Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Pharmaceutical Design
  4. Fast Track Articles
  5. Fast Track Article
image of Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis

Abstract

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available tools is crucial. This review extensively studies various tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cpd/10.2174/0113816128349577240927071706
2024-10-01
2024-11-21

  • From This Site

    /content/journals/cpd/10.2174/0113816128349577240927071706
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. de Chadarevian S. John Kendrew and myoglobin: Protein structure determination in the 1950s. Protein Sci. 2018 27 6 1136 1143 10.1002/pro.3417 29607556
    [Google Scholar]
  2. Olby R. Francis Crick, DNA, and the Central Dogma. Daedalus 1970 99 4 938 987
    [Google Scholar]
  3. Ille A.M. Lamont H. Mathews M.B. The Central Dogma revisited: Insights from protein synthesis, CRISPR, and beyond. Wiley Interdiscip. Rev. RNA 2022 13 5 e1718 10.1002/wrna.1718 35199457
    [Google Scholar]
  4. Johnson I.S. Human insulin from recombinant DNA technology. Science 1983 219 4585 632 637 10.1126/science.6337396 6337396
    [Google Scholar]
  5. Carter P. Site-directed mutagenesis. Biochem. J. 1986 237 1 1 7 10.1042/bj2370001 3541892
    [Google Scholar]
  6. Brannigan J.A. Wilkinson A.J. Protein engineering 20 years on. Nat. Rev. Mol. Cell Biol. 2002 3 12 964 970 10.1038/nrm975 12461562
    [Google Scholar]
  7. Singh R.K. Lee J.K. Selvaraj C. Singh R. Li J. Kim S.Y. Kalia V.C. Protein engineering approaches in the post-genomic era. Curr. Protein Pept. Sci. 2017 19 1 5 15 10.2174/1389203718666161117114243 27855603
    [Google Scholar]
  8. Mathieu C. Martens P.J. Vangoitsenhoven R. One hundred years of insulin therapy. Nat. Rev. Endocrinol. 2021 17 12 715 725 10.1038/s41574‑021‑00542‑w 34404937
    [Google Scholar]
  9. Keen H. Pickup J.C. Bilous R.W. Glynne A. Viberti G.C. Jarrett R.J. Marsden R. Human insulin produced by recombinant DNA technology: Safety and hypoglycaemic potency in healthy men. Lancet 1980 316 8191 398 401 10.1016/S0140‑6736(80)90443‑2 6105520
    [Google Scholar]
  10. Trudeau D.L. Tawfik D.S. Protein engineers turned evolutionists—the quest for the optimal starting point. Curr. Opin. Biotechnol. 2019 60 46 52 10.1016/j.copbio.2018.12.002 30611116
    [Google Scholar]
  11. Goodsell D.S. Zardecki C. Di Costanzo L. Duarte J.M. Hudson B.P. Persikova I. Segura J. Shao C. Voigt M. Westbrook J.D. Young J.Y. Burley S.K. RCSB Protein Data Bank: Enabling biomedical research and drug discovery. Protein Sci. 2020 29 1 52 65 10.1002/pro.3730 31531901
    [Google Scholar]
  12. Yamamoto T. Ryan R.O. Domain swapping reveals that low density lipoprotein (LDL) type A repeat order affects ligand binding to the LDL receptor. J. Biol. Chem. 2009 284 20 13396 13400 10.1074/jbc.M900194200 19329437
    [Google Scholar]
  13. Sternke M. Tripp K.W. Barrick D. The use of consensus sequence information to engineer stability and activity in proteins. Methods Enzymol. 2020 643 149 179 10.1016/bs.mie.2020.06.001 32896279
    [Google Scholar]
  14. Strain-Damerell C. Burgess-Brown N.A. High-Throughput Site-Directed Mutagenesis. Methods Mol. Biol. 2019 2025 281 296 10.1007/978‑1‑4939‑9624‑7_13 31267458
    [Google Scholar]
  15. Gupta K. Varadarajan R. Insights into protein structure, stability and function from saturation mutagenesis. Curr. Opin. Struct. Biol. 2018 50 117 125 10.1016/j.sbi.2018.02.006 29505936
    [Google Scholar]
  16. Chuang Y.C. Hu I.C. Lyu P.C. Hsu S.T.D. Untying a protein knot by circular permutation. J. Mol. Biol. 2019 431 4 857 863 10.1016/j.jmb.2019.01.005 30639189
    [Google Scholar]
  17. Thomas S. Georrge J.J. In silico protein engineering: Methods and Tools. Recent Trends Sci. Technol. 2018 2018 73 80 10.5281/zenodo.4729855
    [Google Scholar]
  18. Kouba P. Kohout P. Haddadi F. Bushuiev A. Samusevich R. Sedlar J. Damborsky J. Pluskal T. Sivic J. Mazurenko S. Machine learning-guided protein engineering. ACS Catal. 2023 13 21 13863 13895 10.1021/acscatal.3c02743 37942269
    [Google Scholar]
  19. Qiu Y. Wei G.W. Artificial intelligence-aided protein engineering: From topological data analysis to deep protein language models. Brief. Bioinform. 2023 24 5 bbad289 10.1093/bib/bbad289 37580175
    [Google Scholar]
  20. Tian T. Zhou X. CRISPR-based biosensing strategies: Technical development and application prospects. Annu. Rev. Anal. Chem. (Palo Alto, Calif.) 2023 16 1 311 332 10.1146/annurev‑anchem‑090822‑014725 37018798
    [Google Scholar]
  21. S V.S. Mishra S.K. Sharma K. Georrge J.J. Designing of a novel curcumin analogue to inhibit mitogen-activated protein kinase: A cheminformatics approach. J. Phytonanotechnol. Pharmaceut. Sci. 2023 3 1 37 47 10.54085/jpps.2023.3.1.5
    [Google Scholar]
  22. Vinjoda P. Mishra S.K. Sharma K. Georrge J.J. In silico identification of novel drug target and its natural product inhibitors for herpes simplex virus. Nanotechnology and In Silico Tools. Kaneria M. Rakholiya K. Egbuna C. Amsterdam Elsevier 2024 377 383 10.1016/B978‑0‑443‑15457‑7.00007‑1
    [Google Scholar]
  23. Mishra S.K. Priya P. Rai G.P. Haque R. Shanker A. Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2. Comput. Biol. Med. 2023 163 107233 10.1016/j.compbiomed.2023.107233 37422941
    [Google Scholar]
  24. Vaghasia V.V. Sharma K. Mishra S.K. Georrge J.J. In silicoidentification of natural product inhibitor for multidrug resistance proteins from selected gram-positive bacteria. Nanotechnology and In Silico Tools. Kaneria M. Rakholiya K. Egbuna C. Amsterdam Elsevier 2024 309 317 10.1016/B978‑0‑443‑15457‑7.00015‑0
    [Google Scholar]
  25. Mishra S.K. Jeba P.J. Georrge J.J. An emerging trends of bioinformatics and big data analytics in healthcare. Digital Transformation in Healthcare 50. Rishabha M. Sonali S. Rajesh Kumar D. Seifedine K. Berlin, Boston De Gruyter 2024 159 188 10.1515/9783111398549‑007
    [Google Scholar]
  26. Dimple K.K. Khoiwal P. Saurav Kumar M. John J.G. In silico based identification of novel inhibitors for selected MDR protein from shigella species: A validation through molecular docking analysis. Edu. Administ. Theory Pract. J. 2024 30 6S 309 316 10.53555/kuey.v30i6s.5380
    [Google Scholar]
  27. Lutz S. Iamurri S.M. Protein Engineering: Past, Present, and Future. Methods Mol. Biol. 2018 1685 1 12 10.1007/978‑1‑4939‑7366‑8_1 29086300
    [Google Scholar]
  28. Raj K. Singh A. Kulkarni N. Thangaraj G. Llp Q. Prediction of hotspot in protein-protein/protein-substrate interaction: A novel computational approach. Int. J. Pharm. Sci. Res. 2022 13 1108 1119
    [Google Scholar]
  29. Kubyshkin V. Budisa N. The alanine world model for the development of the amino acid repertoire in protein biosynthesis. Int. J. Mol. Sci. 2019 20 21 5507 10.3390/ijms20215507 31694194
    [Google Scholar]
  30. Kubyshkin V. Budisa N. Anticipating alien cells with alternative genetic codes: Away from the alanine world! Curr. Opin. Biotechnol. 2019 60 242 249 10.1016/j.copbio.2019.05.006 31279217
    [Google Scholar]
  31. Moreira I.S. Fernandes P.A. Ramos M.J. Computational alanine scanning mutagenesis—An improved methodological approach. J. Comput. Chem. 2007 28 3 644 654 10.1002/jcc.20566 17195156
    [Google Scholar]
  32. Kortemme T. Kim D.E. Baker D. Computational alanine scanning of protein-protein interfaces. Sci. STKE 2004 2004 219 pl2 10.1126/stke.2192004pl2 14872095
    [Google Scholar]
  33. Ye X. Lee Y.C. Gates Z.P. Ling Y. Mortensen J.C. Yang F.S. Lin Y.S. Pentelute B.L. Binary combinatorial scanning reveals potent poly-alanine-substituted inhibitors of protein-protein interactions. Commun. Chem. 2022 5 1 128 10.1038/s42004‑022‑00737‑w 36697672
    [Google Scholar]
  34. Anand P. Nagarajan D. Mukherjee S. Chandra N. ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex. F1000 Res. 2014 3 214 10.12688/f1000research.5165.1 25685322
    [Google Scholar]
  35. Ramadoss V. Dehez F. Chipot C. AlaScan: A graphical user interface for alanine scanning free-energy calculations. J. Chem. Inf. Model. 2016 56 6 1122 1126 10.1021/acs.jcim.6b00162 27214306
    [Google Scholar]
  36. Wood C.W. Ibarra A.A. Bartlett G.J. Wilson A.J. Woolfson D.N. Sessions R.B. BAlaS: Fast, interactive and accessible computational alanine-scanning using BudeAlaScan. Bioinformatics 2020 36 9 2917 2919 10.1093/bioinformatics/btaa026 31930404
    [Google Scholar]
  37. Sukhwal A. Sowdhamini R. PPCheck: A webserver for the quantitative analysis of protein-protein interfaces and prediction of residue hotspots. Bioinform. Biol. Insights 2015 9 BBI.S25928 10.4137/BBI.S25928 26448684
    [Google Scholar]
  38. Wu F.X. Yang J.F. Mei L.C. Wang F. Hao G.F. Yang G.F. PIIMS Server: A web server for mutation hotspot scanning at the protein–protein interface. J. Chem. Inf. Model. 2021 61 1 14 20 10.1021/acs.jcim.0c00966 33400510
    [Google Scholar]
  39. Wang L. Ding M.Y. Wang J. Gao J.G. Liu R.M. Li H.T. Effects of site-directed mutagenesis of cysteine on the structure of sip proteins. Front. Microbiol. 2022 13 805325 10.3389/fmicb.2022.805325 35572629
    [Google Scholar]
  40. Buß O. Rudat J. Ochsenreither K. FoldX as Protein Engineering Tool: Better Than Random Based Approaches? Comput. Struct. Biotechnol. J. 2018 16 25 33 10.1016/j.csbj.2018.01.002 30275935
    [Google Scholar]
  41. Craig D.B. Dombkowski A.A. Disulfide by Design 2.0: A web-based tool for disulfide engineering in proteins. BMC Bioinformatics 2013 14 1 346 10.1186/1471‑2105‑14‑346 24289175
    [Google Scholar]
  42. Wijma H.J. Fürst M.J.L.J. Janssen D.B. A Computational Library Design Protocol for Rapid Improvement of Protein Stability: FRESCO. Methods Mol. Biol. 2018 1685 69 85 10.1007/978‑1‑4939‑7366‑8_5 29086304
    [Google Scholar]
  43. Huang J. Dai S. Chen X. Xu L. Yan J. Yang M. Yan Y. Alteration of Chain-Length Selectivity and Thermostability of Rhizopus oryzae Lipase via Virtual Saturation Mutagenesis Coupled with Disulfide Bond Design. Appl. Environ. Microbiol. 2023 89 1 e01878-22 10.1128/aem.01878‑22 36602359
    [Google Scholar]
  44. Li G. Fang X. Su F. Chen Y. Xu L. Yan Y. Enhancing the Thermostability of Rhizomucor miehei Lipase with a Limited Screening Library by Rational-Design Point Mutations and Disulfide Bonds. Appl. Environ. Microbiol. 2018 84 2 e02129-17 10.1128/AEM.02129‑17 29101200
    [Google Scholar]
  45. Suplatov D. Timonina D. Sharapova Y. Švedas V. Yosshi: A web-server for disulfide engineering by bioinformatic analysis of diverse protein families. Nucleic Acids Res. 2019 47 W1 W308 W314 10.1093/nar/gkz385 31106356
    [Google Scholar]
  46. Moore J.C. Rodriguez-Granillo A. Crespo A. Govindarajan S. Welch M. Hiraga K. Lexa K. Marshall N. Truppo M.D. “Site and Mutation”-Specific Predictions Enable Minimal Directed Evolution Libraries. ACS Synth. Biol. 2018 7 7 1730 1741 10.1021/acssynbio.7b00359 29782150
    [Google Scholar]
  47. Salam N.K. Adzhigirey M. Sherman W. Pearlman D.A. Structure-based approach to the prediction of disulfide bonds in proteins. Protein Eng. Des. Sel. 2014 27 10 365 374 10.1093/protein/gzu017 24817698
    [Google Scholar]
  48. Sim N.L. Kumar P. Hu J. Henikoff S. Schneider G. Ng P.C. SIFT web server: Predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012 40 W1 W452 W457 10.1093/nar/gks539 22689647
    [Google Scholar]
  49. Venkata Subbiah H. Ramesh Babu P. Subbiah U. Determination of deleterious single-nucleotide polymorphisms of human LYZ C gene: An in silico study. J. Genet. Eng. Biotechnol. 2022 20 1 92 10.1186/s43141‑022‑00383‑8 35776277
    [Google Scholar]
  50. Steinhaus R. Proft S. Schuelke M. Cooper D.N. Schwarz J.M. Seelow D. MutationTaster2021. Nucleic Acids Res. 2021 49 W1 W446 W451 10.1093/nar/gkab266 33893808
    [Google Scholar]
  51. Schwarz J.M. Cooper D.N. Schuelke M. Seelow D. MutationTaster2: Mutation prediction for the deep-sequencing age. Nat. Methods 2014 11 4 361 362 10.1038/nmeth.2890 24681721
    [Google Scholar]
  52. Montenegro L.R. Lerário A.M. Nishi M.Y. Jorge A.A.L. Mendonca B.B. Performance of mutation pathogenicity prediction tools on missense variants associated with 46,XY differences of sex development. Clinics (São Paulo) 2021 76 e2052 10.6061/clinics/2021/e2052 33503178
    [Google Scholar]
  53. Pejaver V. Urresti J. Lugo-Martinez J. Pagel K.A. Lin G.N. Nam H.J. Mort M. Cooper D.N. Sebat J. Iakoucheva L.M. Mooney S.D. Radivojac P. Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat. Commun. 2020 11 1 5918 10.1038/s41467‑020‑19669‑x 33219223
    [Google Scholar]
  54. Remali J. Aizat W.M. Ng C.L. Lim Y.C. Mohamed-Hussein Z.A. Fazry S. In silico analysis on the functional and structural impact of Rad50 mutations involved in DNA strand break repair. PeerJ 2020 8 e9197 10.7717/peerj.9197 32509463
    [Google Scholar]
  55. Iida N. Yamao F. Nakamura Y. Iida T. Mudi, a web tool for identifying mutations by bioinformatics analysis of whole‐genome sequence. Genes Cells 2014 19 6 517 527 10.1111/gtc.12151 24766403
    [Google Scholar]
  56. Tokuriki N. Stricher F. Serrano L. Tawfik D.S. How protein stability and new functions trade off. PLOS Comput. Biol. 2008 4 2 e1000002 10.1371/journal.pcbi.1000002 18463696
    [Google Scholar]
  57. Nisthal A. Wang C.Y. Ary M.L. Mayo S.L. Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis. Proc. Natl. Acad. Sci. USA 2019 116 33 16367 16377 10.1073/pnas.1903888116 31371509
    [Google Scholar]
  58. Pires D.E.V. Ascher D.B. Blundell T.L. DUET: A server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res. 2014 42 W1 W314 W319 10.1093/nar/gku411 24829462
    [Google Scholar]
  59. Schymkowitz J. Borg J. Stricher F. Nys R. Rousseau F. Serrano L. The FoldX web server: An online force field. Nucleic Acids Res. 2005 33 Web Server Suppl. 2 W382 W388 10.1093/nar/gki387 15980494
    [Google Scholar]
  60. Cheng J. Randall A. Baldi P. Prediction of protein stability changes for single‐site mutations using support vector machines. Proteins 2006 62 4 1125 1132 10.1002/prot.20810 16372356
    [Google Scholar]
  61. Wainreb G. Wolf L. Ashkenazy H. Dehouck Y. Ben-Tal N. Protein stability: A single recorded mutation aids in predicting the effects of other mutations in the same amino acid site. Bioinformatics 2011 27 23 3286 3292 10.1093/bioinformatics/btr576 21998155
    [Google Scholar]
  62. Pandurangan A.P. Ochoa-Montaño B. Ascher D.B. Blundell T.L. SDM: A server for predicting effects of mutations on protein stability. Nucleic Acids Res. 2017 45 W1 W229 W235 10.1093/nar/gkx439 28525590
    [Google Scholar]
  63. Pires D.E.V. Ascher D.B. Blundell T.L. mCSM: Predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 2014 30 3 335 342 10.1093/bioinformatics/btt691 24281696
    [Google Scholar]
  64. Kumar P. Henikoff S. Ng P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 2009 4 7 1073 1081 10.1038/nprot.2009.86 19561590
    [Google Scholar]
  65. Parthiban V. Gromiha M.M. Schomburg D. CUPSAT: Prediction of protein stability upon point mutations. Nucleic Acids Res. 2006 34 Web Server W239 W242 10.1093/nar/gkl190 16845001
    [Google Scholar]
  66. Quan L. Lv Q. Zhang Y. STRUM: Structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics 2016 32 19 2936 2946 10.1093/bioinformatics/btw361 27318206
    [Google Scholar]
  67. Gonnelli G. Rooman M. Dehouck Y. Structure-based mutant stability predictions on proteins of unknown structure. J. Biotechnol. 2012 161 3 287 293 10.1016/j.jbiotec.2012.06.020 22782143
    [Google Scholar]
  68. Rodrigues C.H.M. Pires D.E.V. Ascher D.B. DynaMut: Predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res. 2018 46 W1 W350 W355 10.1093/nar/gky300 29718330
    [Google Scholar]
  69. Chen C-W. Lin J. Chu Y-W. iStable: Off-the-shelf predictor integration for predicting protein stability changes. BMC Bioinformatics 2013 Suppl 2 Suppl 2 S5
    [Google Scholar]
  70. Sora V. Laspiur A.O. Degn K. Arnaudi M. Utichi M. Beltrame L. De Menezes D. Orlandi M. Stoltze U.K. Rigina O. Sackett P.W. Wadt K. Schmiegelow K. Tiberti M. Papaleo E. RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding. Protein Sci. 2023 32 1 e4527 10.1002/pro.4527 36461907
    [Google Scholar]
  71. Witvliet D.K. Strokach A. Giraldo-Forero A.F. Teyra J. Colak R. Kim P.M. ELASPIC web-server: Proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity. Bioinformatics 2016 32 10 1589 1591 10.1093/bioinformatics/btw031 26801957
    [Google Scholar]
  72. Gong J. Wang J. Zong X. Ma Z. Xu D. Prediction of protein stability changes upon single-point variant using 3D structure profile. Comput. Struct. Biotechnol. J. 2023 21 354 364 10.1016/j.csbj.2022.12.008 36582438
    [Google Scholar]
  73. Savojardo C. Fariselli P. Martelli P.L. Casadio R. INPS-MD: A web server to predict stability of protein variants from sequence and structure. Bioinformatics 2016 32 16 2542 2544 10.1093/bioinformatics/btw192 27153629
    [Google Scholar]
  74. Kaushal N. Baranwal M. Mutational analysis of catalytic site domain of CCHFV L RNA segment. J. Mol. Model. 2023 29 4 88 10.1007/s00894‑023‑05487‑7 36877258
    [Google Scholar]
  75. Paladin L. Piovesan D. Tosatto S.C.E. SODA: Prediction of protein solubility from disorder and aggregation propensity. Nucleic Acids Res. 2017 45 W1 W236 W240 10.1093/nar/gkx412 28505312
    [Google Scholar]
  76. Oeller M. Kang R. Bell R. Ausserwöger H. Sormanni P. Vendruscolo M. Sequence-based prediction of pH-dependent protein solubility using CamSol. Brief. Bioinform. 2023 24 2 bbad004 10.1093/bib/bbad004 36719110
    [Google Scholar]
  77. Kulshreshtha S. Chaudhary V. Goswami G.K. Mathur N. Computational approaches for predicting mutant protein stability. J. Comput. Aided Mol. Des. 2016 30 5 401 412 10.1007/s10822‑016‑9914‑3 27160393
    [Google Scholar]
  78. Bhandari B.K. Gardner P.P. Lim C.S. Solubility-Weighted Index: Fast and accurate prediction of protein solubility. Bioinformatics 2020 36 18 4691 4698 10.1093/bioinformatics/btaa578 32559287
    [Google Scholar]
  79. Wang C. Zou Q. Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE. BMC Biol. 2023 21 1 12 10.1186/s12915‑023‑01510‑8 36694239
    [Google Scholar]
  80. Hebditch M. Carballo-Amador M.A. Charonis S. Curtis R. Warwicker J. Protein–Sol: A web tool for predicting protein solubility from sequence. Bioinformatics 2017 33 19 3098 3100 10.1093/bioinformatics/btx345 28575391
    [Google Scholar]
  81. Lear S. Cobb S.L. Pep-Calc.com: A set of web utilities for the calculation of peptide and peptoid properties and automatic mass spectral peak assignment. J. Comput. Aided Mol. Des. 2016 30 3 271 277 10.1007/s10822‑016‑9902‑7 26909892
    [Google Scholar]
  82. Wu X. Yu L. EPSOL: Sequence-based protein solubility prediction using multidimensional embedding. Bioinformatics 2021 37 23 4314 4320 10.1093/bioinformatics/btab463 34145885
    [Google Scholar]
  83. Hon J. Marusiak M. Martínek T. Kunka A. Zendulka J. Bednář D. Damborsky J. SoluProt: Prediction of soluble protein expression in Escherichia coli. Bioinformatics 2021 37 1 23 28 10.1093/bioinformatics/btaa1102 33416864
    [Google Scholar]
  84. Yang Y. Zeng L. Vihinen M. PON-Sol2: Prediction of Effects of Variants on Protein Solubility. Int. J. Mol. Sci. 2021 22 15 8027 10.3390/ijms22158027 34360790
    [Google Scholar]
  85. Agostini F. Cirillo D. Livi C.M. Delli Ponti R. Tartaglia G.G. cc SOL omics : A webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli. Bioinformatics 2014 30 20 2975 2977 10.1093/bioinformatics/btu420 24990610
    [Google Scholar]
  86. Tu M. Qiao X. Wang C. Liu H. Cheng S. Xu Z. Du M. In vitro and in silico analysis of dual-function peptides derived from casein hydrolysate. Food Sci. Hum. Wellness 2021 10 1 32 37 10.1016/j.fshw.2020.08.014
    [Google Scholar]
  87. Prabakaran R. Rawat P. Thangakani A.M. Kumar S. Gromiha M.M. Protein aggregation: In silico algorithms and applications. Biophys. Rev. 2021 13 1 71 89 10.1007/s12551‑021‑00778‑w 33747245
    [Google Scholar]
  88. Conchillo-Solé O. de Groot N.S. Avilés F.X. Vendrell J. Daura X. Ventura S. AGGRESCAN: A server for the prediction and evaluation of “hot spots” of aggregation in polypeptides. BMC Bioinformatics 2007 8 1 65 10.1186/1471‑2105‑8‑65 17324296
    [Google Scholar]
  89. Tartaglia G.G. Vendruscolo M. The Zyggregator method for predicting protein aggregation propensities. Chem. Soc. Rev. 2008 37 7 1395 1401 10.1039/b706784b 18568165
    [Google Scholar]
  90. Sankar K. Krystek S.R. Jr Carl S.M. Day T. Maier J.K.X. AggScore: Prediction of aggregation‐prone regions in proteins based on the distribution of surface patches. Proteins 2018 86 11 1147 1156 10.1002/prot.25594 30168197
    [Google Scholar]
  91. Navarro S. Ventura S. Computational methods to predict protein aggregation. Curr. Opin. Struct. Biol. 2022 73 102343 10.1016/j.sbi.2022.102343 35240456
    [Google Scholar]
  92. Tsolis A.C. Papandreou N.C. Iconomidou V.A. Hamodrakas S.J. A consensus method for the prediction of ‘aggregation-prone’ peptides in globular proteins. PLoS One 2013 8 1 e54175 10.1371/journal.pone.0054175 23326595
    [Google Scholar]
  93. Yan R. Wang X. Huang L. Yan F. Xue X. Cai W. Prediction of structural features and application to outer membrane protein identification. Sci. Rep. 2015 5 1 11586 10.1038/srep11586 26104144
    [Google Scholar]
  94. Kouza M. Faraggi E. Kolinski A. Kloczkowski A. The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool. Prediction of Protein Secondary Structure. Zhou Y. Kloczkowski A. Faraggi E. Yang Y. New York, NY Springer New York 2017 7 24 10.1007/978‑1‑4939‑6406‑2_2
    [Google Scholar]
  95. Källberg M. Wang H. Wang S. Peng J. Wang Z. Lu H. Xu J. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 2012 7 8 1511 1522 10.1038/nprot.2012.085 22814390
    [Google Scholar]
  96. Combet C. Blanchet C. Geourjon C. Deléage G. NPS@: Network protein sequence analysis. Trends Biochem. Sci. 2000 25 3 147 150 10.1016/S0968‑0004(99)01540‑6 10694887
    [Google Scholar]
  97. Buchan D.W.A. Jones D.T. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res. 2019 47 W1 W402 W407 10.1093/nar/gkz297 31251384
    [Google Scholar]
  98. Klausen M.S. Jespersen M.C. Nielsen H. Jensen K.K. Jurtz V.I. Sønderby C.K. Sommer M.O.A. Winther O. Nielsen M. Petersen B. Marcatili P. NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning. Proteins 2019 87 6 520 527 10.1002/prot.25674 30785653
    [Google Scholar]
  99. Qin X. Liu M. Zhang L. Liu G. Structural protein fold recognition based on secondary structure and evolutionary information using machine learning algorithms. Comput. Biol. Chem. 2021 91 107456 10.1016/j.compbiolchem.2021.107456 33610129
    [Google Scholar]
  100. Lin K. Simossis V.A. Taylor W.R. Heringa J. A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 2005 21 2 152 159 10.1093/bioinformatics/bth487 15377504
    [Google Scholar]
  101. Urban G. Magnan C.N. Baldi P. SSpro/ACCpro 6: Almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, deep learning and structural similarity. Bioinformatics 2022 38 7 2064 2065 10.1093/bioinformatics/btac019 35108364
    [Google Scholar]
  102. Lee A.C.L. Harris J.L. Khanna K.K. Hong J.H. A Comprehensive Review on Current Advances in Peptide Drug Development and Design. Int. J. Mol. Sci. 2019 20 10 2383 10.3390/ijms20102383 31091705
    [Google Scholar]
  103. Fiser A. Šali A. Modeller: Generation and refinement of homology-based protein structure models. Methods Enzymol. 2003 374 461 491 10.1016/S0076‑6879(03)74020‑8 14696385
    [Google Scholar]
  104. Peng J. Xu J. A multiple‐template approach to protein threading. Proteins 2011 79 6 1930 1939 10.1002/prot.23016 21465564
    [Google Scholar]
  105. Zheng W. Zhang C. Bell E.W. Zhang Y. I-TASSER gateway: A protein structure and function prediction server powered by XSEDE. Future Gener. Comput. Syst. 2019 99 73 85 10.1016/j.future.2019.04.011 31427836
    [Google Scholar]
  106. Zhou X. Zheng W. Li Y. Pearce R. Zhang C. Bell E.W. Zhang G. Zhang Y. I-TASSER-MTD: A deep-learning-based platform for multi-domain protein structure and function prediction. Nat. Protoc. 2022 17 10 2326 2353 10.1038/s41596‑022‑00728‑0 35931779
    [Google Scholar]
  107. Leman J.K. Weitzner B.D. Lewis S.M. Adolf-Bryfogle J. Alam N. Alford R.F. Aprahamian M. Baker D. Barlow K.A. Barth P. Basanta B. Bender B.J. Blacklock K. Bonet J. Boyken S.E. Bradley P. Bystroff C. Conway P. Cooper S. Correia B.E. Coventry B. Das R. De Jong R.M. DiMaio F. Dsilva L. Dunbrack R. Ford A.S. Frenz B. Fu D.Y. Geniesse C. Goldschmidt L. Gowthaman R. Gray J.J. Gront D. Guffy S. Horowitz S. Huang P.S. Huber T. Jacobs T.M. Jeliazkov J.R. Johnson D.K. Kappel K. Karanicolas J. Khakzad H. Khar K.R. Khare S.D. Khatib F. Khramushin A. King I.C. Kleffner R. Koepnick B. Kortemme T. Kuenze G. Kuhlman B. Kuroda D. Labonte J.W. Lai J.K. Lapidoth G. Leaver-Fay A. Lindert S. Linsky T. London N. Lubin J.H. Lyskov S. Maguire J. Malmström L. Marcos E. Marcu O. Marze N.A. Meiler J. Moretti R. Mulligan V.K. Nerli S. Norn C. Ó’Conchúir S. Ollikainen N. Ovchinnikov S. Pacella M.S. Pan X. Park H. Pavlovicz R.E. Pethe M. Pierce B.G. Pilla K.B. Raveh B. Renfrew P.D. Burman S.S.R. Rubenstein A. Sauer M.F. Scheck A. Schief W. Schueler-Furman O. Sedan Y. Sevy A.M. Sgourakis N.G. Shi L. Siegel J.B. Silva D.A. Smith S. Song Y. Stein A. Szegedy M. Teets F.D. Thyme S.B. Wang R.Y.R. Watkins A. Zimmerman L. Bonneau R. Macromolecular modeling and design in Rosetta: Recent methods and frameworks. Nat. Methods 2020 17 7 665 680 10.1038/s41592‑020‑0848‑2 32483333
    [Google Scholar]
  108. Sawal H.A. Nighat S. Safdar T. Anees L. Comparative in silico analysis and functional characterization of TANK-binding kinase 1–binding protein 1. Bioinform. Biol. Insights 2023 17 10.1177/11779322231164828 37032976
    [Google Scholar]
  109. Arasu M.V. Vijayaragavan P. Purushothaman S. Rathi M.A. Al-Dhabi N.A. Gopalakrishnan V.K. Choi K.C. Ilavenil S. Molecular docking of monkeypox (mpox) virus proteinase with FDA approved lead molecules. J. Infect. Public Health 2023 16 5 784 791 10.1016/j.jiph.2023.03.004 36958173
    [Google Scholar]
  110. Mollazadeh S. Bakhshesh M. Keyvanfar H. Nikbakht Brujeni G. Identification of Cytotoxic T lymphocyte (CTL) Epitope and design of an immunogenic multi-epitope of Bovine Ephemeral Fever Virus (BEFV) Glycoprotein G for Vaccine Development. Res. Vet. Sci. 2022 144 18 26 10.1016/j.rvsc.2021.12.023 35033847
    [Google Scholar]
  111. Wu X. Lin H. Bai R. Duan H. Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design. Eur. J. Med. Chem. 2024 268 116262 10.1016/j.ejmech.2024.116262 38387334
    [Google Scholar]
  112. Waterhouse A. Bertoni M. Bienert S. Studer G. Tauriello G. Gumienny R. Heer F.T. de Beer T.A.P. Rempfer C. Bordoli L. Lepore R. Schwede T. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 2018 46 W1 W296 W303 10.1093/nar/gky427 29788355
    [Google Scholar]
  113. Eswar N. Webb B. Marti-Renom M.A. Madhusudhan M.S. Eramian D. Shen M.Y. Comparative protein structure modeling using Modeller. Hoboken, New Jersey Wiley 2006 5 6
    [Google Scholar]
  114. Kelley L.A. Mezulis S. Yates C.M. Wass M.N. Sternberg M.J.E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 2015 10 6 845 858 10.1038/nprot.2015.053 25950237
    [Google Scholar]
  115. Yang J. Zhang Y. Protein structure and function prediction using I‐TASSER. Curr. Protoc. Bioinformatics 2015 52 1 8.1 , 15 10.1002/0471250953.bi0508s52 26678386
    [Google Scholar]
  116. Montgomerie S. Cruz J.A. Shrivastava S. Arndt D. Berjanskii M. Wishart D.S. PROTEUS2: A web server for comprehensive protein structure prediction and structure-based annotation. Nucleic Acids Res. 2008 36 Web Server Suppl. 2 W202 W209 10.1093/nar/gkn255 18483082
    [Google Scholar]
  117. McGuffin L.J. Adiyaman R. Maghrabi A.H.A. Shuid A.N. Brackenridge D.A. Nealon J.O. Philomina L.S. IntFOLD: An integrated web resource for high performance protein structure and function prediction. Nucleic Acids Res. 2019 47 W1 W408 W413 10.1093/nar/gkz322 31045208
    [Google Scholar]
  118. Mortuza S.M. Zheng W. Zhang C. Li Y. Pearce R. Zhang Y. Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions. Nat. Commun. 2021 12 1 5011 10.1038/s41467‑021‑25316‑w 34408149
    [Google Scholar]
  119. Thévenet P. Shen Y. Maupetit J. Guyon F. Derreumaux P. Tufféry P. PEP-FOLD: An updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res. 2012 40 W1 W288 W293 10.1093/nar/gks419 22581768
    [Google Scholar]
  120. Badaczewska-Dawid A. Wróblewski K. Kurcinski M. Kmiecik S. Structure prediction of linear and cyclic peptides using CABS-flex. Brief. Bioinform. 2024 25 2 bbae003 10.1093/bib/bbae003 38305457
    [Google Scholar]
  121. Timmons P.B. Hewage C.M. APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures. Brief. Bioinform. 2021 22 6 bbab308 10.1093/bib/bbab308 34396417
    [Google Scholar]
  122. Singh S. Singh H. Tuknait A. Chaudhary K. Singh B. Kumaran S. Raghava G.P.S. PEPstrMOD: Structure prediction of peptides containing natural, non-natural and modified residues. Biol. Direct 2015 10 1 73 10.1186/s13062‑015‑0103‑4 26690490
    [Google Scholar]
  123. McDonald E.F. Jones T. Plate L. Meiler J. Gulsevin A. Benchmarking AlphaFold2 on peptide structure prediction. Structure 2023 31 1 111 119.e2 10.1016/j.str.2022.11.012 36525975
    [Google Scholar]
  124. Pan L. Aller S.G. Tools and procedures for visualization of proteins and other biomolecules. Curr. Protoc. Mol. Biol. 2015 110 1 12.1 , 47 10.1002/0471142727.mb1912s110 25827086
    [Google Scholar]
  125. Garrison L. Bruckner S. Considering best practices in color palettes for molecular visualizations. J. Integr. Bioinform. 2022 19 2 20220016 10.1515/jib‑2022‑0016 35731632
    [Google Scholar]
  126. Baammi S. Daoud R. El Allali A. In silico protein engineering shows that novel mutations affecting NAD+ binding sites may improve phosphite dehydrogenase stability and activity. Sci. Rep. 2023 13 1 1878 10.1038/s41598‑023‑28246‑3 36725973
    [Google Scholar]
  127. Yuan S. Chan H.C.S. Hu Z. Using PyMOL as a platform for computational drug design. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2017 7 2 e1298 10.1002/wcms.1298
    [Google Scholar]
  128. Zhang W. Wang C. Zhang X. Mutplot: An easy-to-use online tool for plotting complex mutation data with flexibility. PLoS One 2019 14 5 e0215838 10.1371/journal.pone.0215838 31091262
    [Google Scholar]
  129. Gromiha M.M. An J. Kono H. Oobatake M. Uedaira H. Prabakaran P. Sarai A. ProTherm, version 2.0: Thermodynamic database for proteins and mutants. Nucleic Acids Res. 2000 28 1 283 285 10.1093/nar/28.1.283 10592247
    [Google Scholar]
  130. Laskowski R.A. Swindells M.B. LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 2011 51 10 2778 2786 10.1021/ci200227u 21919503
    [Google Scholar]
  131. Dulsat J. López-Nieto B. Estrada-Tejedor R. Borrell J.I. Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments. Molecules 2023 28 2 776 10.3390/molecules28020776 36677832
    [Google Scholar]
  132. Santos G.B. Ganesan A. Emery F.S. Oral administration of peptide‐based drugs: Beyond Lipinski’s Rule. ChemMedChem 2016 11 20 2245 2251 10.1002/cmdc.201600288 27596610
    [Google Scholar]
  133. Gasteiger E. Gattiker A. Hoogland C. Ivanyi I. Appel R.D. Bairoch A. ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 2003 31 13 3784 3788 10.1093/nar/gkg563 12824418
    [Google Scholar]
  134. Rathore A.S. Arora A. Choudhury S. Tijare P. Raghava G.P.S. ToxinPred 3.0: An improved method for predicting the toxicity of peptides. bioRxiv 2023 10.1101/2023.08.11.552911
    [Google Scholar]
  135. Arámburo-Gálvez J.G. Arvizu-Flores A.A. Cárdenas-Torres F.I. Cabrera-Chávez F. Ramírez-Torres G.I. Flores-Mendoza L.K. Gastelum-Acosta P.E. Figueroa-Salcido O.G. Ontiveros N. Prediction of ACE-I Inhibitory Peptides Derived from Chickpea (Cicer arietinum L.): In Silico Assessments Using Simulated Enzymatic Hydrolysis, Molecular Docking and ADMET Evaluation. Foods 2022 11 11 1576 10.3390/foods11111576 35681326
    [Google Scholar]
  136. Kumar V. Patiyal S. Dhall A. Sharma N. Raghava G.P.S. B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood–Brain Barrier Penetrating Peptides. Pharmaceutics 2021 13 8 1237 10.3390/pharmaceutics13081237 34452198
    [Google Scholar]
  137. Flores-Holguín N. Frau J. Glossman-Mitnik D. Computational Pharmacokinetics Report, ADMET Study and Conceptual DFT‐Based Estimation of the Chemical Reactivity Properties of Marine Cyclopeptides. ChemistryOpen 2021 10 11 1142 1149 10.1002/open.202100178 34806828
    [Google Scholar]
  138. Stourac J. Borko S. Khan R.T. Pokorna P. Dobias A. Planas-Iglesias J. Mazurenko S. Pinto G. Szotkowska V. Sterba J. Slaby O. Damborsky J. Bednar D. PredictONCO: A web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning. Brief. Bioinform. 2023 25 1 bbad441 10.1093/bib/bbad441 38066711
    [Google Scholar]
  139. Chaudhary K. Kumar R. Singh S. Tuknait A. Gautam A. Mathur D. Anand P. Varshney G.C. Raghava G.P.S. A Web Server and Mobile App for Computing Hemolytic Potency of Peptides. Sci. Rep. 2016 6 1 22843 10.1038/srep22843 26953092
    [Google Scholar]
  140. Timmons P.B. Hewage C.M. HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. Sci. Rep. 2020 10 1 10869 10.1038/s41598‑020‑67701‑3 32616760
    [Google Scholar]
  141. Mulpuru V. Mishra N. Immunoinformatic based identification of cytotoxic T lymphocyte epitopes from the Indian isolate of SARS-CoV-2. Sci. Rep. 2021 11 1 4516 10.1038/s41598‑021‑83949‑9 33633155
    [Google Scholar]
  142. Dimitrov I. Flower D.R. Doytchinova I. AllerTOP - a server for in silico prediction of allergens. BMC Bioinformatics 2013 14 S6 Suppl. 6 S4 10.1186/1471‑2105‑14‑S6‑S4 23735058
    [Google Scholar]
  143. Schaduangrat N. Nantasenamat C. Prachayasittikul V. Shoombuatong W. Meta-iAVP: A sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation. Int. J. Mol. Sci. 2019 20 22 5743 10.3390/ijms20225743 31731751
    [Google Scholar]
  144. Schaduangrat N. Nantasenamat C. Prachayasittikul V. Shoombuatong W. ACPred: A computational tool for the prediction and analysis of anticancer peptides. Molecules 2019 24 10 1973 10.3390/molecules24101973 31121946
    [Google Scholar]
  145. Agrawal P. Bhagat D. Mahalwal M. Sharma N. Raghava G.P.S. AntiCP 2.0: An updated model for predicting anticancer peptides. Brief. Bioinform. 2021 22 3 bbaa153 10.1093/bib/bbaa153 32770192
    [Google Scholar]
  146. Wang C.Y. Chang P.M. Ary M.L. Allen B.D. Chica R.A. Mayo S.L. Olafson B.D. ProtaBank: A repository for protein design and engineering data. Protein Sci. 2019 28 3 672 10.1002/pro.3585 30747468
    [Google Scholar]
  147. Musil M. Stourac J. Bendl J. Brezovsky J. Prokop Z. Zendulka J. Martinek T. Bednar D. Damborsky J. FireProt: Web server for automated design of thermostable proteins. Nucleic Acids Res. 2017 45 W1 W393 W399 10.1093/nar/gkx285 28449074
    [Google Scholar]
  148. Xavier J.S. Nguyen T.B. Karmarkar M. Portelli S. Rezende P.M. Velloso J.P.L. Ascher D.B. Pires D.E.V. ThermoMutDB: A thermodynamic database for missense mutations. Nucleic Acids Res. 2021 49 D1 D475 D479 10.1093/nar/gkaa925 33095862
    [Google Scholar]
  149. Nikam R. Kulandaisamy A. Harini K. Sharma D. Gromiha M.M. ProThermDB: Thermodynamic database for proteins and mutants revisited after 15 years. Nucleic Acids Res. 2021 49 D1 D420 D424 10.1093/nar/gkaa1035 33196841
    [Google Scholar]
  150. Berman H.M. Westbrook J. Feng Z. Gilliland G. Bhat T.N. Weissig H. Shindyalov I.N. Bourne P.E. The Protein Data Bank. Nucleic Acids Res. 2000 28 1 235 242 10.1093/nar/28.1.235 10592235
    [Google Scholar]
  151. Jankauskaitė J. Jiménez-García B. Dapkūnas J. Fernández-Recio J. Moal I.H. SKEMPI 2.0: An updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics 2019 35 3 462 469 10.1093/bioinformatics/bty635 30020414
    [Google Scholar]
  152. Gollapalli P. Kumari N.S. Shetty P. Gnanasekaran T.S. Molecular basis of AR and STK11 genes associated pathogenesis via AMPK pathway and adipocytokine signalling pathway in the development of metabolic disorders in PCOS women. Beni. Suef Univ. J. Basic Appl. Sci. 2022 11 1 23 10.1186/s43088‑022‑00200‑8
    [Google Scholar]
  153. Selvan T.G. Gollapalli P. Kumar S.H.S. Ghate S.D. Early diagnostic and prognostic biomarkers for gastric cancer: Systems-level molecular basis of subsequent alterations in gastric mucosa from chronic atrophic gastritis to gastric cancer. J. Genet. Eng. Biotechnol. 2023 21 1 86 10.1186/s43141‑023‑00539‑0 37594635
    [Google Scholar]
  154. Selvan G.T. Gollapalli P. Shetty P. Kumari N.S. Exploring key molecular signatures of immune responses and pathways associated with tuberculosis in comorbid diabetes mellitus: A systems biology approach. Beni. Suef Univ. J. Basic Appl. Sci. 2022 11 1 77 10.1186/s43088‑022‑00257‑5
    [Google Scholar]
  155. Szklarczyk D. Kirsch R. Koutrouli M. Nastou K. Mehryary F. Hachilif R. Gable A.L. Fang T. Doncheva N.T. Pyysalo S. Bork P. Jensen L.J. von Mering C. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023 51 D1 D638 D646 10.1093/nar/gkac1000 36370105
    [Google Scholar]
  156. Oughtred R. Rust J. Chang C. Breitkreutz B.J. Stark C. Willems A. Boucher L. Leung G. Kolas N. Zhang F. Dolma S. Coulombe-Huntington J. Chatr-aryamontri A. Dolinski K. Tyers M. The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 2021 30 1 187 200 10.1002/pro.3978 33070389
    [Google Scholar]
  157. Uhlén M. Fagerberg L. Hallström B.M. Lindskog C. Oksvold P. Mardinoglu A. Sivertsson Å. Kampf C. Sjöstedt E. Asplund A. Olsson I. Edlund K. Lundberg E. Navani S. Szigyarto C.A.K. Odeberg J. Djureinovic D. Takanen J.O. Hober S. Alm T. Edqvist P.H. Berling H. Tegel H. Mulder J. Rockberg J. Nilsson P. Schwenk J.M. Hamsten M. von Feilitzen K. Forsberg M. Persson L. Johansson F. Zwahlen M. von Heijne G. Nielsen J. Pontén F. Tissue-based map of the human proteome. Science 2015 347 6220 1260419 10.1126/science.1260419 25613900
    [Google Scholar]
  158. Chatr-aryamontri A. Ceol A. Palazzi L.M. Nardelli G. Schneider M.V. Castagnoli L. Cesareni G. MINT: The Molecular INTeraction database. Nucleic Acids Res. 2007 35 Database D572 D574 10.1093/nar/gkl950 17135203
    [Google Scholar]
  159. Das A.A. Sharma O.P. Kumar M.S. Krishna R. Mathur P.P. PepBind: A comprehensive database and computational tool for analysis of protein-peptide interactions. Genomics Proteomics Bioinformatics 2013 11 4 241 246 10.1016/j.gpb.2013.03.002 23896518
    [Google Scholar]
  160. Kalinina O.V. Wichmann O. Apic G. Russell R.B. ProtChemSI: A network of protein-chemical structural interactions. Nucleic Acids Res. 2012 40 D1 D549 D553 10.1093/nar/gkr1049 22110041
    [Google Scholar]
  161. Martins P Mariano D Carvalho FC Bastos LL Moraes L Paixão V Propedia v2.3: A novel representation approach for the peptide-protein interaction database using graph-based structural signatures. Front. Bioinformatics 2023 3 1103103
    [Google Scholar]
  162. Orchard S. Ammari M. Aranda B. Breuza L. Briganti L. Broackes-Carter F. Campbell N.H. Chavali G. Chen C. del-Toro N. Duesbury M. Dumousseau M. Galeota E. Hinz U. Iannuccelli M. Jagannathan S. Jimenez R. Khadake J. Lagreid A. Licata L. Lovering R.C. Meldal B. Melidoni A.N. Milagros M. Peluso D. Perfetto L. Porras P. Raghunath A. Ricard-Blum S. Roechert B. Stutz A. Tognolli M. van Roey K. Cesareni G. Hermjakob H. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014 42 D1 D358 D363 10.1093/nar/gkt1115 24234451
    [Google Scholar]
  163. Wen Z. He J. Tao H. Huang S.Y. PepBDB: A comprehensive structural database of biological peptide–protein interactions. Bioinformatics 2019 35 1 175 177 10.1093/bioinformatics/bty579 29982280
    [Google Scholar]
  164. Sonawani A. Naglekar A. Kharche S. Sengupta D. Assessing protein-protein docking protocols: Case studies of G-protein-coupled receptor interactions. Methods Mol. Biol. 2024 2780 257 280 10.1007/978‑1‑0716‑3985‑6_13 38987472
    [Google Scholar]
  165. Funmilola A.R. Abubakar G. Hassan Z. Molecular Docking in Drug Discovery: A Review on Anti-snake Venom Development. Int. J. Biochem. Res. Rev. 2020 2020 42 49 10.9734/ijbcrr/2020/v29i330179
    [Google Scholar]
  166. Agrawal P. Singh H. Srivastava H.K. Singh S. Kishore G. Raghava G.P.S. Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinformatics 2019 19 S13 Suppl. 13 426 10.1186/s12859‑018‑2449‑y 30717654
    [Google Scholar]
  167. Zhang W. Bell E.W. Yin M. Zhang Y. EDock: Blind protein–ligand docking by replica-exchange monte carlo simulation. J. Cheminform. 2020 12 1 37 10.1186/s13321‑020‑00440‑9 33430966
    [Google Scholar]
  168. Schneidman-Duhovny D. Inbar Y. Nussinov R. Wolfson H.J. PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Res. 2005 33 Web Server W363 W367 10.1093/nar/gki481 15980490
    [Google Scholar]
  169. Jones G. Willett P. Glen R.C. Leach A.R. Taylor R. Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen. J. Mol. Biol. 1997 267 3 727 748 10.1006/jmbi.1996.0897 9126849
    [Google Scholar]
  170. Koes D.R. Baumgartner M.P. Camacho C.J. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J. Chem. Inf. Model. 2013 53 8 1893 1904 10.1021/ci300604z 23379370
    [Google Scholar]
  171. Bitencourt-Ferreira G. de Azevedo W.F. Jr Docking with GemDock. Methods Mol. Biol. 2019 2053 169 188 10.1007/978‑1‑4939‑9752‑7_11 31452105
    [Google Scholar]
  172. Pierce B.G. Wiehe K. Hwang H. Kim B.H. Vreven T. Weng Z. ZDOCK server: Interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics 2014 30 12 1771 1773 10.1093/bioinformatics/btu097 24532726
    [Google Scholar]
  173. Macindoe G. Mavridis L. Venkatraman V. Devignes M.D. Ritchie D.W. HexServer: An FFT-based protein docking server powered by graphics processors. Nucleic Acids Res. 2010 38 Web Server Suppl. 2 W445 W449 10.1093/nar/gkq311 20444869
    [Google Scholar]
  174. Ramírez-Aportela E. López-Blanco J.R. Chacón P. FRODOCK 2.0: Fast protein–protein docking server. Bioinformatics 2016 32 15 2386 2388 10.1093/bioinformatics/btw141 27153583
    [Google Scholar]
  175. Geng C. Narasimhan S. Rodrigues J.P.G.L.M. Bonvin A.M.J.J. Information-driven, ensemble flexible peptide docking using HADDOCK. Modeling Peptide-Protein Interactions. Methods Mol. Biol. 2017 1561 109 138 10.1007/978‑1‑4939‑6798‑8_8 28236236
    [Google Scholar]
  176. Schindler C.E.M. Chauvot de Beauchêne I. de Vries S.J. Zacharias M. Protein‐protein and peptide‐protein docking and refinement using ATTRACT in CAPRI. Proteins 2017 85 3 391 398 10.1002/prot.25196 27785830
    [Google Scholar]
  177. Alekseenko A. Ignatov M. Jones G. Sabitova M. Kozakov D. Protein–Protein and Protein–Peptide Docking with ClusPro Server. Methods Mol. Biol. 2020 2165 157 174 10.1007/978‑1‑0716‑0708‑4_9 32621224
    [Google Scholar]
  178. Andrusier N. Nussinov R. Wolfson H.J. FireDock: Fast interaction refinement in molecular docking. Proteins 2007 69 1 139 159 10.1002/prot.21495 17598144
    [Google Scholar]
  179. Pons C. Solernou A. Perez-Cano L. Grosdidier S. Fernandez-Recio J. Optimization of pyDock for the new CAPRI challenges: Docking of homology‐based models, domain–domain assembly and protein‐RNA binding. Proteins 2010 78 15 3182 3188 10.1002/prot.22773 20602351
    [Google Scholar]
  180. Yang Y. Yao K. Repasky M.P. Leswing K. Abel R. Shoichet B.K. Jerome S.V. Efficient Exploration of Chemical Space with Docking and Deep Learning. J. Chem. Theory Comput. 2021 17 11 7106 7119 10.1021/acs.jctc.1c00810 34592101
    [Google Scholar]
  181. Raveh B. London N. Zimmerman L. Schueler-Furman O. Rosetta FlexPepDock ab-initio: Simultaneous folding, docking and refinement of peptides onto their receptors. PLoS One 2011 6 4 e18934 10.1371/journal.pone.0018934 21572516
    [Google Scholar]
  182. Kurcinski M. Badaczewska-Dawid A. Kolinski M. Kolinski A. Kmiecik S. Flexible docking of peptides to proteins using CABS‐dock. Protein Sci. 2020 29 1 211 222 10.1002/pro.3771 31682301
    [Google Scholar]
  183. Lee H. Heo L. Lee M.S. Seok C. GalaxyPepDock: A protein–peptide docking tool based on interaction similarity and energy optimization. Nucleic Acids Res. 2015 43 W1 W431 W435 10.1093/nar/gkv495 25969449
    [Google Scholar]
  184. Lamiable A. Thévenet P. Rey J. Vavrusa M. Derreumaux P. Tufféry P. PEP-FOLD3: Faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res. 2016 44 W1 W449 W454 10.1093/nar/gkw329 27131374
    [Google Scholar]
  185. Santos K.B. Guedes I.A. Karl A.L.M. Dardenne L.E. Highly flexible ligand docking: Benchmarking of the dockthor program on the LEADS-PEP protein–peptide data set. J. Chem. Inf. Model. 2020 60 2 667 683 10.1021/acs.jcim.9b00905 31922754
    [Google Scholar]
  186. Vidal-Limon A. Aguilar-Toalá J.E. Liceaga A.M. Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides. J. Agric. Food Chem. 2022 70 4 934 943 10.1021/acs.jafc.1c06110 34990125
    [Google Scholar]
  187. Mast T Lupyan D. How to Assign AMBER Parameters to Desmond-generated System with viparr4 v1. Preprint 2023
    [Google Scholar]
  188. Brooks B.R. Brooks C.L. III Mackerell A.D. Jr Nilsson L. Petrella R.J. Roux B. Won Y. Archontis G. Bartels C. Boresch S. Caflisch A. Caves L. Cui Q. Dinner A.R. Feig M. Fischer S. Gao J. Hodoscek M. Im W. Kuczera K. Lazaridis T. Ma J. Ovchinnikov V. Paci E. Pastor R.W. Post C.B. Pu J.Z. Schaefer M. Tidor B. Venable R.M. Woodcock H.L. Wu X. Yang W. York D.M. Karplus M. CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009 30 10 1545 1614 10.1002/jcc.21287 19444816
    [Google Scholar]
  189. Páll S. Zhmurov A. Bauer P. Abraham M. Lundborg M. Gray A. Hess B. Lindahl E. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. J. Chem. Phys. 2020 153 13 134110 10.1063/5.0018516 33032406
    [Google Scholar]
  190. Lier B. Öhlknecht C. de Ruiter A. Gebhardt J. van Gunsteren W.F. Oostenbrink C. A Suite of Advanced Tutorials for the GROMOS Biomolecular Simulation Software. Living J. Comput. Mol. Sci. 2020 2 1 18552
    [Google Scholar]
  191. Bjelkmar P. Larsson P. Cuendet M.A. Hess B. Lindahl E. Implementation of the CHARMM Force Field in GROMACS: Analysis of protein stability effects from correction maps, virtual interaction sites, and water models. J. Chem. Theory Comput. 2010 6 2 459 466 10.1021/ct900549r 26617301
    [Google Scholar]
  192. Narancic T. Almahboub S.A. O’Connor K.E. Unnatural amino acids: Production and biotechnological potential. World J. Microbiol. Biotechnol. 2019 35 4 67 10.1007/s11274‑019‑2642‑9 30963257
    [Google Scholar]
  193. Giannakoulias S. Shringari S.R. Ferrie J.J. Petersson E.J. Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine. Sci. Rep. 2021 11 1 18406 10.1038/s41598‑021‑97965‑2 34526629
    [Google Scholar]
  194. Zhang H. Zheng Z. Dong L. Shi N. Yang Y. Chen H. Shen Y. Xia Q. Rational incorporation of any unnatural amino acid into proteins by machine learning on existing experimental proofs. Comput. Struct. Biotechnol. J. 2022 20 4930 4941 10.1016/j.csbj.2022.08.063 36147660
    [Google Scholar]
  195. Mattei AE Gutierrez AH Martin WD Terry FE Roberts BJ Rosenberg AS In silico immunogenicity assessment for sequences containing unnatural amino acids: A method using existing in silico algorithm infrastructure and a vision for future enhancements. Front Drug Discov (Lausanne) 2022 2 952326 10.3389/fddsv.2022.952326
    [Google Scholar]
  196. Hermann J. Schurgers L. Jankowski V. Identification and characterization of post-translational modifications: Clinical implications. Mol. Aspects Med. 2022 86 101066 10.1016/j.mam.2022.101066 35033366
    [Google Scholar]
  197. Ramazi S. Zahiri J. Post-translational modifications in proteins: Resources, tools and prediction methods. Database (Oxford) 2021 2021 baab012 10.1093/database/baab012 33826699
    [Google Scholar]
  198. Carter A.M. Tan C. Pozo K. Telange R. Molinaro R. Guo A. De Rosa E. Martinez J.O. Zhang S. Kumar N. Takahashi M. Wiederhold T. Ghayee H.K. Oltmann S.C. Pacak K. Woltering E.A. Hatanpaa K.J. Nwariaku F.E. Grubbs E.G. Gill A.J. Robinson B. Gillardon F. Reddy S. Jaskula-Sztul R. Mobley J.A. Mukhtar M.S. Tasciotti E. Chen H. Bibb J.A. Phosphoprotein-based biomarkers as predictors for cancer therapy. Proc. Natl. Acad. Sci. USA 2020 117 31 18401 18411 10.1073/pnas.2010103117 32690709
    [Google Scholar]
  199. Li W. Li F. Zhang X. Lin H.K. Xu C. Insights into the post-translational modification and its emerging role in shaping the tumor microenvironment. Signal Transduct. Target. Ther. 2021 6 1 422 10.1038/s41392‑021‑00825‑8 34924561
    [Google Scholar]
  200. Li Z. Li S. Luo M. Jhong J.H. Li W. Yao L. Pang Y. Wang Z. Wang R. Ma R. Yu J. Huang Y. Zhu X. Cheng Q. Feng H. Zhang J. Wang C. Hsu J.B.K. Chang W.C. Wei F.X. Huang H.D. Lee T.Y. dbPTM in 2022: An updated database for exploring regulatory networks and functional associations of protein post-translational modifications. Nucleic Acids Res. 2022 50 D1 D471 D479 10.1093/nar/gkab1017 34788852
    [Google Scholar]
  201. Craveur P. Rebehmed J. de Brevern A.G. PTM-SD: A database of structurally resolved and annotated posttranslational modifications in proteins. Database (Oxford) 2014 2014 0 bau041 10.1093/database/bau041 24857970
    [Google Scholar]
  202. Yu K. Wang Y. Zheng Y. Liu Z. Zhang Q. Wang S. Zhao Q. Zhang X. Li X. Xu R.H. Liu Z.X. qPTM: An updated database for PTM dynamics in human, mouse, rat and yeast. Nucleic Acids Res. 2023 51 D1 D479 D487 10.1093/nar/gkac820 36165955
    [Google Scholar]
  203. Liu Z. Wang Y. Gao T. Pan Z. Cheng H. Yang Q. Cheng Z. Guo A. Ren J. Xue Y. CPLM: A database of protein lysine modifications. Nucleic Acids Res. 2014 42 D1 D531 D536 10.1093/nar/gkt1093 24214993
    [Google Scholar]
  204. Lin S. Wang C. Zhou J. Shi Y. Ruan C. Tu Y. Yao L. Peng D. Xue Y. EPSD: A well-annotated data resource of protein phosphorylation sites in eukaryotes. Brief. Bioinform. 2021 22 1 298 307 10.1093/bib/bbz169 32008039
    [Google Scholar]
  205. Lee T.Y. Bo-Kai Hsu J. Chang W.C. Huang H.D. RegPhos: A system to explore the protein kinase–substrate phosphorylation network in humans. Nucleic Acids Res. 2011 39 Database issue Suppl. 1 D777 D787 10.1093/nar/gkq970 21037261
    [Google Scholar]
  206. Dinkel H. Chica C. Via A. Gould C.M. Jensen L.J. Gibson T.J. Diella F. Phospho.ELM: A database of phosphorylation sites--update 2011. Nucleic Acids Res. 2011 39 Database D261 D267 10.1093/nar/gkq1104 21062810
    [Google Scholar]
  207. Chen T. Zhou T. He B. Yu H. Guo X. Song X. Sha J. mUbiSiDa: A comprehensive database for protein ubiquitination sites in mammals. PLoS One 2014 9 1 e85744 10.1371/journal.pone.0085744 24465676
    [Google Scholar]
  208. Wang D. Liu D. Yuchi J. He F. Jiang Y. Cai S. Li J. Xu D. MusiteDeep: A deep-learning based webserver for protein post-translational modification site prediction and visualization. Nucleic Acids Res. 2020 48 W1 W140 W146 10.1093/nar/gkaa275 32324217
    [Google Scholar]
  209. Deng W. Wang C. Zhang Y. Xu Y. Zhang S. Liu Z. Xue Y. GPS-PAIL: Prediction of lysine acetyltransferase-specific modification sites from protein sequences. Sci. Rep. 2016 6 1 39787 10.1038/srep39787 28004786
    [Google Scholar]
  210. Nickchi P. Mirzaie M. Baumann M. Saei A.A. Jafari M. Monitoring functional post-translational modifications using a data-driven proteome informatic pipeline based on PEIMAN2. bioRxiv 2022 10.1101/2022.11.09.515610
    [Google Scholar]
  211. Chang C.C. Tung C.H. Chen C.W. Tu C.H. Chu Y.W. SUMOgo: Prediction of sumoylation sites on lysines by motif screening models and the effects of various post-translational modifications. Sci. Rep. 2018 8 1 15512 10.1038/s41598‑018‑33951‑5 30341374
    [Google Scholar]
  212. Fu H. Yang Y. Wang X. Wang H. Xu Y. DeepUbi: A deep learning framework for prediction of ubiquitination sites in proteins. BMC Bioinformatics 2019 20 1 86 10.1186/s12859‑019‑2677‑9 30777029
    [Google Scholar]
  213. Li S.H. Zhang J. Zhao Y.W. Dao F.Y. Ding H. Chen W. Tang H. iPhoPred: A Predictor for Identifying Phosphorylation Sites in Human Protein. IEEE Access 2019 7 177517 177528 10.1109/ACCESS.2019.2953951
    [Google Scholar]
  214. Deng W. Wang Y. Ma L. Zhang Y. Ullah S. Xue Y. Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins. Brief. Bioinform. 2016 18 4 bbw041 10.1093/bib/bbw041 27241573
    [Google Scholar]
  215. Yang H. Wang M. Liu X. Zhao X.M. Li A. PhosIDN: An integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein–protein interaction information. Bioinformatics 2021 37 24 4668 4676 10.1093/bioinformatics/btab551 34320631
    [Google Scholar]
  216. Akbarian M. Khani A. Eghbalpour S. Uversky V.N. Bioactive Peptides: Synthesis, Sources, Applications, and Proposed Mechanisms of Action. Int. J. Mol. Sci. 2022 23 3 1445 10.3390/ijms23031445 35163367
    [Google Scholar]
  217. Wu J. An G. Lin S. Xie J. Zhou W. Sun H. Pan Y. Li G. Solution-phase-peptide synthesis via the group-assisted purification (GAP) chemistry without using chromatography and recrystallization. Chem. Commun. (Camb.) 2014 50 10 1259 1261 10.1039/C3CC48509A 24336500
    [Google Scholar]
  218. Mahindra A. Sharma K.K. Jain R. Rapid microwave-assisted solution-phase peptide synthesis. Tetrahedron Lett. 2012 53 51 6931 6935 10.1016/j.tetlet.2012.10.028
    [Google Scholar]
  219. Conibear A.C. Watson E.E. Payne R.J. Becker C.F.W. Native chemical ligation in protein synthesis and semi-synthesis. Chem. Soc. Rev. 2018 47 24 9046 9068 10.1039/C8CS00573G 30418441
    [Google Scholar]
  220. Fields G.B. Introduction to peptide synthesis. Hoboken, New Jersey, U.S. John Wiley & Sons, Inc. 2002 11 19
    [Google Scholar]
  221. Akintayo D.C. de la Torre B.G. Li Y. Albericio F. Amino-li-resin—a fiber polyacrylamide resin for solid-phase peptide synthesis. Polymers (Basel) 2022 14 5 928 10.3390/polym14050928 35267752
    [Google Scholar]
  222. Souza S.E.G. Malavolta L. Salomoni L.F. Cilli E.M. Schreier S. Jubilut G.N. Nakaie C.R. Evaluation of 4- tert -Butyl-Benzhydrylamine Resin (BUBHAR) as an Alternative Solid Support for Peptide Synthesis. Int. J. Polym. Sci. 2020 2020 1 1 7 10.1155/2020/5479343
    [Google Scholar]
  223. Amblard M. Fehrentz J.A. Martinez J. Subra G. Methods and protocols of modern solid phase Peptide synthesis. Mol. Biotechnol. 2006 33 3 239 254 10.1385/MB:33:3:239 16946453
    [Google Scholar]
  224. Simon M.D. Mijalis A.J. Totaro K.A. Dunkelmann D. Vinogradov A.A. Zhang C. Automated Fast Flow Peptide Synthesis. Total Chemical Synthesis of Proteins Hoboken, New Jersey Wiley 2021 17 57
    [Google Scholar]
  225. Wen C. Zhang J. Zhang H. Duan Y. Ma H. Plant protein-derived antioxidant peptides: Isolation, identification, mechanism of action and application in food systems: A review. Trends Food Sci. Technol. 2020 105 308 322 10.1016/j.tifs.2020.09.019
    [Google Scholar]
  226. Kumar A. Jad Y.E. Collins J.M. Albericio F. de la Torre B.G. Microwave-Assisted Green Solid-Phase Peptide Synthesis Using γ-Valerolactone (GVL) as Solvent. ACS Sustain. Chem.& Eng. 2018 6 6 8034 8039 10.1021/acssuschemeng.8b01531
    [Google Scholar]
  227. Wang G. Ang H.T. Dubbaka S.R. O’Neill P. Wu J. Multistep automated synthesis of pharmaceuticals. Trends Chem. 2023 5 6 432 445 10.1016/j.trechm.2023.03.008
    [Google Scholar]
  228. Kiss K. Ránky S. Gyulai Z. Molnár L. Development of a novel, automated, robotic system for rapid, high-throughput, parallel, solid-phase peptide synthesis. SLAS Technol. 2023 28 2 89 97 10.1016/j.slast.2023.01.002 36649783
    [Google Scholar]
  229. Kaur J. Saxena M. Rishi N. An Overview of Recent Advances in Biomedical Applications of Click Chemistry. Bioconjug. Chem. 2021 32 8 1455 1471 10.1021/acs.bioconjchem.1c00247 34319077
    [Google Scholar]
  230. Meldal M. Diness F. Recent Fascinating Aspects of the CuAAC Click Reaction. Trends Chem. 2020 2 6 569 584 10.1016/j.trechm.2020.03.007
    [Google Scholar]
  231. Timmers M. Kipper A. Frey R. Notermans S. Voievudskyi M. Wilson C. Hentzen N. Ringle M. Bovino C. Stump B. Rijcken C.J.F. Vermonden T. Dijkgraaf I. Liskamp R. Exploring the Chemical Properties and Medicinal Applications of Tetramethylthiocycloheptyne Sulfoximine Used in Strain-Promoted Azide–Alkyne Cycloaddition Reactions. Pharmaceuticals (Basel) 2023 16 8 1155 10.3390/ph16081155 37631074
    [Google Scholar]
  232. Giesler R.J. Erickson P.W. Kay M.S. Enhancing native chemical ligation for challenging chemical protein syntheses. Curr. Opin. Chem. Biol. 2020 58 37 44 10.1016/j.cbpa.2020.04.003 32745915
    [Google Scholar]
  233. Dawson P.E. Muir T.W. Clark-Lewis I. Kent S.B.H. Synthesis of proteins by native chemical ligation. Science 1994 266 5186 776 779 10.1126/science.7973629 7973629
    [Google Scholar]
  234. Agouridas V. El Mahdi O. Diemer V. Cargoët M. Monbaliu J.C.M. Melnyk O. Native Chemical Ligation and Extended Methods: Mechanisms, Catalysis, Scope, and Limitations. Chem. Rev. 2019 119 12 7328 7443 10.1021/acs.chemrev.8b00712 31050890
    [Google Scholar]
  235. Wan Q. Danishefsky S.J. Free-radical-based, specific desulfurization of cysteine: A powerful advance in the synthesis of polypeptides and glycopolypeptides. Angew. Chem. Int. Ed. 2007 46 48 9248 9252 10.1002/anie.200704195 18046687
    [Google Scholar]
  236. Zou J. Zhou M. Xiao X. Liu R. Advance in hybrid peptides synthesis. Macromol. Rapid Commun. 2022 43 23 2200575 10.1002/marc.202200575 35978269
    [Google Scholar]
  237. Mant C.T. Chen Y. Yan Z. Popa T.V. Kovacs J.M. Mills J.B. HPLC Analysis and Purification of Peptides. Peptide Characterization and Application Protocols. Fields G.B. Totowa, NJ Humana Press 2007 3 55
    [Google Scholar]
  238. Strege M.A. Oman T.J. Risley D.S. Muehlbauer L.K. Jalan A. Jerry Lian Z. Enantiomeric purity analysis of synthetic peptide therapeutics by direct chiral high-performance liquid chromatography-electrospray ionization tandem mass spectrometry. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2023 1219 123638 10.1016/j.jchromb.2023.123638 36857849
    [Google Scholar]
  239. Isidro-Llobet A. Kenworthy M.N. Mukherjee S. Kopach M.E. Wegner K. Gallou F. Smith A.G. Roschangar F. Sustainability Challenges in Peptide Synthesis and Purification: From R&D to Production. J. Org. Chem. 2019 84 8 4615 4628 10.1021/acs.joc.8b03001 30900880
    [Google Scholar]
  240. Ferrazzano L. Catani M. Cavazzini A. Martelli G. Corbisiero D. Cantelmi P. Fantoni T. Mattellone A. De Luca C. Felletti S. Cabri W. Tolomelli A. Sustainability in peptide chemistry: Current synthesis and purification technologies and future challenges. Green Chem. 2022 24 3 975 1020 10.1039/D1GC04387K
    [Google Scholar]
  241. Ali A. Alharthi S. Al-Shaalan N. Santali E. Development of Narrow-Bore C18 Column for Fast Separation of Peptides and Proteins in High-Performance Liquid Chromatography. Polymers (Basel) 2022 14 13 2576 10.3390/polym14132576 35808622
    [Google Scholar]
  242. Alharthi S. Ali A. Iqbal M. Ibrar A. Ahmad B. Nisa S. Mabood F. Preparation of mixed-mode stationary phase for separation of peptides and proteins in high performance liquid chromatography. Sci. Rep. 2022 12 1 4061 10.1038/s41598‑022‑08074‑7 35260726
    [Google Scholar]
  243. Zhang H. Zhang S. Chen L. Xu R. Zhu J. LC-HRMS-based metabolomics and lipidomics analyses of a novel probiotic Akkermansia Muciniphila in response to different nutritional stimulations. J. Microbiol. Methods 2024 223 106975 10.1016/j.mimet.2024.106975 38889842
    [Google Scholar]
  244. Ishii C. Tojo Y. Iwasaki K. Fujii A. Akita T. Nagano M. Mita M. Ide T. Hamase K. Development of a two-dimensional LC–MS/MS system for the determination of proline and 4-hydroxyproline enantiomers in biological and food samples. Anal. Sci. 2024 40 5 881 889 10.1007/s44211‑024‑00530‑w 38598049
    [Google Scholar]
  245. Bouvarel T. Camperi J. Guillarme D. Multi‐dimensional technology – Recent advances and applications for biotherapeutic characterization. J. Sep. Sci. 2024 47 5 2300928 10.1002/jssc.202300928 38471977
    [Google Scholar]
  246. El Ouahabi O. Mancera-Arteu M. Latorre I. Salvadó M. Rodríguez-Vidal S. Sanz-Nebot V. Rapid and simple dual extraction for the analysis of lipids and autoantigenic peptides within phosphatidylserine-liposomes. Microchem. J. 2024 206 111420 10.1016/j.microc.2024.111420
    [Google Scholar]
  247. Rygula A. Majzner K. Marzec K.M. Kaczor A. Pilarczyk M. Baranska M. Raman spectroscopy of proteins: A review. J. Raman Spectrosc. 2013 44 8 1061 1076 10.1002/jrs.4335
    [Google Scholar]
  248. Bakshi K. Liyanage M.R. Volkin D.B. Middaugh C.R. Circular dichroism of peptides. Methods Mol. Biol. 2014 1088 247 253 10.1007/978‑1‑62703‑673‑3_17 24146409
    [Google Scholar]
  249. Keiderling T.A. Structure of Condensed Phase Peptides: Insights from Vibrational Circular Dichroism and Raman Optical Activity Techniques. Chem. Rev. 2020 120 7 3381 3419 10.1021/acs.chemrev.9b00636 32101406
    [Google Scholar]
  250. Ji Y. Yang X. Ji Z. Zhu L. Ma N. Chen D. Jia X. Tang J. Cao Y. DFT-Calculated IR Spectrum Amide I, II, and III Band Contributions of N -Methylacetamide Fine Components. ACS Omega 2020 5 15 8572 8578 10.1021/acsomega.9b04421 32337419
    [Google Scholar]
  251. Koenis M.A.J. Visscher L. Buma W.J. Nicu V.P. Analysis of Vibrational Circular Dichroism Spectra of Peptides: A Generalized Coupled Oscillator Approach of a Small Peptide Model Using VCDtools. J. Phys. Chem. B 2020 124 9 acs.jpcb.9b11261 10.1021/acs.jpcb.9b11261 32037822
    [Google Scholar]
  252. Eikås K.D.R. Krupová M. Kristoffersen T. Beerepoot M.T.P. Ruud K. Can the absolute configuration of cyclic peptides be determined with vibrational circular dichroism? Phys. Chem. Chem. Phys. 2023 25 20 14520 14529 10.1039/D2CP04942B 37190985
    [Google Scholar]
  253. Keiderling T.A. Protein and peptide secondary structure and conformational determination with vibrational circular dichroism. Curr. Opin. Chem. Biol. 2002 6 5 682 688 10.1016/S1367‑5931(02)00369‑1 12413554
    [Google Scholar]
  254. Maveyraud L. Mourey L. Protein X-ray Crystallography and Drug Discovery. Molecules 2020 25 5 1030 10.3390/molecules25051030 32106588
    [Google Scholar]
  255. Hawkins B. Cross K. Craik D. Solution structure of the B‐chain of insulin as determined by 1 H NMR spectroscopy Comparison with the crystal structure of the insulin hexamer and with the solution structure of the insulin monomer. Int. J. Pept. Protein Res. 1995 46 5 424 433 10.1111/j.1399‑3011.1995.tb01077.x 8567187
    [Google Scholar]
  256. Yu K Park K Kang S-W Shin SY Hahm Ks Kim Y Solution structure of a cathelicidin-derived antimicrobial peptide, CRAMP as determined by NMR spectroscopy. J. Pept. Res. 2002 60 1 1 9
    [Google Scholar]
  257. Kamagata K. Mano E. Itoh Y. Wakamoto T. Kitahara R. Kanbayashi S. Takahashi H. Murata A. Kameda T. Rational design using sequence information only produces a peptide that binds to the intrinsically disordered region of p53. Sci. Rep. 2019 9 1 8584 10.1038/s41598‑019‑44688‑0 31253862
    [Google Scholar]
  258. Emwas A.H.M. The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods Mol. Biol. 2015 1277 161 193 10.1007/978‑1‑4939‑2377‑9_13 25677154
    [Google Scholar]
  259. Piper S.J. Johnson R.M. Wootten D. Sexton P.M. Membranes under the Magnetic Lens: A Dive into the Diverse World of Membrane Protein Structures Using Cryo-EM. Chem. Rev. 2022 122 17 13989 14017 10.1021/acs.chemrev.1c00837 35849490
    [Google Scholar]
  260. Liang Y.L. Khoshouei M. Radjainia M. Zhang Y. Glukhova A. Tarrasch J. Thal D.M. Furness S.G.B. Christopoulos G. Coudrat T. Danev R. Baumeister W. Miller L.J. Christopoulos A. Kobilka B.K. Wootten D. Skiniotis G. Sexton P.M. Phase-plate cryo-EM structure of a class B GPCR–G-protein complex. Nature 2017 546 7656 118 123 10.1038/nature22327 28437792
    [Google Scholar]
  261. Bachman J. Site-Directed Mutagenesis. Methods Enzymol. 2013 529 241 248 10.1016/B978‑0‑12‑418687‑3.00019‑7 24011050
    [Google Scholar]
  262. Watanabe S. Ito M. Kigawa T. DiRect: Site-directed mutagenesis method for protein engineering by rational design. Biochem. Biophys. Res. Commun. 2021 551 107 113 10.1016/j.bbrc.2021.03.021 33725571
    [Google Scholar]
  263. Drienovská I. Roelfes G. Expanding the enzyme universe with genetically encoded unnatural amino acids. Nat. Catal. 2020 3 3 193 202 10.1038/s41929‑019‑0410‑8
    [Google Scholar]
  264. Nickling J.H. Baumann T. Schmitt F.J. Bartholomae M. Kuipers O.P. Friedrich T. Budisa N. Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids. J. Vis. Exp. 2018 135 57551 10.3791/57551 29781997
    [Google Scholar]
  265. Meineke B. Heimgärtner J. Caridha R. Block M.F. Kimler K.J. Pires M.F. Landreh M. Elsässer S.J. Dual stop codon suppression in mammalian cells with genomically integrated genetic code expansion machinery. Cell Reports Methods 2023 3 11 100626 10.1016/j.crmeth.2023.100626 37935196
    [Google Scholar]
  266. Qiao Y. Yu G. Leeuwon S.Z. Liu W.R. Site-Specific Conversion of Cysteine in a Protein to Dehydroalanine Using 2-Nitro-5-thiocyanatobenzoic Acid. Molecules 2021 26 9 2619 10.3390/molecules26092619 33947165
    [Google Scholar]
  267. De Cena G.L. Scavassa B.V. Conceição K. In silico prediction of anti-infective and cell-penetrating peptides from Thalassophryne nattereri natterin toxins. Pharmaceuticals (Basel) 2022 15 9 1141 10.3390/ph15091141 36145362
    [Google Scholar]
  268. Pal A. Neo K. Rajamani L. Ferrer F.J. Lane D.P. Verma C.S. Mortellaro A. Inhibition of NLRP3 inflammasome activation by cell-permeable stapled peptides. Sci. Rep. 2019 9 1 4913 10.1038/s41598‑019‑41211‑3 30894604
    [Google Scholar]
  269. Lu J. Xu H. Xia J. Ma J. Xu J. Li Y. Feng J. D- and Unnatural Amino Acid Substituted Antimicrobial Peptides With Improved Proteolytic Resistance and Their Proteolytic Degradation Characteristics. Front. Microbiol. 2020 11 563030 10.3389/fmicb.2020.563030 33281761
    [Google Scholar]
  270. Zhang Y. Wang J. Li W. Guo Y. Rational design of stapled helical peptides as antidiabetic PPARγ antagonists to target coactivator site by decreasing unfavorable entropy penalty instead of increasing favorable enthalpy contribution. Eur. Biophys. J. 2022 51 7-8 535 543 10.1007/s00249‑022‑01616‑x 36057906
    [Google Scholar]
  271. Naeem A. Noureen N. Al-Naemi S.K. Al-Emadi J.A. Khan M.J. Computational design of anti-cancer peptides tailored to target specific tumor markers. BMC Chem. 2024 18 1 39 10.1186/s13065‑024‑01143‑0 38388460
    [Google Scholar]
  272. Gonçalves P.B. Sodero A.C.R. Cordeiro Y. Natural products targeting amyloid-β oligomer neurotoxicity in Alzheimer’s disease. Eur. J. Med. Chem. 2024 276 116684 10.1016/j.ejmech.2024.116684 39032401
    [Google Scholar]
  273. Delgado M. Garcia-Sanz J.A. Therapeutic monoclonal antibodies against cancer: Present and Future. Cells 2023 12 24 2837 10.3390/cells12242837 38132155
    [Google Scholar]
  274. Listov D. Goverde C.A. Correia B.E. Fleishman S.J. Opportunities and challenges in design and optimization of protein function. Nat. Rev. Mol. Cell Biol. 2024 25 8 639 653 10.1038/s41580‑024‑00718‑y 38565617
    [Google Scholar]
  275. Sharma K. Sharma K.K. Sharma A. Jain R. Peptide-based drug discovery: Current status and recent advances. Drug Discov. Today 2023 28 2 103464 10.1016/j.drudis.2022.103464 36481586
    [Google Scholar]
  276. Thien N.D. Hai-Nam N. Anh D.T. Baecker D. Piezo1 and its inhibitors: Overview and perspectives. Eur. J. Med. Chem. 2024 273 116502 10.1016/j.ejmech.2024.116502 38761789
    [Google Scholar]
  277. Naeem M. Malik M.I. Umar T. Ashraf S. Ahmad A. A Comprehensive Review About Bioactive Peptides: Sources to Future Perspective. Int. J. Pept. Res. Ther. 2022 28 6 155 10.1007/s10989‑022‑10465‑3
    [Google Scholar]
  278. Heh E. Allen J. Ramirez F. Lovasz D. Fernandez L. Hogg T. Riva H. Holland N. Chacon J. Peptide Drug Conjugates and Their Role in Cancer Therapy. Int. J. Mol. Sci. 2023 24 1 829 10.3390/ijms24010829 36614268
    [Google Scholar]
/content/journals/cpd/10.2174/0113816128349577240927071706
Loading
Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis
Bentham Science Publishers ; https://doi.org/10.2174/0113816128349577240927071706
/content/journals/cpd/10.2174/0113816128349577240927071706
/content/journals/cpd/10.2174/0113816128349577240927071706
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: Drug designing ; protein engineering ; peptide synthesis ; mutation ; in-silico tools

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test