Current Medicinal Chemistry - Volume 31, Issue 26, 2024

Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction.

Background: Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2, which gave rise to coronavirus disease 2019 (COVID-19), has considerably impacted global health. The identification of effective anticoronavirus peptides (ACVPs) and the establishment of robust data storage methods are critical in the fight against COVID-19. Traditional wet-lab peptide discovery approaches are timeconsuming and labor-intensive. With advancements in computer technology and bioinformatics, machine learning has gained prominence in the extraction of functional peptides from extensive datasets. Methods: In this study, we comprehensively review data resources and predictors related to ACVPs published over the past two decades. In addition, we analyze the influence of various factors on model performance. Results: We have reviewed nine ACVP-containing databases, which integrate detailed information on protein fragments effective against coronaviruses, providing crucial references for the development of antiviral drugs and vaccines. Additionally, we have assessed 15 peptide predictors for antiviral or specifically anticoronavirus activity. These predictors employ computational models to swiftly screen potential antiviral candidates, offering an efficient pathway for drug development. Conclusion: Our study provides conclusive results and insights into the performance of different computational methods, and sheds light on the future trajectory of bioinformatics tools for ACVPs. This work offers a representative overview of contributions to the field, with an emphasis on the crucial role of ACVPs in combating COVID-19.

The application of therapeutic peptides in clinical practice has significantly progressed in the past decades. However, immunogenicity remains an inevitable and crucial issue in the development of therapeutic peptides. The prediction of antigenic peptides presented by MHC class II is a critical approach to evaluating the immunogenicity of therapeutic peptides. With the continuous upgrade of algorithms and databases in recent years, the prediction accuracy has been significantly improved. This has made in silico evaluation an important component of immunogenicity assessment in therapeutic peptide development. In this review, we summarize the development of peptide-MHC-II binding prediction methods for antigenic peptides presented by MHC class II molecules and provide a systematic explanation of the most advanced ones, aiming to deepen our understanding of this field that requires particular attention.

The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through specific structures, providing insights into the treatment and prevention of diseases. With the advancement of AI technology in the field of protein structure prediction, computational methods have become increasingly important and promising for studying protein folding mechanisms. In this review, we retrospect the current progress in the field of protein folding mechanisms by computational methods from four perspectives: simulation of an inverse folding pathway from native state to unfolded state; prediction of early folding residues by machine learning; exploration of protein folding pathways through conformational sampling; prediction of protein folding intermediates based on templates. Finally, the challenges and future perspectives of the protein folding problem by computational methods are also discussed.

Background: Peptides play crucial roles in diverse cellular functions and participate in many biological processes by interacting with a variety of proteins, which have also been exploited as a promising class of therapeutic agents to target druggable proteins over the past decades. Understanding the intrinsic association between the structure and affinity of protein-peptide interactions (PpIs) should be considerably valuable for the computational peptidology area, such as guiding protein-peptide docking calculations, developing protein-peptide affinity scoring functions, and designing peptide ligands for specific protein receptors. Objective: We attempted to create a data source for relating PpI structure to affinity. Methods: By exhaustively surveying the whole protein data bank (PDB) database as well as the ontologically enriched literature information, we manually curated a structure- based data set of protein-peptide affinities, PpI[S/A]DS, which assembled over 350 PpI complex samples with both the experimentally measured structure and affinity data. The data set was further reduced to a nonredundant benchmark consisting of 102 culled samples, PpI[S/A]BM, which only selected those of structurally reliable, functionally diverse and evolutionarily nonhomologous. Results: The collected structures were resolved at a high-resolution level with either Xray crystallography or solution NMR, while the deposited affinities were characterized by dissociation constant, i.e. Kd value, which is a direct biophysical measure of the intermolecular interaction strength between protein and peptide, ranging from subnanomolar to millimolar levels. The PpI samples in the set/benchmark were arbitrarily classified into α-helix, partial α-helix, β-sheet formed through binding, β-strand formed through selffolding, mixed, and other irregular ones, totally resulting in six classes according to the secondary structure of their peptide ligands. In addition, we also categorized these PpIs in terms of their biological function and binding behavior. Conclusion: The PpI[S/A]DS set and PpI[S/A]BM benchmark can be considered a valuable data source in the computational peptidology community, aiming to relate the affinity to structure for PpIs.

Background: Thrombosis is one of the major causes of morbidity and mortality in a wide range of vessel diseases. Several studies have been conducted to identify antithrombotic agents from medicinal plants, and phenolic compounds (PCs) have been shown to effectively inhibit plasma coagulation and platelet aggregation. Objectives: This study aimed to conduct a survey of the natural PCs with proven antithrombotic and antiplatelet activities, as well as to evaluate by computational modeling the physicochemical and toxicological properties of these compounds using drug-likeness approaches. Methods: The data were collected from the scientific database: ‘Web of Science’, ‘Scifinder’, ‘Pubmed’, ‘ScienceDirect’ and ‘Google Scholar’, the different classes of PCs with antithrombotic or antiplatelet effects were used as keywords. These molecules were also evaluated for their Drug-Likeness properties and toxicity to verify their profile for being candidates for new antithrombotic drugs. Results: In this review, it was possible to register 85 lignans, 73 flavonoids, 28 coumarins, 21 quinones, 23 phenolic acids, 8 xanthones and 8 simple phenols. Activity records for tannins were not found in the researched databases. Of these 246 compounds, 213 did not violate any of Lipinski's rules of five, of which 125 (59%) showed non-toxicity, being promising candidates for new potential antithrombotic drugs. Conclusion: This review arouses interest in the isolation of phenolic compounds that may allow a new approach for the prevention of both arterial and venous thrombosis, with the potential to become alternatives in the prevention and treatment of cardiovascular diseases.

Bile acids have outstanding chemistry due to their amphiphilic nature and have received a lot of interest in the last few decades in the fields of biomedicine, pharmacology, and supramolecular applications. Bile acids are highly sought after by scientists looking for diverse and effective biological activity due to their chirality, rigidity, and hydroxyl group. The hydroxyl group makes it simple to alter the structure in a way that improves bioactivity and bioavailability. Bile acid-bioactive molecule conjugates are compounds in which a bile acid is linked to a bioactive molecule by a linker in order to increase the bioactivity of the bioactive molecule against the target cancer cells. This method has been used to improve the therapeutic efficacy of cytotoxic drugs while reducing their adverse side effects. These new bile acid conjugates are gaining attention because they overcome bioavailability and stability issues. The design, synthesis, and anticancer effectiveness of various bile acid conjugates are discussed together with recent advances in understanding in this review.

Quercetin, a naturally occurring polyphenolic compound found in abundance in vegetables and fruits, has emerged as a compelling subject of study in cancer treatment. This comprehensive review delves into the significance and originality of quercetin's multifaceted mechanisms of action, with a particular focus on its application in various brain tumors such as glioblastoma, glioma, neuroblastoma, astrocytoma, and medulloblastoma. This review scrutinizes the distinctive facets of quercetin's anti-cancer properties, highlighting its capacity to modulate intricate signaling pathways, trigger apoptosis, impede cell migration, and enhance radiosensitivity in brain tumor cells. Significantly, it synthesizes recent research findings, providing insights into potential structure-activity relationships that hold promise for developing novel quercetin derivatives with heightened effectiveness. By unraveling the unique attributes of quercetin's anti-brain tumor effects and exploring its untapped potential in combination therapies, this review contributes to a deeper comprehension of quercetin's role as a prospective candidate for advancing innovative treatments for brain cancer.

As a physiological condition, pregnancy may cause temporary alterations in the hematological, cardiopulmonary, and immune responses, affecting the maternal susceptibility to viral infections. Pregnant women are vulnerable to infection with the influenza A virus, hepatitis E virus, MERS CoV, and SARS CoV. The agent of Coronavirus disease (COVID-19) is the SARS coronavirus (SARS CoV-2), which affects the cells upon binding to the angiotensin-converting enzyme-2 (ACE2). However, ACE2 expression is elevated in the placental tissue. However, surprisingly, COVID-19 infection in pregnant women tends to have a lower severity and mortality. Therefore, it is interesting to find the immunological mechanisms related to the severity of COVID-19 in pregnancy. Regulatory T cells (Tregs) are a subset of CD4+T cells that may play a central role in maintaining maternal tolerance by regulating immune responses. Pregnancy-induced Tregs are developed to control immune responses against paternal antigens expressed by the semi-allograft fetus. The role of uncontrolled immune responses in COVID-19 pathogenesis has already been identified. This review provides insight into whether pregnancy- induced regulatory T-cell functions could influence the severity of COVID-19 infection during pregnancy.

Background: Ovarian cancer (OC) is the deadliest malignant tumor in women with a poor prognosis due to drug resistance and lack of prediction tools for therapeutic responses to anti- cancer drugs. Objective: The objective of this study was to launch a prediction model for therapeutic responses in OC patients. Methods: The RNA-seq technique was used to identify differentially expressed paclitaxel (PTX)- resistant lncRNAs (DE-lncRNAs). The Cancer Genome Atlas (TCGA)-OV and ImmPort database were used to obtain immune-related lncRNAs (ir-lncRNAs). Univariate, multivariate, and LASSO Cox regression analyses were performed to construct the prediction model. Kaplan- meier plotter, Principal Component Analysis (PCA), nomogram, immune function analysis, and therapeutic response were applied with Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT, and TCGA databases. The biological functions were evaluated in the CCLE database and OC cells. Results: The RNA-seq defined 186 DE-lncRNAs between PTX-resistant A2780-PTX and PTXsensitive A2780 cells. Through the analysis of the TCGA-OV database, 225 ir-lncRNAs were identified. Analyzing 186 DE-lncRNAs and 225 ir-lncRNAs using univariate, multivariate, and LASSO Cox regression analyses, 9 PTX-resistant immune-related lncRNAs (DEir-lncRNAs) acted as biomarkers were discovered as potential biomarkers in the prediction model. Single-cell RNA sequencing (scRNA-seq) data of OC confirmed the relevance of DEir-lncRNAs in immune responsiveness. Patients with a low prediction score had a promising prognosis, whereas patients with a high prediction score were more prone to evade immunotherapy and chemotherapy and had poor prognosis. Conclusion: The novel prediction model with 9 DEir-lncRNAs is a valuable tool for predicting immunotherapeutic and chemotherapeutic responses and prognosis of patients with OC.