Current Computer - Aided Drug Design - Volume 12, Issue 2, 2016

Background: The recent epidemic of Zika virus infections in South and Latin America have raised serious concern on its ramifications for the population in the Americas and spread of the virus worldwide. The Zika virus disease is a relatively new phenomenon for which sufficient and comprehensive data and investigative reports have not been available to date. Objective: To carry out a bioinformatics study of the available Zika virus genomic sequences to characterize the virus. Method: 2D graphical representation method is used for visual rendering and compute sequence parameters and descriptors of the African and Asian-American groups of the Zika viruses to characterize the sequences. We also used MEGA5.2 and other software to compute various biological properties of interest like phylogenetic relationships, transition-transversion ratios, amino acid usage, codon usage bias and hydropathy index of the Zika genomes and virions. Results: The phylogenetic relationships show that the African and Asian-American Zika virus genomes are grouped in two clades. The 2D plots of typical genomes of these types also show dramatic differences indicating that the gene sequences at the 5’-end coding regions for the structural proteins are rather strongly conserved. Among other characteristics, the transition/transversion ratio matrices for the sequences in each of the two clades show that analogous to the dengue virus, the transition rates are about 10 to 15 times the transversion rates. Conclusion: These findings are important for computer-assisted approaches towards surveillance of emerging Zika virus strains as well as in the design of drugs and vaccines to combat the growth and spread of the Zika virus.

The coronavirus Nucleocapsid (N) plays an important role in the virus structure, the replication, and the transcription of CoV. This protein, which has a helix and flexible structure, and is capable of binding on to the viral genomic RNA, is a non-structural protein (nsp3). Many studies suggest that the N protein interaction with nsp3 plays a critical role in the virus replication early in infection. Therefore, it is necessary to know the definition of the interaction mechanism of N and nsp3 protein in terms of the CoV replication transcription mechanism. We report on the homology modeling, molecular dynamics simulation, and docking studies to explain the structure-function relationship and the interaction mechanism. In addition, the prototype MHV is preferred in the wet experiment, so we also based our study on the MHV N and nsp3 proteins that belong to the experimental study. The amino acid sequences of MHV N and nsp3 proteins have similarity between human and severe acute respiratory syndrome coronavirus. Therefore, the 3D structure models of these proteins were built with using the crystal structure of the CoV family members as a template. By following these models, molecular dynamics simulations were applied to attain the most stable conformation. Finally, protein-protein docking was performed to prove accuracy of model structures of the MHV N and to clarify the interaction with nsp3. As a result, Lys 113, Arg 125, Tyr 127, Glu 173, Tyr 190 residues that play an important role in virus replication were determined.

Background: Mutant actins D157E and R183A-D184A are reported to resist the anticancer drug Latrunculin A (LAT); though identified, the mechanism of resistance is not clearly understood. Objective: To design better molecules that can overcome the resistance caused by mutations it is important to define precise pharmacophoric regions in LAT based on the mechanism of resistance on the mutant actin –LAT interactions. Methods: To address this we have conducted 20 nano seconds (ns) simulation of mutant actins – LAT complex and compared it with the 20ns simulation of wild actin – LAT complex. Functions as the binding free energy, distance between LAT and binding site residues, LAT and actin domains, dihedral angle analysis, motional correlation were studied of these simulations. Results: Grounded on these studies, four sites in LAT are identified to be crucial for modification. Bulkier ring moieties containing nitrogen in place of the double bonded oxygen in the macrocyclic lactone ring may be considered to establish interactions with Glu214. The nitrogen in 2-thiazolidinone moiety can be substituted with a hydrophobic ring to stabilise the interaction with the Asp157Glu and the oxygen in the cyclohexane of LAT with hydrophilic groups to strengthen their interaction with Tyr69. The nitrogen of the 2-thiazolidinone moiety can be replaced with nitrogen containing rings to improve inhibition of the actin polymerisation. Apart from this chemical groups on the sulphur of 2-thiazolidinone moiety to improve the hydrophobic interaction with actin is also identified for modification. Conclusion: Based on this a combinatorial library of 46 LAT analogs was generated and docked with the wild and mutant actins to identify potent leads to become anti-actin anticancer drugs.

Background: Methicillin-resistant Staphylococcus aureus (MRSA) has been a global public health problem because MRSA infection often leads to poor clinical outcomes. Currently, the search for an effective candidate has been ongoing. Rhodomyrtone, a natural compound, has been exhibited strong anti-MRSA activity comparable to that of vancomycin, a drug of choice for MRSA treatment. An important procedure to develop the compound in clinical use is elucidation of its molecular mechanism. However, previous attempts were performed to clarify the mechanism but ambiguity still exists. With this aspect, computer aided techniques to identify drug targets is able to enhance a success rate in drug discovery. Methods: Fifty MRSA proteins, playing roles in vital processes, was screened rhodomyrtone molecular targets. The molecular docking study was operated using AutoDock4. To confirm two possible targets, checkerboard assay and cell visualization were further carried out. Results: Rhodomyrtone exhibited an interesting efficacy towards one-fifth of the given proteins. Moreover, metaldependent phosphate binding proteins were excluded from possible targets because of electrostatic forces. Amongst chosen proteins, rhodomyrtone, both enantiomers, displayed significant potency to dihydrofolate reductase (DHFR) and filamenting temperature-sensitive Z (FtsZ) proteins, compared to their natural substrates/inhibitors. However, protein cofactors such as nicotinamide adenine dinucleotide phosphate or guanosine diphosphate decreased rhodomyrtone binding affinity. This information suggested a cofactor free DHFR and a ligand-unbound FtsZ are likely to prove to be rhodomyrtone targets for MRSA inhibition. In addition, checkerboard assay and cell visualization gave a hint on target confirmation. Conclusion: We have proposed potential rhodomyrtone targets, and DHFR and FtsZ caught our interest. Further studies will need to focus on profound molecular information concerning the rhodomyrtone response to these proteins, both in experimental and computational views.

Background: Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. Methods: First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. Results: A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. Conclusions: The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

Background: The influenza A virus M2 proton channel plays a critical role in its life cycle. However, known M2 inhibitors have lost their clinical efficacy due to the spread of resistant mutant channels. Thus, the search for broad-spectrum M2 channel inhibitors is of great importance. Objective: The goal of the present work was to develop a general approach supporting the design of ligands interacting with multiple labile targets and to propose on its basis the potential broad-spectrum inhibitors of the M2 proton channel. Method: The dynamic dimer-of-dimers structures of the three primary M2 target variants, wild-type, S31N and V27A, were modeled by molecular dynamics and thoroughly analyzed in order to define the inhibitor binding sites. The potential inhibitor structures were identified by molecular docking and their binding was verified by molecular dynamics simulation. Results: The binding sites of the M2 proton channel inhibitors were analyzed, a number of potential broad-spectrum inhibitors were identified and the binding modes and probable mechanisms of action of one promising compound were clarified. Conclusion: Using the molecular dynamics and molecular docking techniques, we have refined the dynamic dimer-ofdimers structures of the WT, S31N and V27A variants of the M2 proton channel of the influenza A virus, analyzed the inhibitor binding sites, identified a number of potential broad-spectrum inhibitor structures targeting them, and clarified the binding modes and probable mechanisms of action of one promising compound. The proposed approach is also suitable for the design of ligands interacting with other multiple labile targets.

Background: Diabetes is a metabolic disorder that requires multiple therapeutic approaches. The pancreas loses its functionality to properly produce the insulin hormone in patients with diabetes mellitus. In 2012, more than one million people worldwide died as a result of diabetes, which was the eighth leading cause of death. Objective: Most drugs currently available and approved by the U.S. Food and Drug Administration cannot reach an adequate level of glycemic control in diabetic patients, and have many side effects; thus, new classes of compounds are required. Efforts based on computer-aided drug design (CADD) can mine a large number of databases to produce new and potent hits and minimize the requirement of time and dollars for new discoveries. Methods: Pharmaceutical sciences have made progress with advances in drug design concepts. Virtual screening of large databases is most compatible with different computational methods such as molecular docking, pharmacophore, quantitative structure-activity relationship, and molecular dynamic simulation. Contribution of these methods in selection of antidiabetic compounds has been discussed. Results: The Computer-Aided Drug Design (CADD) approach has contributed to successful discovery of novel antidiabetic agents. This mini-review focuses on CADD approach on currently approved drugs and new therapeutic agents-indevelopment that may achieve suitable glucose levels and decrease the risk of hypoglycemia, which is a major obstacle to glucose control and a special concern for therapies that increase insulin levels. Conclusion: Drug design and development for type 2 diabetes have been actively studied. However, a large number of antidiabetic drugs are still in early stages of development. The conventional target- and structure-based approaches can be regarded as part of the efforts toward therapeutic mechanism-based drug design for treatment of type 2 diabetes. It is expected that further improvement in CADD approach will enhance the new discoveries.