Journal of Computational Biophysics and Chemistry

SARS-CoV-2 Main protease (M^pro) is pivotal in viral replication and transcription. M^pro mediates proteolysis of translated products of replicase genes ORF1a and ORF1ab. Surveying pre-clinical trial M^pro inhibitors suggests potential enhanced efficacy for some moieties. Concordant with promising in vitro and in silico data, the protease inhibitor GC376 was chosen as a lead. Modification of GC376 analogues yielded a series of promising M^pro inhibitors. Design optimization identified compound G59i as lead candidate, displaying a binding energy of $-$10.54 kcal/mol for the complex. Robust interactivity was noted between G59i and M^pro. With commendable ADMET characteristics and enhanced potency, further G59i analysis may be advantageous; moreover, identified key M^pro residues could contribute to the design of neotenic inhibitors.

Rho-associated coiled-coil containing protein kinases (ROCKs) are members of the cyclic adenosine monophosphate-dependent protein kinase/protein kinase G/protein kinase C family that participates in a variety of important physiological functions, including smooth muscle contraction, cell proliferation, cell adhesion, migration, and inflammatory responses. In this study, we focused on ROCK1 and ROCK2, which are targets of the Food and Drug Administration-approved inhibitor 2-(3-(4-((1$H$-Indazol-5-yl)amino)quinazolin-2-yl)phenoxy)-$N$-isopropylacetamide (belumosudil). We constructed four representative belumosudil/ROCK complex structures by molecular docking. The interactions between belumosudil and ROCK were then investigated via molecular dynamics simulations and binding free energy calculations. It was found that belumosudil showed a stronger binding affinity toward ROCK2 than toward ROCK1. Binding free energy calculations and free energy decompositions suggested that the modification of various regions of the belumosudil structure may enhance its binding affinity with ROCK, in addition to improving the selectivity between ROCK1 and ROCK2. This work therefore provides useful information to aid future drug design.

In recent years, scientists have become increasingly interested in finding high-efficacy, low-toxicity anti-tumor compounds. Combretastatin A-4 (CA-4) is supposed to be one of the materials that has excellent anti-tumor properties. This study intends to develop a more acceptable pathway and cross-coupling mechanism to resolve the contest between Negishi and Suzuki–Miyaura cross-coupling to form a combretastatin analogue A-4 by using the CAM-B3LYP-D3 theory level with DEF2-SVP basis set in the presence of $N$,$N$-dimethylformamide as a solvent. First, due to the experimental data for the formation of 4-methyl-$4^{\prime }$-methoxybiphenyl in the Suzuki–Miyaura and Negishi reactions, two designated reactions were used to determine the 4-methyl-$4^{\prime }$-methoxybiphenyl formation cycle. The mechanism for the progression of the regioselective compound 2-methoxy-5-(3-(3,4,5-trimethoxy phenyl) furan-2-yl) phenol via Suzuki–Miyaura and Negishi reactions can theoretically be reconciled with a more appropriate cross-coupling and pathway.

Apatinib is a tyrosine kinase inhibitor that cognately blocks the kinase activity of vascular endothelial growth factor receptor (VEGFR) signaling for the treatment of advanced gastric cancer (GC). However, the drug is also clinically found to reposition a significant suppressing potency on hepatocellular carcinoma (HCC). In this study, we reported the successful use of Apatinib as a bait to fish its potential kinase targets from the HCC druggable kinome pool. In the procedure, cell viability assays observed that the Apatinib has a potent cytotoxicity on human HCC cell lines. Dynamics simulations and affinity scoring systematically created an intermolecular interaction profile of Apatinib with ontologically enriched kinases in the HCC druggable kinome, from which the top-hit kinases were considered as potential candidates. It is revealed that the inhibitor has a weak potency on the well-established HCC target of ErbB pathway, but exhibits potent activity against some known targets or regulators of HCC. In particular, kinase assays substantiated that Apatinib can effectively inhibit four FGFR family members with moderate or high activity. In addition, the clinical FGFR1 gatekeeper mutation V561M was also observed to considerably impair the inhibitory activity, thus causing a drug resistance. Molecular modeling suggested that the Apatinib adopts two distinct binding modes to separately interact with wild-type and gatekeeper-mutant FGFR1 kinase domain.

Over the years, the escalation of cancer cases has been linked to the resistance, less selectivity, and toxicity of available anticancer drugs to normal cells. Therefore, continuous efforts are necessary to find new anticancer drugs with high selectivity of epidermal growth factor receptor tyrosine kinase (EGFR-TK) as a therapeutic target. The EGFR-TK protein has a crucial role in cell proliferation and cancer progression. With about 30% of cancer cases involved with the protein, it has piqued the interest as a therapeutic target. The potential of theoretically designed thiourea derivatives as anticancer agents in this report was evaluated against EGFR-TK via in silico techniques, including molecular docking (AutoDock Vina), molecular dynamics simulations (GROMACS), pharmacokinetics, and drug-likeness properties (SwissADME and Molinspiration). New hybrid molecules of the thiourea derivative moiety were designed in this study based on the fragment-based drug discovery and linked with diverse pharmacophoric fragments with reported anticancer potential ($R_1$) and the modification of the methyl position on phenyl ring ($R_2$). These fragments include pyridine, thiophene, furan, pyrrole and styrene groups. Out of 15 compounds, compound 13 displayed the most potent inhibitory activity, with the lowest binding affinity in docking of $-$8.7 kcal/mol compared to the positive control erlotinib of $-$6.7 kcal/mol. Our molecular dynamics (MD) simulations revealed that molecule 13, comprising styrene and 2-methylphenyl substituents on $R_1$ and $R_2$, respectively, showed adequate compactness, uniqueness and satisfactory stability. Subsequently, the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties and drug-likeness properties also indicate that this theoretically designed inhibitor ( 13) is less toxic and contains high druggable properties. Thus, compound 13 could be promising against EGFR-TK.

Enoyl acyl carrier protein reductase (InhA) is a crucial enzyme for the biosynthesis of mycolic acids which are major compartments of the Mycobacterium tuberculosis (Mtb) cell wall. Direct inhibition of InhA without activation by drug-NADH adduct has clinical utility to overcome drug resistance. We aimed at the in silico identification of triclosan derivatives with the potential inhibitory effect of S94A-InhA as a clinically important mutant form. Caver Web 1.0 server was used to analyze the ligand transport through access tunnels. Two macrocyclic triclosan derivatives ( 4 and 6) could be identified with more energy-favorable transfer routes toward the enzyme active site. Molecular dynamics (MD) simulations (50 ns) of the best-scored compounds revealed the stability of enzyme structure upon binding to 4 and 6. Compound 4 could better retain enzyme stability upon target binding. Results of intermolecular H-bond analysis indicated that both complexes were mediated through hydrophobic contacts. Declined solvent accessible surface area (SASA) for the apo and bound enzyme states indicated non-destabilizing behavior and no structural relaxation. Electrostatic and van der Waals interactions between triclosan derivatives and their surroundings were used to acquire binding free energies through the linear interaction energy (LIE) method based on MD simulations (Average $\mathrm{\Delta }{G}_b$, $-7.83\pm 0.72$ kcal/mol and $-8.89\pm 0.64$ kcal/mol). Both of the triclosan derivatives showed relatively stable energy variations and their steady accommodation inside enzyme active site could be confirmed during 50 ns. These results may be implicated in further structure-guided approaches against drug-resistant Mtb.

Molecular dynamics (MD) simulations have been extensively used to study protein dynamics and subsequently functions. However, MD simulations are often insufficient to explore adequate conformational space for protein functions within reachable timescales. Accordingly, many enhanced sampling methods, including variational autoencoder (VAE) based methods, have been developed to address this issue. The purpose of this study is to evaluate the feasibility of using VAE to assist in the exploration of protein conformational landscapes. Using three modeling systems, we showed that VAE could capture high-level hidden information which distinguishes protein conformations. These models could also be used to generate new physically plausible protein conformations for direct sampling in favorable conformational spaces. We also found that VAE worked better in interpolation than extrapolation and increasing latent space dimension could lead to a trade-off between performances and complexities.

Thanks to the tremendous progress in data, computing power and algorithms, AI-based material mining and design have gained much attention. However, building high-performance AI models requires efficient material structure representation. In this work, we propose a structural characterization method based on the neighborhood path complex for the first time. Specifically, we use persistent neighborhood path homology to obtain the structural features by introducing a filtration. This approach preserves more elemental information, as well as the corresponding physicochemical information, through the directed edges of the neighborhood digraph. To validate our model, we perform cross-validation with the carborane structures. The Pearson coefficient for stability prediction is as high as 0.903, which is a 15.5% improvement compared to the traditional persistent homology method. In addition, we constructed a prediction model based on the neighborhood path complex, and the Pearson coefficients for the prediction of carboranes’ HOMO, LUMO, and HOMO–LUMO gaps were 0.915, 0.946, and 0.941, respectively. The results show that our proposed method can effectively extract structural information and achieve accurate material property prediction.