Structure-based virtual screening workflow to identify antivirals targeting HIV-1 capsid

Discovery of the allosteric inhibitor from actinomyces metabolites to target EGFRCSTMLR mutant protein: molecular modeling and free energy approach

EGFR (epidermal growth factor receptor), a surface protein on the cell, belongs to the tyrosine kinase family, responsible for cell growth and proliferation. Overexpression or mutation in the EGFR gene leads to various types of cancer, i.e., non-small cell lung cancer, breast, and pancreatic cancer. Bioactive molecules identified in this genre were also an essential source of encouragement for researchers who accomplished the design and synthesis of novel compounds with anticancer properties. World Health Organization (WHO) report states that antibiotic resistance is one of the most severe risks to global well-being, food safety, and development. The world needs to take steps to lessen this danger, such as developing new antibiotics and regulating their use. In this study, 6524 compounds derived from Streptomyces sp. were subjected to drug-likeness filters, molecular docking, and molecular dynamic simulation for 1000 ns to find new triple mutant EGFR^CSTMLR (EGFR-L858R/T790M/C797S) inhibitors. Docking outcomes revealed that five compounds showed better binding affinity (- 9.074 to - 9.3 kcal/mol) than both reference drug CH7233163 (- 6.11 kcal/mol) and co-crystallized ligand Osimertinib (- 8.07 kcal/mol). Further, molecular dynamic simulation confirmed that ligand C_42 exhibited the best interaction at the active site of EGFR protein and comprised a better average radius of gyration (3.87 Å) and average SASA (Solvent Accessible Surface Area) (82.91 Å2) value than co-crystallized ligand (4.49 Å, 222.38 Å2). Additionally, its average RMSD (Root Mean Square Deviation) (3.25 Å) and RMSF (Root Mean Square Fluctuation) (1.54 Å) values were highly similar to co-crystallized ligand (3.07 Å, 1.54 Å). Compared to the reference ligand, it also demonstrated conserved H-bond interactions with the residues MET_793 and GLN_791 with strong interaction probability. In conclusion, we have found a potential drug with no violation of the rule of three, Lipinski's rule of five, and 26 other vital parameters having great potential in medicinal and pharmaceutical industries applications and can overcome synthetic drug issues.

Computer-aided drug design in seeking viral capsid modulators

Approved or licensed antiviral drugs have limited applications because of their drug resistance and severe adverse effects. By contrast, by stabilizing or destroying the viral capsid, compounds known as capsid modulators prevent viral replication by acting on new targets and, therefore, overcoming the problem of clinical drug resistance. For example. computer-aided drug design (CADD) methods, using strategies based on structures of biological targets (structure-based drug design; SBDD), such as docking, molecular dynamics (MD) simulations, and virtual screening (VS), have provided opportunities for fast and effective development of viral capsid modulators. In this review, we summarize the application of CADD in the discovery, optimization, and mechanism prediction of capsid-targeting small molecules, providing new insights into antiviral drug discovery modalities. Teaser: Computer-aided drug design will accelerate the development of viral capsid regulators, which brings new hope for the treatment of refractory viral diseases.

Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved 'type-II inhibitors' in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17-20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical 'folded activation loop' DFG-out conformation relative to the active conformation, that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly 'anchored' against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors.

Absolute binding free energy calculations improve enrichment of actives in virtual compound screening

We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process.