Protein Binding Pocket Research Articles

Identification of the Seaweed Metabolites as Potential Anti-tubercular Agents Against Human Pantothenate synthetase: An In Silico Approach.

Tuberculosis is the disease which is caused due to the contagion of Mycobacterium tuberculosis. The multidrug resistance Mycobacterium tuberculosis is the main hassle in the treatment of this worldwide health threats. Pantothenate synthase is a legitimate goal for rational drug designing against Mycobacterium tuberculosis. The enzyme is most active in the presence of magnesium or manganese. Marine algal cell wall is rich in sulfated polysaccharides such as fucoidans (brown algae), κ-carrageenans (red algae), and ulvan (green algae) with various favorable biological activities such as anticoagulant, antiviral, antioxidative, anticancer, and immunomodulating activities. In this study, we have modeled binding modes of selected known anti-tubercular compounds and different solvent extract against pantothenate synthase using advanced docking program AutoDock 4.2 tool. In our current study, in silico experiments were carried out to determine if fucoidan, κ-carrageenan, and ulvan sulfated polysaccharides could be a potential target against PANc (pantothenate synthetase), with the goal of identifying potential inhibitors as anti-TB leads targeting PANc for further wet lab validation. Two bioactive compounds were docked to the Mtb pantothenate synthetase protein binding site, with docking scores ranging from -5.57 to -2.73. κ-carrageenan had the best pose and docking score, with a Ligand fit score of -5.815. Ulvan did not dock with the protein. The molecular dynamics simulations were conducted with substrate and ligand bounded fucoidan and κ-carrageenan for 150ns and the protein Mtb pantothenate synthetase showed a stable conformation in the simulation, with tight amino acid contributions binding to the ligand molecule. RMSD characterizes the conformation and stability of protein ligand complexes, with higher fluctuations indicating low stability and minimal low-level fluctuations indicating equilibration and stability. The graph for RMSF shows significant peaks due to fluctuations in active site regions and other peaks indicating the adaptation of the ligand molecule to the protein binding pocket. From the molecular dynamics study, it is clear that the compounds are having good binding affinity in the active site. The root mean square deviation, root mean square fluctuations, and radius of gyration are supportive evidences which helped us to conclude that the compounds κ-carrageenan and fucoidan are suitable lead molecules for inhibiting pantothenate synthetase. Based on these evidences, the natural compounds from seaweeds can be tested clinically either alone or in combinations against the protein, which could facilitate the designing or the synthesis of new lead molecules as drugs against the tuberculosis.

In silico design based on quantum chemical, molecular docking studies and ADMET predictions of ciprofloxacin derivatives as novel potential antibacterial and antimycrobacterium agents

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. In this paper, we report the prediction of new ciprofloxacin derivatives by quantum chemical, molecular docking studies and pharmacokinetic properties. Theoretical studies were performed by geometry optimization computation using B3LYP level at 6-311 G (d,p) basis set. The absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters were predicted and the result show that all compounds have a great ADMET profile. To study the antibacterial, anti-Mycobacterium tuberculosis activities, ciprofloxacin and its derivatives were interacted with the proteins: Thymidylate Kinase (PDB: 4QGG), Biotin carboxylase (PDB: 3JZF) and β-lactamase BlaC (PDB: 3N7W). The results of the docking studies indicate that one pharmacophore designed presents a great inhibition behavior against gram-positive organism (4QGG) and significant interactions observed between the compound and ARG48, GLN101, ARG105 and GLU37 residues of 4QGG. Also, another derivative designed present the best inhibition against gram-negative organism (3JZF) several interactions were noticed between the compound and GLY165, ILE287, LEU278, HIS236, HIS209, MET169 and LYS159 residues of (3JZF). As well as, one designed candidate is good inhibitors for β-lactamase (3N7W) multiple no bonded interactions were observed between the compound and SER84, ILE117, ASN186, LYS87, ARG187, ASN186 and THR251 residues of(3N7W). Molecular dynamics (MD) simulation study was also performed for 100 ns to confirm the stability behaviour of the main protein and inhibitor complexes. The MD simulation study validated the stability of three compounds in the protein binding pocket as potent binders. Natural bonding orbital analysis, reactivity indices and molecular electrostatic potential were carried out. The research finding of this study can be helpful to design a new potent antibacterial, antimycrobacterium candidate’s drugs that will serve as the basis for future in vitro and in vivo research. Communicated by Ramaswamy H. Sarma

HydraMap v.2: Prediction of Hydration Sites and Desolvation Energy with Refined Statistical Potentials.

The complex network of water molecules within the binding pocket of a target protein undergoes alterations upon ligand binding, presenting a significant challenge for conventional molecular modeling methods to accurately characterize and compute the associated energy changes. We have previously developed an empirical method, HydraMap (J. Chem. Inf. Model. 2020, 60, 4359-4375), which employs statistical potentials to predict hydration sites and compute desolvation energy, achieving a reasonable balance between accuracy and speed. In this work, we present its improved version, namely, HydraMap v.2. We updated the statistical potentials for protein-water interactions through an analysis of 17 042 crystal protein structures. We also introduced a new feature to evaluate ligand-water interactions by incorporating statistical potentials derived from the solvated structures of 9878 small organic molecules produced by molecular dynamics simulations. By combining these potentials, HydraMap v.2 can predict and compare the hydration sites in a binding pocket before and after ligand binding, identifying key water molecules involved in the binding process, such as those forming bridging hydrogen bonds and unstable ones that can be replaced. We demonstrated the application of HydraMap v.2 in explaining the structure-activity relationship of a panel of MCL-1 inhibitors. The desolvation energies calculated by summing the energy change of each hydration site before and after ligand binding showed good correlation with known ligand binding affinities on six target proteins. In conclusion, HydraMap v.2 offers a cost-effective solution for estimating the desolvation energy during protein-ligand binding and also is practical in guiding lead optimization in structure-based drug discovery.

Natural selection of carotenoid binding in Gloeobacter rhodopsin

Energy transfer from the 4-ketocarotenoid antenna (salinixanthin) and echinenone to xanthorhodopsin has been confirmed in Salinibacter ruber and Gloeobacter violaceus, respectively. Hundreds of carotenoids and numerous rhodopsin homologs are spreading across the life domain. Here, we found that ketocarotenoid binds to Gloeobacter rhodopsin (GR), suggesting an increased likelihood of the existence of the carotenoid-rhodopsin complex. Additionally, the recruitment of the secondary chromophore antenna is precisely controlled by rhodopsin. Therefore, comparing the binding of multiple carotenoids is of interest to GR. Particular attention was paid to the structural interaction of ligands within the protein binding pocket. While different degrees of carotenoid association are evident in the GR-carotenoid experimental data and molecular docking results, the retinal isomerization dynamics defined the relative orientation of the β-ionone ring, and structural changes in the protein acted as modest barriers for the best carotenoid for GR. Notably, the selection of carotenoid binding can be summarized in three points: (1) structural matching of a ligand molecule with the binding pocket; (2) strong binding affinity and rapid binding kinetics; (3) dynamic flexibility that is versatile to rhodopsin activation. The selected carotenoids showed significant enhancement in light-driven proton pumping as extra light-harvesting antennae and provided intrinsic protection from stress.

CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties.

Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties. To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It's anticipated that this work can boost de novo molecule design. The code and data underlying this article are freely available at https://github.com/lijianing0902/CProMG.

Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics

Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in wastewater treatment, soil, or through natural processes in the environment. If a metabolite is bioactive, even at sub-lethal levels, and also stable in the environment, then it could provide selection pressure for resistance. (5S)-penicilloic acid of piperacillin has previously been found complexed to the binding pocket of penicillin binding protein 3 (PBP3) of Pseudomonas aeruginosa. Here, we predicted the affinities of all potentially relevant antibiotic metabolites of ten different penicillins to that target protein, using molecular docking and molecular dynamics simulations. Docking predicts that, in addition to penicilloic acid, pseudopenicillin derivatives of these penicillins, as well as 6-aminopenicillanic acid (6APA), could also bind to this target. MD simulations further confirmed that (5R)-pseudopenicillin and 6APA bind the target protein, in addition to (5S)-penicilloic acid. Thus, it is possible that these metabolites are bioactive, and, if stable in the environment, could be contaminants selective for antibiotic resistance. This could have considerable significance for environmental surveillance for antibiotics as a means to reduce antimicrobial resistance, because targeted mass spectrometry could be required for relevant metabolites as well as the native antibiotics.

Abstract B036: Treatment modalities in KRAS-driven cancer

Abstract KRAS is the most common RAS isoform, found to be altered in 22% of RAS-mutated cancers. The mutant protein is found most prevalently in pancreatic cancer followed by colorectal, non-small cell lung cancer and other solid tumors. Due to its structural complexity, KRAS remained “undruggable” for several decades. Recently, the discovery of a specific protein binding pocket has led to the development of small molecule inhibitors that potently bind and inhibit KRAS downstream signalling. The accelerated FDA approval of sotorasib and adagrasib for treatment of NSCLC patients harbouring KRASG12C-mutation and further clinical evaluation of several other inhibitors of KRASG12C are under investigation, however emergence of resistance and limited efficacy beyond non-small cell lung cancer is a major concern. Targeting KRAS mutations other than KRASG12C (i.e., without a mutant-specific cysteine residue) with small molecules, has been considered much more challenging, especially for developing covalent inhibitors. Small molecule targeting of KRASG12D (e.g. MRTX1133), is under active preclinical investigation and small molecule drugs that target other KRAS-activating mutations (G12S, G12R and G12V) are being intensely explored. A variety of novel alternative approaches are also being investigated to target tumors driven by mutant KRAS, such as (i) targeting complementary signalling pathways (ii) enhancing mutant KRAS protein degradation using PROTACS, (iii) developing pan-KRAS small molecules inhibitors, (iv) anti-KRAS immune cell-based therapies, (v) small interfering RNAs to block mutant KRAS expression and (vi) anti-KRAS vaccines. Here, we aim to provide a comprehensive overview of current status and future prospect of these modalities, in the context of RAS-mutated cancers. Citation Format: Darren R. Tyson, Neetu Singh, Prashant K. Bhavar, Partha Pratim Sarma, Uday Kumar Surampudi, Sathish Katike. Treatment modalities in KRAS-driven cancer [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr B036.

Targeting neuroblastoma by small-molecule inhibitors of human ALYREF protein: mechanistic insights using molecular dynamics simulations

Neuroblastoma is a tumour of the sympathetic nervous system mainly prevalent in children. Many strategies have been employed to target several drug-targetable proteins for the clinical management of neuroblastoma. However, the heterogeneous nature of neuroblastoma presents serious challenges in drug development for its treatment. Albeit numerous medications have been developed to target various signalling pathways in neuroblastoma, the redundant nature of the tumour pathways makes its suppression unsuccessful. Recently, the quest for neuroblastoma therapy resulted in the identification of human ALYREF, a nuclear protein that plays an essential role in tumour growth and progression. Therefore, this study used the structure-based drug discovery method to identify the putative inhibitors targeting ALYREF for the Neuroblastoma treatment. Herein, a library of 119 blood-brain barrier crossing small molecules from the ChEMBL database was downloaded and docked against the predicted binding pocket of the human ALYREF protein. Based on docking scores, the top four compounds were considered for intermolecular interactions and molecular dynamics simulation analysis, which revealed CHEMBL3752986 and CHEMBL3753744 with substantial affinity and stability with the ALYREF. These results were further supported by binding free energies and essential dynamics analysis of the respective complexes. Hence, this study advocates the sorted compounds targeting ALYREF for further in vitro and in vivo assessment to develop a drug against neuroblastoma. Communicated by Ramaswamy H. Sarma

Non-Covalent Interactions in the Self-Assembly of Dihydropyridone Supramolecules and In Vitro Anti-Cancer Assessment in Human Lung Adenocarcinoma Cell Line (A549).

The synthesis of dihydropyridone derivatives has been reported by ring rearrangement of pyrans using iodine and formic acid as a catalyst separately. Dihydropyridones were crystallized subjected for single-crystal X-ray crystallography to acquire their structural parameters. The different non-covalent interactions involved within the supramolecular systems were studied and validated using Hirshfeld surface plot analysis. N-H⋅⋅⋅O interactions between the lactam group dominate. Still, other non-covalent interactions such as C-H⋅⋅⋅N, C-H⋅⋅⋅O, C-H⋅⋅⋅C, N-H⋅⋅⋅N, C-H⋅⋅⋅π, and lone pair⋅⋅⋅π systems act as the driving force in facilitating the self-assembly of the dihydropyridone supramolecules. The synthesized compounds were analyzed by in vitro techniques using human lung adenocarcinoma (A549) to evaluate their cytotoxic activities. Ethyl 4-(4-chlorophenyl)-5-cyano-2-methyl-6-oxo-1,4,5,6- tetrahydropyridine-3-carboxylate has shown the highest cytotoxicity among all the synthesized compounds. Molecular recognition properties of the dihydropyridone compounds were also studied, employing molecular docking tools to gain insight into the binding mode inside the allosteric binding pocket of the Eg5 protein through non-covalent interactions.

Docking cholesterol to integral membrane proteins with Rosetta.

Lipid molecules such as cholesterol interact with the surface of integral membrane proteins (IMP) in a mode different from drug-like molecules in a protein binding pocket. These differences are due to the lipid molecule's shape, the membrane's hydrophobic environment, and the lipid's orientation in the membrane. We can use the recent increase in experimental structures in complex with cholesterol to understand protein-cholesterol interactions. We developed the RosettaCholesterol protocol consisting of (1) a prediction phase using an energy grid to sample and score native-like binding poses and (2) a specificity filter to calculate the likelihood that a cholesterol interaction site may be specific. We used a multi-pronged benchmark (self-dock, flip-dock, cross-dock, and global-dock) of protein-cholesterol complexes to validate our method. RosettaCholesterol improved sampling and scoring of native poses over the standard RosettaLigand baseline method in 91% of cases and performs better regardless of benchmark complexity. On the β2AR, our method found one likely-specific site, which is described in the literature. The RosettaCholesterol protocol quantifies cholesterol binding site specificity. Our approach provides a starting point for high-throughput modeling and prediction of cholesterol binding sites for further experimental validation.

Identification and Investigation of a Cryptic Binding Pocket of the P37 Envelope Protein of Monkeypox Virus by Molecular Dynamics Simulations

The spread of the monkeypox virus has surged during the unchecked COVID-19 epidemic. The most crucial target is the viral envelope protein, p37. However, lacking p37's crystal structure is a significant hurdle to rapid therapeutic discovery and mechanism elucidation. Structural modeling and molecular dynamics (MD) of the enzyme with inhibitors reveal a cryptic pocket occluded in the unbound structure. For the first time, the inhibitor's dynamic flip from the active to the cryptic site enlightens p37's allosteric site, which squeezes the active site, impairing its function. A large force is needed for inhibitor dissociation from the allosteric site, ushering in its biological importance. In addition, hot spot residues identified at both locations and discovered drugs more potent than tecovirimat may enable even more robust inhibitor designs against p37 and accelerate the development of monkeypox therapies.

A thermosensitive PCNA allele underlies an ataxia-telangiectasia-like disorder

Proliferating cell nuclear antigen (PCNA) is a sliding clamp protein that coordinates DNA replication with various DNA maintenance events that are critical for human health. Recently, a hypomorphic homozygous serine to isoleucine (S228I) substitution in PCNA was described to underlie a rare DNA repair disorder known as PCNA-associated DNA repair disorder (PARD). PARD symptoms range from UV sensitivity, neurodegeneration, telangiectasia, and premature aging. We, and others, previously showed that the S228I variant changes the protein-binding pocket of PCNA to a conformation that impairs interactions with specific partners. Here, we report a second PCNA substitution (C148S) that also causes PARD. Unlike PCNA-S228I, PCNA-C148S has WT-like structure and affinity toward partners. In contrast, both disease-associated variants possess a thermostability defect. Furthermore, patient-derived cells homozygous for the C148S allele exhibit low levels of chromatin-bound PCNA and display temperature-dependent phenotypes. The stability defect of both PARD variants indicates that PCNA levels are likely an important driver of PARD disease. These results significantly advance our understanding of PARD and will likely stimulate additional work focused on clinical, diagnostic, and therapeutic aspects of this severe disease.

In Silico Identification of Lead Compounds for Pseudomonas Aeruginosa PqsA Enzyme: Computational Study to Block Biofilm Formation.

Pseudomonas aeruginosa is an opportunistic Gram-negative bacterium implicated in acute and chronic nosocomial infections and a leading cause of patient mortality. Pseudomonas aeruginosa infections are frequently associated with the development of biofilms, which give the bacteria additional drug resistance and increase their virulence. The goal of this study was to find strong compounds that block the Anthranilate-CoA ligase enzyme made by the pqsA gene. This would stop the P. aeruginosa quorum signaling system. This enzyme plays a crucial role in the pathogenicity of P. aeruginosa by producing autoinducers for cell-to-cell communication that lead to the production of biofilms. Pharmacophore-based virtual screening was carried out utilizing a library of commercially accessible enzyme inhibitors. The most promising hits obtained during virtual screening were put through molecular docking with the help of MOE. The virtual screening yielded 7/160 and 10/249 hits (ZINC and Chembridge). Finally, 2/7 ZINC hits and 2/10 ChemBridge hits were selected as potent lead compounds employing diverse scaffolds due to their high pqsA enzyme binding affinity. The results of the pharmacophore-based virtual screening were subsequently verified using a molecular dynamic simulation-based study (MDS). Using MDS and post-MDS, the stability of the complexes was evaluated. The most promising lead compounds exhibited a high binding affinity towards protein-binding pocket and interacted with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

Accounting for Solvation Correlation Effects on the Thermodynamics of Water Networks in Protein Cavities

Macromolecular recognition and ligand binding are at the core of biological function and drug discovery efforts. Water molecules play a significant role in mediating the protein-ligand interaction, acting as more than just the surrounding medium by affecting the thermodynamics and thus the outcome of the binding process. As individual water contributions are impossible to measure experimentally, a range of computational methods have emerged to identify hydration sites in protein pockets and characterize their energetic contributions for drug discovery applications. Even though several methods model solvation effects explicitly, they focus on determining the stability of specific water sites independently and neglect solvation correlation effects upon replacement of clusters of water molecules, which typically happens in hit-to-lead optimization. In this work, we rigorously determine the conjoint effects of replacing all combinations of water molecules in protein binding pockets through the use of the RE-EDS multistate free-energy method, which combines Hamiltonian replica exchange (RE) and enveloping distribution sampling (EDS). Applications on the small bovine pancreatic trypsin inhibitor and four proteins of the bromodomain family illustrate the extent of solvation correlation effects on water thermodynamics, with the favorability of replacement of the water sites by pharmacophore probes highly dependent on the composition of the water network and the pocket environment. Given the ubiquity of water networks in biologically relevant protein targets, we believe our approach can be helpful for computer-aided drug discovery by providing a pocket-specific and a priori systematic consideration of solvation effects on ligand binding and selectivity.

GC-MS Screening of Adiantum lunulatum Burm. F Phytochemicals and Interaction with COX-2, TRPV1, and TRPC3 Proteins-bioinformatics Approach

Background:The Adiantum lunulatum is a medicinally important pteridophyte used to treat inflammatory-related diseases. The phytochemical profile of this plant is poorly investigated.Objective:Here, we screened the nonpolar phytochemicals and their interactions with cyclooxygenase 2 (COX-2) enzyme (inflammation), transient receptor potential cation channel V member 1 (TRPV1), and transient receptor potential channel 3 (TRPC3) receptors (pain).Methods:The identification and molecular docking analysis used gas chromatography-mass spectrometry (GC-MS), AutoDock Vina, and BIOVIA discovery studio visualizer 2020. The online computer tools Swiss ADME and admetSAR predicted these compounds' bioavailability and toxicity.Results:GC-MS analysis detected the 12 different compounds. Five compounds with high similarity to mass spectrum were selected for molecular docking. This includes 2, 4 di-tert-butylphenol; n-hexadecanoic acid (palmitic acid); 2 pentadecanone, 6, 10, 14-trimethyl-; Quinoline 1, 2 dihydro 2, 2, 4 trimethyl and 3, 7, 11, 15-tetramethyl hexadec 2-en-1-yl acetate. These compounds showed interaction with the binding pocket of COX-2, TRPV1, and TRPC3 proteins. This interaction with enzyme and receptor activity causes a reduction in inflammatory pathogenesis.Conclusion:This study enhances our fundamental knowledge of biologically important volatile phytochemicals in Adiantum lunulatum dichloromethane extract and its possible effects in reducing inflammatory responses.

Spectroscopic, electronic properties analysis for 2, 6-Bis (phenylamino)-4-(iminophenyl) benzoquinone molecule and molecular docking clarification for its anticancer activity detected by strong inhibition of NQO1 enzyme

Quinone compounds have been the subject of extensive research due to their remarkable efficiency and prospective use as drugs and in a variety of fields. In this study, we report the spectroscopic characterization, electronic structure, ADMET evaluation, and molecular docking assessment of 2,6-Bis(phenylamino)-4-(iminophenyl)benzoquinone as an anticancer drug. By using DFT investigations, the vibrational wavenumbers were calculated and utilized to assign vibrational bands, which were found to be in good accordance with the experimentally observed data. The analysis of the UV-Vis spectra revealing an absorption peak from electronic transitions HOMO→LUMO at 547 nm is found to be in good conformity with its experimental value. The HOMO and LUMO frontier molecular orbitals and their associated energies highlighted the mechanism of charge transfer within the molecule and revealed a small energy gap. The chemically reactive sites identified by the MEP surface helped predict the spots of the molecule's biological activity. According to NBO analysis, the π C9-C10 → π* C11-C12 interaction has the maximum energy stability with 23.74 Kcal/mol., due to π electron delocalization within the ring. At the same level of theory, third-order NLO polarizability was found to be 4-fold stronger than the third-order of P-NA (a prototype NLO molecule). The potential for a safe oral bioavailability drug was identified by computing ADMET parameters and evaluating drug-likeness based on Lipinski's rule of five. The molecular docking study found that the molecule binding to NQO1 receptor protein with superior binding energy -8.69 kcal/mol than previously studied Quinone derivatives. Additionally, molecular dynamics simulations were performed to test the dynamic behavior of the ligand-NQO1 complex. The complex was stable in the binding pocket of the receptor proteins, according to the analysis of the simulation outcomes such as RMSD and RMSF. These results suggests using this molecule as a potential anticancer drug due to its high capability to inhibit the NQO1 enzyme.

Investigation on Phyto‐active Constituent ofClerodendrum paniculatumas Therapeutic Agent against Viral Diseases

AbstractThe ability of alcohol extracted constituents from the leaves ofClerodendrum paniculatumto inhibit SARS‐CoV‐2 was investigated through various computational methods like docking, molecular dynamic simulations and pharmacokinetic predictions. Of the various active constituents, quercetin was identified as a potent inhibitor that can bind strongly to the active site of main protease (Mpro) and spike protein of the virus with respective binding free energy of −41.07 and −40.76 kcal mol−1. Molecular dynamic simulations also supported the binding interactions by the presence of strong hydrogen bonding interactions with the key residues in the binding pocket of the target protein. The results were ascertained experimentally by evaluating the inhibition potential of the extract against spike and Mproproteins of SARS‐CoV‐2.

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism.

Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules. In this work, a new deep learning approach based on the cross-attention mechanism named CAPLA was developed for improved prediction of protein-ligand binding affinity by learning features from sequence-level information of both protein and ligand. Specifically, CAPLA employs the cross-attention mechanism to capture the mutual effect of protein-binding pocket and ligand. We evaluated the performance of our proposed CAPLA on comprehensive benchmarking experiments on binding affinity prediction, demonstrating the superior performance of CAPLA over state-of-the-art baseline approaches. Moreover, we provided the interpretability for CAPLA to uncover critical functional residues that contribute most to the binding affinity through the analysis of the attention scores generated by the cross-attention mechanism. Consequently, these results indicate that CAPLA is an effective approach for binding affinity prediction and may contribute to useful help for further consequent applications. The source code of the method along with trained models is freely available at https://github.com/lennylv/CAPLA. Supplementary data are available at Bioinformatics online.

In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations.

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease Mpro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective Mpro inhibitors against SARS-CoV-2. The 33.8 kDa Mpro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (Mpro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with Mpro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

Integrating microsecond timescale classical and biased molecular dynamics simulations to screen potential molecules for BRD4-BD1

Contemporary pre-clinical drug discovery is complemented by the combinatorial use of numerous computational techniques linked to computer-aided drug design and discovery. We combined inherently different computational analyses derived from multi-scale conventional, steered and umbrella sampling simulations to identify potential drug candidates for the first bromodomain of the human bromodomain containing protein 4 (BRD4-BD1). The BRD4-BD1 protein is a recognized therapeutic target for various diseases including cancer, neurological disorders, inflammation, and obesity. The in-house synthesized benzosuberene-sulfone (BSS) molecules were assessed for their potential to act as inhibitors against the BRD4-BD1 protein. We compared the molecular interactions and docking scores of BSS analogues with six different co-crystal inhibitors of the BRD4-BD1 protein. Further, the thermodynamic free energy of binding for each complex was estimated by the end-state Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) approach. Moreover, umbrella sampling simulations were utilized to validate the results obtained from MM/PBSA analyses and to demonstrate the unbinding/binding process of the most potent BSS analogue from the binding pocket of BRD4-BD1 protein. Our results show the potential of organosulfur molecules to be developed as inhibitors of the BRD4-BD1 protein and endorse their evaluation by suitable in-vitro and in-vivo studies.