Conformational transitions upon ligand binding: holo-structure prediction from apo conformations

How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms

Molecular simulations are an essential asset in the first steps of drug design campaigns. However, the requirement of high-throughput limits applications mainly to qualitative approaches with low computational cost, but also low accuracy. Unlocking the potential of more rigorous quantum mechanical/molecular mechanics (QM/MM) models combined with molecular dynamics-based free energy techniques could have a tremendous impact. Indeed, these two relatively old techniques are emerging as promising methods in the field. This has been favored by the exponential growth of computer power and the proliferation of powerful data-driven methods. Here, we briefly review recent advances and applications, and give our perspective on the impact that QM/MM and free-energy methods combined with machine learning-aided algorithms can have on drug design.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Identification of potent anti-fibrinolytic compounds against plasminogen and tissue-type plasminogen activator employing in silico approaches

The zymogen protease Plasminogen (Plg) and its active form plasmin (Plm) carry out important functions in the blood clot disintegration (breakdown of fibrin fibers) process. Inhibition of plasmin effectively reduces fibrinolysis to circumvent heavy bleeding. Currently, available Plm inhibitor tranexamic acid (TXA) used for treating severe hemorrhages is associated with an increased incidence of seizures which in turn were traced to gamma-aminobutyric acid antagonistic activity (GABAa) in addition to having multiple side effects. Fibrinolysis can be suppressed by targeting the three important protein domains: the kringle-2 domain of tissue plasminogen activator, the kringle-1 domain of plasminogen, and the serine protease domain of plasminogen. In the present study, one million molecules were screened from the ZINC database. These ligands were docked to their respective protein targets using Autodock Vina, Schrödinger Glide, and ParDOCK/BAPPL+. Thereafter, the drug-likeness properties of the ligands were evaluated using Discovery Studio 3.5. Subsequently, we subjected the protein-ligand complexes to molecular dynamics simulation of 200 ns in GROMACS. The identified ligands P76(ZINC09970930), C97(ZINC14888376), and U97(ZINC11839443) for each protein target are found to impart higher stability and greater compactness to the protein-ligand complexes. Principal component analysis (PCA) implicates, that the identified ligands occupy smaller phase space, form stable clusters, and provide greater rigidity to the protein-ligand complexes. Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis reveals that P76, C97, and U97 exhibit better binding free energy (ΔG) when compared to that of the standard ligands. Thus, our findings can be useful for the development of promising anti-fibrinolytic agents.Communicated by Ramaswamy H. Sarma.

Identification of phytocompounds as potent inhibitors of sodium/glucose cotransporter-2 leading to diabetes treatment

Type-II diabetes, a major metabolic disorder has threatened the very existence of a healthy life since long ago. Commercially available antidiabetic drugs are known for several adverse effects. The present study attempted to identify potential phytocompounds as inhibitors of sodium/glucose cotransporter-2 (SGLT2), a major protein that helps in glucose re-absorption from renal tubules. A total of 28 phytocompounds were collected based on the literature survey. 3D co-ordinates of phytocompounds were collected from PubChem database. Molecular docking was carried out with SGLT2 protein and the best 3 docking complexes were subjected to molecular dynamics simulation for 100 ns. Free energy changes were also analyzed using MM/PBSA analysis. Phytocompounds were also analyzed for their drug-likeness and ADMET properties. Docking study observed a strong binding affinity of phytocompounds (> -7.0 kcal/mol). More than 10 phytocompounds showed better binding affinity compared to reference drugs. Further analysis of three best docking complexes when analyzed by MD simulation showed better stability and compactness of the complexes compared to reference drug, empagliflozin. MM/PBSA analysis also revealed that van der Waals force and electrostatic energy are the major binding energy involved in the complex formation. Like docking energy, free energy analysis also observed stronger binding energies (ΔGGAS) in SGLT2-phytocompound complexes compared to empagliflozin complex. All the phytocompounds showed drug-likeness and considerable ADMET properties. The study, therefore, suggests that Trifolirhizin-6'-monoacetate, Aspalathin, and Quercetin-3-glucoside could be a possible inhibitor of SGLT2 protein. However, further studies need to be carried out to reveal the exact mode of activity.Communicated by Ramaswamy H. Sarma.

ATP-dependent conformational dynamics in a photoactivated adenylate cyclase revealed by fluorescence spectroscopy and small-angle X-ray scattering

Structural insights into the photoactivated adenylate cyclases can be used to develop new ways of controlling cellular cyclic adenosine monophosphate (cAMP) levels for optogenetic and other applications. In this work, we use an integrative approach that combines biophysical and structural biology methods to provide insight on the interaction of adenosine triphosphate (ATP) with the dark-adapted state of the photoactivated adenylate cyclase from the cyanobacterium Oscillatoria acuminata (OaPAC). A moderate affinity of the nucleotide for the enzyme was calculated and the thermodynamic parameters of the interaction have been obtained. Stopped-flow fluorescence spectroscopy and small-angle solution scattering have revealed significant conformational changes in the enzyme, presumably in the adenylate cyclase (AC) domain during the allosteric mechanism of ATP binding to OaPAC with small and large-scale movements observed to the best of our knowledge for the first time in the enzyme in solution upon ATP binding. These results are in line with previously reported drastic conformational changes taking place in several class III AC domains upon nucleotide binding.

Computational molecular perspectives on novel carbazole derivative as an anti-cancer molecule against CDK1 of breast and colorectal cancers via gene expression studies, novel two-way docking strategies, molecular mechanics and dynamics

With increase in cancer incidences, alternative strategies for disease management are of utmost importance. Carbazole, is a compound that is being studied extensively as an anti-cancer compound. In this work, we aimed to investigate a carbazole derivative against specific cancer types such as breast and colorectal, based on the off-target analyses of carbazole derivative. The present work shortlisted 6 proteins that have an association in both cancer types, and then employed two different molecular docking strategies to examine the binding stability of carbazole derivative: a blind-docking state, where the pockets were undefined and mutation-docking state, where possible mutations were induced within the proteins. The results showed that CDK1 bound best in both states to carbazole derivative, and performed better than an array of positive controls. Molecular dynamic simulations at 100 ns further proved its stability, with carbazole derivative-CDK1-blind and mutated complex having RMSD values between 3.2 and 3.6 Å, and 2.8-3.2 Å respectively. Molecular-mechanics generalized born and surface area solvation disclosed free energy of binding for the complexes as -28.79 ± 3.97 kcal/mol and -31.86 ± 5.09 kcal/mol respectively, with carbazole derivative bound stably within the binding pocket at every 10 ns of the 100 ns trajectory. Radial distribution functions showed that the bell curve was well within 6 Å, thus showing that carbazole derivative and its atoms do not deviate away from the pocket, suggesting its ability to be used as a good anti-cancer compound against breast and colorectal.

Computational investigation of impact of Pb(II) and Ni(II) ions on hUNG enzyme: insights from molecular dynamics simulations

Human uracil DNA glycosylase (hUNG), a crucial player in the initiation of the base excision repair pathway, is susceptible to alterations in function and conformation induced by the accumulation of toxic metals. Despite the recognized impact of toxic metals on DNA repair enzymes, there exists a notable deficiency in theoretical investigations addressing this phenomenon. This study investigates the impact of toxic heavy metal ions, Pb(II) and Ni(II), on the stability of hUNG through molecular dynamics (MD) simulations. The initial analysis involved the identification of key cavities in the hUNG enzyme. Notably, the active site cavity emerged as a promising site for ligand binding. Subsequently, AutoDockTools software was employed to dock Pb(II) and Ni(II) onto the identified cavities, followed by extensive MD simulations. The MD analysis, encompassing parameters such as root mean square deviation, radius of gyration, solvent accessible surface area, hydrogen bond variations, Ramachandran plot, principal component analysis, and root mean square fluctuations, collectively revealed distinct alterations in the behavior of the enzyme upon complexation with Pb(II) and Ni(II). Interestingly, the enzyme exhibited enhanced structural stability, reduced flexibility, and modified hydrogen bonding patterns in the presence of these toxic metal ions. The observed limitation in structural flexibility implies a more rigid and stable conformation when the enzyme complex with Pb(II) and Ni(II) compared to its free form. This structural alteration may lead to a potential reduction in enzymatic activity, suggesting that toxic metal ions influence the functional dynamics of hUNG. These computational findings offer valuable insights into the molecular interactions between metal ions and enzymes.Communicated by Ramaswamy H. Sarma.

Comprehending interaction mechanism of natural actives of Colchicum autumnale L. for rheumatoid arthritis using integrative chemoinformatic approaches

This research delves into the realm of therapeutic potential within natural compounds derived from Colchicum autumnale L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA). The study focuses on understanding interactions between specific natural actives that target RA. Fifteen RA target proteins were identified from OMIM, GeneScan and PharmaGKB. Their structures were downloaded from RCSB PDB. Two active components of C. autumnale L. were chosen for mass spectrometry investigation. Ligand characteristics were determined using the ADMETlab and SwissADME software tools. Molecular docking was performed, and the top three complexes were simulated for 200 ns, along with identification of free binding energies. The compounds β-sitosterol-IL-10 (-6.50 kcal/mol), colchicine-IL-10 (-6.01 kcal/mol), linoleic acid-IL-10 (-7.22 kcal/mol) and linoleic acid-IL-10 (-7.22 kcal/mol) exhibited best binding energies. β-Sitosterol and colchicine showed the highest stability in simulations, confirmed by molecular mechanics free energy binding calculations. This work provides insights into the molecular interaction of natural compounds against RA targets, offering potential therapeutic anti-RA medications.Communicated by Ramaswamy H. Sarma.

Lipase A from Bacillus subtilis: Substrate Binding, Conformational Dynamics, and Signatures of a Lid

Protein-ligand binding studies are crucial for understanding the molecular basis of biological processes and for further advancing industrial biocatalysis and drug discovery. Using computational modeling and molecular dynamics simulations, we investigated the binding of a butyrate ester substrate to the lipase A (LipA) enzyme of Bacillus subtilis. Besides obtaining a close agreement of the binding free energy with the experimental value, the study reveals a remarkable reorganization of the catalytic triad upon substrate binding, leading to increased essential hydrogen bond populations. The investigation shows the distortion of the oxyanion hole in both the substrate-bound and unbound states of LipA and highlights the strengthening of the same in the tetrahedral intermediate complex. Principal component analysis of the unbound ensemble reveals the dominant motion in LipA to be the movement of Loop-1 (Tyr129-Arg142) between two states that cover and uncover the active site, mirroring that of a lid prevalent in several lipases. This lid-like motion of Loop-1 is also supported by its tendency to spontaneously open up at an oil-water interface. Overall, this study provides valuable insights into the impact of substrate binding on the structure, flexibility, and conformational dynamics of the LipA enzyme.

Investigating the Molecular Interactions of Quinoline Derivatives for Antibacterial Activity Against Bacillus subtilis: Computational Biology and In Vitro Study Interpretations

Bacterial infections are evolving and one of the chief problems is emergence and prevalence of antibacterial resistance. Moreover, certain strains of Bacillus subtilis have become resistant to several antibiotics. To counteract this menace, the present work aimed to comprehend the antibacterial activity of synthesized two quinoline derivatives against Bacillus subtilis. Toxicity predictions via Protox II, SwissADME and T.E.S.T (Toxicity Estimation Software Tool) revealed that these derivatives were non-toxic and had little to no adverse effects. Molecular docking studies carried out in Schrodinger with two quinoline derivatives (referred Q1 and Q2) docked against selected target proteins (PDB IDs: 2VAM and1FSE) of B. subtilis demonstrated ideal binding energies (2VAM-Q1: - 4.63 kcal/mol and 2VAM-Q2: - 4.46 kcal/mol, and 1FSE-Q1: - 3.51 kcal/mol, 1FSE-Q2: - 6.34 kcal/mol). These complexes were simulated at 100 ns and the outcomes revealed their stability with slight conformational changes. Anti-microbial assay via disc diffusion method revealed zones of inhibition showing that B. subtilis was inhibited by both Q1 and Q2, with Q2 performing slightly better than Q1, pointing towards its effectiveness against this organism and necessitating further study on other bacteria in prospective studies. Thus, this study demonstrates that our novel quinoline derivatives exhibit antibacterial properties against Bacillus subtilis and can act as potent anti-bacterials.

De novo designed inhibitor has high affinity to four variants of the RBD of S-glycoprotein of SARS-CoV-2 - an in silico study

In the years since the rapid invasion of SARS-CoV-2, the world community has fully understood the extent of the danger of this new pathogen. And also the speed with which he is able to adapt both to humans as a species and to the means of combat that are introduced. However, this has already resulted in millions of lost lives and this situation may worsen in the future, due to the further inevitable evolution of the virus. Accordingly, the need for effective drugs is urgent. In this work, using an iterative approach, we de novo designed a molecule that revealed significant affinity to four variants of SARS-CoV-2 - Wuhan, Omicron, Delta and Cluster 5. More precisely, to their receptor-binding domain of S-glycoprotein, in particular, to the site that is directly involved in the recognition of human ACE2.What is confirmed in particular by the ΔGbind of the complexes of RBD of all four SARS-CoV-2 variants with a potential inhibitor: it is in significantly negative values. Along with this, the calculated ADMET parameters can generally be considered acceptable. Accordingly, we believe that the molecule we have designed has a high potential for further development as an effective drug against SARS-CoV-2. However, it currently requires further in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

A virtual screening investigation to identify bioactive natural compounds as potential inhibitors of cyclin-dependent kinase 9

Cyclin-dependent kinase 9 (CDK9) is a transcription-associated protein involved in controlling the cell cycle and is often deregulated in stress conditions. CDK9 is being studied as a well-known druggable target for developing effective therapeutics against a wide range of cancer, cardiac dysfunction and inflammatory diseases. Owing to the significance of CDK9 in the etiology of hematological and solid malignancies, its structure, biological activity, regulation and its pharmacological inhibition are being explored for therapeutic management of cancer. We employed a structure-based virtual high-throughput screening of bioactive compounds from the IMPPAT database to discover potential bioactive inhibitors of CDK9. The preliminary results were obtained from the Lipinski criteria, ADMET parameters and sorting compounds without any PAINS patterns. Subsequently, binding affinity and selectivity analyses were used to find effective CDK9 hits. This screening resulted in the identification of two natural compounds, Glabrene and Guggulsterone with high affinity and specificity for the CDK9 binding site. Both compounds exhibit drug-like characteristics, as projected by ADMET analysis, physicochemical data and PASS evaluation. Both compounds preferentially bind to the ATP-binding pocket of CDK9 and interact with functionally important residues. Further, the dynamics and consistency of CDK9 interaction with Glabrene and Guggulsteron were evaluated through all-atom molecular dynamic (MD) simulations which suggested the stability of both complexes. The results might be deployed to introduce novel CDK9 inhibitors that may treat life-threatening diseases, including cancer.Communicated by Ramaswamy H. Sarma.

Exploring the Synergistic Mechanism of AP2A2 Transcription Factor Inhibition via Molecular Modeling and Simulations as a Novel Computational Approach for Combating Breast Cancer: In Silico Interpretations

Studies have shown that transcription factor AP2A2 (activator protein-2 alpha-2) is involved in the expression of DLEC1, a tumor suppressor gene, which, when mutated, will cause breast cancer and is thus an excellent target for breast cancer studies. Therefore, in the present research, a synergistic approach toward combating breast cancer is proposed by blocking AP2A2 factor, and allowing the cancer cells to be sensitive to anti-cancer drugs. The effect of AP2A2 on breast cancer was first understood via gene analysis from cBioPortal. AP2A2 was then modeled using RaptorX and its structure was validated from Ramachandran plots. Using all ligands from MolPort database, molecular docking was performed against AP2A2, from which the top three best docked ligands were studied for toxicity in humans using Protox-II. Once the ligands passed these tests, the best complexes were simulated at 200ns in Desmond Maestro, to comprehend their stabilities, followed by the computations of free energies of binding via Molecular mechanics- Generalized Born Solvent Accessibility method (MM-GBSA). The results showed that molecules MolPort-005-945-556 (sachharolipids), MolPort-001-741-124 (flavonoids), and MolPort-005-944-667 (lignan glycosides) with AP2A2 passed toxicity evaluation and belonged to toxicity classes 6, 5, and 5, respectively, with good docking energies. 200 ns simulations revealed stable complexes with slight conformational changes. Stability of ligands was confirmed via snapshots at every 20 ns of the trajectory. Radial distribution of these molecules against the protein revealed very slight deviation from binding pocket. Additionally, the free binding energies for these complexes were found to be - 54.93 ± 12.982 kcal/mol, - 44.39 ± 14.393 kcal/mol, and - 66.51 ± 13.522 kcal/mol, respectively. A preliminary computational validation of the inability of AP2A2 to bind to DLEC1 in the presence of ligands offers beneficial insights into the potential of these ligands. Therefore, this study sheds light on the potential natural molecules that could stably block AP2A2 with least deviation and act in synergy to aid anti-cancer drugs work on breast cancer cells.

Evolutionary relevance of single nucleotide variants within the forebrain exclusive human accelerated enhancer regions

Background

Human accelerated regions (HARs) are short conserved genomic sequences that have acquired significantly more nucleotide substitutions than expected in the human lineage after divergence from chimpanzees. The fast evolution of HARs may reflect their roles in the origin of human-specific traits. A recent study has reported positively-selected single nucleotide variants (SNVs) within brain-exclusive human accelerated enhancers (BE-HAEs) hs1210 (forebrain), hs563 (hindbrain) and hs304 (midbrain/forebrain). By including data from archaic hominins, these SNVs were shown to be Homo sapiens-specific, residing within transcriptional factors binding sites (TFBSs) for SOX2 (hs1210), RUNX1/3 (hs563), and FOS/JUND (hs304). Although these findings suggest that the predicted modifications in TFBSs may have some role in present-day brain structure, work is required to verify the extent to which these changes translate into functional variation.

Results

To start to fill this gap, we investigate the SOX2 SNV, with both forebrain expression and strong signal of positive selection in humans. We demonstrate that the HMG box of SOX2 binds in vitro with Homo sapiens-specific derived A-allele and ancestral T-allele carrying DNA sites in BE-HAE hs1210. Molecular docking and simulation analysis indicated highly favourable binding of HMG box with derived A-allele containing DNA site when compared to site carrying ancestral T-allele.

Conclusion

These results suggest that adoptive changes in TF affinity within BE-HAE hs1210 and other HAR enhancers in the evolutionary history of Homo sapiens might.

have brought about changes in gene expression patterns and have functional consequences on forebrain formation and evolution.

Methods

The present study employ electrophoretic mobility shift assays (EMSA) and molecular docking and molecular dynamics simulations approaches.

Deciphering the interaction mechanism of natural actives against larval proteins of Aedes aegypti to identify potential larvicides: a computational biology analysis

Aedes aegypti is the target for repellents to curb incidences of vector-borne diseases. Stopping breeding of this mosquito species at its larval stages helps in controlling spread of insect-borne diseases. Therefore, the present study focused on deciphering the mechanism of interaction of selected natural actives against larval proteins of A. aegypti to identify potential natural alternative larvicides. 65 larval proteins were identified from literature, whose structures were modelled and validated using RaptorX and ProCheck. 11 natural actives were selected for predicting their ligand properties and toxicities via Toxicity Estimation Software Tool and ProTox-II. Molecular docking studies were carried out using POAP followed by 100 ns molecular dynamic simulation studies for top three best docked complexes to better understand the robustness of docking, complex stabilities and molecular mechanisms of interactions. Toxicity predictions revealed that 6 molecules belonged to toxicity class 4, and five to toxicity class 5, implying that they were all safe to use. Complexes goniothalamin-translation elongation factor (-10 kcal/mol), andrographolide-acetyl-CoA C-myristoyltransferase (-9.2 kcal/mol) and capillin-translation elongation factor (-8.4 kcal/mol) showed best binding energies. When simulated, capillin-translation elongation factor showed most stability, while the remaining two also evidenced robust docking. Evolutionary studies for top two larval proteins disclosed 100 other insect species in which these proteins can be targeted using various larvicides. Protein-protein interaction network analysis revealed several protein pathways that might be affected due to aforesaid naturals. Therefore, this study provides computational insights into the molecular interaction of naturals against larval proteins, acting as potential natural larvicides.Communicated by Ramaswamy H. Sarma.

Big Data analytics for improved prediction of ligand binding and conformational selection

This research introduces new machine learning and deep learning approaches, collectively referred to as Big Data analytics techniques that are unique to address the protein conformational selection mechanism for protein:ligands complexes. The novel Big Data analytics techniques presented in this work enables efficient data processing of a large number of protein:ligand complexes, and provides better identification of specific protein properties that are responsible for a high probability of correct prediction of protein:ligand binding. The GPCR proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2), OPRD1 (Opioid receptor Delta 1) and OPRK1 (Opioid Receptor Kappa 1) are examined in this study using Big Data analytics techniques, which can efficiently process a huge ensemble of protein conformations, and significantly enhance the prediction of binding protein conformation (i.e., the protein conformations that will be selected by the ligands for binding) about 10-38 times better than its random selection counterpart for protein conformation selection. In addition to providing a Big Data approach to the conformational selection mechanism, this also opens the door to the systematic identification of such "binding conformations" for proteins. The physico-chemical features that are useful in predicting the "binding conformations" are largely, but not entirely, shared among the test proteins, indicating that the biophysical properties that drive the conformation selection mechanism may, to an extent, be protein-specific for the protein properties used in this work.

Molecular docking-based virtual screening: Challenges in hits identification for Anti-SARS-Cov-2 activity

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) requires finding new drugs or repurposing drugs for clinical use. Molecular docking belongs to structure-based drug design providing a fast method for identifying the hit compounds with antiviral activity against SARS-Cov-2. However, the weakness of the docking method is compounded by the limited crystallographic information and comparison drugs due to the novelty of this virus can present challenges in identifying hits of anti-SARS-Cov-2. In the current review, we highlighted several aspects, especially those related to the target structure, docking validation, and virtual hit selection, that need to be considered to obtain reliable docking results. Here, we discussed several cases pertaining to the issue highlighted and approaches that could be used to solve them.

Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors

Tyrosine threonine kinase (TTK) is the key component of the spindle assembly checkpoint (SAC) that ensures correct attachment of chromosomes to the mitotic spindle and thereby their precise segregation into daughter cells by phosphorylating specific substrate proteins. The overexpression of TTK has been associated with various human malignancies, including breast, colorectal and thyroid carcinomas. TTK has been validated as a target for drug development, and several TTK inhibitors have been discovered. In this study, ligand and structure-based alignment as well as various partial charge models were used to perform 3D-QSAR modelling on 1H-Pyrrolo[3,2-c] pyridine core containing reported inhibitors of TTK protein using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches to design better active compounds. Different statistical methods i.e., correlation coefficient of non-cross validation (r2), correlation coefficient of leave-one-out cross-validation (q2), Fisher's test (F) and bootstrapping were used to validate the developed models. Out of several charge models and alignment-based approaches, Merck Molecular Force Field (MMFF94) charges using structure-based alignment yielded highly predictive CoMFA (q2 = 0.583, Predr2 = 0.751) and CoMSIA (q2 = 0.690, Predr2 = 0.767) models. The models exhibited that electrostatic, steric, HBA, HBD, and hydrophobic fields play a key role in structure activity relationship of these compounds. Using the contour maps information of the best predictive model, new compounds were designed and docked at the TTK active site to predict their plausible binding modes. The structural stability of the TTK complexes with new compounds was confirmed using MD simulations. The simulation studies revealed that all compounds formed stable complexes. Similarly, MM/PBSA method based free energy calculations showed that these compounds bind with reasonably good affinity to the TTK protein. Overall molecular modelling results suggest that newly designed compounds can act as lead compounds for the optimization of TTK inhibitors.

Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel

(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.

In silico drug repurposing for coronavirus (COVID-19): screening known HCV drugs against the SARS-CoV-2 spike protein bound to angiotensin-converting enzyme 2 (ACE2) (6M0J)

In this study, FDA-approved HCV antiviral drugs and their structural analogues—several of them in clinical trials—were tested for their inhibitory properties toward the SARS-CoV-2 spike protein bound to angiotensin-converting enzyme 2 (6M0J) using a virtual screening approach and computational chemistry methods. The most stable structures and the corresponding binding affinities of thirteen such antiviral compounds were obtained. Frontier molecular orbital theory, global reactivity descriptors, molecular docking calculations and electrostatic potential analysis were used to hypothesize the bioactivity of these drugs against 6M0J. It is found that an increased affinity for the protein is shown by inhibitors with large compound volume, relatively higher electrophilicity index, aromatic rings and heteroatoms that participate in hydrogen bonding. Among the tested drugs, four compounds 10–13 showed excellent results—binding affinities − 11.2 to − 11.5 kcal mol⁻¹. These four top scoring compounds may act as lead compounds for further experimental validation, clinical trials and even for the development of more potent antiviral agents against the SARS-CoV-2.

Dynamically induced conformation depending on excited normal modes of fast oscillation

We present dynamical effects on conformation in a simple bead-spring model consisting of three beads connected by two stiff springs. The conformation defined by the bending angle between the two springs is determined not only by a given potential energy function depending on the bending angle, but also by fast motion of the springs which constructs the effective potential. A conformation corresponding with a local minimum of the effective potential is hence called the dynamically induced conformation. We develop a theory to derive the effective potential using multiple-scale analysis and the averaging method. A remarkable consequence is that the effective potential depends on the excited normal modes of the springs and amount of the spring energy. Efficiency of the obtained effective potential is numerically verified.

Connecting conformational stiffness of the protein with energy landscape by a single experiment

The structure-function dynamics of a protein as a flexible polymer is essential to describe its biological functions. Here, using single-molecule magnetic tweezers, we have studied the effect of ionic strength on the folding mechanics of protein L, and probed its folding-associated physical properties by re-measuring the same protein in a range of ammonium sulfate concentrations from 150 mM to 650 mM. We observed an electrolyte-dependent conformational dynamics and folding landscape of the protein in a single experiment. Salt increases the refolding kinetics, while decreasing the unfolding kinetics under force, which in turn modifies the barrier heights towards the folded state. Additionally, salt enhances the molecular compaction by decreasing the Kuhn length of the protein polymer from 1.18 nm to 0.58 nm, which we have explained by modifying the freely jointed chain model. Finally, we correlated polymer chain physics to the folding dynamics, and thus provided an analytical framework for understanding compaction-induced folding mechanics across a range of ionic strengths from a single experiment.

Computation of the Protein Conformational Transition Pathway on Ligand Binding by Linear Response-Driven Molecular Dynamics

While extremely important for relating the protein structure to its biological function, determination of the protein conformational transition pathway upon ligand binding is made difficult due to the transient nature of intermediates, a large and rugged conformational space, and coupling between protein dynamics and ligand-protein interactions. Existing methods that rely on prior knowledge of the bound (holo) state structure are restrictive. A second concern relates to the correspondence of intermediates obtained to the metastable states on the apo → holo transition pathway. Here, we have taken the protein apo structure and ligand-binding site as only inputs and combined an elastic network model (ENM) representation of the protein Hamiltonian with linear response theory (LRT) for protein-ligand interactions to identify the set of slow normal modes of protein vibrations that have a high overlap with the direction of the protein conformational change. The structural displacement along the chosen direction was performed using excited normal modes molecular dynamics (MDeNM) simulations rather than by the direct use of LRT. Herein, the MDeNM excitation velocity was optimized on-the-fly on the basis of its coupling to protein dynamics and ligand-protein interactions. Thus, a determined set of structures was validated against crystallographic and simulation data on four protein-ligand systems, namely, adenylate kinase-di(adenosine-5')pentaphosphate, ribose binding protein-β-d-ribopyranose, DNA β-glucosyltransferase-uridine-5'-diphosphate, and G-protein α subunit-guanosine-5'-triphosphate, which present important differences in protein conformational heterogeneity, ligand binding mechanism, viz. induced-fit or conformational selection, extent, and nonlinearity in protein conformational changes upon ligand binding, and presence of allosteric effects. The obtained set of intermediates was used as an input to path metadynamics simulations to obtain the free energy profile for the apo → holo transition.

Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics

Compared to all-atom molecular dynamics (AA-MD) simulations, coarse-grained (CG) MD simulations can significantly reduce calculation costs. However, existing CG-MD methods are unsuitable for sampling structures that depart significantly from the initial structure without any biased force. In this study, we developed a new adaptive CG elastic network model (ENM), in which the dynamic cross-correlation coefficient based on short-time AA-MD of at most ns order is considered. By applying Bayesian optimization to search for a suitable parameter among the vast parameter space of adaptive CG-ENM, we succeeded in reducing the searching cost to approximately 10% of those for random sampling and exhaustive sampling. To evaluate the performance of adaptive CG-ENM, we applied the new methodology to adenylate kinase (ADK) and glutamine binding protein (GBP) in the apo state. The results showed that the structural ensembles explored by adaptive CG-ENM could be considerably more diverse than those by conventional ENMs with enhanced sampling such as temperature replica exchange MD and long-time AA-MD of 1 μs. In particular, some of the structures sampled by adaptive ENM are relatively close to the holo-type structures of ADK and GBP. Furthermore, as a challenging task, to demonstrate the advantages of the CG model with lower calculation cost, we applied our new methodology to a larger biomolecule, integrin (αV) in the inactive state. Then, we sampled various structural ensembles, including extended structures that are apparently different from inactive ones.

New biochemical insights into the dynamics of water molecules at the GMP or IMP binding site of human GMPR enzyme: A molecular dynamics study

Human GMP reductase (hGMPR) enzyme is involved in a cellular metabolic pathway, converting GMP into IMP, and also it is an important target for anti-leukemic agents. Present computational investigations explain dynamical behavior of water molecules during the conformational transition process from GMP to IMP using molecular dynamics simulations. Residues at substrate-binding site of cancerous protein (PDB Id. 2C6Q) are mostly more dynamic in nature than the normal protein (PDB Id. 2BLE). Nineteen conserved water molecules are identified at the GMP/IMP binding site and are classified as (i) conserved stable dynamic and (ii) infrequent dynamic. Water molecules W11, W14, and W16 are classified as conserved stable dynamic due to their immobile character, whereas remaining water molecules (W1, W2, W3, W4, W5, W7, W8, W9, W10, W12, W13, W15, W17, W18, and W19) are infrequent with dynamic nature. Entrance or displacement of these infrequent water molecules at GMP/IMP sites may occur due to forward and backward movement of reference residues involving ligands. Four water molecules of hGMPR-I and nine water molecules of hGMPR-II are observed in repetitive transitions from GMP to IMP pathway, which indicates discrimination between two isoforms of hGMPRs. Water molecules in cancerous protein are more dynamic and unstable compared to normal protein. These water molecules execute rare dynamical events at GMP binding site and could assist in detailed understanding of conformational transitions that influence the hGMPR's biological functionality. The present study should be of interest to the experimental community engaged in leukemia research and drug discovery for CML cancer.

Computational screening of camostat and related compounds against human TMPRSS2: A Potential Treatment of COVID-19

The global coronavirus pandemic has burdened the human population with mass fatalities and disastrous socio-economic consequences. The frequent occurrence of these new variants has fueled the already prevailing challenge. There is still a necessity for highly effective small molecular agents to prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we targeted the human transmembrane surface protease TMPRSS2, which is essential for proteolytic activation of SARS-CoV-2. Camostat is a well-known inhibitor of serine proteases and an effective TMPRSS2 inhibitor. A virtual library of camostat-like compounds was computationally screened against the catalytic site of TMPRSS2. Following a sequential in-depth molecular docking and dynamics simulation, we report the compounds that exhibited promising efficacy against TMPRSS2. The molecular docking and MM/PBSA free energy calculation study indicates these compounds carry excellent binding affinity against TMPRSS2 and found them more effective than camostat. The study will open doors for the effective treatment of coronavirus disease 2019.

Multiscale Model for Quantitative Prediction of Insulin Aggregation Nucleation Kinetics

We combined kinetic, thermodynamic, and structural information from single-molecule (protein folding) and two-molecule (association) explicit-solvent simulations for determination of kinetic parameters in protein aggregation nucleation with insulin as the model protein. A structural bioinformatics approach was developed to account for heterogeneity of aggregation-prone species, with the transition complex theory found applicable in modeling association kinetics involving non-native species. Specifically, the kinetic pathway for formation of aggregation-prone oligomeric species was found to contain a structurally specific dominant binding mode, making the kinetic process similar to native protein association. The kinetic parameters thus obtained were used in a population balance model, and accurate predictions for aggregation nucleation time varying over 2 orders of magnitude with changes in either insulin concentration or an aggregation-inhibitor ligand concentration were obtained, while an empirical parameter set was not found to be transferable for prediction of ligand effects. Further, this physically determined kinetic parameter set provided several mechanistic insights, such as identification of the rate-limiting step in aggregation nucleation and a quantitative explanation for the switch from Arrhenius to non-Arrhenius aggregation kinetics around the melting temperature of insulin.

Thymoquinone and quercetin induce enhanced apoptosis in non-small cell lung cancer in combination through the Bax/Bcl2 cascade

The treatments available for non-small cell lung cancer exert various side effects in patients, and the burden of treatment cost is high. Therefore, exploring the alternative system of medicines, including therapies based on natural compounds, has become inevitable in developing anticancer therapeutics. This study used an integrated approach involving in-silico and in-vitro methods to explore natural compounds targeting Bax and Bcl2 for their apoptotic potential. Molecular docking followed by molecular dynamics (MD) simulation of thymoquinone (Tq) and quercetin (Qu) with Bax and Bcl2 were carried out to explore their interactions and stability under explicit solvent conditions. Tq and Qu showed appreciable binding affinities toward Bax (-6.2 and -7.1 kcal/mol, respectively) and Bcl2 (-5.6 and -6.4 kcal/mol, respectively) with well-organized conformational fitting compatibility. The MD simulation results revealed the development of stable complexes maintained by various noncovalent interactions that were preserved throughout the 100 ns trajectories. Further studies with these compounds were carried out using various in-vitro experimental approaches like MTT assay, apoptotic assay, and Western blot. IC₅₀ values of Tq and Qu alone in A549 cells were found to be 45.78 and 35.69 µM, while in combination, it comes down to 22.49 µM, which is quite impressive. Similarly, in apoptosis assay, a combination of Tq and Qu shows 50.9% early apoptosis compared to Tq (40.6%) and Qu (33.3%) when taken alone. These assays signify their apoptotic induction potential, whereas both compounds significantly reduce the expression of antiapoptotic protein Bcl2 and induce proapoptotic Bax, suggestive of sensitizing NSCLS cells toward apoptosis.

Discovery of Natural Compounds as Potential Inhibitors of Human Carbonic Anhydrase II: An Integrated Virtual Screening, Docking, and Molecular Dynamics Simulation Study

Carbonic anhydrase II (CAII) is one of the zinc metalloenzymes that catalyze the reversible hydration of carbon dioxide, leading to the formation of bicarbonate and proton. CAII plays a significant role in health and disease. For example, CAII helps to maintain eye pressure while regulating the pH of the tumor microenvironment, and by extension, contributing to cancer progression. Owing to its remarkable role in cancer, visual health, and other human diseases, CAII can serve as an attractive therapeutic target. We report an original study based on high-throughput virtual screening of natural compounds from the ZINC database in search of potential inhibitors of CAII. We selected the hits based on the physicochemical, absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, pan-assay interference compound (PAINS) patterns, and interaction analysis. Importantly, two natural compounds were identified, ZINC08918123 and ZINC00952700, bearing considerable affinity and specific interactions to the residues of the CAII-binding pocket with well-organized conformational fitting compatibility. We investigated the conformational dynamics of CAII in complex with the identified compounds through molecular dynamics simulation, which revealed the formation of a stable complex preserved throughout the 100 ns trajectories. The stability of the protein/ligand complexes is maintained by significant numbers of noncovalent interactions throughout the simulations. In conclusion, natural compounds identified in the present study specifically and computer-assisted drug design broadly offer a reliable resource and strategy to discover potential promising therapeutic inhibitors of CAII to cure various cancers and glaucoma after further experimental validation and clinical studies.

In silico screening and molecular dynamics simulation of deleterious PAH mutations responsible for phenylketonuria genetic disorder

Phenylketonuria (PKU) is a genetic disorder that if left untreated can lead to behavioral problems, epilepsy, and even mental retardation. PKU results from mutations within the phenylalanine-4-hydroxylase (PAH) gene that encodes for the PAH protein. The study of all PAH causing mutations is improbable using experimental techniques. In this study, a collection of in silico resources, sorting intolerant from tolerant, Polyphen-2, PhD-SNP, and MutPred were used to identify possible pathogenetic and deleterious PAH non-synonymous single nucleotide polymorphisms (nsSNPs). We identified two variants of PAH, I65N and L311P, to be the most deleterious and disease causing nsSNPs. Molecular dynamics (MD) simulations were carried out to characterize these point mutations on the atomic level. MD simulations revealed increased flexibility and a decrease in the hydrogen bond network for both mutants compared to the native protein. Free energy calculations using the MM/GBSA approach found that BH4 , a drug-based therapy for PKU patients, had a higher binding affinity for I65N and L311P mutants compared to the wildtype protein. We also identify important residues in the BH4 binding pocket that may be of interest for the rational drug design of other PAH drug-based therapies. Lastly, free energy calculations also determined that the I65N mutation may impair the dimerization of the N-terminal regulatory domain of PAH.

Structure and ligand-based drug discovery of IL-4 inhibitors via interaction-energy-based learning approaches

Interleukin-4 (IL-4), an anti-inflammatory cytokine plays significant in the development of various diseases especially asthmatic allergies. Previous structural and functional studies of IL-4 with its receptor bring forth different types of inhibitors to block their interaction but each of them failed in clinical trials. Since, no synthetic molecules have been identified against IL-4, so far. Therefore, 21 in-house tested IL-4 inhibitors were blindly docked over the entire surface of IL-4 to predict a suitable and druggable binding site as the crystal structure of IL-4 protein in complex with ligand has not been reported yet. After binding site prediction, both ligand-based and structure-based pharmacophore were generated to screen three ZINC libraries (24.5 M) i.e. purchasable, natural product and natural derivative. A total 5,800 top-scored compounds were further subjected towards score-based screening to find the potential leads. Following protein-ligand interaction fingerprints (PLIF) and molecular visualization of selected hits, six top-scored compounds (five from purchasable and one from natural product library) were further moved towards their stability dynamics, followed by their absolute binding free energy and residue-based energy decomposition calculation by MM-GBSA method. These efforts help us to reveal the key factors responsible for ligand binding that might help to improve the binding and stability of these newly discovered hits by structural modifications.Communicated by Freddie R. Salsbury.

Potential repurposing of four FDA approved compounds with antiplasmodial activity identified through proteome scale computational drug discovery and in vitro assay

Malaria elimination can benefit from time and cost-efficient approaches for antimalarials such as drug repurposing. In this work, 796 DrugBank compounds were screened against 36 Plasmodium falciparum targets using QuickVina-W. Hits were selected after rescoring using GRaph Interaction Matching (GRIM) and ligand efficiency metrics: surface efficiency index (SEI), binding efficiency index (BEI) and lipophilic efficiency (LipE). They were further evaluated in Molecular dynamics (MD). Twenty-five protein-ligand complexes were finally retained from the 28,656 (36 × 796) dockings. Hit GRIM scores (0.58 to 0.78) showed their molecular interaction similarity to co-crystallized ligands. Minimum LipE (3), SEI (23) and BEI (7) were in at least acceptable thresholds for hits. Binding energies ranged from -6 to -11 kcal/mol. Ligands showed stability in MD simulation with good hydrogen bonding and favorable protein-ligand interactions energy (the poorest being -140.12 kcal/mol). In vitro testing showed 4 active compounds with two having IC50 values in the single-digit μM range.

Integron mediated antimicrobial resistance in diarrheagenic Escherichia coli in children: in vitro and in silico analysis

The exchange of genes between bacterial chromosome and plasmid(s) and their integration into integrons are mainly responsible for acquisition and dissemination of antibiotic resistance. We investigated the role of integrons and their underlying molecular mechanisms leading to development of adaptability in E. coli and eventual resistance to antimicrobials. Escherichia coli isolates (n = 120); including 40 diarrheagenic isolates, an even number of isolates from cases other than diarrhea, and equal number of isolates from healthy children recovered from fresh stool samples were used for identification of integron genes and gene cassettes. The association of integrons with antibiotic resistance was assayed before phylogenetic analysis. DNA sequence analysis revealed class 1 and 2 integrons in 55.83% and 21.66% isolates, respectively. The integron presence was found significantly associated with the probability of antibiotic resistance in E. coli; the association being highest with class 1 integron. Modelling and molecular docking along with molecular dynamics simulation analyses found ceftriaxone and amoxicillin as potential inhibitors of dihydrofolate reductase (DHFR). The class 1 integrons of these pathogenic isolates can serve as prospective therapeutic targets using specific silencing strategies and combinational antimicrobial therapy. The findings may be useful for the development of a potent and versatile drug for DHFR inhibition.

Isolation, Characterization and Structure Elucidation of a Novel Lantibiotic From Paenibacillus sp

We have isolated and characterized a novel antibacterial peptide, CMB001, following an extensive screening effort of bacterial species isolated from diverse environmental sources. The bacterium that produces CMB001 is characterized as a Gram (+) bacillus sharing approximately 98.9% 16S rRNA sequence homology with its closest match, Paenibacillus kyungheensis. The molecule has been purified to homogeneity from its cell-free supernatant by a three-step preparative chromatography process. Based on its primary structure, CMB001 shares 81% identity with subtilin and 62% with nisin. CMB001 is active mainly against Gram-positive bacteria and Mycobacteriaceae but it is also active against certain Gram-negative bacteria, including multi-drug resistant Acinetobacter baumannii. It retains full antibacterial activity at neutral pH and displays a low propensity to select for resistance among targeted bacteria. Based on NMR and mass spectrometry, CMB001 forms a unique 3D-structure comprising of a compact backbone with one α-helix and two pseudo-α-helical regions. Screening the structure against the Protein Data Bank (PDB) revealed a partial match with nisin-lipid II (1WCO), but none of the lantibiotics with known structures showed significant structural similarity. Due to its unique structure, resistance profile, relatively broad spectrum and stability under physiological conditions, CMB001 is a promising drug candidate for evaluation in animal models of bacterial infection.

Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana glycosyltransferases

Glycosylation of secondary metabolites involves plant UDP-dependent glycosyltransferases (UGTs). UGTs have shown promise as catalysts in the synthesis of glycosides for medical treatment. However, limited understanding at the molecular level due to insufficient biochemical and structural information has hindered potential applications of most of these UGTs. In the absence of experimental crystal structures, we employed advanced molecular modeling and simulations in conjunction with biochemical characterization to design a workflow to study five Group H Arabidopsis thaliana (76E1, 76E2, 76E4, 76E5, 76D1) UGTs. Based on our rational structural manipulation and analysis, we identified key amino acids (P129 in 76D1; D374 in 76E2; K275 in 76E4), which when mutated improved donor substrate recognition than wildtype UGTs. Molecular dynamics simulations and deep learning analysis identified structural differences, which drive substrate preferences. The design of these UGTs with broader substrate specificity may play important role in biotechnological and industrial applications. These findings can also serve as basis to study other plant UGTs and thereby advancing UGT enzyme engineering.

Exploring Conformational Space with Thermal Fluctuations Obtained by Normal-Mode Analysis

Proteins in their native states can be represented as ensembles of conformers in dynamical equilibrium. Thermal fluctuations are responsible for transitions between these conformers. Normal-modes analysis (NMA) using elastic network models (ENMs) provides an efficient procedure to explore global dynamics of proteins commonly associated with conformational transitions. In the present work, we present an iterative approach to explore protein conformational spaces by introducing structural distortions according to their equilibrium dynamics at room temperature. The approach can be used either to perform unbiased explorations of conformational space or to explore guided pathways connecting two different conformations, e.g., apo and holo forms. In order to test its performance, four proteins with different magnitudes of structural distortions upon ligand binding have been tested. In all cases, the conformational selection model has been confirmed and the conformational space between apo and holo forms has been encompassed. Different strategies have been tested that impact on the efficiency either to achieve a desired conformational change or to achieve a balanced exploration of the protein conformational multiplicity.

Structural modification of aspirin to design a new potential cyclooxygenase (COX-2) inhibitors

Aspirin (Asp) is one of the most important and ancient member of nonsteroidal anti-inflammatory drugs (NSAID), commonly used in medication of fever, pain and inflammation. It can inhibit the synthesis of prostaglandin by blocking the cyclooxygenase (COX). Attempts have been taken to analyze aspirin together with some of its modified derivatives applying quantum mechanical calculations in order to compare their physicochemical and biochemical properties. Density functional theory (DFT) with B3LYP/6-31G (d, p) basis set has been employed to elucidate their thermal, molecular orbital, equilibrium geometrical properties in gas phase. Molecular docking and nonbonding interactions have been performed against human cyclooxygenase-2 protein 5F1A to investigate the binding affinity and mode(s) of newly designed aspirin derivatives. ADMET prediction has been utilized to compare the absorption, metabolism, and carcinogenic properties of new derivatives with parent drug (Asp). Thermal and geometrical results support the thermochemical stability and equilibrium geometry of all the structures. From the molecular docking simulation, most of the derivatives exhibited better binding affinity than parent drug (Asp) with the receptor protein (5F1A). ADMET prediction disclosed the improved pharmacokinetic properties with lower acute oral toxicity of some derivatives. Based on quantum chemical, molecular docking and ADMET analysis, this investigation can be useful to understand the physicochemical and biochemical/biological activities of Asp and its modified derivatives to search a new antipyretic analgesic drug.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets

The TRAnsient Pockets in Proteins (TRAPP) webserver provides an automated workflow that allows users to explore the dynamics of a protein binding site and to detect pockets or sub-pockets that may transiently open due to protein internal motion. These transient or cryptic sub-pockets may be of interest in the design and optimization of small molecular inhibitors for a protein target of interest. The TRAPP workflow consists of the following three modules: (i) TRAPP structure— generation of an ensemble of structures using one or more of four possible molecular simulation methods; (ii) TRAPP analysis—superposition and clustering of the binding site conformations either in an ensemble of structures generated in step (i) or in PDB structures or trajectories uploaded by the user; and (iii) TRAPP pocket—detection, analysis, and visualization of the binding pocket dynamics and characteristics, such as volume, solvent-exposed area or properties of surrounding residues. A standard sequence conservation score per residue or a differential score per residue, for comparing on- and off-targets, can be calculated and displayed on the binding pocket for an uploaded multiple sequence alignment file, and known protein sequence annotations can be displayed simultaneously. The TRAPP webserver is freely available at http://trapp.h-its.org.

Optimization of structures, biochemical properties of ketorolac and its degradation products based on computational studies

BACKGROUND

Ketorolac (KTR) is used as an analgesic drug with an efficacy close to that of the opioid family. It is mainly used for the short term treatment of post-operative pain. It can inhibit the prostaglandin synthesis by blocking cyclooxygenase (COX).

METHODS

In this investigation, the inherent stability and biochemical interaction of Ketorolac (KTR) and its degradation products have been studiedon the basis of quantum mechanical approaches. Density functional theory (DFT) with B3LYP/ 6-31G (d) has been employed to optimize the structures. Thermodynamic properties, frontier molecular orbital features, dipole moment, electrostatic potential, equilibrium geometry, vibrational frequencies and atomic partial charges of these optimized structureswere investigated. Molecular docking has been performed against prostaglandin H2 (PGH2) synthase protein 5F19 to search the binding affinity and mode(s). ADMET prediction has performed to evaluate the absorption, metabolism and carcinogenic properties.

RESULTS

The equilibrium geometry calculations support the optimized structures. Thermodynamic results disclosed the thermal stability of all structures. From molecular orbital data, all the degradents are chemically more reactive than parent drug (except K3). However, the substitution of carboxymethyl radicalin K4 improved the physicochemical properties and binding affinity. ADMET calculations predict the improved pharmacokinetic and non-carcinogenic properties of all degradents.

CONCLUSION

Based on physicochemical, molecular docking, and ADMET calculation, this study can be helpful to understand the biochemical activities of Ketorolac and its degradents and to design a potent analgesic drug.

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening: A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors

Accurate protein structure in the ligand-bound state is a prerequisite for successful structure-based virtual screening (SBVS). Therefore, applications of SBVS against targets for which only an apo structure is available may be severely limited. To address this constraint, we developed a computational strategy to explore the ligand-bound state of a target protein, by combined use of molecular dynamics simulation, MM/GBSA binding energy calculation, and fragment-centric topographical mapping. Our computational strategy is validated against low-molecular weight protein tyrosine phosphatase (LMW-PTP) and then successfully employed in the SBVS against protein tyrosine phosphatase receptor type O (PTPRO), a potential therapeutic target for various diseases. The most potent hit compound GP03 showed an IC₅₀ value of 2.89 μM for PTPRO and possessed a certain degree of selectivity toward other protein phosphatases. Importantly, we also found that neglecting the ligand energy penalty upon binding partially accounts for the false positive SBVS hits. The preliminary structure-activity relationships of GP03 analogs are also reported.

Identification of Effective Dimeric Gramicidin-D Peptide as Antimicrobial Therapeutics over Drug Resistance: In-Silico Approach

Discovering and developing the antimicrobial peptides are recently focused on pharmaceutical firm, since they serve as complementary to antibiotics in prevailing over drug resistance by eliciting the disruption of microbial membrane. Still, there are lots of challenges to bring up the structurally stable and functionally efficient antimicrobial peptides. It is well known that gramicidin D is the prominent antimicrobial peptide that exists as g-AB, g-BC, and g-AC. This study analyzes the structural stability and the functional activity of hetero-dimeric double-stranded gramicidin-D peptides, thereby demonstrating its potent antimicrobial activity against antibiotic-resistant micro-organisms. To investigate the structural stability and functionality of gramicidin D, we performed static and dynamic analysis. Initially, we observed a maximum number of intermolecular interactions and membrane penetration in g-AB as compared to g-BC and g-AC. To substantiate further, the geometrical and thermodynamic parameters revealed the retention of maximum stability in g-AB than g-AC and g-BC. Thus, the conformational free energy and the binding free energy showed the variation among gramicidin-D peptides for the prediction of increased stability and functionality. In conclusion, g-AB peptide has definitely demonstrated adequate structural stability and functionality and this work will need to be considered in peptide-based drug discovery.

Computational Analysis for the Rational Design of Anti-Amyloid Beta (Aβ) Antibodies

Alzheimer's disease (AD) is a neurodegenerative disorder that lacks effective treatment options. Anti-amyloid beta (Aβ) antibodies are the leading drug candidates to treat AD, but the results of clinical trials have been disappointing. Introducing rational mutations into anti-Aβ antibodies to increase their effectiveness is a way forward, but the path to take is unclear. In this study, we demonstrate the use of computational fragment-based docking and MMPBSA binding free energy calculations in the analysis of anti-Aβ antibodies for rational drug design efforts. Our fragment-based docking method successfully predicts the emergence of the common EFRH epitope. MD simulations coupled with MMPBSA binding free energy calculations are used to analyze scenarios described in prior studies, and we computationally introduce rational mutations into PFA1 to predict mutations that can improve its binding affinity toward the pE3-Aβ_3-8 form of Aβ. Two out of our four proposed mutations are predicted to stabilize binding. Our study demonstrates that a computational approach may lead to an improved drug candidate for AD in the future.

How Diverse Are the Protein-Bound Conformations of Small-Molecule Drugs and Cofactors?

Knowledge of the bioactive conformations of small molecules or the ability to predict them with theoretical methods is of key importance to the design of bioactive compounds such as drugs, agrochemicals, and cosmetics. Using an elaborate cheminformatics pipeline, which also evaluates the support of individual atom coordinates by the measured electron density, we compiled a complete set ("Sperrylite Dataset") of high-quality structures of protein-bound ligand conformations from the PDB. The Sperrylite Dataset consists of a total of 10,936 high-quality structures of 4,548 unique ligands. Based on this dataset, we assessed the variability of the bioactive conformations of 91 small molecules-each represented by a minimum of ten structures-and found it to be largely independent of the number of rotatable bonds. Sixty-nine molecules had at least two distinct conformations (defined by an RMSD greater than 1 Å). For a representative subset of 17 approved drugs and cofactors we observed a clear trend for the formation of few clusters of highly similar conformers. Even for proteins that share a very low sequence identity, ligands were regularly found to adopt similar conformations. For cofactors, a clear trend for extended conformations was measured, although in few cases also coiled conformers were observed. The Sperrylite Dataset is available for download from http://www.zbh.uni-hamburg.de/sperrylite_dataset.

The Bioactive Protein-Ligand Conformation of GluN2C-Selective Positive Allosteric Modulators Bound to the NMDA Receptor

N-methyl-d-aspartate (NMDA) receptors are ligand-gated, cation-selective channels that mediate a slow component of excitatory synaptic transmission. Subunit-selective positive allosteric modulators of NMDA receptor function have therapeutically relevant effects on multiple processes in the brain. A series of pyrrolidinones, such as PYD-106, that selectively potentiate NMDA receptors that contain the GluN2C subunit have structural determinants of activity that reside between the GluN2C amino terminal domain and the GluN2C agonist binding domain, suggesting a unique site of action. Here we use molecular biology and homology modeling to identify residues that line a candidate binding pocket for GluN2C-selective pyrrolidinones. We also show that occupancy of only one site in diheteromeric receptors is required for potentiation. Both GluN2A and GluN2B can dominate the sensitivity of triheteromeric receptors to eliminate the actions of pyrrolidinones, thus rendering this series uniquely sensitive to subunit stoichiometry. We experimentally identified NMR-derived conformers in solution, which combined with molecular modeling allows the prediction of the bioactive binding pose for this series of GluN2C-selective positive allosteric modulators of NMDA receptors. These data advance our understanding of the site and nature of the ligand-protein interaction for GluN2C-selective positive allosteric modulators for NMDA receptors.