Structural insights from an in silico molecular docking simulation of complement component 3a receptor 1 with an antagonist

Complement component 3a receptor 1 (C3aR) is an anaphylatoxin receptor that mediates inflammatory processes. Although considerable effort has gone into discovering the antagonists and agonists of C3aR, structural insights are required to search for effective ligands and to elucidate their binding modes and the mechanism of activation and inactivation. No experimental structural data of C3aR have yet been reported. We investigated the binding mode of an antagonist of C3aR using a combination of homology modeling, ligand docking, molecular dynamics simulations, and binding free energy calculations. We produced a plausible binding model consistent with the reported experimental data. We believe that this model is appropriate for the identification of new C3aR antagonists, as it can distinguish between antagonists and decoy compounds.

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC₅₀ values below 10 μM and the most potent one (compound 1) with IC₅₀ of 1.18 ± 0.04 μM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC₅₀ of 0.27 ± 0.02 μM. Then, the structure-activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.

Tetrazoles as PPARγ ligands: A structural and computational investigation

Diabetes is an important chronic disease affecting about 10% of the adult population in the US and over 420 million people worldwide, resulting in 1.6 million deaths every year, according to the World Health Organization. The most common type of the disease, type 2 diabetes, can be pharmacologically managed using oral hypoglycemic agents or thiazolidinediones (TZDs), such as pioglitazone, which act by activating the Peroxisome Proliferated-Activated Receptor γ. Despite their beneficial effects in diabetes treatment, TZDs like rosiglitazone and troglitazone were withdrawn due to safety reasons, creating a void in the pharmacological options for the treatment of this important disease. Here, we explored a structure-based approach in the screening for new chemical probes for a deeper investigation of the effects of PPARγ activation. A class of tetrazole compounds was identified and the compounds named T1, T2 and T3 were purchased and evaluated for their ability to interact with the PPARγ ligand binding domain (LBD). The compounds were binders with micromolar range affinity, as determined by their IC₅₀ values. A Monte Carlo simulation of the compound T2 revealed that the tetrazole ring makes favorable interaction with the polar arm of the receptor binding pocket. Finally, the crystal structure of the PPARγ-LBD-T2 complex was solved at 2.3 Å, confirming the binding mode for this compound. The structure also revealed that, when the helix H12 is mispositioned, an alternative binding conformation is observed for the ligand suggesting an H12-dependent binding conformation for the tetrazole compound.

Identification of potential inhibitors of Schistosoma mansoni purine nucleoside phosphorylase from neolignan compounds using molecular modelling approaches

Schistosomiasis is a parasitic disease that is part of the neglected tropical diseases (NTDs), which cause significant levels of morbidity and mortality in millions of people throughout the world. The enzyme purine nucleoside phosphorylase from Schistosoma mansoni (SmPNP) represents a potential target for discovering new agents, and neolignans stand out as an important class of compounds. In this work, molecular modeling studies and biological assays of a set of neolignans were conducted against the PNP enzymes of the parasite and the human homologue (HssPNP). The results of the molecular docking described that the neolignans showed good complementarity by the active site of SmPNP. Molecular dynamics (MD) studies revealed that both complexes (Sm/HssPNP - neolignan compounds) were stable by analyzing the root mean square deviation (RMSD) values, and the binding free energy values suggest that the selected structures can interact and inhibit the catalytic activity of the SmPNP. Finally, the biological assay indicated that the selected neolignans presented a better molecular profile of inhibition compared to the human enzyme, as these ligands did not have the capacity to inhibit enzymatic activity, indicating that these compounds are promising candidates and that they can be used in future research in chemotherapy for schistosomiasis.Communicated by Ramaswamy H. Sarma.

Virtual Screening of Chinese Medicine Small Molecule Compounds Targeting SARS-CoV-2 3CL Protease (3CL pro)

Background:

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has attracted worldwide attention due to its high infectivity and pathogenicity.

Objective:

The purpose of this study is to develop drugs with therapeutic potentials for COVID-19.

Methods:

we selected the crystal structure of 3CL pro to perform virtual screening against natural products in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Then, molecular dynamics (MD) simulation was carried out to explore the binding mode between compounds and 3CL pro.

Results and Discussion:

A total of 6 candidates with good theoretical binding affinity to 3CL pro were identified. The binding mode after MD shows that hydrogen bonding and hydrophobic interaction play an important role in the binding process. Finally, based on the free binding energy analysis, the candidate natural product Gypenoside LXXV may bind to 3CL pro with high binding affinity.

Conclusion:

The natural product Gypenoside LXXV may have good potential anti-SARS-COV-2 activity.

Molecular mechanisms of inhibitor bindings to A-FABP deciphered by using molecular dynamics simulations and calculations of MM-GBSA

Adipocyte fatty-acid binding protein (A-FABP) plays a central role in many aspects of metabolic diseases. It is an important target in drug design for treatment of FABP-related diseases. In this study, molecular dynamics (MD) simulations followed by calculations of molecular mechanics generalized Born surface area (MM-GBSA) and principal components analysis (PCA) were implemented to decipher molecular mechanism correlating with binding of inhibitors 57Q, 57P and L96 to A-FABP. The results show that van der Waals interactions are the leading factors to control associations of 57Q, 57P, and L96 with A-FABP, which reveals an energetic basis for designing of clinically available inhibitors towards A-FABP. The information from PCA and cross-correlation analysis rationally unveils that inhibitor bindings affect conformational changes of A-FABP and change relative movements between residues. Decomposition of binding affinity into contributions of individual residues not only detects hot spots of inhibitor/A-FABP binding but also shows that polar interactions of the positively charged residue Arg126 with three inhibitors provide a significant contribution for stabilization of the inhibitor/A-FABP bindings. Furthermore, the binding strength of L96 to residues Ser55, Phe57 and Lys58 are stronger than that of inhibitors 57Q and 57P to these residues.

Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

Predicting accurate protein-ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite the recent advances in the application of deep convolutional and graph neural network-based approaches, it remains unclear what the relative advantages of each approach are and how they compare with physics-based methodologies that have found more mainstream success in virtual screening pipelines. We present fusion models that combine features and inference from complementary representations to improve binding affinity prediction. This, to our knowledge, is the first comprehensive study that uses a common series of evaluations to directly compare the performance of three-dimensional (3D)-convolutional neural networks (3D-CNNs), spatial graph neural networks (SG-CNNs), and their fusion. We use temporal and structure-based splits to assess performance on novel protein targets. To test the practical applicability of our models, we examine their performance in cases that assume that the crystal structure is not available. In these cases, binding free energies are predicted using docking pose coordinates as the inputs to each model. In addition, we compare these deep learning approaches to predictions based on docking scores and molecular mechanic/generalized Born surface area (MM/GBSA) calculations. Our results show that the fusion models make more accurate predictions than their constituent neural network models as well as docking scoring and MM/GBSA rescoring, with the benefit of greater computational efficiency than the MM/GBSA method. Finally, we provide the code to reproduce our results and the parameter files of the trained models used in this work. The software is available as open source at https://github.com/llnl/fast. Model parameter files are available at ftp://gdo-bioinformatics.ucllnl.org/fast/pdbbind2016_model_checkpoints/.

An Active Site Inhibitor Induces Conformational Penalties for ACE2 Recognition by the Spike Protein of SARS-CoV-2

The novel RNA virus, severe acute respiratory syndrome coronavirus II (SARS-CoV-2), is currently the leading cause of mortality in 2020, having led to over 1.6 million deaths and infecting over 75 million people worldwide by December 2020. While vaccination has started and several clinical trials for a number of vaccines are currently underway, there is a pressing need for a cure for those already infected with the virus. Of particular interest in the design of anti-SARS-CoV-2 therapeutics is the human protein angiotensin converting enzyme II (ACE2) to which this virus adheres before entry into the host cell. The SARS-CoV-2 virion binds to cell-surface bound ACE2 via interactions of the spike protein (s-protein) on the viral surface with ACE2. In this paper, we use all-atom molecular dynamics simulations and binding enthalpy calculations to determine the effect that a bound ACE2 active site inhibitor (MLN-4760) would have on the binding affinity of SARS-CoV-2 s-protein with ACE2. Our analysis indicates that the binding enthalpy could be reduced for s-protein adherence to the active site inhibitor-bound ACE2 protein by as much as 1.48-fold as an upper limit. This weakening of binding strength was observed to be due to the destabilization of the interactions between ACE2 residues Glu-35, Glu-37, Tyr-83, Lys-353, and Arg-393 and the SARS-CoV-2 s-protein receptor binding domain (RBD). The conformational changes were shown to lead to weakening of ACE2 interactions with SARS-CoV-2 s-protein, therefore reducing s-protein binding strength. Further, we observed increased conformational lability of the N-terminal helix and a conformational shift of a significant portion of the ACE2 motifs involved in s-protein binding, which may affect the kinetics of the s-protein binding when the small molecule inhibitor is bound to the ACE2 active site. These observations suggest potential new ways for interfering with the SARS-CoV-2 adhesion by modulating ACE2 conformation through distal active site inhibitor binding.

Insights into interaction mechanism of inhibitors E3T, E3H and E3B with CREB binding protein by using molecular dynamics simulations and MM-GBSA calculations

CREB binding protein (CBP) and its paralog E1A binding protein (p300) are related to the development of inflammatory diseases, cancers and other diseases, and have been potential targets for the treatment of human diseases. In this work, interaction mechanism of three small molecules E3T, E3H, and E3B with CBP was investigated by employing molecular dynamics (MD) simulations, principal component analysis (PCA), and molecular mechanics/generalized born surface area (MM-GBSA) method. The results indicate that inhibitor bindings cause the changes of movement modes and structural flexibility of CBP, and van der Waals interactions mostly drive associations of inhibitors with CBP. In the meantime, the results based on inhibitor-residue interactions not only show that eight residues of CBP can strongly interact with E3T, E3H and E3B but also verify that the CH-CH, CH-π, and π-π interactions are responsible for vital contributions in associations of E3T, E3H and E3B with CBP. In addition, the H-O radial distribution functions (RDFs) were computed to assess the stability of hydrogen bonding interactions between inhibitors and CBP, and the obtained information identifies several key hydrogen bonds playing key roles in bindings of E3T, E3H and E3B to CBP. Potential new inhibitors have been proposed.

Effects of H2A.B incorporation on nucleosome structures and dynamics

The H2A.B histone variant is an epigenetic regulator involved in transcriptional upregulation, DNA synthesis, and splicing that functions by replacing the canonical H2A histone in the nucleosome core particle. Introduction of H2A.B results in less compact nucleosome states with increased DNA unwinding and accessibility at the nucleosomal entry and exit sites. Despite being well characterized experimentally, the molecular mechanisms by which H2A.B incorporation alters nucleosome stability and dynamics remain poorly understood. To study the molecular mechanisms of H2A.B, we have performed a series of conventional and enhanced sampling molecular dynamics simulation of H2A.B- and canonical H2A-containing nucleosomes. Results of conventional simulations show that H2A.B weakens protein-protein and protein-DNA interactions at specific locations throughout the nucleosome. These weakened interactions result in significantly more DNA opening from both the entry and exit sites in enhanced sampling simulations. Furthermore, free energy profiles show that H2A.B-containing nucleosomes have significantly broader free wells and that H2A.B allows for sampling of states with increased DNA breathing, which are shown to be stable on the hundreds of nanoseconds timescale with further conventional simulations. Together, our results show the molecular mechanisms by which H2A.B creates less compacted nucleosome states as a means of increasing genetic accessibility and gene transcription.

Exploring the Distinct Binding and Activation Mechanisms for Different CagA Oncoproteins and SHP2 by Molecular Dynamics Simulations

CagA is a major virulence factor of Helicobacter pylori. H. pylori CagA is geographically subclassified into East Asian CagA and Western CagA, which are characterized by the presence of a EPIYA-D or EPIYA-C segment. The East Asian CagA is more closely associated with gastric cancer than the Western CagA. In this study, molecular dynamic (MD) simulations were performed to investigate the binding details of SHP2 and EPIYA segments, and to explore the allosteric regulation mechanism of SHP2. Our results show that the EPIYA-D has a stronger binding affinity to the N-SH2 domain of SHP2 than EPIYA-C. In addition, a single EPIYA-D binding to N-SH2 domain of SHP2 can cause a deflection of the key helix B, and the deflected helix B could squeeze the N-SH2 and PTP domains to break the autoinhibition pocket of SHP2. However, a single EPIYA-C binding to the N-SH2 domain of SHP2 cannot break the autoinhibition of SHP2 because the secondary structure of the key helix B is destroyed. However, the tandem EPIYA-C not only increases its binding affinity to SHP2, but also does not significantly break the secondary structure of the key helix B. Our study can help us better understand the mechanism of gastric cancer caused by Helicobacter pylori infection.

Selective inhibition mechanism of nitroxoline to the BET family: Insight from molecular simulations

Although the proteins in bromodomain and extra-terminal domain (BET) family are promising therapy drug targets for numerous human diseases, the binding effectiveness is interfered by the competition from non-BET protein BRD9. In this study, molecular docking, molecular dynamics simulations, binding free energy calculations and per-residue energy decomposition methods were employed to clarify the selective inhibition mechanism of nitroxoline. The results showed that the different cavity volume of effective embedding inhibitor and the changes in conserved residues were associated with the significant higher selectivity of inhibitor nitroxoline for BET family than non-BET protein (BRD9). In addition, the non-polar interactions occurred in Phe83, Val87 at ZA loop, and the polar interaction appeared in Met132, Asn135 at BC loop. Therefore, when designing a new inhibitor, it could better improve the inhibitor activity by introducing the heteroatom conjugated pyridine-like moiety and the strong electron-withdrawing nitro-like moiety. Overall, this study not only clarified the molecular mechanism of the selective inhibition of nitroxoline, but also provided insight into designing more effective BET inhibitors in next step.

Modifications on the Tetrahydroquinoline Scaffold Targeting a Phenylalanine Cluster on GPER as Antiproliferative Compounds against Renal, Liver and Pancreatic Cancer Cells

The implementation of chemo- and bioinformatics tools is a crucial step in the design of structure-based drugs, enabling the identification of more specific and effective molecules against cancer without side effects. In this study, three new compounds were designed and synthesized with suitable absorption, distribution, metabolism, excretion and toxicity (ADME-tox) properties and high affinity for the G protein-coupled estrogen receptor (GPER) binding site by in silico methods, which correlated with the growth inhibitory activity tested in a cluster of cancer cell lines. Docking and molecular dynamics (MD) simulations accompanied by a molecular mechanics/generalized Born surface area (MMGBSA) approach yielded the binding modes and energetic features of the proposed compounds on GPER. These in silico studies showed that the compounds reached the GPER binding site, establishing interactions with a phenylalanine cluster (F206, F208 and F278) required for GPER molecular recognition of its agonist and antagonist ligands. Finally, a 3-(4,5-dimethylthiazol-2-yl)2,5-diphenyltetrazolium bromide (MTT) assay showed growth inhibitory activity of compounds 4, 5 and 7 in three different cancer cell lines-MIA Paca-2, RCC4-VA and Hep G2-at micromolar concentrations. These new molecules with specific chemical modifications of the GPER pharmacophore open up the possibility of generating new compounds capable of reaching the GPER binding site with potential growth inhibitory activities against nonconventional GPER cell models.

Target SARS-CoV-2: computation of binding energies with drugs of dexamethasone/umifenovir by molecular dynamics using OPLS-AA force field

Introduction

In recent times, myriads of public have been infected with a novel SARS-CoV-2, and the fatality toll has reached thousands and been mounting step by step, which is a major crisis in the world. The challenge for this burning issue pertinent to repurposed medicines which prevent novel coronavirus is of immense concern for all scientists around the globe until the arrival of the vaccine.

Methods

Because of the global high priority rating on the search for the repurposed drugs which outfits clinical suitability to SARS-CoV-2, a unique theoretical methodology is proposed. The approach is based on explorations of biothermodynamics computed via molecular dynamics, root-mean-square deviation (RMSD), radius of gyration (Rg) and interactions. This unique methodology is tested for umifenovir/dexamethasone drugs on (SARS-CoV-2) main protease.

Results

This theoretical exploration not only suggested the presence of strong interactions between (SARS-CoV-2 + umifenovir/dexamethasone) but also emphasized the clinical suitability of dexamethasone over umifenovir to treat SARS-CoV-2. This supremacy of dexamethasone is well supported by the results of global clinical trials and COVID-19 therapeutic approved management guidelines of countries.

Conclusions

Thus, this work will pave a way for incremental advancement towards future design and development of more specific inhibitors for the treatment of SARS-CoV-2 infection in humans.

Computational and theoretical exploration for clinical suitability of Remdesivir drug to SARS-CoV-2

A methodology for the exploration of clinical suitability of Remdesivir drug to SARS-CoV-2 main protease based on the computational, theoretical analysis pertinent to Gibb's free energy computed from the Molecular Dynamic simulations with OPLS-AA force field at 300 K/atmospheric pressure and the variation of thermodynamic potentials over the entire simulation run of 100 ns. This study emphasized the suitability of Remdesivir drug to SARS-CoV-2 protein and the same is emphasized by the results of global clinical trials. This methodology can be applied for future design, development of more specific repurposed inhibitors for the treatment of SARS-CoV-2 infection.

LEADS-FRAG: A Benchmark Data Set for Assessment of Fragment Docking Performance

Fragment-based drug design is a popular approach in drug discovery, which makes use of computational methods such as molecular docking. To assess fragment placement performance of molecular docking programs, we constructed LEADS-FRAG, a benchmark data set containing 93 high-quality protein-fragment complexes that were selected from the Protein Data Bank using a rational and unbiased process. The data set contains fully prepared protein and fragment structures and is publicly available. Moreover, we used LEADS-FRAG for evaluating the small-molecule docking programs AutoDock, AutoDock Vina, FlexX, and GOLD for their fragment docking performance. GOLD in combination with the scoring function ChemPLP and AutoDock Vina performed best and generated near-native conformations (root mean square deviation <1.5 Å) for more than 50% of the data set considering the top-ranked docking pose. Taking into account all docking poses, the tested programs generated near-native conformations for up to 86% of the fragments in LEADS-FRAG. By rescoring all docking poses with the GOLD scoring functions and the Protein-Ligand Informatics force field, the number of near-native conformations increased up to 40% with respect to the top-rescored poses. Our results show that conventional small-molecule docking programs achieve a satisfactory fragment docking performance when utilizing rescoring.

Studying the Binding Modes of Novel 2-Aminopyridine Derivatives as Effective and Selective c-Met Kinase Type 1 Inhibitors Using Molecular Modeling Approaches

The mesenchymal epithelial cell transforming factor c-Met, encoded by c-Met proto-oncogene and known as a high-affinity receptor for Hepatocyte Growth Factor (HGF), is one of the receptor tyrosine kinases (RTKs) members. The HGF/c-Met signaling pathway has close correlation with tumor growth, invasion and metastasis. Thus, c-Met kinase has emerged as a prominent therapeutic target for cancer drug discovery. Recently a series of novel 2-aminopyridine derivatives targeting c-Met kinase with high biological activity were reported. In this study, 3D quantitative structure-activity relationship (QSAR), molecular docking and molecular dynamics simulations (MD) were employed to research the binding modes of these inhibitors.The results show that both the atom-based and docking-based CoMFA (Q² = 0.596, R² = 0.950 in atom-based model and Q² = 0.563, R² = 0.985 in docking-based model) and CoMSIA (Q² = 0.646, R² = 0.931 in atom-based model and Q² = 0.568, R² = 0.983 in docking-based model) models own satisfactory performance with good reliabilities and powerful external predictabilities. Molecular docking study suggests that Tyr1230 and Arg1208 might be the key residues, and electrostatic and hydrogen bond interactions were shown to be vital to the activity, concordance with QSAR analysis. Then MD simulation was performed to further explore the binding mode of the most potent inhibitor. The obtained results provide important references for further rational design of c-Met Kinase type I inhibitors.

Combined in silico and in vitro studies to identify novel antidiabetic flavonoids targeting glycogen phosphorylase

Novel pharmacological strategies for the treatment of diabetic patients are now focusing on inhibiting glycogenolysis steps. In this regard, glycogen phosphorylase (GP) is a validated target for the discovery of innovative antihyperglycemic molecules. Natural products, and in particular flavonoids, have been reported as potent inhibitors of GP at the cellular level. Herein, free-energy calculations and microscale thermophoresis approaches were performed to get an in-depth assessment of the binding affinities and elucidate intermolecular interactions of several flavonoids at the inhibitor site of GP. To our knowledge, this is the first study indicating genistein, 8-prenylgenistein, apigenin, 8-prenylapigenin, 8-prenylnaringenin, galangin and valoneic acid dilactone as natural molecules with high inhibitory potency toward GP. We identified: i) the residues Phe285, Tyr613, Glu382 and/or Arg770 as the most relevant for the binding of the best flavonoids to the inhibitor site of GP, and ii) the 5-OH, 7-OH, 8-prenyl substitutions in ring A and the 4'-OH insertion in ring B to favor flavonoid binding at this site. Our results are invaluable to plan further structural modifications through organic synthesis approaches and develop more effective pharmaceuticals for Type 2 Diabetes treatment, and serve as the starting point for the exploration of food products for therapeutic usage, as well as for the development of novel bio-functional food and dietary supplements/herbal medicines.

Discovery of a species-specific novel antifungal compound against Fusarium graminearum through an integrated molecular modeling strategy

BACKGROUND

The cyanoacrylate fungicide phenamacril targeting fungal myosin I has been widely used for controlling Fusarium head blight (FHB) of wheat caused by the pathogenic fungus Fusarium graminearum worldwide. Therefore, there is great interest in the discovery and development of novel FgMyo1 inhibitors through structure-based drug design for the treatment of FHB.

RESULTS

In this study, the binding mechanism of phenamacril with FgMyo1 was predicted by an integrated molecular modeling strategy. The predicted key phenamacril-binding residues of FgMyo1 were further experimentally validated by point mutagenesis and phenamacril sensitivity assessment. Four novel key residues responsible for phenamacril binding were identified, highlighting the reliability of the theoretical predictions. The subsequent optimization of phenamacril derivatives led to the discovery of a novel compound (10) which shows better activity than phenamacril against conidial germination of F. graminearum, but not against other fungal species. Moreover, 10 also inhibits conidial germination of phenamacril-resistant strains effectively. Further experiments illustrated that application of 10 could dramatically inhibit deoxynivalenol biosynthesis.

CONCLUSION

Overall, our results further optimize and develop the binding model of phenamacril-myosin I. Furthermore, 10 was found and has the potential to be developed as a species-specific fungicide for management of FHB. © 2020 Society of Chemical Industry.

Exploring the Allosteric Mechanism of Src Homology-2 Domain-Containing Protein Tyrosine Phosphatase 2 (SHP2) by Molecular Dynamics Simulations

The Src homology-2 (SH2) domain-containing protein tyrosine phosphatase 2 (SHP2, encoded by PTPN11) is a critical allosteric phosphatase for many signaling pathways. Programmed cell death 1 (PD-1) could be phosphorylated at its immunoreceptor tyrosine-based inhibitory motif (ITIM) and immunoreceptor tyrosine-based switch motif (ITSM) and can bind to SHP2 to initiate T cell inactivation. Although the interaction of SHP2-PD-1 plays an important role in the immune process, the complex structure and the allosteric regulation mechanism remain unknown. In this study, molecular dynamics (MD) simulations were performed to study the binding details of SHP2 and PD-1, and explore the allosteric regulation mechanism of SHP2. The results show that ITIM has a preference to bind to the N-SH2 domain and ITSM has almost the same binding affinity to the N-SH2 and C-SH2 domain. Only when ITIM binds to the N-SH2 domain and ITSM binds to the C-SH2 domain can the full activation of SHP2 be obtained. The binding of ITIM and ITSM could change the motion mode of SHP2 and switch it to the activated state.

Molecular simulation studies to reveal the binding mechanisms of shikonin derivatives inhibiting VEGFR-2 kinase

Traditional vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors can manage angiogenesis; however, severe toxicity and resistance limit their long-term applications in clinical therapy. Shikonin (SHK) and its derivatives could be promising to inhibit the VEGFR-2 mediated angiogenesis, as they are reported to bind in the catalytic kinase domain with low affinity. However, the detailed molecular insights and binding dynamics of these natural inhibitors are unknown, which is crucial for potential SHK based lead design. Therefore, the present study employed molecular modeling and simulations techniques to get insight into the binding behaviors of SHK and its two derivates, β-hydroxyisovalerylshikonin (β-HIVS) and acetylshikonin (ACS). Here the intermolecular interactions between protein and ligands were studied by induced fit docking approach, which were further evaluated by treating QM/MM (quantum mechanics/molecular mechanics) and molecular dynamics (MD) simulation. The result showed that the naphthazarin ring of the SHK derivates is vital for strong binding to the catalytic domain; however, the binding stability can be modulated by the side chain modification. Because of having electrostatic potential, this ring makes essential interactions with the DFG (Asp1046 and Phe1047) motif and also allows interacting with the allosteric binding site. Taken together, the studies will advance our knowledge and scope for the development of new selective VEGFR-2 inhibitors based on SHK and its analogs.

Searching for target-specific and multi-targeting organics for Covid-19 in the Drugbank database with a double scoring approach

The current outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major threat to human healthcare. The virus has already infected more than 44 M people and the number of deaths reported has reached more than 1.1 M which may be attributed to lack of medicine. The traditional drug discovery approach involves many years of rigorous research and development and demands for a huge investment which cannot be adopted for the ongoing pandemic infection. Rather we need a swift and cost-effective approach to inhibit and control the viral infection. With the help of computational screening approaches and by choosing appropriate chemical space, it is possible to identify lead drug-like compounds for Covid-19. In this study, we have used the Drugbank database to screen compounds against the most important viral targets namely 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the spike (S) protein. These targets play a major role in the replication/transcription and host cell recognition, therefore, are vital for the viral reproduction and spread of infection. As the structure based computational screening approaches are more reliable, we used the crystal structures for 3C-like main protease and spike protein. For the remaining targets, we used the structures based on homology modeling. Further, we employed two scoring methods based on binding free energies implemented in AutoDock Vina and molecular mechanics-generalized Born surface area approach. Based on these results, we propose drug cocktails active against the three viral targets namely 3CLpro, PLpro and RdRp. Interestingly, one of the identified compounds in this study i.e. Baloxavir marboxil has been under clinical trial for the treatment of Covid-19 infection. In addition, we have identified a few compounds such as Phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine, R-428 which can bind to all three targets simultaneously and can serve as multi-targeting drugs. Our study also included calculation of binding energies for various compounds currently under drug trials. Among these compounds, it is found that Remdesivir binds to targets, 3CLpro and RdRp with high binding affinity. Moreover, Baricitinib and Umifenovir were found to have superior target-specific binding while Darunavir is found to be a potential multi-targeting drug. As far as we know this is the first study where the compounds from the Drugbank database are screened against four vital targets of SARS-CoV-2 and illustrates that the computational screening using a double scoring approach can yield potential drug-like compounds against Covid-19 infection.

Scaffold morphing of arbidol (umifenovir) in search of multi-targeting therapy halting the interaction of SARS-CoV-2 with ACE2 and other proteases involved in COVID-19

The rapid emergence of novel coronavirus, SARS-coronavirus 2 (SARS-CoV-2), originated from Wuhan, China, imposed a global health emergency. Angiotensin-converting enzyme 2 (ACE2) receptor serves as an entry point for this deadly virus while the proteases like furin, transmembrane protease serine 2 (TMPRSS2) and 3 chymotrypsin-like protease (3CLpro) are involved in the further processing and replication of SARS-CoV-2. The interaction of SP with ACE2 and these proteases results in the SARS-CoV-2 invasion and fast epidemic spread. The small molecular inhibitors are reported to limit the interaction of SP with ACE2 and other proteases. Arbidol, a membrane fusion inhibitor approved for influenza virus is currently undergoing clinical trials against COVID-19. In this context, we report some analogues of arbidol designed by scaffold morphing and structure-based designing approaches with a superior therapeutic profile. The representative compounds A_BR4, A_BR9, A_BR18, A_BR22 and A_BR28 restricted the interaction of SARS-CoV-2 SP with ACE2 and host proteases furin and TMPRSS2. For 3CLPro, Compounds A_BR5, A_BR6, A_BR9 and A_BR18 exhibited high binding affinity, docking score and key residue interactions. Overall, A_BR18 and A_BR28 demonstrated multi-targeting potential against all the targets. Among these top-scoring molecules A_BR9, A_BR18, A_BR22 and A_BR28 were predicted to confer favorable ADME properties.

Theoretical Studies on the Selectivity Mechanisms of Glycogen Synthase Kinase 3β (GSK3β) with Pyrazine ATP-competitive Inhibitors by 3DQSAR, Molecular Docking, Molecular Dynamics Simulation and Free Energy Calculations

Background

Glycogen synthase kinase-3 (GSK3) is associated with various key biological processes and has been considered as an important therapeutic target for the treatment of many diseases. Great efforts have been made on the development of GSK3 inhibitors, especially ATP-competitive GSK3β inhibitor, but it is still a great challenge to develop selective GSK3β inhibitors because of the high sequence homology with other kinases.

Objective

In order to reveal the selectivity mechanisms of GSK3β inhibition at the molecular level, a series of ATP-competitive GSK3β inhibitor was analyzed by a systematic computational method, combining 3D-QSAR, molecular docking, molecular dynamic simulations and free energy calculations.

Methods

Firstly, 3D-QSAR with CoMFA was built to explore the general structure activity relationships. Secondly, CDOCKER and Flexible docking were employed to predicted the reasonable docking poses of all studied inhibitors. And then, both GSK3β and CDK2 complexes were selected to conduct molecular dynamics simulations. Finally, the free energy calculations were employed to find the key selective-residues.

Results

CoMFA model suggested the steric, hydrophobic fields play key roles in the bioactivities of inhibitors, and the binding mechanisms were well analyzed through molecular docking. The binding free energies predicted are in good agreement with the experimental bioactivities and the free energy calculations showed that the binding of GSK3β/inhibitors was mainly contributed from hydrogen bonding and hydrophobic interaction.

Conclusion

Some key residues for selective binding were highlighted, which may afford important guidance for the rational design of novel ATP-competitive GSK3β inhibitors.

DNA backbone interactions impact the sequence specificity of DNA sulfur-binding domains: revelations from structural analyses

The sulfur atom of phosphorothioated DNA (PT-DNA) is coordinated by a surface cavity in the conserved sulfur-binding domain (SBD) of type IV restriction enzymes. However, some SBDs cannot recognize the sulfur atom in some sequence contexts. To illustrate the structural determinants for sequence specificity, we resolved the structure of SBD_Spr, from endonuclease SprMcrA, in complex with DNA of G_PSGCC, G_PSATC and G_PSAAC contexts. Structural and computational analyses explained why it binds the above PT-DNAs with an affinity in a decreasing order. The structural analysis of SBD_Spr–G_PSGCC and SBD_Sco–G_PSGCC, the latter only recognizes DNA of G_PSGCC, revealed that a positively charged loop above the sulfur-coordination cavity electrostatically interacts with the neighboring DNA phosphate linkage. The structural analysis indicated that the DNA–protein hydrogen bonding pattern and weak non-bonded interaction played important roles in sequence specificity of SBD protein. Exchanges of the positively-charged amino acid residues with the negatively-charged residues in the loop would enable SBD_Sco to extend recognization for more PT-DNA sequences, implying that type IV endonucleases can be engineered to recognize PT-DNA in novel target sequences.

Identification of inhibitors targeting HIF-2α/c-Myc by molecular docking and MM-GBSA technology

The treatment of ccRCC by targeting hypoxia-inducible factor HIF-2α is currently a direct and effective method. Studies have shown that HIF-2α and c-Myc cooperate to promote ccRCC tumor progression, and the overexpression of c-Myc is related to the progress and drug resistance of most human cancers. Although HIF-2α and c-Myc are important drug targets, their dual inhibitors are still lacking. We used virtual screening tools (mainly including molecular docking and MM-GBSA technology) to obtain some well-listed compounds that can potentially target HIF-2α and c-Myc and used molecular dynamics simulations to study their binding with these protein systems. Using a structure-based screening scheme, a batch of top-ranking compounds were selected, and their binding affinities were predicted of these compounds were performed. Representative compound C93106, C43257, and C41580 all showed good comprehensive binding score. Our results indicate that the target compounds can all form key interactions with the active site of the protein, and 30 ns molecular dynamic simulation of the complex system indicates a stable binding conformation. This research laid the foundation for the development of more effective and specific HIF-2α and c-Myc dual-target inhibitors.

Molecular Dynamics Investigations of Binding Mechanism for Triazoles Inhibitors to CYP51

The sterol 14α demethylase enzyme (CYP51) is an important target of fungal infections. However, the molecular mechanism between triazoles inhibitors and CYP51 remains obscure. In this study, we have investigated the binding mechanism and tunnel characteristic upon four triazoles inhibitors with CYP51 based on the molecular docking and molecular dynamics simulations. The results indicate the four inhibitors stabilize in the binding cavity of CYP51 in a similar binding mode. We discover a hydrophobic cavity (F58, Y64, Y118, L121, Y132, L376, S378, S506, S507, and M508) and the hydrophobic interaction is the main driving force for inhibitors binding to CYP51. The long-tailed inhibitors (posaconazole and itraconazole) have stronger binding affinities than short-tailed inhibitors (fluconazole and voriconazole) because long-tailed inhibitors can form more hydrophobic interactions with CYP51. The tunnel 2f is the predominant pathway for inhibitors ingress/egress protein, which is similar to the other works of CYP51. This study could provide the theoretical basis for the development of efficient azoles inhibitors and may lead a better insight into structure-function relationships of CYP51.

Pyrazinamide resistance of novel mutations in pncA and their dynamic behavior

Pyrazinamide (PZA) is one of the essential anti-mycobacterium drugs, active against non-replicating Mycobacterium tuberculosis (MTB) isolates. PZA is converted into its active state, called pyrazinoic acid (POA), by action of pncA encoding pyrazinamidase (PZase). In the majority of PZA-resistance isolates, pncA harbored mutations in the coding region. In our recent report, we detected a number of novel variants in PZA-resistance (PZA^R) MTB isolates, whose resistance mechanisms were yet to be determined. Here we performed several analyses to unveil the PZA^R mechanism of R123P, T76P, G150A, and H71R mutants (MTs) through molecular dynamics (MD) simulations. In brief, culture positive MTB isolates were subjected to PZA susceptibility tests using the WHO recommended concentration of PZA (100 μg ml⁻¹). The PZA^R samples were screened for mutations in pncA along sensitive isolates through polymerase chain reactions and sequencing. A large number of variants (GeneBank accession no. MH461111), including R123P, T76P, G150A, and H71R, have been spotted in more than 70% of isolates. However, the mechanism of PZA^R for mutants (MTs) R123P, T76P, G150A, and H71R was unknown. For the MTs and native PZase structures (WT), thermodynamic properties were compared using molecular dynamics simulations for 100 ns. The MTs structural activity was compared to the WT. Folding effect and pocket volume variations have been detected when comparing between WT and MTs. Geometric matching further confirmed the effect of R123P, T76P, G150A, and H71R mutations on PZase dynamics, making them vulnerable for activating the pro-drug into POA. This study offers a better understanding for management of PZA^R TB. The results may be used as alternative diagnostic tools to infer PZA resistance at a structural dynamics level.

We performed several analyses to unveil the pyrazinamide-resistance mechanism of R123P, T76P, G150A, and H71R mutants through molecular dynamics simulations.

Insight into glycogen synthase kinase-3β inhibitory activity of phyto-constituents from Melissa officinalis: in silico studies

Over activity of Glycogen synthase kinase-3β (GSK-3β), a serine/threonine-protein kinase has been implicated in a number of diseases including stroke, type II diabetes and Alzheimer disease (AD). This study aimed to find novel inhibitors of GSK-3β from phyto-constituents of Melissa officinalis with the aid of computational analysis. Molecular docking, induced-fit docking (IFD), calculation of binding free energy via the MM-GBSA approach and Lipinski's rule of five (RO5) were employed to filter the compounds and determine their druggability. Most importantly, the compounds pIC50 were predicted by machine learning-based model generated by AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best model obtained was Model kpls_desc_38 (R2 = 0.8467 and Q2 = 0.8069), and this external validated model was utilized to predict the bioactivities of the lead compounds. While a number of characterized compounds from Melissa officinalis showed better docking score, binding free energy alongside adherence to RO5 than co-cystallized ligand, only three compounds (salvianolic acid C, ellagic acid and naringenin) showed more satisfactory pIC50. The results obtained in this study can be useful to design potent inhibitors of GSK-3β.

Identification of Potential COX-2 Inhibitors for the Treatment of Inflammatory Diseases Using Molecular Modeling Approaches

Non-steroidal anti-inflammatory drugs are inhibitors of cyclooxygenase-2 (COX-2) that were developed in order to avoid the side effects of non-selective inhibitors of COX-1. Thus, the present study aims to identify new selective chemical entities for the COX-2 enzyme via molecular modeling approaches. The best pharmacophore model was used to identify compounds within the ZINC database. The molecular properties were determined and selected with Pearson’s correlation for the construction of quantitative structure–activity relationship (QSAR) models to predict the biological activities of the compounds obtained with virtual screening. The pharmacokinetic/toxicological profiles of the compounds were determined, as well as the binding modes through molecular docking compared to commercial compounds (rofecoxib and celecoxib). The QSAR analysis showed a fit with R = 0.9617, R² = 0.9250, standard error of estimate (SEE) = 0.2238, and F = 46.2739, with the tetra-parametric regression model. After the analysis, only three promising inhibitors were selected, Z-964, Z-627, and Z-814, with their predicted pIC₅₀ (−log IC₅₀) values, Z-814 = 7.9484, Z-627 = 9.3458, and Z-964 = 9.5272. All candidates inhibitors complied with Lipinski’s rule of five, which predicts a good oral availability and can be used in in vitro and in vivo tests in the zebrafish model in order to confirm the obtained in silico data.

Benchmarking the performance of MM/PBSA in virtual screening enrichment using the GPCR-Bench dataset

Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF_1% and nine targets at EF_5%, with the gains in enrichment being more pronounced at the EF_1% level. We additionally assessed the utility of rescoring the top ten poses from docking and the ability of short MD simulations to refine the binding poses prior to MM/PBSA calculations. There was no clear trend of the benefit observed in both cases, suggesting that utilizing a single energy minimized structure for MM/PBSA calculations may be the most computationally efficient approach in virtual screening. Overall, the performance of MM/PBSA rescoring in improving virtual screening enrichment obtained from docking of the GPCR-Bench dataset was found to be relatively modest and target-specific, highlighting the need for validation of MM/PBSA-based protocols prior to prospective use.

Probing molecular mechanism of inhibitor bindings to bromodomain-containing protein 4 based on molecular dynamics simulations and principal component analysis

It is well known that bromodomain-containing protein 4 (BRD4) has been thought as a promising target utilized for treating various human diseases, such as inflammatory disorders, malignant tumours, acute myelogenous leukaemia (AML), bone diseases, etc. For this study, molecular dynamics (MD) simulations, binding free energy calculations, and principal component analysis (PCA) were integrated together to uncover binding modes of inhibitors 8P9, 8PU, and 8PX to BRD4(1). The results obtained from binding free energy calculations show that van der Waals interactions act as the main regulator in bindings of inhibitors to BRD4(1). The information stemming from PCA reveals that inhibitor associations extremely affect conformational changes, internal dynamics, and movement patterns of BRD4(1). Residue-based free energy decomposition method was wielded to unveil contributions of independent residues to inhibitor bindings and the data signify that hydrogen bonding interactions and hydrophobic interactions are decisive factors affecting bindings of inhibitors to BRD4(1). Meanwhile, eight residues Trp81, Pro82, Val87, Leu92, Leu94, Cys136, Asn140, and Ile146 are recognized as the common hot interaction spots of three inhibitors with BRD4(1). The results from this work are expected to provide a meaningfully theoretical guidance for design and development of effective inhibitors inhibiting of the activity of BRD4.

Fast Identification of Possible Drug Treatment of Coronavirus Disease-19 (COVID-19) through Computational Drug Repurposing Study

The recent outbreak of novel coronavirus disease-19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D structures of key viral proteins are resolved. The virus causing COVID-19 is SARS-CoV-2. Taking advantage of a recently released crystal structure of SARS-CoV-2 main protease in complex with a covalently bonded inhibitor, N3 (Liu et al., 10.2210/pdb6LU7/pdb), I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an end point method called MM-PBSA-WSAS (molecular mechanics/Poisson-Boltzmann surface area/weighted solvent-accessible surface area; Wang, Chem. Rev. 2019, 119, 9478; Wang, Curr. Comput.-Aided Drug Des. 2006, 2, 287; Wang; ; Hou J. Chem. Inf. Model., 2012, 52, 1199). Several promising known drugs stand out as potential inhibitors of SARS-CoV-2 main protease, including carfilzomib, eravacycline, valrubicin, lopinavir, and elbasvir. Carfilzomib, an approved anticancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, -13.8 kcal/mol. The second-best repurposing drug candidate, eravacycline, is synthetic halogenated tetracycline class antibiotic. Streptomycin, another antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (-3.8 kcal/mol) is not nearly as low as that of the neutral form (-7.9 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of -12.9 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hot spot residue, His41, is a conserved residue across many viruses including SARS-CoV, SARS-CoV-2, MERS-CoV, and hepatitis C virus (HCV). The findings of this study can facilitate rational drug design targeting the SARS-CoV-2 main protease.

Effects of Disulfide Bonds on Binding of Inhibitors to β-Amyloid Cleaving Enzyme 1 Decoded by Multiple Replica Accelerated Molecular Dynamics Simulations

The β-amyloid cleaving enzyme 1 (BACE1) has been thought to be an efficient target for treatment of Alzheimer's disease (AD). Deep insight into inhibitor-BACE1 binding mechanism is of significance for design of potent drugs toward BACE1. In this work, multiple replica accelerated molecular dynamics (MR-aMD) simulations, principal component (PC) analysis, and free energy landscapes were integrated to decode the effect of disulfide bonds (SSBs) in BACE1 on bindings of three inhibitors 3KO, 3KT, and 779 to BACE1. The results from cross-correlation analysis suggest that the breaking of SSBs exerts significant influence on structural flexibility and internal dynamics of inhibitor-bound BACE1. PC analysis and free energy landscapes reveal that the breaking of SSBs not only evidently induces the conformational rearrangement of BACE1 but also highly changes binding poses of three inhibitors in BACE1 and leads to more disordered binding of three inhibitors to BACE1, which is further supported by the increase in binding entropy of inhibitors to BACE1 due to the breaking of SSBs. Residue-based free energy decomposition method was utilized to evaluate contributions of separate residues to inhibitor-BACE1 binding. The results suggest that although the breaking of SSBs in BACE1 does not destroy the interaction network of inhibitors with BACE1 it changes interaction strength of some residues with inhibitors. Meanwhile, the information from residue-based free energy decomposition indicates that residues L91, S96, V130, Y132, Q134, W137, F169, I171, and I179 can be used as efficient targets of drug design toward BACE1.

The effect of multiple simulation parameters on MM/PBSA performance for binding affinity prediction of CB1 cannabinoid receptor agonists and antagonists

Both the inactive- and active-state CB1 receptor crystal structures have now been solved, allowing their application in various structure-based drug design methods. One potential method utilizing these crystal structures is the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method of predicting relative binding free.

An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity

The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method is constantly used to calculate the binding free energy of protein-ligand complexes, and has been shown to effectively balance computational cost against accuracy. The relative binding affinities obtained by the MM/PBSA approach are acceptable, while it usually overestimates the absolute binding free energy. This paper proposes four free energy estimators based on the MM/PBSA for enthalpy change combined with interaction entropy (IE) for entropy change using different weights for individual energy terms. The ΔG_{PBSA_IE} method is determined to be an optimal estimator based on its performance in terms of the correlation between experimental and theoretical values and error estimations. This approach is optimized using high-quality experimental values from a training set containing 84 protein-ligand systems, and the coefficients for the sum of electrostatic energy and polar solvation free energy, van der Waals (vdW) energy, non-polar solvation energy and entropy change are obtained by multivariate linear fitting to the corresponding experimental values. A comparison between the traditional MM/PBSA method and this method shows that the correlation coefficient is improved from 0.46 to 0.72 and the slope of the regression line increases from 0.10 to 1.00. More importantly, the mean absolute error (MAE) is significantly reduced from 22.52 to 1.59 kcal mol^-1. Furthermore, the numerical stability of this method is validated on a test set with a similar correlation coefficient, slope and MAE to those of the training set. Based on the above advantages, the ΔG_{PBSA_IE} method can be a powerful tool for a reliable and accurate estimation of binding free energy and plays a significant role in a detailed energetic investigation of protein-ligand interaction.

Structural insights of catalytic mechanism in mutant pyrazinamidase of Mycobacterium tuberculosis

Pyrazinamidase (PZase) is a member of Fe-dependent amidohydrolases that activates pyrazinamide (PZA) into active pyrazinoic acid (POA). PZA, a nicotinamide analogue, is an essential first-line drug used in Mycobacterium tuberculosis (Mtb) treatment. The active form of PZA, POA, is toxic and potently inhibits the growth of latent Mtb, which makes it possible to shorten the conventional 9-month tuberculosis treatment to 6 months. In this study, an extensive molecular dynamics simulation was carried out to the study the resistance mechanism offered by the three mutations Q10P and D12A and G97D. Our results showed that two regions Gln10-His43, Phe50-Gly75 are profoundly affected by these mutations. Among the three mutations, Q10P and D12A mutations strongly disturb the communication among the catalytic triad (Asp8, Lys98 and Cys138). The oxyanion hole is formed between the backbone nitrogen atoms of A134 and C138 which stabilizes the hydroxyl anion of nicotinamide. The D12A mutation greatly disturbs the oxyanion hole formation followed by the Q10P and G97D. Our results also showed that these mutations destabilize the interaction between Fe²⁺ ion and Asp49, His51, His57 and His71. The binding pocket analysis showed that these mutations increase the cavity volume, which results in loose binding of PZA. MMGBSA analyzes have shown that these mutations reduce the binding affinity to the PZA drug. Our results may provide useful information for the design of new and effective PZase inhibitors based on structural information of WT and mutant PZases.Communicated by Ramaswamy H. Sarma.

Revealing binding selectivity of inhibitors toward bromodomain-containing proteins 2 and 4 using multiple short molecular dynamics simulations and free energy analyses

Emerging evidences indicate bromodomain-containing proteins 2 and 4 (BRD2 and BRD4) play critical roles in cancers, inflammations, cardiovascular diseases and other pathologies. Multiple short molecular dynamics (MSMD) simulations combined with molecular mechanics generalized Born surface area (MM-GBSA) method were applied to investigate the binding selectivity of three inhibitors 87D, 88M and 89G towards BRD2 over BRD4. The root-mean-square fluctuation (RMSF) analysis indicates that the structural flexibility of BRD4 is stronger than that of BRD2. Moreover the calculated distances between the C_α atoms in the centres of the ZA_loop and BC_loop of BRD4 are also bigger than that of BRD2. The rank of binding free energies calculated using MM-GBSA method agrees well with that determined by experimental data. The results show that 87D can bind more favourably to BRD2 than BRD4, while 88M has better selectivity on BRD4 over BRD2. Residue-based free-energy decomposition method was utilized to estimate the inhibitor-residue interaction spectrum and the results not only identify the hot interaction spots of inhibitors with BRD2 and BRD4, but also demonstrate that several common residues, including (W370, W374), (P371, P375), (V376, V380) and (L381, L385) belonging to (BRD2, BRD4), generate significant binding difference of inhibitors to BRD2 and BRD4.

The binding mechanism of nitroreductase fluorescent probe: Active pocket deformation and intramolecular hydrogen bonds

Nitroreductase (NTR), a member of the flavoenzyme family, could react with nicotinamide adenine dinucleotide by reducing nitro to amino at hypoxic tumor, which can be monitored by some fluorescent probes in vivo. Here, molecular docking and molecular dynamics simulation techniques were used to explore the molecular mechanisms between NTR and probes. The results showed that formation of hydrogen bond in 1F5V-13 between A@His215 and B@Ser41 with 74.53% occupancy might be the main reason for the decrease of probe fluorescence emission in experiment. Moreover, Probe 16 was rotated by nearly 60 degrees with respect to the position of other probes in protein binding pocket, deforming the protein active pocket, changing the hydrogen bond formation, which leads to the fluorescence performance of 16 with electron donor and electron acceptor groups was superior to other probes in experiment. The deformation of protein active pocket and the formation of intramolecular hydrogen bonds revealed the difference in performance of NTR fluorescent probe at molecular level, which provide theoretical guidance for latter design of fluorescent probes with better performance.

Application of MM-PBSA Methods in Virtual Screening

Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

Molecular Mechanism of Selective Binding of NMS-P118 to PARP-1 and PARP-2: A Computational Perspective

The nuclear protein poly (ADP-ribose) polymerase-1 (PARP-1) inhibitors have been proven effective to potentiate both chemotherapeutic agents and radiotherapy. However, a major problem of most current PARP inhibitors is their lack of selectivity for PARP-1 and its closest isoform PARP-2. NMS-P118 is a highly selective PARP inhibitor that binds PARP-1 stronger than PARP-2 and has many advantages such as excellent pharmacokinetic profiles. In this study, molecular dynamics (MD) simulations of NMS-P118 in complex with PARP-1 and PARP-2 were performed to understand the molecular mechanism of its selectivity. Alanine scanning together with free energy calculation using MM/GBSA and interaction entropy reveal key residues that are responsible for the selectivity. Although the conformation of the binding pockets and NMS-P118 are very similar in PARP-1 and PARP-2, most of the hot-spot residues in PARP-1 have stronger binding free energy than the corresponding residues in PARP-2. Detailed analysis of the binding energy shows that the 4′4-difluorocyclohexyl ring on NMS-P118 form favorable hydrophobic interaction with Y889 in PARP-1. In addition, the H862 residue in PARP-1 has stronger binding free energy than H428 in PARP-2, which is due to shorter distance and stronger hydrogen bonds. Moreover, the negatively charged E763 residue in PARP-1 forms stronger electrostatic interaction energy with the positively charged NMS-P118 than the Q332 residue in PARP-2. These results rationalize the selectivity of NMS-P118 and may be useful for designing novel selective PARP inhibitors.

Gibbs Free Energy Calculation of Mutation in PncA and RpsA Associated With Pyrazinamide Resistance

A central approach for better understanding the forces involved in maintaining protein structures is to investigate the protein folding and thermodynamic properties. The effect of the folding process is often disturbed in mutated states. To explore the dynamic properties behind mutations, molecular dynamic (MD) simulations have been widely performed, especially in unveiling the mechanism of drug failure behind mutation. When comparing wild type (WT) and mutants (MTs), the structural changes along with solvation free energy (SFE), and Gibbs free energy (GFE) are calculated after the MD simulation, to measure the effect of mutations on protein structure. Pyrazinamide (PZA) is one of the first-line drugs, effective against latent Mycobacterium tuberculosis isolates, affecting the global TB control program 2030. Resistance to this drug emerges due to mutations in pncA and rpsA genes, encoding pyrazinamidase (PZase) and ribosomal protein S1 (RpsA) respectively. The question of how the GFE may be a measure of PZase and RpsA stabilities, has been addressed in the current review. The GFE and SFE of MTs have been compared with WT, which were already found to be PZA-resistant. WT structures attained a more stable state in comparison with MTs. The physiological effect of a mutation in PZase and RpsA may be due to the difference in energies. This difference between WT and MTs, depicted through GFE plots, might be useful in predicting the stability and PZA-resistance behind mutation. This study provides useful information for better management of drug resistance, to control the global TB problem.