Accelerated Molecular Dynamics Simulation for Helical Proteins Folding in Explicit Water

Rational design of thiazolidine-4-one-gallic acid hybrid derivatives as selective partial PPARγ modulators: an in-silico approach for type 2 diabetes treatment

Type 2 diabetes mellitus is a bipolar metabolic disorder characterized by abnormalities in insulin production from β-cells and insulin resistance. Thiazolidinediones are potent anti-diabetic agents that act through the modulation of the peroxisome proliferator-activated receptor γ (PPARγ), a nuclear receptor. However, their full agonistic activity leads to severe side effects by stabilizing Helix12 through strong hydrogen bonding with the TYR473 residue. Partial and selective PPARγ modulators (GW0072, GQ16, VSP-51, MRL-20, MBX-213, INT131) have demonstrated superior results compared to full agonists without causing adverse effects, as reported in existing data. To address this uncertainty and advance therapeutic options, we identified and designed a novel class of compounds (A1-A23) based on a hybrid structure combining phenolic and Thiazolidine-4-one's moieties. Our rational drug design strategy incorporated structural-activity relationship principle, and validated the docking studies through calculated the root mean square deviation. Additionally, we conducted molecular docking, binding energy, molecular dynamics simulations, and post-molecular dynamics calculations to evaluate the dynamics behavior between the ligands and protein. The selected ligands demonstrated highly favorable docking scores and binding energies, comparable to the co-crystal (rosiglitazone) such as A12 (-13.9 kcal/mol and -86.2 kcal/mol), A1 (-11.1 kcal/mol and -79.5 kcal/mol), A13 (-11.3 kcal/mol and -91.4 kcal/mol), and the co-crystal itself (-9.8 kcal/mol and -76 kcal/mol), respectively. Finally, the MD revealed that, the selected ligands were equally contributed for stabilization of Helix12 and β-sheets. It was concluded, the designed ligands (A12, A1, and A13) exhibited weaker hydrogen-bond interactions with specific residue TYR473 which partially modulated the PPARγ protein.Communicated by Ramaswamy H. Sarma.

Identifying potential inhibitors against galectin-3-binding protein (LGALS3BP) for therapeutic targeting of glioma

Glioblastoma is one of the most lethal types of Gliomas, and its treatment greatly depends on the stage of its detection. Galactin-3-binding protein (LGALS3BP) serves as a novel circulatory biomarker for detecting glioma at an early stage. This protein is also responsible for metastasis, proliferative signaling, angiogenesis, and immune system evasion in the case of brain tumors. Inhibition of LGALS3BP can help in the reduction of metastasis and progression of the disease. Currently, no effective drug is available that can completely treat glioma. In this study, we have virtually screened the National Cancer Institute (NCI) drug databank to discover potential inhibitors of LGALS3BP. Based on the binding free energy calculations using MMPBSA, three compounds, 627861 (-16.69 kcal/mol), 329090 (-13.66 kcal/mol), and 627855 (-10.01 kcal/mol), were selected as potent inhibitors. 200 ns MD simulation studies further complemented this study. Finally, we recommend three molecules, 627861, 329090, and 627855, can be potential inhibitors of LGAL3SBP. The structural scaffolds of these molecules can also lead to the optimization of better inhibitors of LGALS3BP and be implicated in the therapeutic management of glioma after desired experimental validations.

Accelerated molecular dynamics study of the interaction mechanism between small molecule inhibitors and phosphoglycerate mutase 1

In 2020, cancer-related deaths reached 9.96 million globally, of which China accounted for 3 million, ranking first in the world. Phosphoglycerate mutase 1 (PGAM1) is a key metabolic enzyme in glycolysis, catalysing the conversion of 3-phosphoglycerate to 2-phosphoglycerate. Based on the excellent anticancer activity of PGMI-004A and HKB99, new small molecules with an anthraquinone core were synthesised to inhibit tumour growth. Developing small molecules with an anthraquinone core targeting PGAM1 may be an effective strategy for treating cancer. In this study, accelerated molecular dynamics (aMD) simulation, dynamic cross-correlation map (DCCM) calculation, principal component analysis (PCA) and free energy landscape (FEL) analysis were used to analyse conformational changes of PGAM1 caused by binding of inhibitors 8KX, 9HU and HKB. DCCM calculations and PCA showed that inhibitor binding significantly affected the kinetic behaviour of PGAM1 and conformational rearrangement of PGAM1. The binding ability and mechanism of 8KX, 9HU and HKB to PGAM1 were studied using the molecular mechanics generalised Born surface area (MM-GBSA) method. The results showed that compared with 8KX, the binding ability of 9HU and HKB to PGAM1 was enhanced by sulphonamide reversal and aminocarboxyl trifluoromethyl substitution. There were several hydrophobic interactions between inhibitors and PGAM1, providing significant contributions for inhibitor binding. Calculation of residue-based free energy decomposition revealed that F22, R90, Y92, L95, V112, W115, R116, V121, P123, P124, R191 and M206 were key residues of the PGAM1-inhibitor interaction and could be used as effective targets for designing drugs that inhibit the activity of PGAM1.

Structural and free energy landscape analysis for the discovery of antiviral compounds targeting the cap-binding domain of influenza polymerase PB2

Influenza poses a significant threat to global health, with the ability to cause severe epidemics and pandemics. The polymerase basic protein 2 (PB2) of the influenza virus plays a crucial role in the viral replication process, making the CAP-binding domain of PB2 an attractive target for antiviral drug development. This study aimed to identify and evaluate potential inhibitors of the influenza polymerase PB2 CAP-binding domain using computational drug discovery methods. We employed a comprehensive computational approach involving virtual screening, molecular docking, and 500 ns molecular dynamics (MD) simulations. Compounds were selected from the Diverse lib database and assessed for their binding affinity and stability in interaction with the PB2 CAP-binding domain. The study utilized the generalized amber force field (GAFF) for MD simulations to further evaluate the dynamic behaviour and stability of the interactions. Among the screened compounds, compounds 1, 3, and 4 showed promising binding affinities. Compound 4 demonstrated the highest binding stability and the most favourable free energy profile, indicating strong and consistent interaction with the target domain. Compound 3 displayed moderate stability with dynamic conformational changes, while Compound 1 maintained robust interactions throughout the simulations. Comparative analyses of these compounds against a control compound highlighted their potential efficacy. Compound 4 emerged as the most promising inhibitor, with substantial stability and strong binding affinity to the PB2 CAP-binding domain. These findings suggest that compound 4, along with compounds 1 and 3, holds the potential for further development into effective antiviral agents against influenza. Future studies should focus on experimental validation of these compounds and exploration of resistance mechanisms to enhance their therapeutic utility.

Antifungal potential of marine bacterial compounds in inhibiting Candida albicans Yck2 to overcome echinocandin resistance: a molecular dynamics study

Candida albicans (C. albicans), a common fungal pathogen, poses a significant threat to immunocompromised individuals, particularly due to the emergence of resistance against echinocandins, a primary class of antifungal agents. Yck2 protein, a key regulator of cell wall integrity and signaling pathways in C. albicans, was targeted to overcome this resistance. A virtual screening was used to identify Yck2 inhibitors from marine bacterial compounds. Further re-docking, molecular dynamics simulations, and various analyses such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bonding, free binding energy calculations, and RG-RMSD-based free energy landscape were conducted to evaluate the efficacy and stability of the identified compounds. Among the compounds screened, CMNPD27166 and CMNPD27283 emerged as the most promising candidates, demonstrating superior binding affinities, enhanced stability, and favorable interaction dynamics with Yck2, surpassing both the control and other compounds in efficacy. In contrast, CMNPD19660 and CMNPD24402, while effective, showed lesser potential. These findings highlight the utility of computational drug discovery techniques in identifying and optimizing potential therapeutic agents and suggest that marine-derived molecules could significantly impact the development of novel antifungal therapies. Further experimental validation of the leading candidates, CMNPD27166 and CMNPD27283, is recommended to confirm their potential as effective antifungal agents against echinocandin-resistant C. albicans infections.

Structure-Based Discovery of Phytocompounds from Azadirachta indica as Potential Inhibitors of Thioredoxin Glutathione Reductase in Schistosoma mansoni

Schistosomiasis, a parasitic disease caused by Schistosoma species such as S. haematobium, S. mansoni, and S. japonicum, poses a significant global health burden. The thioredoxin glutathione reductase (TGR) enzyme, crucial for maintaining the parasite's redox balance and preventing oxidative stress, has been identified as a promising target for anti-schistosomal drug development. This study aims to identify potential TGR inhibitors from Azadirachta indica phytochemicals using molecular modeling approaches. We screened 60 compounds derived from A. indica bark and leaves through molecular docking to assess their binding affinity, followed by the evaluation of binding-free energies for the most promising candidates. Drug-likeness and pharmacokinetic properties were assessed, and molecular dynamics simulations were conducted to explore the conformational stability of the protein-ligand complexes. Our findings revealed that several A. indica compounds exhibited significantly lower docking scores (up to -9.669 kcal/mol) compared to the standard drug praziquantel (-4.349 kcal/mol). Notably, Isorhamnetin, Isomargolonone, Nimbaflavone, Quercetin, and Nimbionol demonstrated strong interactions with TGR, although Isorhamnetin showed potential mutagenicity. Further binding free energy calculations and molecular dynamics simulations confirmed the stability of Isomargolonone, Nimbionol, and Quercetin as potential TGR inhibitors. In conclusion, these findings suggest that Isomargolonone, Nimbionol, and Quercetin warrant further experimental validation as promising candidates for anti-schistosomal therapy.

Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning

BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (MM-GBSA) method are integrated to elucidate the molecular mechanism underlying the effect of D93 and D289 protonation on binding of inhibitors OV6 and 4B2 to BACE1. The GaMD trajectory-based DL successfully identifies significant function domains. Dynamic analysis shows that the protonation of D93 and D289 strongly affects the structural flexibility and dynamic behaviour of BACE1. Free energy landscapes indicate that inhibitor-bound BACE1s have more conformational states in the protonated states than the wild-type (WT) BACE1, and show more binding poses of inhibitors. Binding affinities calculated using the MM-GBSA method indicate that the protonation of D93 and D289 highly disturbs the binding ability of inhibitors to BACE1. In addition, the protonation of two residues significantly affects the hydrogen bonding interactions (HBIs) of OV6 and 4B2 with BACE1, altering their binding activity to BACE1. The binding hot spots of BACE1 recognized by residue-based free energy estimations provide rational targeting sites for drug design towards BACE1. This study is anticipated to provide theoretical aids for drug development towards treatment of AD.

Computational insights into the mechanisms underlying structural destabilization and recovery in trafficking-deficient hERG mutants

Cardiovascular diseases are a major global health concern, responsible for a significant number of deaths each year, often linked to cardiac arrhythmias resulting from dysfunction in ion channels. Hereditary Long QT Syndrome (LQTS) is a condition characterized by a prolonged QT interval on ECG, increasing the risk of sudden cardiac death. The most common type of LQTS, LQT2, is caused by mutations in the hERG gene, affecting a potassium ion channel. The majority of these mutations disrupt the channel's trafficking to the cell membrane, leading to intracellular retention. Specific high-affinity hERG blockers (e.g., E-4031) can rescue this mutant phenotype, but the exact mechanism is unknown. This study used accelerated molecular dynamics simulations to investigate how these mutations affect the hERG channel's structure, folding, endoplasmic reticulum (ER) retention, and trafficking. We reveal that these mutations induce structural changes in the channel, narrowing its central pore and altering the conformation of the intracellular domains. These changes expose internalization signals that contribute to ER retention and degradation of the mutant hERG channels. Moreover, the study found that the trafficking rescue drug E-4031 can inhibit these structural changes, potentially rescuing the mutant channels. This research offers valuable insights into the structural issues responsible for the degradation of rescuable transmembrane trafficking mutants. Understanding the defective trafficking structure of the hERG channel could help identify binding sites for small molecules capable of restoring proper folding and facilitating channel trafficking. This knowledge has the potential to lead to mechanism-based therapies that address the condition at the cellular level, which may prove more effective than treating clinical symptoms, ultimately offering hope for individuals with hereditary Long QT Syndrome.

Employing advanced computational drug discovery techniques to identify novel inhibitors against ML2640c protein: a potential therapeutic approach for combatting leprosy

Leprosy, caused by Mycobacterium leprae, remains a significant global health challenge, necessitating innovative approaches to therapeutic intervention. This study employs advanced computational drug discovery techniques to identify potential inhibitors against the ML2640c protein, a key factor in the bacterium's ability to infect and persist within host cells. Utilizing a comprehensive methodology, including virtual screening, re-docking, molecular dynamics simulations, and free energy calculations, we screened a library of compounds for their interaction with ML2640c. Four compounds (24349836, 26616083, 26648979, and 26651264) demonstrated promising inhibitory potential, each exhibiting unique binding energies and interaction patterns that suggest a strong likelihood of disrupting the protein function. The study highlights the efficacy of computational methods in identifying potential therapeutic candidates, presenting compound 26616083 as a notably potent inhibitor due to its excellent binding affinity and stability. Our findings offer a foundation for future experimental validation and optimization, marking a significant step forward in the development of new treatments for leprosy. This research not only advances the fight against leprosy but also showcases the broader applicability of computational drug discovery in tackling infectious diseases.

Identification of potential anti-mucor agents by targeting endothelial cell receptor glucose-regulated protein-78 using in silico approach

Mucormycosis is a fungal infection of the sinuses, brain and lungs that is the cause of approximately 50% mortality rate despite the available first-line therapy. Glucose-Regulated Protein 78 (GRP78) is already reported to be a novel host receptor that mediates invasion and damage of human endothelial cells by Rhizopus oryzae and Rhizopus delemar, the most common etiologic species of Mucorales. The expression of GRP78 is also regulated by the levels of iron and glucose in the blood. There are several antifungal drugs in the market but they pose a serious side effect to the vital organs of the body. Therefore, there is an immediate need to discover effective drug molecules having increased efficacy with no side effects. With the help of various computational tools, the current study was attempted to determine potential antimucor agents against GRP78. The receptor molecule GRP78 was screened against 8820 known drugs deposited in DrugBank library using high-throughput virtual screening method. Total top 10 compounds were selected based on the binding energies greater than the reference co-crystal molecule. Furthermore, molecular dynamic (MD) simulations using AMBER were performed to calculate the stability of the top-ranked compounds in the active site of GRP78. After extensive computational studies, we propose that two compounds (CID439153 and CID5289104) have inhibitory potency against mucormycosis and can serve as potential drugs that can form the basis of treating mucormycosis disease.Communicated by Ramaswamy H. Sarma.

In silico study of androgen receptor N-terminal domain and exploration of its modulators

The androgen receptor (AR, Uniprot: P10275) signaling plays a key role in the progression of prostate cancer, various AR-related ligands have been reported to treat prostate cancer. However, some resistance mechanisms limited the treating effect of these ligands. Since DBD binding or the allosteric binding sites in LBD of AR may allow the circumvention of some drug resistance mechanisms, anti-resistance is expected especially through the NTD (N-terminal domain) targeting. What's more, studies have shown that compounds including EPI-001 and its derivatives which bind to the Tau-5 region on NTD could be promising molecules for AR-based therapeutics. Herein, we employed aMD (accelerated molecular dynamics) simulation to fold Tau-5 unit proteins into native structure correctly. Subsequently, based on the predicted structural features of Tau-5, the virtual screening was conducted to discover new compounds targeting AR-NTD. We picked up 8 compounds (according to their docking scores and partly similar structural consists as known AR ligands) and analyzed their interaction with Tau-5, compared with the positive control EPI-001, four of the pick-up compounds showed better glide scores. Interestingly, although compound 8 had a lower docking score, it consisted of a similar component as the ligand EIQPN and the amide derivatives, this predicts that compound 8 has also the potential to be modified into an excellent AR-NTD binding molecule. These 8 compounds were all commercially available and could be tested to check whether there was a hit compound to bind the AR-NTD and to regulate its bio-activities. Together, this study described an in silico VLS approach to discover AR-NTD ligands and provided more choices for developing AR-targeted therapies.Communicated by Ramaswamy H. Sarma.

6-Octadecenoic and Oleic Acid in Liquid Smoke Rice Husk Showed COVID-19 Inhibitor Properties

In recent years, liquid smoke rice husk (LSRH) has shown its therapeutic potency to diabetes, wound healing, stomatitis, and periodontitis. The phenol, 6-octadecenoic acid, oleic acid, and 9-octadecanoic acid were responsible for their therapeutic effect. The LSRH also demonstrated their potential for infectious diseases such as coronavirus disease (COVID-19). Therefore, the molecular dynamics (MDs) simulation and pharmacophore analysis was performed to analyse the binding stability of 6-octadecenoic and oleic acid. Based on MD simulation, 6-octadecenoic and oleic acids seemed to retain their interactions with Ser144 and Thr24, respectively, with hydrogen bond distance less than 2.9 Å. This interaction was stable during the simulation and has hydrophobic and hydrogen bonds/acceptors. The 6-octadecenoic acid and oleic acid were confirmed to have great potency as inhibitors for COVID-19. These compounds also showed that the existence of hydrophobic and hydrogen bonds/acceptors could increase biological activity.

Applications of Molecular Dynamics Simulations in Drug Discovery

In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.

Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters

Rational predictions on binding kinetics parameters of drugs to targets play significant roles in future drug designs. Full conformational samplings of targets are requisite for accurate predictions of binding kinetic parameters. In this review, we mainly focus on the applications of enhanced sampling technologies in calculations of binding kinetics parameters and residence time of drugs. The methods involved in molecular dynamics simulations are applied to not only probe conformational changes of targets but also reveal calculations of residence time that is significant for drug efficiency. For this review, special attention are paid to accelerated molecular dynamics (aMD) and Gaussian aMD (GaMD) simulations that have been adopted to predict the association or disassociation rate constant. We also expect that this review can provide useful information for future drug design.

Binding selectivity analysis of AURKs inhibitors through molecular dynamics simulation studies

Aurora kinases (AURKs) have been identified as promising biological targets for the treatment of cancer. In this study, molecular dynamics simulations were employed to investigate the binding selectivity of three inhibitors (HPM, MPY, and VX6) towards AURKA and AURKB by predicting their binding free energies. The results show that the inhibitors HPM, MPY, and VX6 have more favorable interactions with AURKB as compared to AURKA. The binding energy decomposition analysis revealed that four common residue pairs (L139, L83), (V147, V91), (L210, L154), and (L263, L207) showed significant binding energies with HPM, MPY, and VX6, hence responsible for the binding selectivity of AURKA and AURKB to the inhibitors. The MD trajectory analysis also revealed that the inhibitors affect the dynamic flexibility of protein structure, which is also responsible for the partial selectivity of HPM, MPY, and VX6 towards AURKA and AURKB. As expected, this study provides useful insights for the design of potential inhibitors with high selectivity for AURKA and AURKB.

Computational Exploration of Fluorocyclopentenyl-purines and-pyrimidines Derivatives as Potential Inhibitors of Epidermal Growth Factor Receptor (EGFR) for the Treatment of Breast Cancer

The Epidermal Growth Factor Receptor (EGFR) is an important therapeutic target for the treatment of a variety of epithelial malignancies, including breast cancer, in which EGFR is aberrantly expressed.The fluorocyclopentenyl-purine-pyrimidines derivatives, which have previously been described as powerful compounds against breast cancer, were selected to investigate their potential against EGFR using computational tools in an effort to obtain potent inhibitors with fewer adverse effects. The molecule's chemical reactivity and stability were assessed by determining the HOMO-LUMO energy gap using density functional theory (DFT) calculations. Among all the selected compounds, PU4 displayed a HOMO-LUMO gap of 0.191 eV. Additionally, molecular docking analysis was performed to assess the binding affinities of PU4 within the active pocket of EGFR-TK. The compound PU4 showed potent interactions with EGFR exhibiting -32.3 kJ/mol binding energy which was found best as compared to gefitinib i. e., -27.4 kJ/mol which was further validated by molecular dynamics simulations and ADMET analysis. The results of these analyses indicate that the top hits obtained from the virtual screening possess the ability to act as effective EGFR inhibitor. Therefore, it is recommended to further investigate the inhibitory potential of these identified compounds using in vitro and in vivo approaches.

Toward a structural identification of metastable molecular conformations

Interpreting high-dimensional data from molecular dynamics simulations is a persistent challenge. In this paper, we show that for a small peptide, deca-alanine, metastable states can be identified through a neural net based on structural information alone. While processing molecular dynamics data, dimensionality reduction is a necessary step that projects high-dimensional data onto a low-dimensional representation that, ideally, captures the conformational changes in the underlying data. Conventional methods make use of the temporal information contained in trajectories generated through integrating the equations of motion, which forgoes more efficient sampling schemes. We demonstrate that EncoderMap, an autoencoder architecture with an additional distance metric, can find a suitable low-dimensional representation to identify long-lived molecular conformations using exclusively structural information. For deca-alanine, which exhibits several helix-forming pathways, we show that this approach allows us to combine simulations with different biasing forces and yields representations comparable in quality to other established methods. Our results contribute to computational strategies for the rapid automatic exploration of the configuration space of peptides and proteins.

Elevated concentrations cause upright alpha-synuclein conformation at lipid interfaces

The amyloid aggregation of α-synuclein (αS), related to Parkinson's disease, can be catalyzed by lipid membranes. Despite the importance of lipid surfaces, the 3D-structure and orientation of lipid-bound αS is still not known in detail. Here, we report interface-specific vibrational sum-frequency generation (VSFG) experiments that reveal how monomeric αS binds to an anionic lipid interface over a large range of αS-lipid ratios. To interpret the experimental data, we present a frame-selection method ("ViscaSelect") in which out-of-equilibrium molecular dynamics simulations are used to generate structural hypotheses that are compared to experimental amide-I spectra via excitonic spectral calculations. At low and physiological αS concentrations, we derive flat-lying helical structures as previously reported. However, at elevated and potentially disease-related concentrations, a transition to interface-protruding αS structures occurs. Such an upright conformation promotes lateral interactions between αS monomers and may explain how lipid membranes catalyze the formation of αS amyloids at elevated protein concentrations.

Local Xenon-Protein Interaction Produces Global Conformational Change and Allosteric Inhibition in Lysozyme

Noble gases have well-established biological effects, yet their molecular mechanisms remain poorly understood. Here, we investigated, both experimentally and computationally, the molecular modes of xenon (Xe) action in bacteriophage T4 lysozyme (T4L). By combining indirect gassing methods with a colorimetric lysozyme activity assay, a reversible, Xe-specific (20 ± 3)% inhibition effect was observed. Accelerated molecular dynamic simulations revealed that Xe exerts allosteric inhibition on the protein by expanding a C-terminal hydrophobic cavity. Xe-induced cavity expansion results in global conformational changes, with long-range transduction distorting the active site where peptidoglycan binds. Interestingly, the peptide substrate binding site that enables lysozyme specificity does not change conformation. Two T4L mutants designed to reshape the C-terminal Xe cavity established a correlation between cavity expansion and enzyme inhibition. This work also highlights the use of Xe flooding simulations to identify new cryptic binding pockets. These results enrich our understanding of Xe-protein interactions at the molecular level and inspire further biochemical investigations with noble gases.

Impact of non-proteinogenic amino acid norvaline and proteinogenic valine misincorporation on a secondary structure of a model peptide

Norvaline is a straight-chain, hydrophobic, non-proteinogenic amino acid, isomeric with valine. Both amino acids can be misincorporated into proteins at isoleucine positions by isoleucyl-tRNA synthetase when the mechanisms of translation fidelity are impaired. Our previous study showed that the proteome-wide substitution of isoleucine with norvaline resulted in higher toxicity in comparison to the proteome-wide substitution of isoleucine with valine. Although mistranslated proteins/peptides are considered to have non-native structures responsible for their toxicity, the observed difference in protein stability between norvaline and valine misincorporation has not yet been fully understood. To examine the observed effect, we chose the model peptide with three isoleucines in the native structure, introduced selected amino acids at isoleucine positions and applied molecular dynamics simulations at different temperatures. The obtained results showed that norvaline has the highest destructive effect on the β-sheet structure and suggested that the higher toxicity of norvaline over valine is predominantly due to the misincorporation within the β-sheet secondary elements.

Accelerated Molecular Dynamics for Peptide Folding: Benchmarking Different Combinations of Force Fields and Explicit Solvent Models

Accelerated molecular dynamics (aMD) protocols were assessed on predicting the secondary structure of eight peptides, of which two are helical, three are β-hairpins, and three are disordered. Protocols consisted of combinations of three force fields (ff99SB, ff14SB, ff19SB) and two explicit solvation models (TIP3P and OPC), and were evaluated in two independent aMD simulations, one starting from an extended conformation, the other starting from a misfolded conformation. The results of these analyses indicate that all three combinations performed well on helical peptides. As for β-hairpins, ff19SB performed well with both solvation methods, with a slight preference for the TIP3P solvation model, even though performance was dependent on both peptide sequence and initial conformation. The ff19SB/OPC combination had the best performance on intrinsically disordered peptides. In general, ff14SB/TIP3P suffered the strongest helical bias.

Molecular Insights into the Improved Bioactivity of Interferon Conjugates Attached to a Helical Polyglutamate

Attaching polymers, especially polyethylene glycol (PEG), to protein drugs has emerged as a successful strategy to prolong circulation time in the bloodstream. The hypothesis is that the flexible chain wobbles on the protein's surface, thus resisting potential nonspecific adsorption. Such a theoretical framework may be challenged when a helical polyglutamate is used to conjugate with target proteins. In this study, we investigated the structure-activity relationships of polyglutamate-interferon conjugates P(EG₃Glu)-IFN using molecular simulations. Our results show that the local crowding effect induced by oligoethylene glycols (i.e., EG₃) is the primary driving force for helix formation in P(EG₃Glu), and its helicity can be effectively increased by reducing the free volume of the two termini. Furthermore, it was found that the steric hindrance induced by IFN is not conductive to the helicity of P(EG₃Glu) but contributes to its dominant orientation relative to interferon. The orientation of IFN relative to the helical P(EG₃Glu) can help to protect the protein drug from neutralizing antibodies while maintaining its bioactivity. These findings suggest that the helical structure and its orientation are critical factors to consider when updating the theoretical framework for protein-polymer conjugates.

Deciphering Selectivity Mechanism of BRD9 and TAF1(2) toward Inhibitors Based on Multiple Short Molecular Dynamics Simulations and MM-GBSA Calculations

BRD9 and TAF1(2) have been regarded as significant targets of drug design for clinically treating acute myeloid leukemia, malignancies, and inflammatory diseases. In this study, multiple short molecular dynamics simulations combined with the molecular mechanics generalized Born surface area method were employed to investigate the binding selectivity of three ligands, 67B, 67C, and 69G, to BRD9/TAF1(2) with IC50 values of 230/59 nM, 1400/46 nM, and 160/410 nM, respectively. The computed binding free energies from the MM-GBSA method displayed good correlations with that provided by the experimental data. The results indicate that the enthalpic contributions played a critical factor in the selectivity recognition of inhibitors toward BRD9 and TAF1(2), indicating that 67B and 67C could more favorably bind to TAF1(2) than BRD9, while 69G had better selectivity toward BRD9 over TAF1(2). In addition, the residue-based free energy decomposition approach was adopted to calculate the inhibitor–residue interaction spectrum, and the results determined the gatekeeper (Y106 in BRD9 and Y1589 in TAF1(2)) and lipophilic shelf (G43, F44, and F45 in BRD9 and W1526, P1527, and F1528 in TAF1(2)), which could be identified as hotspots for designing efficient selective inhibitors toward BRD9 and TAF1(2). This work is also expected to provide significant theoretical guidance and insightful molecular mechanisms for the rational designs of efficient selective inhibitors targeting BRD9 and TAF1(2).

Dimeric Lectin Chimeras as Novel Candidates for Gb3-Mediated Transcytotic Drug Delivery through Cellular Barriers

Receptor-mediated transcytosis is an elegant and promising strategy for drug delivery across biological barriers. Here, we describe a novel ligand-receptor pair based on a dimeric, engineered derivative of the Pseudomonas aeruginosa lectin LecA, here termed Di-LecA, and the host cell glycosphingolipid Gb3. We characterized the trafficking kinetics and transcytosis efficiencies in polarized Gb3-positive and -negative MDCK cells using mainly immunofluorescence in combination with confocal microscopy. To evaluate the delivery capacity of dimeric LecA chimeras, EGFP was chosen as a fluorescent model protein representing macromolecules, such as antibody fragments, and fused to either the N- or C-terminus of monomeric LecA using recombinant DNA technology. Both LecA/EGFP fusion proteins crossed cellular monolayers in vitro. Of note, the conjugate with EGFP at the N-terminus of LecA (EGFP-LecA) showed a higher release rate than the conjugate with EGFP at the C-terminus (LecA-EGFP). Based on molecular dynamics simulations and cross-linking studies of giant unilamellar vesicles, we speculate that EGFP-LecA tends to be a dimer while LecA-EGFP forms a tetramer. Overall, we confidently propose the dimeric LecA chimeras as transcytotic drug delivery tools through Gb3-positive cellular barriers for future in vivo tests.

Defective ORF8 dimerization in SARS-CoV-2 delta variant leads to a better adaptive immune response due to abrogation of ORF8-MHC1 interaction

In India, during the second wave of the COVID-19 pandemic, the breakthrough infections were mainly caused by the SARS-COV-2 delta variant (B.1.617.2). It was reported that, among majority of the infections due to the delta variant, only 9.8% percent cases required hospitalization, whereas only 0.4% fatality was observed. Sudden dropdown in COVID-19 infections cases were observed within a short timeframe, suggesting better host adaptation with evolved delta variant. Downregulation of host immune response against SARS-CoV-2 by ORF8 induced MHC-I degradation has been reported earlier. The Delta variant carried mutations (deletion) at Asp119 and Phe120 amino acids which are critical for ORF8 dimerization. The deletions of amino acids Asp119 and Phe120 in ORF8 of delta variant resulted in structural instability of ORF8 dimer caused by disruption of hydrogen bonds and salt bridges as revealed by structural analysis and MD simulation studies. Further, flexible docking of wild type and mutant ORF8 dimer revealed reduced interaction of mutant ORF8 dimer with MHC-I as compared to wild-type ORF8 dimer with MHC-1, thus implicating its possible role in MHC-I expression and host immune response against SARS-CoV-2. We thus propose that mutant ORF8 of SARS-CoV-2 delta variant may not be hindering the MHC-I expression thereby resulting in a better immune response against the SARS-CoV-2 delta variant, which partly explains the possible reason for sudden drop of SARS-CoV-2 infection rate in the second wave of SARS-CoV-2 predominated by delta variant in India.

Structure prediction and discovery of inhibitors against phosphopantothenoyl cysteine synthetase of Acinetobacter baumannii

Acinetobacter baumannii is an extremely dangerous multidrug-resistant (MDR) gram-negative pathogen which poses a serious life-threatening risk in immunocompromised patients. Phosphopantothenoyl cysteine synthetase (PPCS) catalyzes the formation of an amide bond between L-cysteine and phosphopantothenic acid (PPA) to form 4'- Phosphopantothenoylcysteine during Coenzyme A (CoA) biosynthesis. CoA is a crucial cofactor for cellular survival and inhibiting its synthesis will result in cell death. Bacterial PPCS differs from eukaryotic PPCS in a number of ways like it exists as a C-terminal domain of a PPCDC/PPCS fusion protein whereas eukaryotic PPCS exists as an independent protein. This difference makes it an attractive drug target. For which a conventional iterative approach of SBDD (structure-based drug design) was used, which began with three-dimensional structure prediction of AbPPCS using PHYRE 2.0. A database of FDA-approved compounds (Drug Bank) was then screened against the target of interest by means of docking score and glide energy, leading to the identification of 6 prominent drug candidates. The shortlisted 6 molecules were further subjected to all-atom MD simulation studies in explicit-solvent conditions (using AMBER force field). The MD simulation studies revealed that the ligands DB65103, DB449108 and DB443210, maintained several H-bonds with intense van der Waals contacts at the active site of the protein with high binding free energies: -11.42 kcal/mol, -10.49 kcal/mol and -10.98 kcal/mol, respectively, calculated via MM-PBSA method. Overall, binding of these compounds at the active site was found to be the most stable and robust highlighting the potential of these compounds to serve as antibacterials.Communicated by Ramaswamy H. Sarma.

CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs

Implicit solvent models are widely used because they are advantageous to speed up simulations by drastically decreasing the number of solvent degrees of freedom, which allows one to achieve long simulation time scales for large system sizes. CHARMM-GUI, a web-based platform, has been developed to support the setup of complex multicomponent molecular systems and prepare input files. This study describes an Implicit Solvent Modeler (ISM) in CHARMM-GUI for various generalized Born (GB) implicit solvent simulations in different molecular dynamics programs such as AMBER, CHARMM, GENESIS, NAMD, OpenMM, and Tinker. The GB models available in ISM include GB-HCT, GB-OBC, GB-neck, GBMV, and GBSW with the CHARMM and Amber force fields for protein, DNA, RNA, glycan, and ligand systems. Using the system and input files generated by ISM, implicit solvent simulations of protein, DNA, and RNA systems produce similar results for different simulation packages with the same input information. Protein-ligand systems are also considered to further validate the systems and input files generated by ISM. Simple ligand root-mean-square deviation (RMSD) and molecular mechanics generalized Born surface area (MM/GBSA) calculations show that the performance of implicit simulations is better than docking and can be used for early stage ligand screening. These reasonable results indicate that ISM is a useful and reliable tool to provide various implicit solvent simulation applications.

Protein Model Quality Estimation Using Molecular Dynamics Simulation

The estimation of protein model quality remains a challenging task and is important for protein structural model utilization. In the last decade, existing methods that rely on machine learning to deep learning have been developed and shown progressive improvement. Despite utilizing more sophisticated techniques and introducing new features, none of these methods employ explicit protein structure stability information. Hypothetically, protein model quality might be indicated by its structural stability in an in silico system disclosed by the structural difference from its initial structure. One of the possible methods to exploit such information is by implementing molecular dynamics simulations that have shown successful applications in many research fields. We present a novel approach by introducing explicit protein structure stability information using molecular dynamics simulation. Despite using only simple features, small data with no training process required, and a short molecular dynamics simulation time, our method shows comparable performance to the state-of-the-art deep learning-based method.

Structural Validation by the G-Factor Properly Regulates Boost Potentials Imposed in Conformational Sampling of Proteins

Free energy landscapes (FELs) of proteins are indispensable for evaluating thermodynamic properties. Molecular dynamics (MD) simulation is a computational method for calculating FELs; however, conventional MD simulation frequently fails to search a broad conformational subspace due to its accessible timescale, which results in the calculation of an unreliable FEL. To search a broad subspace, an external bias can be imposed on a protein system, and biased sampling tends to cause a strong perturbation that might collapse the protein structures, indicating that the strength of the external bias should be properly regulated. This regulation can be challenging, and empirical parameters are frequently employed to impose an optimal bias. To address this issue, several methods regulate the external bias by referring to system energies. Herein, we focused on protein structural information for this regulation. In this study, a well-established structural indicator (the G-factor) was used to obtain structural information. Based on the G-factor, we proposed a scheme for regulating biased sampling, which is referred to as a G-factor-based external bias limiter (GERBIL). With GERBIL, the configurations were structurally validated by the G-factor during biased sampling. As an example of biased sampling, an accelerated MD (aMD) simulation was adopted in GERBIL (aMD-GERBIL), whereby the aMD simulation was repeatedly performed by increasing the strength of the boost potential. Furthermore, the configurations sampled by the aMD simulation were structurally validated by their G-factor values, and aMD-GERBIL stopped increasing the strength of the boost potential when the sampled configurations were regarded as low-quality (collapsed) structures. This structural validation is regarded as a "Brake" of the boost potential. For demonstrations, aMD-GERBIL was applied to globular proteins (ribose binding and maltose-binding proteins) to promote their large-amplitude open-closed transitions and successfully identify their domain motions.

A fast-slow method to treat solute dynamics in explicit solvent

Aiming to reduce the computational cost in the current explicit solvent molecular dynamics (MD) simulation, this paper proposes a fast-slow method for the fast MD simulation of biomolecules in explicit solvent. This fast-slow method divides the entire system into two parts: a core layer (typically solute or biomolecule) and a peripheral layer (typically solvent molecules). The core layer is treated using standard MD method but the peripheral layer is treated by a slower dynamics method to reduce the computational cost. We compared four different simulation models in testing calculations for several small proteins. These include gas-phase, implicit solvent, fast-slow explicit solvent and standard explicit solvent MD simulations. Our study shows that gas-phase and implicit solvent models do not provide a realistic solvent environment and fail to correctly produce reliable dynamic structures of proteins. On the other hand, the fast-slow method can essentially reproduce the same solvent effect as the standard explicit solvent model while gaining an order of magnitude in efficiency. This fast-slow method thus provides an efficient approach for accelerating the MD simulation of biomolecules in explicit solvent.

In silico evaluation of food-derived carotenoids against SARS-CoV-2 drug targets: Crocin is a promising dietary supplement candidate for COVID-19

The current COVID‐19 pandemic is severely threatening public healthcare systems around the globe. Some supporting therapies such as remdesivir, favipiravir, and ivermectin are still under the process of a clinical trial, it is thus urgent to find alternative treatment and prevention options for SARS‐CoV‐2. In this regard, although many natural products have been tested and/or suggested for the treatment and prophylaxis of COVID‐19, carotenoids as an important class of natural products were underexplored. The dietary supplementation of some carotenoids was already suggested to be potentially effective in the treatment of COVID‐19 due to their strong antioxidant properties. In this study, we performed an in silico screening of common food‐derived carotenoids against druggable target proteins of SARS‐CoV‐2 including main protease, helicase, replication complex, spike protein and its mutants for the recent variants of concern, and ADP‐ribose phosphatase. Molecular docking results revealed that some of the carotenoids had low binding energies toward multiple receptors. Particularly, crocin had the strongest binding affinity (−10.5 kcal/mol) toward the replication complex of SARS‐CoV‐2 and indeed possessed quite low binding energy scores for other targets as well. The stability of crocin in the corresponding receptors was confirmed by molecular dynamics simulations. Our study, therefore, suggests that carotenoids, especially crocin, can be considered an effective alternative therapeutics and a dietary supplement candidate for the prophylaxis and treatment of SARS‐CoV‐2.

Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach

The overexpression of matrix metalloproteinase-9 (MMP-9) is associated with tumor development and angiogenesis, and hence, it has been considered an attractive drug target for anticancer therapy. To assist in drug design endeavors for MMP-9 targets, an in silico study was presented to investigate whether our compounds inhibit MMP-9 by binding to the catalytic domain, similar to their inhibitor or not. For that, in the initial stage, a deep-learning algorithm was used for the predictive modeling of the CHEMBL321 dataset of MMP-9 inhibitors. Several regression models were built and evaluated based on R2, MAE MSE, RMSE, and Loss. The best model was utilized to screen the drug bank database containing 9,102 compounds to seek novel compounds as MMP-9 inhibitors. Then top high score compounds were selected for molecular docking based on the comparison between the score of the reference molecule. Furthermore, molecules having the highest docking scores were selected, and interaction mechanisms with respect to S1 pocket and catalytic zinc ion of these compounds were also discussed. Those compounds, involving binding to the catalytic zinc ion and the S1 pocket of MMP-9, were considered preferentially for molecular dynamics studies (100 ns) and an MM-PBSA (last 30 ns) analysis. Based on the results, we proposed several novel compounds as potential candidates for MMP-9 inhibition and investigated their binding properties with MMP-9. The findings suggested that these compounds may be useful in the design and development of MMP-9 inhibitors in the future.

SARS-CoV-2 Spike Protein Unlikely to Bind to Integrins via the Arg-Gly-Asp (RGD) Motif of the Receptor Binding Domain: Evidence From Structural Analysis and Microscale Accelerated Molecular Dynamics

The Receptor Binding Domain (RBD) of SARS-CoV-2 virus harbors a sequence of Arg-Gly-Asp tripeptide named RGD motif, which has also been identified in extracellular matrix proteins that bind integrins as well as other disintegrins and viruses. Accordingly, integrins have been proposed as host receptors for SARS-CoV-2. However, given that the microenvironment of the RGD motif imposes a structural hindrance to the protein-protein association, the validity of this hypothesis is still uncertain. Here, we used normal mode analysis, accelerated molecular dynamics microscale simulation, and protein-protein docking to investigate the putative role of RGD motif of SARS-CoV-2 RBD for interacting with integrins. We found, that neither RGD motif nor its microenvironment showed any significant conformational shift in the RBD structure. Highly populated clusters of RBD showed no capability to interact with the RGD binding site in integrins. The free energy landscape revealed that the RGD conformation within RBD could not acquire an optimal geometry to allow the interaction with integrins. In light of these results, and in the event where integrins are confirmed to be host receptors for SARS-CoV-2, we suggest a possible involvement of other residues to stabilize the interaction.

Q61 mutant-mediated dynamics changes of the GTP-KRAS complex probed by Gaussian accelerated molecular dynamics and free energy landscapes

Understanding the molecular mechanism of the GTP-KRAS binding is significant for improving the target roles of KRAS in cancer treatment. In this work, multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations were applied to decode the effect of Q61A, Q61H and Q61L on the activity of KRAS. Dynamics analyses based on MR-GaMD trajectory reveal that motion modes and dynamics behavior of the switch domain in KRAS are heavily affected by the three Q61 mutants. Information of free energy landscapes (FELs) shows that Q61A, Q61H and Q61L induce structural disorder of the switch domain and disturb the activity of KRAS. Analysis of the interaction network uncovers that the decrease in the stability of hydrogen bonding interactions (HBIs) of GTP with residues V29 and D30 induced by Q61A, Q61H and Q61L is responsible for the structural disorder of the switch-I and that in the occupancy of the hydrogen bond between GTP and residue G60 leads to the structural disorder of the switch-II. Thus, the high disorder of the switch domain caused by three current Q61 mutants produces a significant effect on binding of KRAS to its effectors. This work is expected to provide useful information for further understanding function and target roles of KRAS in anti-cancer drug development.

Peptide stapling by late-stage Suzuki–Miyaura cross-coupling

The development of peptide stapling techniques to stabilise α-helical secondary structure motifs of peptides led to the design of modulators of protein–protein interactions, which had been considered undruggable for a long time. We disclose a novel approach towards peptide stapling utilising macrocyclisation by late-stage Suzuki–Miyaura cross-coupling of bromotryptophan-containing peptides of the catenin-binding domain of axin. Optimisation of the linker length in order to find a compromise between both sufficient linker rigidity and flexibility resulted in a peptide with an increased α-helicity and enhanced binding affinity to its native binding partner β-catenin. An increased proteolytic stability against proteinase K has been demonstrated.

Theoretical exploration of the binding selectivity of inhibitors to BRD7 and BRD9 with multiple short molecular dynamics simulations

Bromodomain-containing proteins 7 and 9 (BRD7 and BRD9) have been considered as potential targets of clinical drug design toward treatment of human cancers and other diseases. Multiple short molecular dynamics simulations and binding free energy predictions were carried out to decipher the binding selectivity of three inhibitors 4L2, 5U6, and 6KT toward BRD7 and BRD9. The results show that 4L2 has more favorable binding ability to BRD7 over BRD9 compared to 5U6 and 6KT, while 5U6 and 6KT possess more favorable associations with BRD9 than BRD7. Furthermore, estimations of residue-based free energy decompositions further identify that four common residue pairs, including (F155, F44), (V160, V49), (Y168, Y57) and (Y217, Y106) in (BRD7, BRD9) generate obvious binding differences with 4L2, 5U6, and 6KT, which mostly drives the binding selectivity of 4L2, 5U6, and 6KT to BRD7 and BRD9. Dynamic information arising from trajectory analysis also suggests that inhibitor bindings affect structural flexibility and motion modes, which is responsible for the partial selectivity of 4L2, 5U6, and 6KT toward BRD7 and BRD9. As per our expectation, this study theoretically provides useful hints for design of dual inhibitors with high selectivity on BRD7 and BRD9.

Bromodomains (BRDs) are structurally conserved epigenetic reader modules observed in numerous chromatin- and transcription-associated proteins that have a capability to identify acetylated lysine residues.

Deciphering binding mechanism of inhibitors to SARS-COV-2 main protease through multiple replica accelerated molecular dynamics simulations and free energy landscapes

The pneumonia outbreak caused by the SARS-CoV-2 virus poses a serious threat to human health and the world economy. Development of safe and highly effective antiviral drugs is of great...

Ligand-based pharmacophore modeling and molecular dynamic simulation approaches to identify putative MMP-9 inhibitors

MMP-9 is a calcium-dependent zinc endopeptidase that plays a crucial role in various diseases and is a ubiquitous target for many classes of drugs. The availability of MMP-9 crystal structure in combination with aryl sulfonamide anthranilate hydroxamate inhibitor facilitates to accentuate the computer-aided screening of MMP-9 inhibitors with the presumed binding mode. In the current study, ligand-based pharmacophore modeling and 3D-QSAR analysis were performed using 67 reported MMP-9 inhibitors possessing pIC50 in the range of 5.221 to 9.000. The established five-point hypothesis model DDHRR_1 was statistically validated using various parameters R 2 (0.9076), Q 2 (0.8170), and F value (83.5) at a partial least square of four. Hypothesis validation and enrichment analysis were performed for the generated hypothesis. Further, Y-scrambling and Xternal validation using mean-absolute error-based criteria were performed to evaluate the reliability of the model. Docking in the XP mode and binding free energy was calculated for 67 selected ligands to explore the key binding interactions and binding affinity against the MMP-9 enzyme. Additionally, high-throughput virtual screening was carried out for 2.3 million chemical molecules to explore the potential virtual hits, and their predicted activity was calculated. Thus, the results obtained aid in developing novel MMP-9 inhibitors with significant activity and binding affinity.

Binding of Inhibitors to BACE1 Affected by pH-Dependent Protonation: An Exploration from Multiple Replica Gaussian Accelerated Molecular Dynamics and MM-GBSA Calculations

To date, inhibiting the activity of β-amyloid cleaving enzyme 1 (BACE1) has been considered an efficient approach for treating Alzheimer's disease (AD). In the current work, multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations and the molecular mechanics general Born surface area (MM-GBSA) method were combined to investigate the effect of pH-dependent protonation on the binding of the inhibitors CS9, C6U, and 6WE to BACE1. Dynamic analyses based on the MR-GaMD trajectory show that pH-dependent protonation strongly affects the structural flexibility, correlated motions, and dynamic behavior of inhibitor-bound BACE1. According to the constructed free energy profiles, in the protonated state at low pH, inhibitor-bound BACE1 tends to populate at more conformations than in high pH. The binding free energies calculated by MM-GBSA suggest that inhibitors possess stronger binding abilities under the protonation conditions at high pH than under the protonation conditions at low pH. Moreover, pH-dependent protonation exerts a significant effect on the hydrogen bonding interactions of CS9, C6U, and 6WE to BACE1, which correspondingly alters the binding abilities of the three inhibitors to BACE1. Furthermore, in different protonated environments, three inhibitors share common interaction clusters and similar binding sites in BACE1, which are reliably used as efficient targets for the design of potent inhibitors of BACE1.

Structure and Thermal Stability of wtRop and RM6 Proteins through All-Atom Molecular Dynamics Simulations and Experiments

In the current work we study, via molecular simulations and experiments, the folding and stability of proteins from the tertiary motif of 4-α-helical bundles, a recurrent motif consisting of four amphipathic α-helices packed in a parallel or antiparallel fashion. The focus is on the role of the loop region in the structure and the properties of the wild-type Rop (wtRop) and RM6 proteins, exploring the key factors which can affect them, through all-atom molecular dynamics (MD) simulations and supporting by experimental findings. A detailed investigation of structural and conformational properties of wtRop and its RM6 loopless mutation is presented, which display different physical characteristics even in their native states. Then, the thermal stability of both proteins is explored showing RM6 as more thermostable than wtRop through all studied measures. Deviations from native structures are detected mostly in tails and loop regions and most flexible residues are indicated. Decrease of hydrogen bonds with the increase of temperature is observed, as well as reduction of hydrophobic contacts in both proteins. Experimental data from circular dichroism spectroscopy (CD), are also presented, highlighting the effect of temperature on the structural integrity of wtRop and RM6. The central goal of this study is to explore on the atomic level how a protein mutation can cause major changes in its physical properties, like its structural stability.

Virtual screening of quinoline derived library for SARS-COV-2 targeting viral entry and replication

The COVID-19 pandemic infection has claimed many lives and added to the social, economic, and psychological distress. The contagious disease has quickly spread to almost 218 countries and territories following the regional outbreak in China. As the number of infected populations increases exponentially, there is a pressing demand for anti-COVID drugs and vaccines. Virtual screening provides possible leads while extensively cutting down the time and resources required for ab-initio drug design. We report structure-based virtual screening of a hundred plus library of quinoline drugs with established antiviral, antimalarial, antibiotic or kinase inhibitor activity. In this study, targets having a role in viral entry, viral assembly, and viral replication have been selected. The targets include: 1) RBD of receptor-binding domain spike protein S 2) M^pro Chymotrypsin main protease 3) P^pro Papain protease 4) RNA binding domain of Nucleocapsid Protein, and 5) RNA Dependent RNA polymerase from SARS-COV-2. An in-depth analysis of the interactions and G-score compared to the controls like hydroxyquinoline and remdesivir has been presented. The salient results are (1) higher scoring of antivirals as potential drugs (2) potential of afatinib by scoring as better inhibitor, and (3) biological explanation of the potency of afatinib. Further MD simulations and MM-PBSA calculations showed that afatinib works best to interfere with the the activity of RNA dependent RNA polymerase of SARS-COV-2, thereby inhibiting replication process of single stranded RNA virus.

Communicated by Ramaswamy H. Sarma

Simulating Peptide Monolayer Formation: GnRH-I on Silica

Molecular dynamics (MD) simulations can provide a detailed view of molecule behaviour at an atomic level, which can be useful when attempting to interpret experiments or design new systems. The decapeptide gonadotrophin-releasing hormone I (GnRH-I) is known to control fertility in mammals for both sexes. It was previously shown that inoculation with silica nanoparticles (SiNPs) coated with GnRH-I makes an effective anti-fertility vaccine due to how the peptide adsorbs to the nanoparticle and is presented to the immune system. In this paper, we develop and employ a protocol to simulate the development of a GnRH-I peptide adlayer by allowing peptides to diffuse and adsorb in a staged series of trajectories. The peptides start the simulation in an immobile state in solution above the model silica surface, and are then released sequentially. This facile approach allows the adlayer to develop in a natural manner and appears to be quite versatile. We find that the GnRH-I adlayer tends to be sparse, with electrostatics dominating the interactions. The peptides are collapsed to the surface and are seemingly free to interact with additional solutes, supporting the interpretations of the GNRH-I/SiNP vaccine system.

Binding mechanism of inhibitors to p38α MAP kinase deciphered by using multiple replica Gaussian accelerated molecular dynamics and calculations of binding free energies

The p38α MAP Kinase has been an important target of drug design for treatment of inflammatory diseases and cancers. This work applies multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations and the molecular mechanics generalized Born surface area (MM-GBSA) method to probe the binding mechanism of inhibitors L51, R24 and 1AU to p38α. Dynamics analyses show that inhibitor bindings exert significant effect on conformational changes of the active helix α2 and the conserved DFG loop. The rank of binding free energies calculated with MM-GBSA not only agrees well with that determined by the experimental IC50 values but also suggests that mutual compensation between the enthalpy and entropy changes can improve binding of inhibitors to p38α. The analyses of free energy landscapes indicate that the L51, R24 and 1AU bound p38α display a DFG-out conformation. The residue-based free energy decomposition method is used to evaluate contributions of separate residues to the inhibitor-p38α binding and the results imply that residues V30, V38, L74, L75, I84, T106, H107, L108, M109, L167, F169 and D168 can be utilized as efficient targets of potent inhibitors toward p38α.

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

Fibril fragments from the amyloid core of lysozyme: An accelerated molecular dynamics study

Protein aggregation and formation of amyloid fibrils are associated with many diseases and present a ubiquitous problem in protein science. Hen egg white lysozyme (HEWL) can form fibrils both from the full length protein and from its fragments. In the present study, we simulated unfolding of the amyloidogenic fragment of HEWL encompassing residues 49-101 to study the conformational aspects of amyloidogenesis. The accelerated molecular dynamics approach was used to speed up the sampling of the fragment conformers under enhanced temperature. Analysis of conformational transformation and intermediate structures was performed. During the unfolding, the novel short-living and long-living β-structures are formed along with the unstructured random coils. Such β-structure enriched monomers can interact with each other and propagate into fibril-like forms. The stability of oligomers assembled from these monomers was evaluated in the course of MD simulations with explicit water. The residues playing a key role in fibril stabilization were determined. The work provides new insights into the processes occurring at the early stages of amyloid fibril assembly.

Unveiling conformational dynamics changes of H-Ras induced by mutations based on accelerated molecular dynamics

Uncovering molecular basis with regard to the conformational change of two switches I and II in the GppNHp (GNP)-bound H-Ras is highly significant for the understanding of Ras signaling. For this purpose, accelerated molecular dynamics (aMD) simulations and principal component (PC) analysis are integrated to probe the effect of mutations G12V, T35S and Q61K on conformational transformation between two switches of the GNP-bound H-Ras. The RMSF and cross-correlation analyses suggest that three mutations exert a vital effect on the flexibility and internal dynamics of two switches in the GNP-bound H-Ras. The results stemming from PC analysis indicate that two switches in the GNP-bound WT H-Ras tend to form a closed state in most conformations, while those in the GNP-bound mutated H-Ras display transformation between different states. This conclusion is further supported by free energy landscapes constructed by using the distances of residues 12 away from 35 and 35 away from 61 as reaction coordinates and different experimental studies. Interaction scanning is performed on aMD trajectories and the information shows that conformational transformations of two switches I and II induced by mutations extremely affect the GNP-residue interactions. Meanwhile, the scanning results also signify that residues G15, A18, F28, K117, A146 and K147 form stable contacts with GNP, while residues D30, E31, Y32, D33, P34 and E62 in two switches I and II produce unstable contacts with GNP. This study not only reveals dynamic behavior changes of two switches in H-Ras induced by mutations, but also unveils general principles and mechanisms with regard to functional conformational changes of H-Ras.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Structure-Based Virtual Screening and Biochemical Validation to Discover a Potential Inhibitor of the SARS-CoV-2 Main Protease

The recent pandemic caused by SARS-CoV-2 has led the world to a standstill, causing a medical and economic crisis worldwide. This crisis has triggered an urgent need to discover a possible treatment strategy against this novel virus using already-approved drugs. The main protease (Mpro) of this virus plays a critical role in cleaving the translated polypeptides that makes it a potential drug target against COVID-19. Taking advantage of the recently discovered three-dimensional structure of Mpro, we screened approved drugs from the Drug Bank to find a possible inhibitor against Mpro using computational methods and further validating them with biochemical studies. The docking and molecular dynamics study revealed that DB04983 (denufosol) showed the best glide docking score, -11.884 kcal/mol, and MM-PBSA binding free energy, -10.96 kcal/mol. Cobicistat, cangrelor (previous computational studies in our lab), and denufosol (current study) were tested for the in vitro inhibitory effects on Mpro. The IC50 values of these drugs were ∼6.7 μM, 0.9 mM, and 1.3 mM, respectively, while the values of dissociation constants calculated using surface plasmon resonance were ∼2.1 μM, 0.7 mM, and 1.4 mM, respectively. We found that cobicistat is the most efficient inhibitor of Mpro both in silico and in vitro. In conclusion, cobicistat, which is already an FDA-approved drug being used against HIV, may serve as a good inhibitor against the main protease of SARS-CoV-2 that, in turn, can help in combating COVID-19, and these results can also form the basis for the rational structure-based drug design against COVID-19.

Tuning the Biological Activity of RGD Peptides with Halotryptophans†

An array of l- and d-halotryptophans with different substituents at the indole moiety was synthesized employing either enzymatic halogenation by halogenases or incorporation of haloindoles using tryptophan synthase. Introduction of these Trp derivatives into RGD peptides as a benchmark system was performed to investigate their influence on bioactivity. Halotryptophan-containing RGD peptides display increased affinity toward integrin α_vβ₃ and enhanced selectivity over integrin α₅β₁. In addition, bromotryptophan was exploited as a platform for late-stage diversification by Suzuki-Miyaura cross-coupling (SMC), resulting in new-to-nature biaryl motifs. These peptides show enhanced affinity toward α_vβ₃, good affinity to α_vβ₈, and remarkable selectivity over α₅β₁ and α_IIbβ₃ while featuring fluorogenic properties. Their feasibility as a probe was demonstrated in vitro. Extensive molecular dynamics simulations were undertaken to elucidate NMR and high-performance liquid chromatography (HPLC) data for these late-stage diversified cyclic RGD peptides and to further characterize their conformational preferences.

An α-helix mimetic oligopyridylamide, ADH-31, modulates Aβ42 monomer aggregation and destabilizes protofibril structures: insights from molecular dynamics simulations

Alzheimer's disease (AD), an epidemic growing worldwide due to no effective medical aid available in the market, is a neurological disorder. AD is known to be directly associated with the toxicity of amyloid-β (Aβ) aggregates. In search of potent inhibitors of Aβ aggregation, Hamilton and co-workers reported an α-helix mimetic, ADH-31, which acts as a powerful antagonist of Aβ42 aggregation. To identify the key interactions between protein-ligand complexes and to gain insights into the inhibitory mechanism of ADH-31 against Aβ42 aggregation, molecular dynamics (MD) simulations were performed in the present study. The MD simulations highlighted that ADH-31 showed distinct binding capabilities with residues spanning from the N-terminal to the central hydrophobic core (CHC) region of Aβ42 and restricted the conformational transition of the helix-rich structure of Aβ42 into another form of secondary structures (coil/turn/β-sheet). Hydrophobic contacts, hydrogen bonding and π-π interaction contribute to the strong binding between ADH-31 and Aβ42 monomer. The Dictionary of Secondary Structure of Proteins (DSSP) analysis highlighted that the probability of helical content increases from 38.5% to 50.2% and the turn content reduces from 14.7% to 6.2% with almost complete loss of the β-sheet structure (4.5% to 0%) in the Aβ42 monomer + ADH-31 complex. The per-residue binding free energy analysis demonstrated that Arg5, Tyr10, His14, Gln15, Lys16, Val18, Phe19 and Lys28 residues of Aβ42 are responsible for the favourable binding free energy in Aβ42 monomer + ADH-31 complex, which is consistent with the 2D HSQC NMR of the Aβ42 monomer that depicted a change in the chemical shift of residues spanning from Glu11 to Phe20 in the presence of ADH-31. The MD simulations highlighted the prevention of sampling of amyloidogenic β-strand conformations in Aβ42 trimer in the presence of ADH-31 as well as the ability of ADH-31 to destabilize Aβ42 trimer and protofibril structures. The lower binding affinity between Aβ42 trimer chains in the presence of ADH-31 highlights the destabilization of the Aβ42 trimer structure. Overall, MD results highlighted that ADH-31 inhibited Aβ42 aggregation by constraining Aβ peptides into helical conformation and destabilized Aβ42 trimer as well as protofibril structures. The present study provides a theoretical insight into the atomic level details of the inhibitory mechanism of ADH-31 against Aβ42 aggregation as well as protofibril destabilization and could be implemented in the structure-based drug design of potent therapeutic agents for AD.