Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

Identification and molecular mechanism of novel hypoglycemic peptide in ripened pu-erh tea: Molecular docking, dynamic simulation, and cell experiments

Ripened pu-erh tea is known to have beneficial hypoglycemic properties. However, it remains unclear whether the bioactive peptides produced during fermentation are also related to hypoglycemic potential. This study aimed to identify hypoglycemic peptides in ripened pu-erh tea and to elucidate their bioactive mechanisms using physicochemical property prediction, molecular docking, molecular dynamics simulations, and cell experiments. Thirteen peptides were identified by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). Among them, AADTDYRFS (AS-9) and AGDGTPYVR (AR-9) exhibited high α-glucosidase inhibitory activity, with half-maximal inhibitory concentration (IC50) values of 0.820 and 3.942 mg/mL, respectively. Molecular docking and dynamics simulations revealed that hydrogen bonding, hydrophobic interactions, and van der Waals forces assist peptides AS-9 and AR-9 in forming stable and tight complexes with α-glucosidase. An insulin-resistance (IR)-HepG2 cell model was established. AS-9 was non-toxic to IR-HepG2 cells and significantly increased the glucose consumption capacity, hexokinase, and pyruvate kinase activities of IR-HepG2 cells (p < 0.05). AS-9 alleviated glucose metabolism disorders and ameliorated IR by activating the IRS-1/PI3K/Akt signaling pathway and increasing the expression levels of MDM2, IRS-1, Akt, PI3K, GLUT4, and GSK3β genes. In addition, no hemolysis of mice red blood cells red blood cells occurred at concentrations below 1 mg/mL. This work first explored hypoglycemic peptides in ripened pu-erh tea, providing novel insights for enhancing its functional value.

Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations

Active learning (AL) is a specific instance of sequential experimental design and uses machine learning to intelligently choose the next data point or batch of molecular structures to be evaluated. In this sense, it closely mimics the iterative design-make-test-analysis cycle of laboratory experiments to find optimized compounds for a given design task. Here, we describe an AL protocol which combines generative molecular AI, using REINVENT, and physics-based absolute binding free energy molecular dynamics simulation, using ESMACS, to discover new ligands for two different target proteins, 3CLpro and TNKS2. We have deployed our generative active learning (GAL) protocol on Frontier, the world's only exa-scale machine. We show that the protocol can find higher-scoring molecules compared to the baseline, a surrogate ML docking model for 3CLpro and compounds with experimentally determined binding affinities for TNKS2. The ligands found are also chemically diverse and occupy a different chemical space than the baseline. We vary the batch sizes that are put forward for free energy assessment in each GAL cycle to assess the impact on their efficiency on the GAL protocol and recommend their optimal values in different scenarios. Overall, we demonstrate a powerful capability of the combination of physics-based and AI methods which yields effective chemical space sampling at an unprecedented scale and is of immediate and direct relevance to modern, data-driven drug discovery.

Sturgeon (Acipenser schrenckii) spinal cord peptides: Antioxidative and acetylcholinesterase inhibitory efficacy and mechanisms

Providing antioxidants and targeting acetylcholinesterase (AChE) are key strategies in treating neurocognitive dysfunction. In this study, bioactive sturgeon (Acipenser schrenckii) spinal cord peptides (SSCPs) with antioxidant and AChE inhibitory potency were extracted and separated from sturgeon spinal cord by enzymatic hydrolysis and ultrafiltration, and targeted peptide PGGW was screened via computer simulated molecular docking. Further, the molecular dynamic interactions of the PGGW with superoxide dismutase (SOD) and AChE were analyzed, and the protective effect of PGGW on glutamate-induced PC12 cells in vitro was evaluated. The <3 kDa fraction of SSCPs displays the most potent antioxidative efficacy (1 mg/mL, DPPH•: 89.07%, ABTS+: 76.35%). Molecular dynamics simulation showed that PGGW was stable within AChE and tightly bound to residues SER203, PHE295, ILE294 and TRP236. When combined with SOD, the indole group of PGGW was stuck inside SOD, but the tail chain PGG fluctuated greatly outside. Surface plasmon resonance demonstrated that PGGW has a high binding affinity for AChE (KD = 1.4 mM) and 0.01 mg/mL PGGW provided good protection against glutamate-induced apoptosis. The findings suggest a promising strategy for drug research on neurodegenerative diseases.

Adsorption of Flavonoids in a Transcriptional Regulator TtgR: Relative Binding Free Energies and Intermolecular Interactions

Antimicrobial resistance in bacteria often arises from their ability to actively identify and expel toxic compounds. The bacterium strain Pseudomonas putida DOT-T1E utilizes its TtgABC efflux pump to confer robust resistance against antibiotics, flavonoids, and organic solvents. This resistance mechanism is intricately regulated at the transcriptional level by the TtgR protein. Through molecular dynamics and alchemical free energy simulations, we systematically examine the binding of seven flavonoids and their derivatives with the TtgR transcriptional regulator. Our simulations reveal distinct binding geometries and free energies for the flavonoids in the active site of the protein, which are driven by a range of noncovalent forces encompassing van der Waals, electrostatic, and hydrogen bonding interactions. The interplay of molecular structures, substituent patterns, and intermolecular interactions effectively stabilizes the bound flavonoids, confining their movements within the TtgR binding pocket. These findings yield valuable insights into the molecular determinants that govern ligand recognition in TtgR and shed light on the mechanism of antimicrobial resistance in P. putida DOT-T1E.

Structure-based computational design of antibody mimetics: challenges and perspectives

The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.

Molecular docking and molecular dynamics simulation of wheat gluten-derived antioxidant peptides acting through the Keap1-Nrf2 pathway

BACKGROUND

In our previous study, we successfully identified five peptides from wheat gluten: Ala-Pro-Ser-Tyr (APSY), Leu-Tyr (LY), Pro-Tyr (PY), Arg-Gly-Gly-Tyr (RGGY) and Tyr-Gln (YQ). Molecular docking and molecular dynamics simulation methods were employed to investigate the interaction between these antioxidant peptides and the Kelch-like ECH-associated protein 1 (Keap1 protein), revealing the molecular mechanism of their non-competitive binding. In addition, the total antioxidant capacity of the five peptides was determined using the 2,2'-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) method.

RESULTS

The affinities of APSY, LY, PY, RGGY and YQ were -8.9, -8.3, -8.5, -9.1 and - 7.9 kcal mol-1, respectively. The five peptides effectively bound to Keap1 protein through hydrogen, π-σ, π-alkyl and alkyl interactions. Significant roles were observed for the P1 pocket residue ARG-415 and the P3 pocket residue ALA-556 in the interactions of the Keap1-peptide complexes. Molecular dynamics simulations further elucidated the dynamic process of peptide binding to the Keap1 protein. All five peptides formed stable complexes with Keap1 protein, with van der Waals forces playing crucial roles in these complex systems, indicative of the peptides' strong binding ability to Keap1 protein. The van der Waals forces were -178.74, -123.11, -134.36, -132.59, and -121.44 kJ mol-1 for the Keap1-APSY, Keap1-LY, Keap1-PY, Keap1-RGGY and Keap1-YQ complexes, respectively. These peptides exhibited excellent antioxidant effects. Among them, the YQ peptide exhibited the highest total antioxidant capacity, with an activity value of 1.18 ± 0.06 mmol Trolox equivalent (TE) L-1 at a concentration of 0.10 mg mL-1. The RGGY, PY, LY and APSY peptides followed in descending order, with activity values of 0.91 ± 0.05, 0.72 ± 0.06, 0.62 ± 0.04 and 0.60 ± 0.05 mmol TE L-1, respectively.

CONCLUSION

These results unveiled the molecular mechanism by which the five antioxidant peptides act on active pockets through the Keap1-Nrf2 signaling pathway, providing a theoretical basis for the development of antioxidants. © 2024 Society of Chemical Industry.

Traversing DNA-Protein Interactions Between Mesophilic and Thermophilic Bacteria: Implications from Their Cold Shock Response

Cold shock proteins (CSPs) are small, acidic proteins which contain a conserved nucleic acid-binding domain. These perform mRNA translation acting as "RNA chaperones" when triggered by low temperatures initiating their cold shock response. CSP- RNA interactions have been predominantly studied. Our focus will be CSP-DNA interaction examination, to analyse the diverse interaction patterns such as electrostatic, hydrogen and hydrophobic bonding in both thermophilic and mesophilic bacteria. The differences in the molecular mechanism of these contrasting bacterial proteins are studied. Computational techniques such as modelling, energy refinement, simulation and docking were operated to obtain data for comparative analysis. The thermostability factors which stabilise a thermophilic bacterium and their effect on their molecular regulation is investigated. Conformational deviation, atomic residual fluctuations, binding affinity, Electrostatic energy and Solvent Accessibility energy were determined during stimulation along with their conformational study. The study revealed that mesophilic bacteria E. coli CSP have higher binding affinity to DNA than thermophilic G. stearothermophilus. This was further evident by low conformation deviation and atomic fluctuations during simulation.

Structural insight into the mechanisms and interacting features of endocrine disruptor Bisphenol A and its analogs with human estrogen-related receptor gamma

Bisphenol A (BPA) is a very important chemical from the commercial perspective. Many useful products are made from it, so its production is increasing day by day. It is widely known that Bisphenol A (BPA) and its analogs are present in the environment and that they enter our body through various routes on a daily basis as we use things made of this chemical in our daily lives. BPA has already been reported to be an endocrine disruptor. Studies have shown that BPA binds strongly to the human estrogen-related receptor gamma (ERRγ) and is an important target of it. This study seeks to understand how it interacts with ERRγ. Molecular docking of BPA and its analogs with ERRγ was performed, and estradiol was taken as a reference. Then, physico-chemical and toxicological analysis of BPA compounds was performed. Subsequently, the dynamic behavior of ERRγ and ERRγ-BPA compound complexes was studied by molecular dynamics simulations over 500 ns, and using this simulated data, their binding energies were again calculated using the MM-PBSA method. We observed that the binding affinity of BPA and its analogs was much higher than that of estradiol, and apart from being toxic, they can be easily absorbed in our body as their physicochemical properties are similar to those of oral medicines. Therefore, this study facilitates the understanding of the structure-activity relationship of ERRγ and BPA compounds and provides information about the key amino acid residues of ERRγ that interact with BPA compounds, which can be helpful to design competitive inhibitors so that we can interrupt the interaction of BPA with ERRγ. In addition, it provides information on BPA and its analogs and will also be helpful in developing new therapeutics.

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Accelerating COVID-19 Drug Discovery with High-Performance Computing

The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has there been such global demand for a therapeutic treatment to be identified as a matter of such urgency. Unfortunately, this is a scenario likely to repeat itself in future, so it is of interest to explore ways in which to accelerate drug discovery at pandemic speed. Computational methods naturally lend themselves to this because they can be performed rapidly if sufficient computational resources are available. Recently, high-performance computing (HPC) technologies have led to remarkable achievements in computational drug discovery and yielded a series of new platforms, algorithms, and workflows. The application of artificial intelligence (AI) and machine learning (ML) approaches is also a promising and relatively new avenue to revolutionize the drug design process and therefore reduce costs. In this review, I describe how molecular dynamics simulations (MD) were successfully integrated with ML and adapted to HPC to form a powerful tool to study inhibitors for four of the COVID-19 target proteins. The emphasis of this review is on the strategy that was used with an explanation of each of the steps in the accelerated drug discovery workflow. For specific technical details, the reader is directed to the relevant research publications.

Contribution of Nischarin/IRAS in CNS development, injury and diseases

BACKGROUND

Murine Nischarin and its human homolog IRAS are scaffold proteins highly expressed in the central nervous system (CNS). Nischarin was initially discovered as a tumor suppressor protein, and recent studies have also explored its potential value in the CNS. Research on IRAS has largely focused on its effect on opioid dependence. Although the role of Nischarin/IRAS in the physiological function and pathological process of the CNS has gradually attracted attention and the related research results are expected to be applied in clinical practice, there is no systematic review of the role and mechanisms of Nischarin/IRAS in the CNS so far.

AIM OF REVIEW

This review will systematically analyze the role and mechanism of Nischarin/IRAS in the CNS, and provide necessary references and possible targets for the treatment of neurological diseases, thereby broadening the direction of Nischarin/IRAS research and facilitating clinical translation.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The pathophysiological processes affected by dysregulation of Nischarin/IRAS expression in the CNS are mainly introduced, including spinal cord injury (SCI), opioid dependence, anxiety, depression, and autism. The molecular mechanisms such as factors regulating Nischarin/IRAS expression and signal transduction pathways regulated by Nischarin/IRAS are systematically summarized. Finally, the clinical application of Nischarin/IRAS has been prospected.

Ensemble-Based Approaches Ensure Reliability and Reproducibility

It is increasingly widely recognized that ensemble-based approaches are required to achieve reliability, accuracy, and precision in molecular dynamics calculations. The purpose of the present article is to address a frequently raised question: what is the optimal way to perform ensemble simulation to calculate quantities of interest?

Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions

Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.

Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle

BACKGROUND

Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance.

RESULTS

In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method.

CONCLUSION

Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

Advancement of Computational Design Drug Delivery System in COVID-19: Current Updates and Future Crosstalk- A Critical update

Positive strides have been achieved in developing vaccines to combat the coronavirus-2019 infection (COVID-19) pandemic. Still, the outline of variations, particularly the most current delta divergent, has posed significant health encounters for people. Therefore, developing strong treatment strategies, such as an anti-COVID-19 medicine plan, may help deal with the pandemic more effectively. During the COVID-19 pandemic, some drug design techniques were effectively used to develop and substantiate relevant critical medications. Extensive research, both experimental and computational, has been dedicated to comprehending and characterizing the devastating COVID-19 disease. The urgency of the situation has led to the publication of over 130,000 COVID-19-related research papers in peer-reviewed journals and preprint servers. A significant focus of these efforts has been the identification of novel drug candidates and the repurposing of existing drugs to combat the virus. Many projects have utilized computational or computer-aided approaches to facilitate their studies. In this overview, we will explore the key computational methods and their applications in the discovery of small-molecule therapeutics for COVID-19, as reported in the research literature. We believe that the true effectiveness of computational tools lies in their ability to provide actionable and experimentally testable hypotheses, which in turn facilitate the discovery of new drugs and combinations thereof. Additionally, we recognize that open science and the rapid sharing of research findings are vital in expediting the development of much-needed therapeutics for COVID-19.

Can Free Energy Perturbation Simulations Coupled with Replica-Exchange Molecular Dynamics Study Ligands with Distributed Binding Sites?

Free energy perturbation coupled with replica exchange with solute tempering (FEP/REST) offers a rigorous approach to compute relative free energy changes for ligands. To determine the applicability of FEP/REST for the ligands with distributed binding poses, we considered two alchemical transformations involving three putative inhibitors I0, I1, and I2 of the Venezuelan equine encephalitis virus nuclear localization signal sequence binding to the importin-α (impα) transporter protein. I0 → I1 and I0 → I2 transformations, respectively, increase or decrease the polarity of the parent molecule. Our objective was three-fold─(i) to verify FEP/REST technical performance and convergence, (ii) to estimate changes in binding free energy ΔΔG, and (iii) to determine the utility of FEP/REST simulations for conformational binding analysis. Our results are as follows. First, our FEP/REST implementation properly follows FEP/REST formalism and produces converged ΔΔG estimates. Due to ligand inherent unbinding, the better FEP/REST strategy lies in performing multiple independent trajectories rather than extending their length. Second, I0 → I1 and I0 → I2 transformations result in overall minor changes in inhibitor binding free energy, slightly strengthening the affinity of I1 and weakening that of I2. Electrostatic interactions dominate binding interactions, determining the enthalpic changes. The two transformations cause opposite entropic changes, which ultimately govern binding affinities. Importantly, we confirm the validity of FEP/REST free energy estimates by comparing them with our previous REST simulations, directly probing binding of three ligands to impα. Third, we established that FEP/REST simulations can sample binding ensembles of ligands. Thus, FEP/REST can be applied (i) to study the energetics of the ligand binding without defined poses and showing minor differences in affinities |ΔΔG| ≲ 0.5 kcal/mol and (ii) to collect ligand binding conformational ensembles.

Structure and dynamics of an archetypal DNA nanoarchitecture revealed via cryo-EM and molecular dynamics simulations

DNA can be folded into rationally designed, unique, and functional materials. To fully realise the potential of these DNA materials, a fundamental understanding of their structure and dynamics is necessary, both in simple solvents as well as more complex and diverse anisotropic environments. Here we analyse an archetypal six-duplex DNA nanoarchitecture with single-particle cryo-electron microscopy and molecular dynamics simulations in solvents of tunable ionic strength and within the anisotropic environment of biological membranes. Outside lipid bilayers, the six-duplex bundle lacks the designed symmetrical barrel-type architecture. Rather, duplexes are arranged in non-hexagonal fashion and are disorted to form a wider, less elongated structure. Insertion into lipid membranes, however, restores the anticipated barrel shape due to lateral duplex compression by the bilayer. The salt concentration has a drastic impact on the stability of the inserted barrel-shaped DNA nanopore given the tunable electrostatic repulsion between the negatively charged duplexes. By synergistically combining experiments and simulations, we increase fundamental understanding into the environment-dependent structural dynamics of a widely used nanoarchitecture. This insight will pave the way for future engineering and biosensing applications.

The nucleotide excision repair proteins through the lens of molecular dynamics simulations

Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.

Targeting the PEDV 3CL protease for identification of small molecule inhibitors: an insight from virtual screening, ADMET prediction, molecular dynamics, free energy landscape, and binding energy calculations

BACKGROUND

The porcine epidemic diarrhea virus (PEDV) represents a major health issue for piglets worldwide and does significant damage to the pork industry. Thus, new therapeutic approaches are urgently needed to manage PEDV infections. Due to the current lack of a reliable remedy, this present study aims to identify novel compounds that inhibit the 3CL protease of the virus involved in replication and pathogenesis.

RESULTS

To identify potent antiviral compounds against the 3CL protease, a virtual screening of natural compounds (n = 97,999) was conducted. The top 10 compounds were selected based on the lowest binding energy and the protein-ligand interaction analyzed. Further, the top five compounds that demonstrated a strong binding affinity were subjected to drug-likeness analysis using the ADMET prediction, which was followed by molecular dynamics simulations (500 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method. Based on these parameters, four putative lead (ZINC38167083, ZINC09517223, ZINC04339983, and ZINC09517238) compounds were identified that represent potentially effective inhibitors of the 3CL protease.

CONCLUSION

Therefore, these can be utilized for the development of novel antiviral drugs against PEDV. However, this requires further validation through in vitro and in vivo studies.

In Silico Analyses of a Promising Drug Candidate for the Treatment of Amyotrophic Lateral Sclerosis Targeting Superoxide Dismutase I Protein

Amyotrophic lateral sclerosis (ALS) is the most prevalent motor neuron disorder in adults, which is associated with a highly disabling condition. To date, ALS remains incurable, and the only drugs approved by the FDA for its treatment confer a limited survival benefit. Recently, SOD1 binding ligand 1 (SBL-1) was shown to inhibit in vitro the oxidation of a critical residue for SOD1 aggregation, which is a central event in ALS-related neurodegeneration. In this work, we investigated the interactions between SOD1 wild-type and its most frequent variants, i.e., A4V (NP_000445.1:p.Ala5Val) and D90A (NP_000445.1:p.Asp91Val), with SBL-1 using molecular dynamics (MD) simulations. The pharmacokinetics and toxicological profile of SBL-1 were also characterized in silico. The MD results suggest that the complex SOD1-SBL-1 remains relatively stable and interacts within a close distance during the simulations. This analysis also suggests that the mechanism of action proposed by SBL-1 and its binding affinity to SOD1 may be preserved upon mutations A4V and D90A. The pharmacokinetics and toxicological assessments suggest that SBL-1 has drug-likeness characteristics with low toxicity. Our findings, therefore, suggested that SBL-1 may be a promising strategy to treat ALS based on an unprecedented mechanism, including for patients with these frequent mutations.

Computational Methods for Molecular Understanding of the Antibiotic-Aminoacyl tRNA Synthetase Interaction

Developing an understanding of the interactions between an antibiotic and its binding site in a pathogen cell is the key to antibiotic design-an important cost-saving methodology compared to the costly and time-consuming random trial-and-error approach. The rapid development of antibiotic resistance provides an impetus for such studies. Recent years have witnessed the beginning of the use of combined computational techniques, including computer simulations and quantum mechanical computations, to understand how antibiotics bind at the active site of aminoacyl tRNA synthetases (aaRSs) from pathogens. Such computational protocols assist the knowledge-based design of antibiotics targeting aaRSs, which are their validated targets. After the ideas behind the protocols and their strategic planning are discussed, the protocols are described along with their major outcomes. This is followed by an integration of results from the different basic protocols. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Analysis of active-site residues from primary sequence of synthetase and transfer RNAs Basic Protocol 2: Molecular dynamics simulation-based protocol to study the structure and dynamics of the aaRS active site:antibiotic complex Basic Protocol 3: Quantum mechanical method-based protocol to study the structure and dynamics of the aaRS active site:antibiotic complex.

Virtual screening of natural products inspired in-house library to discover potential lead molecules against the SARS-CoV-2 main protease

SARS-CoV-2, a new coronavirus emerged in 2019, causing a global healthcare epidemic. Although a variety of drug targets have been identified as potential antiviral therapies, and effective candidate against SARS-CoV-2 remains elusive. One of the most promising targets for combating COVID-19 is SARS-CoV-2 Main protease (M^pro, a protein responsible for viral replication. In this work, an in-house curated library was thoroughly evaluated for druggability against M^pro. We identified four ligands (FG, Q5, P5, and PJ4) as potential inhibitors based on docking scores, predicted binding energies (MMGBSA), in silico ADME, and RMSD trajectory analysis. Among the selected ligands, FG, a natural product from Andrographis nallamalayana, exhibited the highest binding energy of -10.31 kcal/mol close to the docking score of clinical candidates Boceprevir and GC376. Other ligands (P5, natural product from cardiospermum halicacabum and two synthetic molecules Q5 and PJ4) have shown comparable docking scores ranging -7.65 kcal/mol to -7.18 kcal/mol. Interestingly, we found all four top ligands had Pi bond interaction with the main amino acid residues HIS41 and CYS145 (catalytic dyad), H-bonding interactions with GLU166, ARG188, and GLN189, and hydrophobic interactions with MET49 and MET165 in the binding site of M^pro. According to the ADME analysis, Q5 and P5 are within the acceptable range of drug likeliness, compared to FG and PJ4. The interaction stability of the lead molecules with viral protease was verified using replicated MD simulations. Thus, the present study opens up the opportunity of developing drug candidates targeting SARS-CoV-2 main protease (M^pro) to mitigate the disease.Communicated by Ramaswamy H. Sarma.

Identifying Drug Candidates for COVID-19 with Large-Scale Drug Screening

Papain-like protease (PL^pro) is critical to COVID-19 infection. Therefore, it is a significant target protein for drug development. We virtually screened a 26,193 compound library against the PL^pro of SARS-CoV-2 and identified several drug candidates with convincing binding affinities. The three best compounds all had better estimated binding energy than those of the drug candidates proposed in previous studies. By analyzing the docking results for the drug candidates identified in this and previous studies, we demonstrate that the critical interactions between the compounds and PL^pro proposed by the computational approaches are consistent with those proposed by the biological experiments. In addition, the predicted binding energies of the compounds in the dataset showed a similar trend as their IC₅₀ values. The predicted ADME and drug-likeness properties also suggested that these identified compounds can be used for COVID-19 treatment.

Impact of Ambient Vapors on Spectra of 4-Nitroaniline Recorded under Atmospheric Solids Analysis Probe (ASAP) Mass Spectrometric Conditions

Thermally desorbed 4-nitroaniline (4-NA), upon atmospheric pressure chemical ionization (APCI), generates gaseous ions for its protonated species. The APCI mass spectrum recorded under mild in-source ion-activating conditions from 4-NA showed a peak at m/z 139, whereas that acquired under high ion-activating conditions showed two additional peaks at m/z 122 (^•OH loss) and 92 (^•NO loss). The spectrum changed instantaneously when acetonitrile vapor was introduced to the source. In the new spectrum, both m/z 122 and 92 peaks were absent, while a new peak appeared at m/z 93. Ion-mobility separation carried out with the m/z 139 ion revealed that the initial ion represented the thermodynamically favored nitro-protonated tautomer. The ion population changed to an ensemble dominated by the less-favored amino-protomer when acetonitrile vapor was introduced to the ion source. The amino-protomer, upon collisional activation, loses ^•NO₂ to generate an m/z 93 ion, which was confirmed to be the 4-dehydroanilinium ion. Ion mobility provided a practical way to monitor the changes secured by acetonitrile vapor because the two protomers showed different arrival times. Under spray-ionization conditions, the formation of the thermodynamically less favored protomer has been attributed to kinetic trapping. Our study demonstrated that the less favored amino-protomer could be generated by introducing acetonitrile vapor under nonspray conditions. Apparently, under APCI conditions, protonated water vapor attaches to the nitro group to generate a proton-bound heterodimer, which upon activation dissociates to yield the nitro-protomer. In contrast, protonated acetonitrile makes a tighter complex preferentially with the amino group, which upon activation breaks to the amino-protomer.

TIES 2.0: A Dual-Topology Open Source Relative Binding Free Energy Builder with Web Portal

Relative binding free energy (RBFE) calculations are widely used to aid the process of drug discovery. TIES, Thermodynamic Integration with Enhanced Sampling, is a dual-topology approach to RBFE calculations with support for NAMD and OpenMM molecular dynamics engines. The software has been thoroughly validated on publicly available datasets. Here we describe the open source software along with a web portal (https://ccs-ties.org) that enables users to perform such calculations correctly and rapidly.

Binding affinity estimation from restrained umbrella sampling simulations

The protein-ligand binding affinity quantifies the binding strength between a protein and its ligand. Computer modeling and simulations can be used to estimate the binding affinity or binding free energy using data- or physics-driven methods or a combination thereof. Here we discuss a purely physics-based sampling approach based on biased molecular dynamics simulations. Our proposed method generalizes and simplifies previously suggested stratification strategies that use umbrella sampling or other enhanced sampling simulations with additional collective-variable-based restraints. The approach presented here uses a flexible scheme that can be easily tailored for any system of interest. We estimate the binding affinity of human fibroblast growth factor 1 to heparin hexasaccharide based on the available crystal structure of the complex as the initial model and four different variations of the proposed method to compare against the experimentally determined binding affinity obtained from isothermal titration calorimetry experiments.

Accuracy and precision of binding free energy prediction for a tacrine related lead inhibitor of acetylcholinesterase with an arsenal of supercomputerized molecular modelling methods: a comparative study

Nowadays, advanced computational chemistry methods offer various strategies for revealing prospective hit structures in drug development essentially through accurate binding free energy predictions. After the era of molecular docking and quantitative structure-activity relationships, much interest has been lately oriented to perturbed molecular dynamic approaches like replica exchange with solute tempering and free energy perturbation (REST/FEP) and the potential of the mean force with adaptive biasing and accelerated weight histograms (PMF/AWH). Both of these receptor-based techniques can exploit exascale CPU&GPU supercomputers to achieve high throughput performance. In this fundamental study, we have compared the predictive power of a panel of supercomputerized molecular modelling methods to distinguish the major binding modes and the corresponding binding free energies of a promising tacrine related potential antialzheimerics in human acetylcholinesterase. The binding free energies were estimated using flexible molecular docking, molecular mechanics/generalized Born surface area/Poisson-Boltzmann surface area (MM/GBSA/PBSA), transmutation REST/FEP with 12 x 5 ns/λ windows, annihilation FEP with 20 x 5 ns/λ steps, PMF with weight histogram analysis method (WHAM) and 40 x 5 ns samples, and PMF/AWH with 10 x 100 ns replicas. Confrontation of the classical approaches such as canonical molecular dynamics and molecular docking with alchemical calculations and steered molecular dynamics enabled us to show how large errors in ΔG predictions can be expected if these in silico methods are employed in the elucidation of a common case of enzyme inhibition.Communicated by Ramaswamy H. Sarma.

General Theory of Specific Binding: Insights from a Genetic-Mechano-Chemical Protein Model

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. However, despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics, and genetics and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination-by varying degrees of flexibility and shape/chemistry complementarity-but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces, and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

Interaction of miR-155 with Human Serum Albumin: An Atomic Force Spectroscopy, Fluorescence, FRET, and Computational Modelling Evidence

This study investigated the interaction between Human Serum Albumin (HSA) and microRNA 155 (miR-155) through spectroscopic, nanoscopic and computational methods. Atomic force spectroscopy together with static and time-resolved fluorescence demonstrated the formation of an HSA/miR-155 complex characterized by a moderate affinity constant (KA in the order of 104 M−1). Förster Resonance Energy Transfer (FRET) experiments allowed us to measure a distance of (3.9 ± 0.2) nm between the lone HSA Trp214 and an acceptor dye bound to miR-155 within such a complex. This structural parameter, combined with computational docking and binding free energy calculations, led us to identify two possible models for the structure of the complex, both characterized by a topography in which miR-155 is located within two positively charged pockets of HSA. These results align with the interaction found for HSA and miR-4749, reinforcing the thesis that native HSA is a suitable miRNA carrier under physiological conditions for delivering to appropriate targets.

Therapeutic Potential of a Novel Vitamin D3 Oxime Analogue, VD1-6, with CYP24A1 Enzyme Inhibitory Activity and Negligible Vitamin D Receptor Binding

The regulation of vitamin D3 actions in humans occurs mainly through the Cytochrome P450 24-hydroxylase (CYP24A1) enzyme activity. CYP24A1 hydroxylates both 25-hydroxycholecalciferol (25(OH)D3) and 1,25-dihydroxycholecalciferol (1,25(OH)2D3), which is the first step of vitamin D catabolism. An abnormal status of the upregulation of CYP24A1 occurs in many diseases, including chronic kidney disease (CKD). CYP24A1 upregulation in CKD and diminished activation of vitamin D3 contribute to secondary hyperparathyroidism (SHPT), progressive bone deterioration, and soft tissue and cardiovascular calcification. Previous studies have indicated that CYP24A1 inhibition may be an effective strategy to increase endogenous vitamin D activity and decrease SHPT. This study has designed and synthesized a novel C-24 O-methyloxime analogue of vitamin D3 (VD1-6) to have specific CYP24A1 inhibitory properties. VD1-6 did not bind to the vitamin D receptor (VDR) in concentrations up to 10−7 M, assessed by a VDR binding assay. The absence of VDR binding by VD1-6 was confirmed in human embryonic kidney HEK293T cultures through the lack of CYP24A1 induction. However, in silico docking experiments demonstrated that VD1-6 was predicted to have superior binding to CYP24A1, when compared to that of 1,25(OH)2D3. The inhibition of CYP24A1 by VD1-6 was also evident by the synergistic potentiation of 1,25(OH)2D3-mediated transcription and reduced 1,25(OH)2D3 catabolism over 24 h. A further indication of CYP24A1 inhibition by VD1-6 was the reduced accumulation of the 24,25(OH)D3 , the first metabolite of 25(OH)D catabolism by CYP24A1. Our findings suggest the potent CYP24A1 inhibitory properties of VD1-6 and its potential for testing as an alternative therapeutic candidate for treating SHPT.

The performance of ensemble-based free energy protocols in computing binding affinities to ROS1 kinase

Optimization of binding affinities for compounds to their target protein is a primary objective in drug discovery. Herein we report on a collaborative study that evaluates a set of compounds binding to ROS1 kinase. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to rank the binding free energies. The predicted binding free energies from ESMACS simulations show good correlations with experimental data for subsets of the compounds. Consistent binding free energy differences are generated for TIES and ESMACS. Although an unexplained overestimation exists, we obtain excellent statistical rankings across the set of compounds from the TIES protocol, with a Pearson correlation coefficient of 0.90 between calculated and experimental activities.

Investigating Multi-Target Antiviral Compounds by Screening of Phytochemicals From Neem (Azadirachta indica) Against PRRSV: A Vetinformatics Approach

Porcine reproductive and respiratory syndrome virus (PRRSV) is a global health problem for pigs. PRRSV is highly destructive and responsible for significant losses to the swine industry. Vaccines are available but incapable of providing adequate and long-term protection. As a result, effective and safe strategies are urgently needed to combat the virus. The scavenger receptor cysteine-rich domain 5 (SRCR5) in porcine CD163, non-structural protein 4 (Nsp4), and Nsp10 are known to play significant roles in PRRSV infection and disease development. Therefore, we targeted these proteins to identify multi-target antiviral compounds. To identify potent inhibitors, molecular docking of neem phytochemicals was conducted; three compounds [7-deacetyl-7-oxogedunin (CID:1886), Kulactone (CID:15560423), and Nimocin (CASID:104522-76-1)] were selected based on the lowest binding energy and multi-target inhibitory nature. The efficacy and safety of the selected compounds were revealed through the pharmacokinetics analysis and toxicity assessment. Moreover, 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability and dynamic behavior of target proteins and their docked complexes with selected compounds. Besides, molecular mechanics Poisson–Boltzmann surface area method was used to estimate the binding free energy of each protein-ligand complex obtained from the MD simulations and validate the affinities of selected compounds to target proteins. Based on our analysis, we concluded that the identified multi-target compounds can be utilized as lead compounds for the development of natural drugs against PRRSV. If further validated in clinical studies, these compounds can be used individually or in combination against the virus.

Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat a myriad of diseases. In this work, we examine the computation of alchemical relative binding free energies with an eye for assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2, and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2, and NAMD3 alpha. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between them of 0.50 kcal/mol. The correlation between packages is very good, with the lowest Spearman's, Pearson's and Kendall's tau correlation coefficients being 0.92, 0.91, and 0.76, respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

Improving the Potential of Mean Force and Nonequilibrium Pulling Simulations by Simultaneous Alchemical Modifications

We present an approach combining alchemical modifications and physical-pathway methods to calculate absolute binding free energies. The employed physical-pathway method is either a stratified umbrella sampling to calculate a potential of mean force or nonequilibrium pulling. We devised two basic approaches: the simultaneous approach (S-approach), where, along the physical unbinding pathway, an alchemical transformation of ligand-protein interactions is installed and deinstalled, and the prior-plus-simultaneous approach (PPS-approach), where, prior to the physical-pathway simulation, an alchemical transformation of ligand-protein interactions is installed in the binding site and deinstalled during the physical-pathway simulation. Using a mutant of T4 lysozyme with a benzene ligand as an example, we show that installation and deinstallation of soft-core interactions concurrent with physical ligand unbinding (S-approach) allow successful potential of mean force calculations and nonequilibrium pulling simulations despite the problems posed by the occluded nature of the lysozyme binding pocket. Good agreement between the potential of the mean-force-based S-approach and double decoupling simulations as well as a remarkable efficiency and accuracy of the nonequilibrium-pulling-based S-approach is found. The latter turned out to be more compute-efficient than the potential of mean force calculation by approximately 70%. Furthermore, we illustrate the merits of reducing ligand-protein interactions prior to potential of mean force calculations using the murine double minute homologue protein MDM2 with a p53-derived peptide ligand (PPS-approach). Here, the problem of breaking strong interactions in the binding pocket is transferred to a prior alchemical transformation that reduces the free-energy barrier between the bound and unbound state in the potential of mean force. Besides, disentangling physical ligand displacement from the deinstallation of ligand-protein interactions was seen to allow a more uniform sampling of distance histograms in the umbrella sampling. In the future, physical ligand unbinding combined with simultaneous alchemical modifications may prove useful in the calculation of protein-protein binding free energies, where sampling problems posed by multiple, possibly sticky interactions and potential steric clashes can thus be reduced.

MutCov: A pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), is a major threat to public health worldwide. Previous studies have shown that the spike protein of SARS-CoV-2 determines viral infectivity and major antigenicity. However, the spike protein has been undergoing various mutations, which bring a great challenge to the prevention and treatment of COVID-19. Here we present the MutCov, a pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2 by calculating the binding free energy between spike protein and angiotensin-converting enzyme 2 (ACE2) or neutralizing monoclonal antibody (mAb). The predicted infectivity and antigenicity were highly consistent with biologically experimental results, and demonstrated that the MutCov achieved good prediction performance. In conclusion, the MutCov is of high importance for systematically evaluating the effect of novel mutations and improving the prevention and treatment of COVID-19. The source code and installation instruction of MutCov are freely available at http://jianglab.org.cn/MutCov.

In silico analyses of acetylcholinesterase (AChE) and its genetic variants in interaction with the anti-Alzheimer drug Rivastigmine

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Despite causing great social and economic impact, there is currently no cure for AD. The most effective therapy to manage AD symptoms is based on acetylcholinesterase inhibitors (AChEi), from which rivastigmine presented numerous benefits. However, mutations in AChE, which affect approximately 5% of the population, can modify protein structure and function, changing the individual response to Alzheimer's treatment. In this study, we performed computer simulations of AChE wild type and variants R34Q, P135A, V333E, and H353N, identified by one or more genome-wide association studies, to evaluate their effects on protein structure and interaction with rivastigmine. The functional effects of AChE variants were predicted using eight machine learning algorithms, while the evolutionary conservation of AChE residues was analyzed using the ConSurf server. Autodock4.2.6 was used to predict the binding modes for the hAChE-rivastigmine complex, which is still unknown. Molecular dynamics (MD) simulations were performed in triplicates for the AChE wild type and mutants using the GROMACS packages. Among the analyzed variants, P135A was classified as deleterious by all the functional prediction algorithms, in addition to occurring at highly conserved positions, which may have harmful consequences on protein function. The molecular docking results suggested that rivastigmine interacts with hAChE at the upper active-site gorge, which was further confirmed by MD simulations. Our MD findings also suggested that the complex hAChE-rivastigmine remains stable over time. The essential dynamics revealed flexibility alterations at the active-site gorge upon mutations P135A, V333E, and H353N, which may lead to strong and nonintuitive consequences to hAChE binding. Nonetheless, similar binding affinities were registered in the MMPBSA analysis for the hAChE wild type and variants when complexed to rivastigmine. Finally, our findings indicated that the rivastigmine binding to hAChE is an energetically favorable process mainly driven by negatively charged amino acids.

Ensemble Simulations and Experimental Free Energy Distributions: Evaluation and Characterization of Isoxazole Amides as SMYD3 Inhibitors

Optimization of binding affinities for ligands to their target protein is a primary objective in rational drug discovery. Herein, we report on a collaborative study that evaluates various compounds designed to bind to the SET and MYND domain-containing protein 3 (SMYD3). SMYD3 is a histone methyltransferase and plays an important role in transcriptional regulation in cell proliferation, cell cycle, and human carcinogenesis. Experimental measurements using the scintillation proximity assay show that the distributions of binding free energies from a large number of independent measurements exhibit non-normal properties. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to predict the binding free energies and to provide a detailed chemical insight into the nature of ligand-protein binding. Our results show that the 1-trajectory ESMACS protocol works well for the set of ligands studied here. Although one unexplained outlier exists, we obtain excellent statistical ranking across the set of compounds from the ESMACS protocol and good agreement between calculations and experiments for the relative binding free energies from the TIES protocol. ESMACS and TIES are again found to be powerful protocols for the accurate comparison of the binding free energies.

Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

The accurate and reliable prediction of protein-ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling). Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behavior. We find that the "enhanced sampling" method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure, and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

How Do Modulators Affect the Orthosteric and Allosteric Binding Pockets?

Allosteric modulators (AMs) that bind allosteric sites can exhibit greater selectivity than the orthosteric ligands and can either enhance agonist-induced receptor activity (termed positive allosteric modulator or PAM), inhibit agonist-induced activity (negative AM or NAM), or have no effect on activity (silent AM or SAM). Until now, it is not clear what the exact effects of AMs are on the orthosteric active site or the allosteric binding pocket(s). In the present work, we collected both the three-dimensional (3D) structures of receptor-orthosteric ligand and receptor-orthosteric ligand-AM complexes of a specific target protein. Using our novel algorithm toolset, molecular complex characterizing system (MCCS), we were able to quantify the key residues in both the orthosteric and allosteric binding sites along with potential changes of the binding pockets. After analyzing 21 pairs of 3D crystal or cryo-electron microscopy (cryo-EM) complexes, including 4 pairs of GPCRs, 5 pairs of ion channels, 11 pairs of enzymes, and 1 pair of transcription factors, we found that the binding of AMs had little impact on both the orthosteric and allosteric binding pockets. In return, given the accurately predicted allosteric binding pocket(s) of a drug target of medicinal interest, we can confidently conduct the virtual screening or lead optimization without concern that the huge conformational change of the pocket could lead to the low accuracy of virtual screening.

Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies

BACKGROUND

Porcine reproductive and respiratory syndrome respiratory sickness in weaned and growing pigs, as well as sow reproductive failure, and its infection is regarded as one of the most serious swine illnesses worldwide. Given the current lack of an effective treatment, in this study, we identified natural compounds capable of inhibiting non-structural protein 4 (Nsp4) of the virus, which is involved in their replication and pathogenesis.

RESULTS

We screened natural compounds (n = 97,999) obtained from the ZINC database against Nsp4 and selected the top 10 compounds for analysing protein-ligand interactions and physicochemical properties. The five compounds demonstrating strong binding affinity were then subjected to molecular dynamics simulations (100 ns) and binding free energy calculations. Based on analysis, we identified four possible lead compounds that represent potentially effective drug-like inhibitors.

CONCLUSIONS

These methods identified that these natural compounds are capable of inhibiting Nsp4 and possibly effective as antiviral therapeutics against PRRSV.

Computational Simulations Highlight the IL2Rα Binding Potential of Polyphenol Stilbenes from Fenugreek

Widely used in global households, fenugreek is well known for its culinary and medicinal uses. The various reported medicinal properties of fenugreek are by virtue of the different natural phytochemicals present in it. Regarded as a promising target, interleukin 2 receptor subunit alpha (IL2Rα) has been shown to influence immune responses. In the present research, using in silico techniques, we have demonstrated the potential IL2Rα binding properties of three polyphenol stilbenes (desoxyrhaponticin, rhaponticin, rhapontigenin) from fenugreek. As the first step, molecular docking was performed to assess the binding potential of the fenugreek phytochemicals with IL2Rα. All three phytochemicals demonstrated interactions with active site residues. To confirm the reliability of our molecular docking results, 100 ns molecular dynamics simulations studies were undertaken. As discerned by the RMSD and RMSF analyses, IL2Rα in complex with the desoxyrhaponticin, rhaponticin, and rhapontigenin indicated stability. The RMSD analysis of the phytochemicals alone also demonstrated no significant structural changes. Based on the stable molecular interactions and comparatively slightly better MM/PBSA binding free energy, rhaponticin seems promising. Additionally, ADMET analysis performed for the stilbenes indicated that all of them obey the ADMET rules. Our computational study thus supports further in vitro IL2Rα binding studies on these stilbenes, especially rhaponticin.

Thermodynamic and structural insights into the repurposing of drugs that bind to SARS-CoV-2 main protease

Although researchers have been working tirelessly since the COVID-19 outbreak, so far only three drugs - remdesivir, ronapreve and molnupiravir - have been approved for use in some countries which directly target the SARS-CoV-2 virus. Given the slow pace and substantial costs of new drug discovery and development, together with the urgency of the matter, repurposing of existing drugs for the ongoing disease is an attractive proposition. In a recent study, a high-throughput X-ray crystallographic screen was performed for a selection of drugs which have been approved or are in clinical trials. Thirty-seven compounds have been identified from drug libraries all of which bind to the SARS-CoV-2 main protease (3CL^pro). In the current study, we use molecular dynamics simulation and an ensemble-based free energy approach, namely, enhanced sampling of molecular dynamics with approximation of continuum solvent (ESMACS), to investigate a subset of the aforementioned compounds. The drugs studied here are highly diverse, interacting with different binding sites and/or subsites of 3CL^pro. The predicted free energies are compared with experimental results wherever they are available and they are found to be in excellent agreement. Our study also provides detailed energetic insights into the nature of the associated drug-protein binding, in turn shedding light on the design and discovery of potential drugs.

Computational anti-COVID-19 drug design: progress and challenges

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Impact of Drug Repurposing on SARS-Cov-2 Main Protease

The recent emergence of the severe acute respiratory disease caused by a novel coronavirus remains a concern posing many challenges to public health and the global economy. The resolved crystal structure of the main protease of SARS-CoV-2 or SCV2 (M^pro) has led to its identification as an attractive target for designing potent antiviral drugs. Herein, we provide a comparative molecular impact of hydroxychloroquine (HCQ), remdesivir, and β-D-N⁴-Hydroxycytidine (NHC) binding on SCV2 M^pro using various computational approaches like molecular docking and molecular dynamics (MD) simulation. Data analyses showed that HCQ, remdesivir, and NHC binding to SARS-CoV-2 M^pro decrease the protease loop capacity to fluctuate. These binding influences the drugs’ optimum orientation in the conformational space of SCV2 M^pro and produce noticeable steric effects on the interactive residues. An increased hydrogen bond formation was observed in SCV2 M^pro–NHC complex with a decreased receptor residence time during NHC binding. The binding mode of remdesivir to SCV2 M^pro differs from other drugs having van der Waals interaction as the force stabilizing protein–remdesivir complex. Electrostatic interaction dominates in the SCV2 M^pro−HCQ and SCV2 M^pro–NHC. Residue Glu166 was highly involved in the stability of remdesivir and NHC binding at the SCV2 M^pro active site, while Asp187 provides stability for HCQ binding.

Three-Dimensional Modeling of Thyroid Hormone Metabolites Binding to the Cancer-Relevant αvβ3 Integrin: In-Silico Based Study

BACKGROUND

Thyroid hormones (TH), T4 and T3, mediate pro-mitogenic effects in cancer cells through binding the membrane receptor αvβ3 integrin. The deaminated analogue tetrac effectively blocks TH binding to this receptor and prevents their action. While computational data on TH binding to the αvβ3 integrin was published, a comprehensive analysis of additional TH metabolites is lacking.

METHODS

In-silico docking of 26 TH metabolites, including the biologically active thyroid hormones (T3 and T4) and an array of sulfated, deiodinated, deaminated or decarboxylated metabolites, to the αvβ3 receptor binding pocket was performed using DOCK6, based on the three-dimensional representation of the crystallographic structure of the integrin. As the TH binding site upon the integrin is at close proximity to the well-defined RGD binding site, linear and cyclic RGD were included as a reference. Binding energy was calculated for each receptor-ligand complex using Grid score and Amber score with distance movable region protocol.

RESULTS

All TH molecules demonstrated negative free energy, suggesting affinity to the αvβ3 integrin. Notably, based on both Grid and Amber scores sulfated forms of 3,3' T2 (3,3' T2S) and T4 (T4S) demonstrated the highest binding affinity to the integrin, compared to both cyclic RGD and an array of examined TH metabolites. The major thyroid hormones, T3 and T4, showed high affinity to the integrin, which was superior to that of linear RGD. For all hormone metabolites, decarboxylation led to decreased affinity. This corresponds with the observation that the carboxylic group mediates binding to the integrin pocket via divalent cations at the metal-ion-dependent adhesion (MIDAS) motif site. A similar reduced affinity was documented for deaminated forms of T3 (triac) and T4 (tetrac). Lastly, the reverse forms of T3, T3S, and T3AM showed higher Amber scores relative to their native form, indicating that iodination at position 5 is associated with increased binding affinity compared to position 5'.

SUMMARY

Three-dimensional docking of various TH metabolites uncovered a structural basis for a differential computational free energy to the αvβ3 integrin. These findings may suggest that naturally occurring endogenous TH metabolites may impact integrin-mediate intracellular pathways in physiology and cancer.