Discovery of a Novel Binding Trench in HIV Integrase

Advancing Drug-Target Interaction prediction with BERT and subsequence embedding

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for the DTI prediction task on three different benchmark datasets. The experimental results show that the model achieves an average prediction metrics higher than most baseline methods. In order to verify the importance of transfer learning, we conducted an ablation study on datasets, and the results show the superiority of transfer learning. In addition, we test the scalability of the model on the dataset in unseen drugs and proteins, and the results of the experiments show that it is acceptable in scalability.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Conformational dynamics of complement protease C1r inhibitor proteins from Lyme disease- and relapsing fever-causing spirochetes

Borrelial pathogens are vector-borne etiological agents known to cause Lyme disease, relapsing fever, and Borrelia miyamotoi disease. These spirochetes each encode several surface-localized lipoproteins that bind components of the human complement system to evade host immunity. One borrelial lipoprotein, BBK32, protects the Lyme disease spirochete from complement-mediated attack via an alpha helical C-terminal domain that interacts directly with the initiating protease of the classical complement pathway, C1r. In addition, the B. miyamotoi BBK32 orthologs FbpA and FbpB also inhibit C1r, albeit via distinct recognition mechanisms. The C1r-inhibitory activities of a third ortholog termed FbpC, which is found exclusively in relapsing fever-causing spirochetes, remains unknown. Here, we report the crystal structure of the C-terminal domain of Borrelia hermsii FbpC to a limiting resolution of 1.5 Å. We used surface plasmon resonance and assays of complement function to demonstrate that FbpC retains potent BBK32-like anticomplement activities. Based on the structure of FbpC, we hypothesized that conformational dynamics of the complement inhibitory domains of borrelial C1r inhibitors may differ. To test this, we utilized the crystal structures of the C-terminal domains of BBK32, FbpA, FbpB, and FbpC to carry out molecular dynamics simulations, which revealed borrelial C1r inhibitors adopt energetically favored open and closed states defined by two functionally critical regions. Taken together, these results advance our understanding of how protein dynamics contribute to the function of bacterial immune evasion proteins and reveal a surprising plasticity in the structures of borrelial C1r inhibitors.

Worth the weight: Sub-Pocket EXplorer (SubPEx), a weighted-ensemble method to enhance binding-pocket conformational sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly curated dataset of 39 experimentally confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures likely contain cryptic pockets, vastly expanding the potentially druggable proteome.

"Conformational dynamics of C1r inhibitor proteins from Lyme disease and relapsing fever spirochetes"

Borrelial pathogens are vector-borne etiological agents of Lyme disease, relapsing fever, and Borrelia miyamotoi disease. These spirochetes each encode several surface-localized lipoproteins that bind to components of the human complement system. BBK32 is an example of a borrelial lipoprotein that protects the Lyme disease spirochete from complement-mediated attack. The complement inhibitory activity of BBK32 arises from an alpha helical C-terminal domain that interacts directly with the initiating protease of the classical pathway, C1r. Borrelia miyamotoi spirochetes encode BBK32 orthologs termed FbpA and FbpB, and these proteins also inhibit C1r, albeit via distinct recognition mechanisms. The C1r-inhibitory activities of a third ortholog termed FbpC, which is found exclusively in relapsing fever spirochetes, remains unknown. Here we report the crystal structure of the C-terminal domain of B. hermsii FbpC to a limiting resolution of 1.5 Å. Surface plasmon resonance studies and assays of complement function demonstrate that FbpC retains potent BBK32-like anti-complement activities. Based on the structure of FbpC, we hypothesized that conformational dynamics of the complement inhibitory domains of borrelial C1r inhibitors may differ. To test this, we utilized the crystal structures of the C-terminal domains of BBK32, FbpA, FbpB, and FbpC to carry out 1 µs molecular dynamics simulations, which revealed borrelial C1r inhibitors adopt energetically favored open and closed states defined by two functionally critical regions. This study advances our understanding of how protein dynamics contribute to the function of bacterial immune evasion proteins and reveals a surprising plasticity in the structures of borrelial C1r inhibitors.

Transient binding sites at the surface of haloalkane dehalogenase LinB as locations for fine-tuning enzymatic activity

The haloalkane dehalogenase LinB is a well-known enzyme that contains buried active site and is used for many modelling studies. Using classical molecular dynamics simulations of enzymes and substrates, we searched for transient binding sites on the surface of the LinB protein by calculating maps of enzyme-ligand interactions that were then transformed into sparse matrices. All residues considered as functionally important for enzyme performance (e.g., tunnel entrances) were excluded from the analysis to concentrate rather on non-obvious surface residues. From a set of 130 surface residues, twenty-six were proposed as a promising improvement of enzyme performance. Eventually, based on rational selection and filtering out the potentially unstable mutants, a small library of ten mutants was proposed to validate the possibility of fine-tuning the LinB protein. Nearly half of the predicted mutant structures showed improved activity towards the selected substrates, which demonstrates that the proposed approach could be applied to identify non-obvious yet beneficial mutations for enzyme performance especially when obvious locations have already been explored.

Allosteric Modulation of the Main Protease (MPro) of SARS-CoV-2 by Casticin—Insights from Molecular Dynamics Simulations

Inhibition of the main protease (M^pro) of SARS-CoV-2 has been suggested to be vital in shutting down viral replication in a host. Most efforts aimed at inhibiting M^Pro activity have been channeled into competitive inhibition at the active site, but this strategy will require a high inhibitor concentration and impressive inhibitor-M^Pro binding affinity. Allosteric inhibition can potentially serve as an effective strategy for alleviating these limitations. In this study, the ability of antiviral natural products to inhibit M^Pro in an allosteric fashion was explored with in silico techniques. Molecular docking revealed a strong interaction between casticin, an antiviral flavonoid, and M^pro at a site distant from the active site. This site, characterized as a distal site, has been shown to have an interdependent dynamic effect with the active site region. M^pro only, M^pro-peptide (binary) and M^pro-peptide-casticin (ternary) complexes were subjected to molecular dynamics simulations for 50 ns to investigate the modulatory activity of casticin binding on M^pro. Molecular dynamic simulations revealed that binding of casticin at the distal site interferes with the proper orientation of the peptide substrate in the oxyanion hole of the active site, and this could lead to a halt or decrease in catalytic activity. This study therefore highlights casticin as a potential allosteric modulator of the SARS-CoV-2 main protease, which could be optimized and developed into a potential lead compound for anti-SARS-CoV-2 chemotherapy.

Computational modeling and druggability assessment of Aggregatibacter actinomycetemcomitans leukotoxin

The leukotoxin (LtxA) of Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) is a protein exotoxin belonging to the repeat-in-toxin family (RTX). Numerous studies have demonstrated that LtxA may play a critical role in the pathogenicity of A. actinomycetemcomitans since hyper-leukotoxic strains have been associated with severe disease. Accordingly, considerable effort has been made to elucidate the mechanisms by which LtxA interacts with host cells and induce their death. However, these attempts have been hampered by the unavailability of a tertiary structure of the toxin, which limits the understanding of its molecular properties and mechanisms. In this paper, we used homology and template free modeling algorithms to build the complete tertiary model of LtxA at atomic level in its calcium-bound Holo-state. The resulting model was refined by energy minimization, validated by Molprobity and ProSA tools, and subsequently subjected to a cumulative 600ns of all-atom classical molecular dynamics simulation to evaluate its structural aspects. The druggability of the proposed model was assessed using Fpocket and FTMap tools, resulting in the identification of four putative cavities and fifteen binding hotspots that could be targeted by rational drug design tools to find new ligands to inhibit LtxA activity.

An In Silico Study of the Interactions of Alkaloids from Cryptolepis sanguinolenta with Plasmodium falciparum Dihydrofolate Reductase and Dihydroorotate Dehydrogenase

The Plasmodium falciparum dihydrofolate reductase (PfDHFR) and dihydroorotate dehydrogenase (PfDHODH) are essential for Plasmodium falciparum growth and development, and have been validated as targets for the development of new antimalarial agents. Several alkaloids isolated from Cryptolepis sanguinolenta have been reported to have antiplasmodial activity, but their protein targets are unknown. Therefore, molecular docking and molecular dynamics simulations were used to investigate the interactions and stability of the alkaloids with PfDHFR and PfDHODH. Based on physicochemical characteristics, alkaloids were grouped as sterically bulky (sb) or planar (pg). Docking results revealed strong binding affinities (−6.0 to −13.4 kcal/mol) of the alkaloids against PfDHODH and various strains of PfDHFR while interacting with key residues such as Asp54 and Phe58 in PfDHFR. The pg alkaloids had high binding affinity and preference for the inhibitor binding domain over the flavin mononucleotide (FMN) binding domain in PfDHODH due to size considerations. From the molecular dynamics trajectories, protein-alkaloid complexes were stable throughout the simulation, with supporting evidence from root mean square deviations, root mean square fluctuations, radius of gyration, free binding energies, and other parameters. We report herein that biscryptolepine and cryptomisrine (sb class), as well as cryptolepinone, cryptoheptine, cryptolepine, and neocryptolepine (pg class), are capable of inhibiting PfDHFR effectively in pyrimethamine sensitive and resistant cells. Also, our results show that alkaloids of the pg class can inhibit PfDHODH as FMN decoys, as well as direct enzyme inhibitors, thereby halting crucial protein function.

An Overview of Antiviral Peptides and Rational Biodesign Considerations

Viral diseases have contributed significantly to worldwide morbidity and mortality throughout history. Despite the existence of therapeutic treatments for many viral infections, antiviral resistance and the threat posed by novel viruses highlight the need for an increased number of effective therapeutics. In addition to small molecule drugs and biologics, antimicrobial peptides (AMPs) represent an emerging class of potential antiviral therapeutics. While AMPs have traditionally been regarded in the context of their antibacterial activities, many AMPs are now known to be antiviral. These antiviral peptides (AVPs) have been shown to target and perturb viral membrane envelopes and inhibit various stages of the viral life cycle, from preattachment inhibition through viral release from infected host cells. Rational design of AMPs has also proven effective in identifying highly active and specific peptides and can aid in the discovery of lead peptides with high therapeutic selectivity. In this review, we highlight AVPs with strong antiviral activity largely curated from a publicly available AMP database. We then compile the sequences present in our AVP database to generate structural predictions of generic AVP motifs. Finally, we cover the rational design approaches available for AVPs taking into account approaches currently used for the rational design of AMPs.

A cryptic pocket in Ebola VP35 allosterically controls RNA binding

Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola's replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins.

Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8

TLR7 and TLR8 are key members of the Toll-like receptor family, playing crucial roles in the signaling pathways of innate immunity, and thus become attractive therapeutic targets of many diseases including infections and cancer. Although TLR7 and TLR8 show a high degree of sequence homology, their biological response to small molecule binding is very different. Aiming to understand the mechanism of selective profiles of small molecule modulators against TLR7 and TLR8, we carried out molecular dynamic simulations on three imidazoquinoline derivatives bound to the receptors separately. They are Resiquimod (R), Hybrid-2 (H), and Gardiquimod (G), selective agonists of TLR7 and TLR8. Our MD trajectories indicated that in the complex of TLR7-R and TLR7-G, the two chains forming the TLR7 dimer tended to remain “open” conformation, while the rest systems maintained in the closed format. The agonists R, H, and G developed conformational deviation mainly on the aliphatic tail. Furthermore, we attempted to quantify the selectivity between TLR7 and TLR8 by binding free energies via MM-GBSA method. It showed that the three selected modulators were more favorable for TLR7 than TLR8, and the ranking from the strongest to the weakest was H, R and G, aligning well with experimental data. In the TLR7, the flexible and hydrophobic aliphatic side chain of H has stronger van der Waals interactions with V381 and F351 but only pick up interaction with one amino acid residue i.e. Y353 of TLR8. Unsurprisingly, the positively charged side chain of G has less favorable interaction with I585 of TLR7 and V573 of TLR8 explaining G is weak agonist of both TLR7 and TLR8. All three imidazoquinoline derivatives can form stable hydrogen bonds with D555 of TLR7 and the corresponding D543 of TLR8. In brief, the set of total 400ns MD studies sheds light on the potential selectivity mechanisms of agonists towards TLR7 and TLR8, indicating the van der Waals interaction as the driving force for the agonists binding, thus provides us insights for designing more potent and selective modulators to cooperate with the hydrophobic nature of the binding pocket.

Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena

Molecular dynamics simulation is the most popular computational technique for investigating the structural and dynamical behaviour of proteins, in search of the molecular basis of their function. Far from being a completely settled field of research, simulations are still evolving to best capture the essential features of the atomic interactions that govern a protein's inner motions. Modern force fields are becoming increasingly accurate in providing a physical description adequate to this purpose, and allow us to model complex biological systems under fairly realistic conditions. Furthermore, the use of accelerated sampling techniques is improving our access to the observation of progressively larger molecular structures, longer time scales, and more hidden functional events. In this review, the basic principles of molecular dynamics simulations and a number of key applications in the area of protein science are summarized, and some of the most important results are discussed. Examples include the study of the structure, dynamics and binding properties of 'difficult' targets, such as intrinsically disordered proteins and membrane receptors, and the investigation of challenging phenomena like hydration-driven processes and protein aggregation. The findings described provide an overall picture of the current state of this research field, and indicate new perspectives on the road ahead to the upcoming future of molecular simulations.

Accelerated Ligand-Mapping Molecular Dynamics Simulations for the Detection of Recalcitrant Cryptic Pockets and Occluded Binding Sites

The identification and characterization of binding sites is a critical component of structure-based drug design (SBDD). Probe-based/cosolvent molecular dynamics (MD) methods that allow for protein flexibility have been developed to predict ligand binding sites. However, cryptic pockets that appear only upon ligand binding and occluded binding sites with no access to the solvent pose significant challenges to these methods. Here, we report the development of accelerated ligand-mapping MD (aLMMD), which combines accelerated MD with LMMD, for the detection of these challenging binding sites. The method was validated on five proteins with what we term "recalcitrant" cryptic pockets, which are deeply buried pockets that require extensive movement of the protein backbone to expose, and three proteins with occluded binding sites. In all the cases, aLMMD was able to detect and sample the binding sites. Our results suggest that aLMMD could be used as a general approach for the detection of such elusive binding sites in protein targets, thus providing valuable information for SBDD.

Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides

In silico tools, such as molecular docking, are widely applied to study interactions and binding affinity of biological activity of proteins and peptides. However, restricted sampling of both ligand and receptor conformations and use of approximated scoring functions can produce results that do not correlate with actual experimental binding affinities. Molecular dynamics simulations (MDS) can provide valuable information in deciphering functional mechanisms of proteins/peptides and other biomolecules, overcoming the rigid sampling limitations in docking analysis. This review will discuss the information related to the traditional use of in silico models, such as molecular docking, and its application for studying food proteins and bioactive peptides, followed by an in-depth introduction to the theory of MDS and description of why these molecular simulation techniques are important in the theoretical prediction of structural and functional dynamics of food proteins and bioactive peptides. Applications, limitations, and future prospects of MDS will also be discussed.

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a -1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.

Hidden allosteric sites and De-Novo drug design

INTRODUCTION

Hidden allosteric sites are not visible in apo-crystal structures, but they may be visible in holo-structures when a certain ligand binds and maintains the ligand intended conformation. Several computational and experimental techniques have been used to investigate these hidden sites but identifying them remains a challenge.

AREAS COVERED

This review provides a summary of the many theoretical approaches for predicting hidden allosteric sites in disease-related proteins. Furthermore, promising cases have been thoroughly examined to reveal the hidden allosteric site and its modulator.

EXPERT OPINION

In the recent past, with the development in scientific techniques and bioinformatics tools, the number of drug targets for complex human diseases has significantly increased but unfortunately most of these targets are undruggable due to several reasons. Alternative strategies such as finding cryptic (hidden) allosteric sites are an attractive approach for exploitation of the discovery of new targets. These hidden sites are difficult to recognize compared to allosteric sites, mainly due to a lack of visibility in the crystal structure. In our opinion, after many years of development, MD simulations are finally becoming successful for obtaining a detailed molecular description of drug-target interaction.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Opening of a cryptic pocket in β-lactamase increases penicillinase activity

A protein is a shape-shifter, but it is currently unclear which of the many structures a protein can adopt are relevant for its function. Here, we examine conformations that contain a “cryptic” pocket (i.e., a pocket absent in ligand-free structures). Cryptic pockets have potential utility in drug discovery efforts because they provide a means to target “undruggable” proteins (i.e., proteins lacking known pockets) or enhance rather than inhibit protein function. In this study, we use a combination of thiol-labeling and kinetic assays, NMR, and molecular dynamic simulations to identify the function of the Ω-loop cryptic pocket in β-lactamase enzymes. We find that an open pocket population is beneficial for hydrolysis of the substrate benzylpenicillin.

Understanding the functional role of protein-excited states has important implications in protein design and drug discovery. However, because these states are difficult to find and study, it is still unclear if excited states simply result from thermal fluctuations and generally detract from function or if these states can actually enhance protein function. To investigate this question, we consider excited states in β-lactamases and particularly a subset of states containing a cryptic pocket which forms under the Ω-loop. Given the known importance of the Ω-loop and the presence of this pocket in at least two homologs, we hypothesized that these excited states enhance enzyme activity. Using thiol-labeling assays to probe Ω-loop pocket dynamics and kinetic assays to probe activity, we find that while this pocket is not completely conserved across β-lactamase homologs, those with the Ω-loop pocket have a higher activity against the substrate benzylpenicillin. We also find that this is true for TEM β-lactamase variants with greater open Ω-loop pocket populations. We further investigate the open population using a combination of NMR chemical exchange saturation transfer experiments and molecular dynamics simulations. To test our understanding of the Ω-loop pocket’s functional role, we designed mutations to enhance/suppress pocket opening and observed that benzylpenicillin activity is proportional to the probability of pocket opening in our designed variants. The work described here suggests that excited states containing cryptic pockets can be advantageous for function and may be favored by natural selection, increasing the potential utility of such cryptic pockets as drug targets.

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning

One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.

Computational modeling reveals key molecular properties and dynamic behavior of disruptor of telomeric silencing 1-like (DOT1L) and partnering complexes involved in leukemogenesis

Disruptor of telomeric silencing 1‐like (DOT1L) is the only non‐SET domain histone lysine methyltransferase (KMT) and writer of H3K79 methylation on nucleosomes marked by H2B ubiquitination. DOT1L has elicited significant attention because of its interaction or fusion with members of the AF protein family in blood cell biology and leukemogenic transformation. Here, our goal was to extend previous structural information by performing a robust molecular dynamic study of DOT1L and its leukemogenic partners combined with mutational analysis. We show that statically and dynamically, D161, G163, E186, and F223 make frequent time‐dependent interactions with SAM, while additional residues T139, K187, and N241 interact with SAM only under dynamics. Dynamics models reveal DOT1L, SAM, and H4 moving as one and show that more than twice the number of DOT1L residues interacts with these partners, relative to the static structure. Mutational analyses indicate that six of these residues are intolerant to substitution. We describe the dynamic behavior of DOT1L interacting with AF10 and AF9. Studies on the dynamics of a heterotrimeric complex of DOT1L1‐AF10 illuminated describe coordinated motions that impact the relative position of the DOT1L HMT domain to the nucleosome. The molecular motions of the DOT1L–AF9 complex are less extensive and highly dynamic, resembling a swivel‐like mechanics. Through molecular dynamics and mutational analysis, we extend the knowledge previous provided by static measurements. These results are important to consider when describing the biochemical properties of DOT1L, under normal and in disease conditions, as well as for the development of novel therapeutic agents.

Targeting protein-protein interactions in the DNA damage response pathways for cancer chemotherapy

Cellular DNA damage response (DDR) is an extensive signaling network that orchestrates DNA damage recognition, repair and avoidance, cell cycle progression and cell death. DDR alteration is a hallmark of cancer, with the deficiency in one DDR capability often compensated by a dependency on alternative pathways endowing cancer cells with survival and growth advantage. Targeting these DDR pathways has provided multiple opportunities for the development of cancer therapies. Traditional drug discovery has mainly focused on catalytic inhibitors that block enzyme active sites, which limits the number of potential drug targets within the DDR pathways. This review article describes the emerging approach to the development of cancer therapeutics targeting essential protein-protein interactions (PPIs) in the DDR network. The overall strategy for the structure-based design of small molecule PPI inhibitors is discussed, followed by an overview of the major DNA damage sensing, DNA repair, and DNA damage tolerance pathways with a specific focus on PPI targets for anti-cancer drug design. The existing small molecule inhibitors of DDR PPIs are summarized that selectively kill cancer cells and/or sensitize cancers to front-line genotoxic therapies, and a range of new PPI targets are proposed that may lead to the development of novel chemotherapeutics.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Thermodynamic Origin of Differential Excipient-Lysozyme Interactions

Understanding the intricate interplay of interactions between proteins, excipients, ions and water is important to achieve the effective purification and stable formulation of protein therapeutics. The free energy of lysozyme interacting with two kinds of polyanionic excipients, citrate and tripolyphosphate, together with sodium chloride and TRIS-buffer, are analysed in multiple-walker metadynamics simulations to understand why tripolyphosphate causes lysozyme to precipitate but citrate does not. The resulting multiscale decomposition of energy and entropy components for water, sodium chloride, excipients and lysozyme reveals that lysozyme is more stabilised by the interaction of tripolyphosphate with basic residues. This is accompanied by more sodium ions being released into solution from tripolyphosphate than for citrate, whilst the latter instead has more water molecules released into solution. Even though lysozyme aggregation is not directly probed in this study, these different mechanisms are suspected to drive the cross-linking between lysozyme molecules with vacant basic residues, ultimately leading to precipitation.

Mutations in passive residues modulate 3D-structure of NDM (New Delhi metallo-β-lactamase) protein that endue in drug resistance: a MD simulation approach

The ability of antimicrobial resistance developed by bacteria enhanced the complexity of bacterial treatment leading a serious threat to human health. Production of β-lactamase by bacteria that inactivates β-lactam is a generic cause of resistance. One such β-lactamase enzyme is New Delhi Metallo-β-lactamase (NDM) which is recently reported to have clinically more importance and recognized as an antibiotic resistance marker. Mutations in active and passive residues of NDM protein play a fateful role in the substrate and inhibitor specificity. In this study, in silico point mutations of residues near the active site and flexible regions of protein were investigated. Hybrid modelling and molecular dynamics (MD) simulations were carried to build up the mutant models and monitored structural stability. Molecular docking results articulated that mutant proteins had lesser binding affinities with methicillin, oxacillin and doripenem drugs. Further, to scrutinize the structural alterations and rescore the binding energies per-residue basis, MD simulations of wildtype (WT) and mutant (MT) NDM proteins with methicillin, oxacillin and doripenem were performed. Our results demonstrated that mutations in N193A, S217A, G219A and T262A residues led to protein destabilization and amend their binding affinities with methicillin, oxacillin and doripenem. The present study exploited computational approaches which displayed differential binding of drugs with WT and MT NDM proteins that confer resistance to oxacillin and doripenem. The study features the significance of passive residues, thus provides a clue to accelerate the process of designing an ergastic antibiotic against NDM protein. Communicated by Ramaswamy H. Sarma.

Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 main protease complexes reveal the role of lateral pocket in enhancing the ligand affinity

The 2019 novel coronavirus pandemic caused by SARS-CoV-2 remains a serious health threat to humans and there is an urgent need to develop therapeutics against this deadly virus. Recent scientific evidences have suggested that the main protease (Mpro) enzyme in SARS-CoV-2 can be an ideal drug target due to its crucial role in the viral replication and transcription processes. Therefore, there are ongoing research efforts to identify drug candidates against SARS-CoV-2 Mpro that resulted in hundreds of X-ray crystal structures of ligand-bound Mpro complexes in the Protein Data Bank (PDB) describing the interactions of different fragment chemotypes within different sites of the Mpro. In this work, we performed rigorous molecular dynamics (MD) simulation of 62 reversible ligand-Mpro complexes in the PDB to gain mechanistic insights about their interactions at the atomic level. Using a total of over 3 µs long MD trajectories, we characterized different pockets in the apo Mpro structure, and analyzed the dynamic interactions and binding affinity of ligands within those pockets. Our results identified the key residues that stabilize the ligands in the catalytic sites and other pockets of Mpro. Our analyses unraveled the role of a lateral pocket in the catalytic site in Mpro that is critical for enhancing the ligand binding to the enzyme. We also highlighted the important contribution from HIS163 in the lateral pocket towards ligand binding and affinity against Mpro through computational mutation analyses. Further, we revealed the effects of explicit water molecules and Mpro dimerization in the ligand association with the target. Thus, comprehensive molecular-level insights gained from this work can be useful to identify or design potent small molecule inhibitors against SARS-CoV-2 Mpro.

A multiscale coarse-grained model of the SARS-CoV-2 virion

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and ongoing development of a largely "bottom-up" coarse-grained (CG) model of the SARS-CoV-2 virion. Data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data become publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion.

The Picornavirus Precursor 3CD Has Different Conformational Dynamics Compared to 3Cpro and 3Dpol in Functionally Relevant Regions

Viruses have evolved numerous strategies to maximize the use of their limited genetic material, including proteolytic cleavage of polyproteins to yield products with different functions. The poliovirus polyprotein 3CD is involved in important protein-protein, protein-RNA and protein-lipid interactions in viral replication and infection. It is a precursor to the 3C protease and 3D RNA-dependent RNA polymerase, but has different protease specificity, is not an active polymerase, and participates in other interactions differently than its processed products. These functional differences are poorly explained by the known X-ray crystal structures. It has been proposed that functional differences might be due to differences in conformational dynamics between 3C, 3D and 3CD. To address this possibility, we conducted nuclear magnetic resonance spectroscopy experiments, including multiple quantum relaxation dispersion, chemical exchange saturation transfer and methyl spin-spin relaxation, to probe conformational dynamics across multiple timescales. Indeed, these studies identified differences in conformational dynamics in functionally important regions, including enzyme active sites, and RNA and lipid binding sites. Expansion of the conformational ensemble available to 3CD may allow it to perform additional functions not observed in 3C and 3D alone despite having nearly identical lowest-energy structures.

Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations

The use of methods at molecular scale for the discovery of new potential active ligands, as well as previously unknown binding sites for target proteins, is now an established reality. Literature offers many successful stories of active compounds developed starting from insights obtained in silico and approved by Food and Drug Administration (FDA). One of the most famous examples is raltegravir, a HIV integrase inhibitor, which was developed after the discovery of a previously unknown transient binding area thanks to molecular dynamics simulations. Molecular simulations have the potential to also improve the design and engineering of drug delivery devices, which are still largely based on fundamental conservation equations. Although they can highlight the dominant release mechanism and quantitatively link the release rate to design parameters (size, drug loading, et cetera), their spatial resolution does not allow to fully capture how phenomena at molecular scale influence system behavior. In this scenario, the "computational microscope" offered by simulations at atomic scale can shed light on the impact of molecular interactions on crucial parameters such as release rate and the response of the drug delivery device to external stimuli, providing insights that are difficult or impossible to obtain experimentally. Moreover, the new paradigm brought by nanomedicine further underlined the importance of such computational microscope to study the interactions between nanoparticles and biological components with an unprecedented level of detail. Such knowledge is a fundamental pillar to perform device engineering and to achieve efficient and safe formulations. After a brief theoretical background, this review aims at discussing the potential of molecular simulations for the rational design of drug delivery systems.

Comparative modelling unravels the structural features of eukaryotic TCTP implicated in its multifunctional properties: an in silico approach

Comparative modelling helps compare the structure and functions of a given protein, to track the path of its origin and evolution and also guide in structure-based drug discovery. Presently, this has been applied for modelling the tertiary structure of highly conserved eukaryotic TCTP (translationally controlled tumour protein) which is involved in a plethora of functions during growth and development and also acts as a biomarker for many cancers like lung, breast, and prostate cancer. The modelled TCTP structures of different organisms belonging to the eukaryotic group showed similar spatial arrangement of structural units except loops and similar patterns of root mean square deviation (RMSD), root mean square fluctuation, and radius of gyration (Rg) inspected through molecular dynamics simulations. Essential dynamics (ED) analyses revealed different domains that exhibited different motions for the assistance in its multifunctional properties. Construction of a free-energy landscape (FEL) based on Rg versus RMSD was employed to characterize the folding behaviours of structures and observe that all proteins had nearly similar conformation and topologies, indicating common thermodynamic/kinetic pathways. A physico-chemical interaction study demonstrated the helices and sheets were well stabilized with ample amounts of bonding compared to turns or loops and charged residues were more accessible to solvent molecules. Hence, the current study reveals the important structural features of TCTP that aid in diverse functions in a wide range of organisms, thus extending our knowledge of TCTP and also providing a venue for designing the potent inhibitors against it.

Mathematical Multidimensional Modelling and Structural Artificial Intelligence Pipelines Provide Insights for the Designing of Highly Specific AntiSARS-CoV2 Agents

COVID19 is the most impactful pandemic of recent times worldwide. It is a highly infectious disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus), To date there is specific drug nor vaccination against COVID19. Therefor the need for novel and pioneering anti-COVID19 is of paramount importance. In this direction, computer-aided drug design constitutes a very promising antiviral approach for the discovery and analysis of drugs and molecules with biological activity against SARS-CoV2. In silico modelling takes advantage of the massive amounts of biological and chemical data available on the nature of the interactions between the targeted systems and molecules, as well as the rapid progress of computational tools and software. Herein, we describe the potential of the merging of mathematical modelling, artificial intelligence and learning techniques into seamless computational pipelines for the rapid and efficient discovery and design of potent anti- SARS-CoV-2 modulators.

Allosteric Response of DNA Recognition Helices of Catabolite Activator Protein to cAMP and DNA Binding

The homodimeric catabolite activator protein (CAP) regulates the transcription of several bacterial genes based on the cellular concentration of cyclic adenosine monophosphate (cAMP). The binding of cAMP to CAP triggers allosteric communication between the cAMP binding domains (CBD) and DNA binding domains (DBD) of CAP, which entails repositioning of DNA recognition helices (F-helices) in the DBD to dock favorably to the target DNA. Despite considerable progress, much remains to be understood about the mechanistic details of DNA recognition by CAP and about the map of allosteric pathways involved in CAP-mediated gene transcription. The present study uses molecular dynamics and umbrella sampling simulations to investigate the mechanism of cAMP- and DNA-induced changes in the conformation and energetics of F-helices observed during the allosteric regulation of CAP by cAMP and the subsequent binding to the DNA promoter region. Using novel collective variables, the free energy profiles associated with the orientation and dynamics of F-helices in the unliganded, cAMP-bound, and cAMP-DNA-bound states of CAP are calculated and compared. The binding-induced alterations in the resultant free energy profiles reveal important flexibility constraints imposed on DBD upon cAMP and DNA binding. A comprehensive analysis of residue-wise interaction maps reveals potential allosteric pathways between CBD and DBD that facilitate the allosteric transduction of regulatory signals in CAP. The revelation that the predicted allosteric pathways crisscross the intersubunit interface offers important clues on the microscopic origin of the intersubunit cooperativity and dimer stability of CAP.

A Multiscale Coarse-grained Model of the SARS-CoV-2 Virion

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations, however, are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and on-going development of a largely "bottom-up" coarse-grained (CG) model of the SARS-CoV-2 virion. Structural data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data becomes publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion.

Significance Statement

This study reports the construction of a molecular model for the SARS-CoV-2 virion and details our multiscale approach towards model refinement. The resulting model and methods can be applied to and enable the simulation of SARS-CoV-2 virions.

Molecular Docking, Antioxidant, Anticancer and Antileishmanial Effects of Newly Synthesized Quinoline Derivatives

Background:

Due to the pressing need and adverse effects associated with the available anti-cancer agents, an attempt was made to develop the new anti-cancer agents with better activity and lesser adverse effects.

Objective:

Synthetic approaches based on chemical modification of quinoline derivatives have been undertaken with the aim of improving anti-cancer agents’ safety profile.

Methods:

In the present study, quinoline derivatives 6-hydroxy-2-(4-methoxyphenyl) quinoline-4-carboxylic acid (M1) and 2-(4-chlorophenyl)-6-hydroxyquinoline-4-carboxylic acid (M3) were synthesized by the reaction of aldehyde and pyruvic acid. The complete reaction was indicated by thin-layer chromatography. Newly synthesized M1and M3were tested for in silico and in vitro studies.

Results:

M1 and M3 were docked against selected targets. Both the test compounds showed good affinity against all targets except the p300\CBP-associated factor target as there was no H-bond formed by M1. IC50 values of M1 and M3 against 1, 1-diphenyl-picrylhydrazyl free radical scavenging activity were 562 and 136.56ng/mL, respectively. In brine shrimp lethality assay, M1 and M3 showed IC50 value of 81.98 and 139.2ng/mL, respectively. IC50 values recorded for M1 and M3 in tumor inhibition activity were 129 and 219μg/mL, respectively. M1 and M3 exhibited concentration-dependent anti-cancer effects against human cell lines of hepatocellular carcinoma (HepG2) and colon cancer (HCT-116). Against HepG2 cells, M1 and M3 exhibited IC50 of 88.6 and 43.62μg/mL, respectively. M1 and M3 utilized against HCT-116 cell lines possessed IC50 values of 62.5 and 15.3μg/mL. M1 and M3 also showed an anti-leishmanial effect with IC50 values of 336.64 and 530.142μg/mL, respectively.

Conclusion:

From the results of pharmacological studies, we conclude that the newly synthesized compound showed enhanced anti-oxidant, anti-cancer and anti-leishmanial profile with good yield.

The AutoDock suite at 30

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.

A Benzene-Mapping Approach for Uncovering Cryptic Pockets in Membrane-Bound Proteins

Molecular dynamics (MD) simulations in combination with small organic probes present in the solvent have previously been used as a method to reveal cryptic pockets that may not have been identified in experimental structures. We report such a method implemented within the CHARMM force field using the GROMACS simulation package to effectively explore cryptic pockets on the surfaces of membrane-embedded proteins using benzene as a probe molecule. This method, for which we have made implementation files freely available, relies on modified nonbonded parameters in addition to repulsive potentials between membrane lipids and benzene molecules. The method was tested on part of the outer shell of the dengue virus (DENV), for which research into a safe and effective neutralizing antibody or drug molecule is still ongoing. In particular, the envelope (E) protein, associated with the membrane (M) protein, is a lipid membrane-embedded complex which forms a dimer in the mature viral envelope. Solvent mapping was performed for the full, membrane-embedded EM protein complex and compared with similar calculations performed for the isolated, soluble E protein ectodomain dimer in the solvent. Ectodomain-only simulations with benzene exhibited unfolding effects not observed in the more physiologically relevant membrane-associated systems. A cryptic pocket which has been experimentally shown to bind n-octyl-β-d-glucoside detergent was consistently revealed in all benzene-containing simulations. The addition of benzene also enhanced the flexibility and hydrophobic exposure of cryptic pockets at a key, functional interface in the E protein and revealed a novel, potentially druggable pocket that may be targeted to prevent conformational changes associated with viral entry into the cell.

Atomic-scale characterization of mature HIV-1 capsid stabilization by inositol hexakisphosphate (IP6)

Inositol hexakisphosphates (IP6) are cellular cofactors that promote the assembly of mature capsids of HIV. These negatively charged molecules coordinate an electropositive ring of arginines at the center of pores distributed throughout the capsid surface. Kinetic studies indicate that the binding of IP6 increases the stable lifetimes of the capsid by several orders of magnitude from minutes to hours. Using all-atom molecular dynamics simulations, we uncover the mechanisms that underlie the unusually high stability of mature capsids in complex with IP6 We find that capsid hexamers and pentamers have differential binding modes for IP6 Ligand density calculations show three sites of interaction with IP6 including at a known capsid inhibitor binding pocket. Free energy calculations demonstrate that IP6 preferentially stabilizes pentamers over hexamers to enhance fullerene modes of assembly. These results elucidate the molecular role of IP6 in stabilizing and assembling the retroviral capsid.

Introducing Virtual Oligomerization Inhibition to Identify Potent Inhibitors of Aβ Oligomerization

Amyloid-β (Aβ) oligomers are known as the most toxic form of Aβ peptides, and they are a major contributor to Alzheimer's disease. Therefore, developing antagonist screening methods for the formation of Aβ oligomers is urgent and of great interest. In this study, we introduce virtual oligomerization inhibition (VOI), a novel virtual screening protocol that applies atomistic simulation to quantitatively investigate the ability of a ligand in interfering Aβ oligomerization and the formation of Aβ oligomers. Results from the VOI performance on six known inhibitors of Aβ aggregation (brazilin, curcumin, EGCG, ELND005, resveratrol, and tacrine) are in excellent agreement with the results of expensive experiments. Moreover, VOI can reveal the mechanism and kinetics of the inhibition process at the atomistic level. VOI not only improves the efficiency of the antagonist screening for Aβ oligomerization but also reduces the cost of performing the task. Attractively, the principle of VOI can also be applied to inhibitor screening for the aggregation of other amyloid proteins/peptides.

Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor

BACKGROUND

Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A₁ receptor (A₁AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A₁AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A₁AR.

METHODS

Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A₁AR.

RESULTS

Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased.

CONCLUSIONS

Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A₁AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking.

GENERAL SIGNIFICANCE

Ensemble docking is a promising approach for drug discovery targeting flexible receptors.

Investigating Cryptic Binding Sites by Molecular Dynamics Simulations

This Account highlights recent advances and discusses major challenges in investigations of cryptic (hidden) binding sites by molecular simulations. Cryptic binding sites are not visible in protein targets crystallized without a ligand and only become visible crystallographically upon binding events. These sites have been shown to be druggable and might provide a rare opportunity to target difficult proteins. However, due to their hidden nature, they are difficult to find through experimental screening. Computational methods based on atomistic molecular simulations remain one of the best approaches to identify and characterize cryptic binding sites. However, not all methods are equally efficient. Some are more apt at quickly probing protein dynamics but do not provide thermodynamic or druggability information, while others that are able to provide such data are demanding in terms of time and resources. Here, we review the recent contributions of mixed-solvent simulations, metadynamics, Markov state models, and other enhanced sampling methods to the field of cryptic site identification and characterization. We discuss how these methods were able to provide precious information on the nature of the site opening mechanisms, to predict previously unknown sites which were used to design new ligands, and to compute the free energy landscapes and kinetics associated with the opening of the sites and the binding of the ligands. We highlight the potential and the importance of such predictions in drug discovery, especially for difficult ("undruggable") targets. We also discuss the major challenges in the field and their possible solutions.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

A computational study on the role of water and conformational fluctuations in Hsp90 in response to inhibitors

Molecular chaperone Heat Shock Protein 90 (Hsp90) represents an interesting chemotherapeutic target for cancer treatments as it plays a role in cancer proliferation. Thus, continued effort to identify novel inhibitors of this target is an important task. Drug design using computational approach has gained significant attention in recent years. This work aims to propose docking protocols to re-purpose FDA-approved drugs targeting Hsp90. Sensitivity of results to different docking protocols such apo, holo and receptor ensembles (relaxed complex) structures, the role of water and conformational changes of Hsp90, are described. We show that the protein conformation and water have effects on drug binding. Holo relaxed complex receptors ensembles improves the binding energy of ligands to the protein. We also compare and contrast structural stability of three drugs namely: ezetimibe, pitavastatin and vilazodon in the Hsp90 protein. The results obtained serves as a possible basis towards developing Hsp90 inhibitors.