Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description

Toward Understanding the Mechanism of Client-Selective Small Molecule Inhibitors of the Sec61 Translocon

The Sec61 translocon mediates the translocation of numerous, newly synthesized precursor proteins into the lumen of the endoplasmic reticulum or their integration into its membrane. Recently, structural biology revealed conformations of idle or substrate-engaged Sec61, and likewise its interactions with the accessory membrane proteins Sec62, Sec63, and TRAP, respectively. Several natural and synthetic small molecules have been shown to block Sec61-mediated protein translocation. Since this is a key step in protein biogenesis, broad inhibition is generally cytotoxic, which may be problematic for a putative drug target. Interestingly, several compounds exhibit client-selective modes of action, such that only translocation of certain precursor proteins was affected. Here, we discuss recent advances of structural biology, molecular modelling, and molecular screening that aim to use Sec61 as feasible drug target.

Advancing rational pesticide development against Drosophila suzukii: bioinformatics tools and applications-a systematic review

CONTEXT

Drosophila suzukii (Matsumura, 1931) is a widespread agricultural pest responsible for significant damage to various soft-skinned fruit hosts. The revolutionary potential of bioinformatics in agriculture emerges from its ability to provide extensive information on pests, fungi, chemical resistance, implications of non-target species, and other critical aspects. This wealth of information allows researchers to engage in projects and applied research in diverse agricultural domains that face these challenges. In this context, bioinformatics tools play a fundamental role. The negative impact of pests on crops, resulting in substantial economic losses, has highlighted the importance of in silico methods.

METHODS

To achieve this, we conducted a systematic search in scientific databases using as keywords "Drosophila suzukii," "biopesticides," "simulations computational," and "in-silico." After applying the filters of relevance and publication date, we organized the articles and prioritized those that directly addressed that matched the keywords and the use of bioinformatics tools. Additionally, we included studies focusing on in silico assays of biopesticides, such as molecular docking. Our review aimed to present a collection of recent literature on biopesticides against Drosophila suzukii, emphasizing bioinformatics methods. Through this work, we strive to contribute to the literature of new perspectives on the development and efficiency of biopesticides, along with to advance research that may improve pest control strategies.

RESULTS

In the results of the systematic review, we found 2734 articles related to the selected keywords. Six of these articles directly address Drosophila suzukii and the use of bioinformatics tools in the search for alternatives in pest control. In the selected studies, we observed that two articles tend to focus on phylogenetic approaches, searching for gene sequences, amino acids, and constructing phylogenetic trees. The other three articles used molecular modeling and docking of receptors such as GABA and TRP with plant-derived and synthetic compounds to study intermolecular interactions. However, we identified gaps in these studies that could lead to further research in the biorational development of biopesticides using bioinformatics tools.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods

Histone deacetylases (HDACs) are crucial in gene transcription, removing acetyl groups from histones. They also influence the deacetylation of non-histone proteins, contributing to the regulation of various biological processes. Thus, HDACs play pivotal roles in various diseases, including cancer, neurodegenerative disorders, and inflammatory conditions, highlighting their potential as therapeutic targets. This paper reviews the structure and function of the four classes of human HDACs. While four HDAC inhibitors are currently available for treating hematological malignancies, numerous others are undergoing clinical trials. However, their non-selective toxicity necessitates ongoing research into safer and more efficient class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches, such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships, and structure-based virtual screening (molecular docking). Moreover, recent developments in the field of molecular dynamics simulations, combined with Poisson-Boltzmann/molecular mechanics generalized Born surface area techniques, have improved the prediction of ligand binding affinity. In this review, we delve into the ways in which these methods have contributed to designing and identifying HDAC inhibitors.

The Potential of Anti-coronavirus Plant Secondary Metabolites in COVID-19 Drug Discovery as an Alternative to Repurposed Drugs: A Review

In early 2020, a global pandemic was announced due to the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known to cause COVID-19. Despite worldwide efforts, there are only limited options regarding antiviral drug treatments for COVID-19. Although vaccines are now available, issues such as declining efficacy against different SARS-CoV-2 variants and the aging of vaccine-induced immunity highlight the importance of finding more antiviral drugs as a second line of defense against the disease. Drug repurposing has been used to rapidly find COVID-19 therapeutic options. Due to the lack of clinical evidence for the therapeutic benefits and certain serious side effects of repurposed antivirals, the search for an antiviral drug against SARS-CoV-2 with fewer side effects continues. In recent years, numerous studies have included antiviral chemicals from a variety of plant species. A better knowledge of the possible antiviral natural products and their mechanism against SARS-CoV-2 will help to develop stronger and more targeted direct-acting antiviral agents. The aim of the present study was to compile the current data on potential plant metabolites that can be investigated in COVID-19 drug discovery and development. This review represents a collection of plant secondary metabolites and their mode of action against SARS-CoV and SARS-CoV-2.

Drugging the entire human proteome: Are we there yet?

Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.

In the quest for histone deacetylase inhibitors: current trends in the application of multilayered computational methods

Histone deacetylase (HDAC) inhibitors have gained attention over the past three decades because of their potential in the treatment of different diseases including various forms of cancers, neurodegenerative disorders, autoimmune, inflammatory diseases, and other metabolic disorders. To date, 5 HDAC inhibitor drugs are marketed for the treatment of hematological malignancies and several drug-candidate HDAC inhibitors are at different stages of clinical trials. However, due to the toxic side effects of these drugs resulting from the lack of target selectivity, active studies are ongoing to design and develop either class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships (3D-QSAR); and structure-based virtual screening (molecular docking). The current trends involve the application of the combination of these methods and incorporating molecular dynamics simulations coupled with Poisson-Boltzmann/molecular mechanics generalized Born surface area (MM-PBSA/MM-GBSA) to improve the prediction of ligand binding affinity. This review aimed at understanding the current trends in applying these multilayered strategies and their contribution to the design/identification of HDAC inhibitors.

Artificial intelligence: Machine learning approach for screening large database and drug discovery

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.

Molecular docking-based interaction studies on imidazo[1,2-a] pyridine ethers and squaramides as anti-tubercular agents

Development of new anti-tubercular agents is required in the wake of resistance to the existing and newly approved drugs through novel-validated targets like ATP synthase, etc. The major limitation of poor correlation between docking scores and biological activity by SBDD was overcome by a novel approach of quantitatively correlating the interactions of different amino acid residues present in the target protein structure with the activity. This approach well predicted the ATP synthase inhibitory activity of imidazo[1,2-a] pyridine ethers and squaramides (r = 0.84) in terms of Glu65b interactions. Hence, the models were developed on combined (r = 0.78), and training (r = 0.82) sets of 52, and 27 molecules, respectively. The training set model well predicted the diverse dataset (r = 0.84), test set (r = 0.755), and, external dataset (r_ext = 0.76). This model predicted three compounds from a focused library generated by incorporating the essential features of the ATP synthase inhibition with the pIC₅₀ values in the range of 0.0508-0.1494 µM. Molecular dynamics simulation studies ascertain the stability of the protein structure and the docked poses of the ligands. The developed model(s) may be useful in the identification and optimization of novel compounds against TB.

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking

Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.

How good are AlphaFold models for docking-based virtual screening?

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.

Computational and data mining studies to understand the distribution and dynamics of Temoneria (TEM) β-lactamase and their interaction with β-lactam and β-lactamase inhibitors

β-lactams are large group of antibiotics widely used to suppress the bacterial growth by inhibiting cell wall synthesis. Bacterial resistance against β-lactam antibiotics is primarily mediated through the production of Temoneria (TEM) β-lactamase (BLs), with almost 474 variants identified in Lactamase Engineering Database (LacED). The present study aims to develop a model to track the evolution of TEM BLs and their interactions with β-lactam and BLs inhibitors through data mining and computational approaches. Further, the model will be used to predict the effective combinations of β-lactam and BLs inhibitors to treat the bacterial infection harbouring emerging variants of β-lactamase. The molecular docking study results demonstrated that most TEM mutants recorded the least binding energy to penicillin and cephalosporin (I/II/III/IV/V generations) class of antibiotics. On the contrary, the same mutants recorded higher binding energy to carbapenem and Monobactam class of antibiotics. Among the BLs inhibitors, tazobactam recorded the least binding energy against most of the TEM mutants, indicating that it can lower the catalytic activity of TEM BLs, thereby potentiating antibiotic action. Similarly, data mining work has assisted us in creating a database of TEM mutants that has comprehensive data on mutations, bacterial diversity, Km, MIC, and IRT types. It has been noted that earlier released antibiotics like amoxicillin and ampicillin had lower Km and higher MIC values, which indicates the prevalence of bacterial resistance. By analysing the differential binding energy (ΔBE) of the selected TEM mutants against β-lactam and BLs inhibitors, the most effective combination of β-lactam (carbapenem and monobactam class of antibiotics) and BLs inhibitors (tazobactam) was identified, to cure bacterial diseases/infections and to prevent similar antibiotic resistance outbreaks. Therefore, our study opens a new avenue in developing strategies to manage antibiotic resistance in bacteria.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

Characterization of Secondary Structure and Thermal Stability by Biophysical Methods of the D-alanyl,D-alanine Ligase B Protein from Escherichia coli

Background:

Peptidoglycan (PG) is a key structural component of the bacterial cell wall and interruption of its biosynthesis is a validated target for antimicrobials. Of the enzymes involved in PG biosynthesis, D-alanyl,D-alanine ligase B (DdlB), is responsible for the condensation of two alanines, forming D-Ala-D-Ala, which is required for subsequent extracellular transpeptidase crosslinking of the mature peptidoglycan polymer.

Objectives:

We aimed the biophysical characterization of recombinant Escherichia coli DdlB (EcDdlB), regarding parameters of melting temperature (Tm), calorimetry and van’t Hoff enthalpy changes of denaturation ( and ), as well as characterization of elements of secondary structure at three different pHs.

Methods:

DdlB was overexpressed in E. coli BL21 and purified by affinity chromatography. Thermal stability and structural characteristics of the purified enzyme were analyzed by circular dichroism (CD), differential scanning calorimetry and fluorescence spectroscopy.

Results:

The stability of EcDdlB increased with proximity to its pI of 5.0, reaching the maximum at pH 5.4 with Tm and of 52.68 ºC and 484 kJ.mol-1, respectively. Deconvolutions of the CD spectra at 20 ºC showed a majority percentage of α-helix at pH 5.4 and 9.4, whereas for pH 7.4, an equal contribution of β-structures and α-helices was calculated. Thermal denaturation process of EcDdlB proved to be irreversible with an increase in β-structures that can contribute to the formation of protein aggregates.

Conclutions:

Such results will be useful for energy minimization of structural models aimed at virtual screening simulations, providing useful information in the search for drugs that inhibit peptidoglycan synthesis.

Molecular docking-based interactions in QSAR studies on Mycobacterium tuberculosis ATP synthase inhibitors

Tuberculosis (TB) is a global threat with a large burden across the continents in terms of mortality, morbidity, and financial losses. The disease has evolved into multi-drug-resistant (MDR-TB) and extensively drug-resistant (XDR-TB) tuberculosis owing to numerous factors ranging from patients' non-compliance to demographical implications. There have been very few new drugs for resistant TB. Resistance has already been reported even for the newly introduced drug bedaquiline. An attempt has been made to integrate both structure-based and QSAR drug design techniques (QSAR-SBDD) for the identification of novel leads. The docking scores normally do not correlate with the activity. Hence, the docking results have been analysed in terms of the number of interactions rather than docking scores. The parameters derived from interactions have been used in developing the QSAR models. The best model shows a good correlation (r = 0.908) between the activity and interaction parameter 'C' describing the sum of all the interactions with each amino acid residue. This model also predicts external dataset with a good correlation (r_ext = 0.851) and can be used for the identification of novel chemical entities (NCEs) and repurposed drugs for TB therapeutics.

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.

P2Y12 antagonists: Approved drugs, potential naturally isolated and synthesised compounds, and related in-silico studies

P2Y₁₂ is a platelet surface protein which is responsible for the amplification of P2Y₁ response. It plays a crucial role in platelet aggregation and thrombus formation through an ADP-induced platelet activation mechanism. Despite that P2Y₁₂ platelets' receptor is an excellent target for developing antiplatelet agents, only five approved medications are currently in clinical use which are classified into thienopyridines and nucleoside-nucleotide derivatives. In the past years, many attempts for developing new candidates as P2Y₁₂ inhibitors have been made. This review highlights the importance and the role of P2Y₁₂ receptor as part of the coagulation cascade, its reported congenital defects, and the type of assays which are used to verify and measure its activity. Furthermore, an overview is given of the clinically approved medications, the potential naturally isolated inhibitors, and the synthesised candidates which were tested either in-vitro, in-vivo and/or clinically. Finally, we outline the in-silico attempts which were carried out using virtual screening, molecular docking and dynamics simulations in efforts of designing novel P2Y₁₂ antagonists. Various phytochemical classes might be considered as a corner stone for the discovery of novel P2Y₁₂ inhibitors, whereas a wide range of ring systems can be deliberated as leading scaffolds in that area synthetically and theoretically.

Fine tuning for success in structure-based virtual screening

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.

Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.

The new methodology named Pose/Ranking Consensus (PRC) combines both pose and ranking consensus strategies. It displays an enhanced performance in terms of enrichment factor and hit rate, ensuring the recovery of a suitable number of ligands.

Guided structure-based ligand identification and design via artificial intelligence modeling

INTRODUCTION

The implementation of Artificial Intelligence (AI) methodologies to drug discovery (DD) are on the rise. Several applications have been developed for structure-based DD, where AI methods provide an alternative framework for the identification of ligands for validated therapeutic targets, as well as the de novo design of ligands through generative models.

AREAS COVERED

Herein, the authors review the contributions between the 2019 to present period regarding the application of AI methods to structure-based virtual screening (SBVS) which encompasses mainly molecular docking applications - binding pose prediction and binary classification for ligand or hit identification-, as well as de novo drug design driven by machine learning (ML) generative models, and the validation of AI models in structure-based screening. Studies are reviewed in terms of their main objective, used databases, implemented methodology, input and output, and key results .

EXPERT OPINION

More profound analyses regarding the validity and applicability of AI methods in DD have begun to appear. In the near future, we expect to see more structure-based generative models- which are scarce in comparison to ligand-based generative models-, the implementation of standard guidelines for validating the generated structures, and more analyses regarding the validation of AI methods in structure-based DD.

A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics

The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein's druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

Computational identification of potential chemoprophylactic agents according to dynamic behavior of peroxisome proliferator-activated receptor gamma

Peroxisome proliferator-activated receptor gamma (PPARγ) is an attractive target for chemoprevention of lung carcinoma, however its highly dynamic nature has plagued drug development for decades, with difficulties in receptor modeling for structure-based design. In this work, an integrated receptor-based virtual screening (VS) strategy was applied to identify PPARγ agonists as chemoprophylactic agents by using extensive docking and conformational sampling methods. Our results showed that the conformational plasticity of PPARγ, especially the H2 & S245 loop, H2' & Ω loop and AF-2 surface, is markedly affected by binding of full/partial agonists. To fully take the dynamic behavior of PPARγ into account, the VS approach effectively sorts out five commercial agents with reported antineoplastic properties. Among them, ZINC03775146 (gusperimus) and ZINC14087743 (miltefosine) might be novel PPARγ agonists with the potential for chemoprophylaxis, that simultaneously take part in a flexible switch of the AF-2 surface and state change of the Ω loop. Furthermore, the dynamic structural coupling between the H2 & S245 and H2' & Ω loops offers enticing hope for PPARγ-targeted therapeutics, by blocking kinase accessibility to PPARγ. These results might aid the development of chemopreventive drugs, and the integrated VS strategy could be conducive to drug design for highly flexible biomacromolecules.

Artificial intelligence in the early stages of drug discovery

Although the use of computational methods within the pharmaceutical industry is well established, there is an urgent need for new approaches that can improve and optimize the pipeline of drug discovery and development. In spite of the fact that there is no unique solution for this need for innovation, there has recently been a strong interest in the use of Artificial Intelligence for this purpose. As a matter of fact, not only there have been major contributions from the scientific community in this respect, but there has also been a growing partnership between the pharmaceutical industry and Artificial Intelligence companies. Beyond these contributions and efforts there is an underlying question, which we intend to discuss in this review: can the intrinsic difficulties within the drug discovery process be overcome with the implementation of Artificial Intelligence? While this is an open question, in this work we will focus on the advantages that these algorithms provide over the traditional methods in the context of early drug discovery.

Stability of Human Serum Amyloid A Fibrils

In systemic amyloidosis, serum amyloid A (SAA) fibril deposits cause widespread damages to tissues and organs that eventually may lead to death. A therapeutically intervention therefore has either to dissolve these fibrils or inhibit their formation. However, only recently has the human SAA fibril structure been resolved at a resolution that is sufficient for development of drug candidates. Here, we use molecular dynamic simulations to probe the factors that modulate the stability of this fibril model. Our simulations suggest that fibril formation starts with the stacking of two misfolded monomers into metastable dimers, with the stacking depending on the N-terminal amyloidogenic regions of different chains forming anchors. The resulting dimers pack in a second step into a 2-fold two-layer tetramer that is stable enough to nucleate fibril formation. The stability of the initial dimers is enhanced under acidic conditions by a strong salt bridge and side-chain hydrogen bond network in the C-terminal cavity (residues 23-51) but is not affected by the presence of the disordered C-terminal tail.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources

P2 receptors are a family of transmembrane receptors activated by nucleotides and nucleosides. Two classes have been described in mammals, P2X and P2Y, which are implicated in various diseases. Currently, only P2Y12 has medicines approved for clinical use as antiplatelet agents and natural products have emerged as a source of new drugs with action on P2 receptors due to the diversity of chemical structures. In drug discovery, in silico virtual screening (VS) techniques have become popular because they have numerous advantages, which include the evaluation of thousands of molecules against a target, usually proteins, faster and cheaper than classical high throughput screening (HTS). The number of studies using VS techniques has been growing in recent years and has led to the discovery of new molecules of natural origin with action on different P2X and P2Y receptors. Using different algorithms it is possible to obtain information on absorption, distribution, metabolism, toxicity, as well as predictions on biological activity and the lead-likeness of the selected hits. Selected biomolecules may then be tested by molecular dynamics and, if necessary, rationally designed or modified to improve their interaction for the target. The algorithms of these in silico tools are being improved to permit the precision development of new drugs and, in the future, this process will take the front of drug development against some central nervous system (CNS) disorders. Therefore, this review discusses the methodologies of in silico tools concerning P2 receptors, as well as future perspectives and discoveries, such as the employment of artificial intelligence in drug discovery.

Demystifying the Molecular Basis of Pyrazoloquinolinones Recognition at the Extracellular α1+/β3- Interface of the GABAA Receptor by Molecular Modeling

GABA_A receptors are pentameric ligand-gated ion channels that serve as major inhibitory neurotransmitter receptors in the mammalian brain and the target of numerous clinically relevant drugs interacting with different ligand binding sites. Here, we report an in silico approach to investigate the binding of pyrazoloquinolinones (PQs) that mediate allosteric effects through the extracellular α+/β- interface of GABA_A receptors. First, we docked a potent prototype of PQs into the α1+/β3- site of a homology model of the human α1β3γ2 subtype of the GABA_A receptor. Next, for each docking pose, we computationally derived protein-ligand complexes for 18 PQ analogs with known experimental potency. Subsequently, binding energy was calculated for all complexes using the molecular mechanics-generalized Born surface area method. Finally, docking poses were quantitatively assessed in the light of experimental data to derive a binding hypothesis. Collectively, the results indicate that PQs at the α1+/β3- site likely exhibit a common binding mode that can be characterized by a hydrogen bond interaction with β3Q64 and hydrophobic interactions involving residues α1F99, β3Y62, β3M115, α1Y159, and α1Y209. Importantly, our results are in good agreement with the recently resolved cryo-Electron Microscopy structures of the human α1β3γ2 and α1β2γ2 subtypes of GABA_A receptors.

Virtual screening of potentially endocrine-disrupting chemicals against nuclear receptors and its application to identify PPARγ-bound fatty acids

Nuclear receptors (NRs) are key regulators of energy homeostasis, body development, and sexual reproduction. Xenobiotics binding to NRs may disrupt natural hormonal systems and induce undesired adverse effects in the body. However, many chemicals of concerns have limited or no experimental data on their potential or lack-of-potential endocrine-disrupting effects. Here, we propose a virtual screening method based on molecular docking for predicting potential endocrine-disrupting chemicals (EDCs) that bind to NRs. For 12 NRs, we systematically analyzed how multiple crystal structures can be used to distinguish actives and inactives found in previous high-throughput experiments. Our method is based on (i) consensus docking scores from multiple structures at a single functional state (agonist-bound or antagonist-bound), (ii) multiple functional states (agonist-bound and antagonist-bound), and (iii) multiple pockets (orthosteric site and alternative sites) of these NRs. We found that the consensus enrichment from multiple structures is better than or comparable to the best enrichment from a single structure. The discriminating power of this consensus strategy was further enhanced by a chemical similarity-weighted scoring scheme, yielding better or comparable enrichment for all studied NRs. Applying this optimized method, we screened 252 fatty acids against peroxisome proliferator-activated receptor gamma (PPARγ) and successfully identified 3 previously unknown fatty acids with Kd = 100-250 μM including two furan fatty acids: furannonanoic acid (FNA) and furanundecanoic acid (FUA), and one cyclopropane fatty acid: phytomonic acid (PTA). These results suggested that the proposed method can be used to rapidly screen and prioritize potential EDCs for further experimental evaluations.

In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Consensus Ranking

In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, over 21 million people have been infected, with more than 750.000 casualties. Today, no vaccine or antiviral drug is available. While the development of a vaccine might take at least a year, and for a novel drug, even longer; finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a docking-based screening using a quantum mechanical scoring of a library built from approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure-based compound screening, we propose several structurally diverse compounds (either FDA-approved or in clinical trials) that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19.

In silico discovery and biological validation of ligands of FAD synthase, a promising new antimicrobial target

New treatments for diseases caused by antimicrobial-resistant microorganisms can be developed by identifying unexplored therapeutic targets and by designing efficient drug screening protocols. In this study, we have screened a library of compounds to find ligands for the flavin-adenine dinucleotide synthase (FADS) -a potential target for drug design against tuberculosis and pneumonia- by implementing a new and efficient virtual screening protocol. The protocol has been developed for the in silico search of ligands of unexplored therapeutic targets, for which limited information about ligands or ligand-receptor structures is available. It implements an integrative funnel-like strategy with filtering layers that increase in computational accuracy. The protocol starts with a pharmacophore-based virtual screening strategy that uses ligand-free receptor conformations from molecular dynamics (MD) simulations. Then, it performs a molecular docking stage using several docking programs and an exponential consensus ranking strategy. The last filter, samples the conformations of compounds bound to the target using MD simulations. The MD conformations are scored using several traditional scoring functions in combination with a newly-proposed score that takes into account the fluctuations of the molecule with a Morse-based potential. The protocol was optimized and validated using a compound library with known ligands of the Corynebacterium ammoniagenes FADS. Then, it was used to find new FADS ligands from a compound library of 14,000 molecules. A small set of 17 in silico filtered molecules were tested experimentally. We identified five inhibitors of the activity of the flavin adenylyl transferase module of the FADS, and some of them were able to inhibit growth of three bacterial species: C. ammoniagenes, Mycobacterium tuberculosis, and Streptococcus pneumoniae, where the last two are human pathogens. Overall, the results show that the integrative VS protocol is a cost-effective solution for the discovery of ligands of unexplored therapeutic targets.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors

A promising protein target for computational drug development, the human cluster of differentiation 38 (CD38), plays a crucial role in many physiological and pathological processes, primarily through the upstream regulation of factors that control cytoplasmic Ca²⁺ concentrations. Recently, a small-molecule inhibitor of CD38 was shown to slow down pathways relating to aging and DNA damage. We examined the performance of seven docking programs for their ability to model protein-ligand interactions with CD38. A test set of twelve CD38 crystal structures, containing crystallized biologically relevant substrates, were used to assess pose prediction. The rankings for each program based on the median RMSD between the native and predicted were Vina, AD4 > PLANTS, Gold, Glide, Molegro > rDock. Forty-two compounds with known affinities were docked to assess the accuracy of the programs at affinity/ranking predictions. The rankings based on scoring power were: Vina, PLANTS > Glide, Gold > Molegro >> AutoDock 4 >> rDock. Out of the top four performing programs, Glide had the only scoring function that did not appear to show bias towards overpredicting the affinity of the ligand-based on its size. Factors that affect the reliability of pose prediction and scoring are discussed. General limitations and known biases of scoring functions are examined, aided in part by using molecular fingerprints and Random Forest classifiers. This machine learning approach may be used to systematically diagnose molecular features that are correlated with poor scoring accuracy.

Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery

The virtual high-throughput screening (vHTS) approach has been widely used for large database screening to identify potential lead compounds for drug discovery. Due to its high computational demands, docking that allows receptor flexibility has been a challenging problem for virtual screening. Therefore, the selection of protein target conformations is crucial to produce useful vHTS results. Since only a single protein structure is used to screen large databases in most vHTS studies, the main challenge is to reduce false negative rates in selecting compounds for in vitro tests. False negatives are most likely to occur when using apo structures or homology models of protein targets due to the small volume of the binding pocket formed by incorrect side-chain conformations. Even holo protein structures can exhibit high false negative rates due to ligand-induced fit effects, since the shape of the binding pocket highly depends on its bound ligand. To reduce false negative rates and improve success rates for vHTS in drug discovery, we have developed a new Monte Carlo-based approach that optimizes the binding pocket of protein targets. This newly developed Monte Carlo pocket optimization (MCPO) approach was assessed on several datasets showing promising results. The binding pocket optimization approach could be a useful tool for vHTS-based drug discovery, especially in cases when only apo structures or homology models are available.

Development of the first in vivo GPR17 ligand through an iterative drug discovery pipeline: A novel disease-modifying strategy for multiple sclerosis

The GPR17 receptor, expressed on oligodendroglial precursors (OPCs, the myelin producing cells), has emerged as an attractive target for a pro-myelinating strategy in multiple sclerosis (MS). However, the proof-of-concept that selective GPR17 ligands actually exert protective activity in vivo is still missing. Here, we exploited an iterative drug discovery pipeline to prioritize novel and selective GPR17 pro-myelinating agents out of more than 1,000,000 compounds. We first performed an in silico high-throughput screening on GPR17 structural model to identify three chemically-diverse ligand families that were then combinatorially exploded and refined. Top-scoring compounds were sequentially tested on reference pharmacological in vitro assays with increasing complexity, ending with myelinating OPC-neuron co-cultures. Successful ligands were filtered through in silico simulations of metabolism and pharmacokinetics, to select the most promising hits, whose dose and ability to target the central nervous system were then determined in vivo. Finally, we show that, when administered according to a preventive protocol, one of them (named by us as galinex) is able to significantly delay the onset of experimental autoimmune encephalomyelitis (EAE), a mouse model of MS. This outcome validates the predictivity of our pipeline to identify novel MS-modifying agents.

High-Throughput Docking Using Quantum Mechanical Scoring

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

Phillygenin inhibits LPS-induced activation and inflammation of LX2 cells by TLR4/MyD88/NF-κB signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

The traditional Chinese medicine Forsythiae Fructus is the dried fruit of Forsythia suspensa (Thunb.) Vahl. It is commonly used to clear heat and detoxify, reduce swelling and disperse knot, and evacuate wind and heat.

AIM OF THE STUDY

Inflammation is involved in liver fibrosis. Phillygenin (PHI) is a kind of lignans extracted and separated from Forsythiae Fructus, which has been reported to have a good anti-inflammatory effect. Therefore, we aimed to explore whether PHI has a therapeutic effect on liver fibrosis caused by inflammation.

MATERIALS AND METHODS

Firstly, the induction of the LX2 cells inflammatory model and fibrosis model by LPS with different concentrations were studied. Then, high, medium and low doses PHI was given for intervention therapy. The secretion of IL-6, IL-1β and TNF-α inflammatory factors were detected by ELISA kit, and the expression of collagen I and α-SMA was detected by Western blot and RT-qPCR. The possible mechanism of PHI on TLR4/MyD88/NF-κB signal pathway was studied by computer-aided drug design software and the results were further verified by Western blot and RT-qPCR experiments.

RESULTS

The results showed that LPS could promote the expression of IL-6, IL-1β and TNF-α and the expression of collagen I and α-SMA, indicating that LPS could induce inflammation and fibrosis in LX2 cells. PHI could inhibit LX2 cell activation and fibrotic cytokine expression by inhibiting LPS-induced pro-inflammatory reaction. Molecular docking results showed that PHI could successfully dock with TLR4, MyD88, IKKβ, p65, IκBα, and TAK1 proteins. Subsequently, Western blot and qPCR results further proved that PHI could inhibit the proteins expression in TLR4/MyD88/NF-κB signal pathway which were consistent with the molecular docking results.

CONCLUSION

PHI can inhibit LPS-induced pro-inflammatory reaction and LX2 cell activation through TLR4/MyD88/NF-κB signaling pathway, thereby inhibiting liver fibrosis.

Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.

Molecular Docking Using Quantum Mechanical-Based Methods

Computational methods are a powerful and consolidated tool in the early stage of the drug lead discovery process. Among these techniques, high-throughput molecular docking has proved to be extremely useful in identifying novel bioactive compounds within large chemical libraries. In the docking procedure, the predominant binding mode of each small molecule within a target binding site is assessed, and a docking score reflective of the likelihood of binding is assigned to them. These methods also shed light on how a given hit could be modified in order to improve protein-ligand interactions and are thus able to guide lead optimization. The possibility of reducing time and cost compared to experimental approaches made this technology highly appealing. Due to methodological developments and the increase of computational power, the application of quantum mechanical methods to study macromolecular systems has gained substantial attention in the last decade. A quantum mechanical description of the interactions involved in molecular association of biomolecules may lead to better accuracy compared to molecular mechanics, since there are many physical phenomena that cannot be correctly described within a classical framework, such as covalent bond formation, polarization effects, charge transfer, bond rearrangements, halogen bonding, and others, that require electrons to be explicitly accounted for. Considering the fact that quantum mechanics-based approaches in biomolecular simulation constitute an active and important field of research, we highlight in this work the recent developments of quantum mechanical-based molecular docking and high-throughput docking.

Cheminformatics Explorations of Natural Products

The chemistry of natural products is fascinating and has continuously attracted the attention of the scientific community for many reasons including, but not limited to, biosynthesis pathways, chemical diversity, the source of bioactive compounds and their marked impact on drug discovery. There is a broad range of experimental and computational techniques (molecular modeling and cheminformatics) that have evolved over the years and have assisted the investigation of natural products. Herein, we discuss cheminformatics strategies to explore the chemistry and applications of natural products. Since the potential synergisms between cheminformatics and natural products are vast, we will focus on three major aspects: (1) exploration of the chemical space of natural products to identify bioactive compounds, with emphasis on drug discovery; (2) assessment of the toxicity profile of natural products; and (3) diversity analysis of natural product collections and the design of chemical collections inspired by natural sources.

ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance

Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all-against-all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking. In search for a robust approach to protein structure selection, we have come up with an integrated mAchine Learning AnD DockINg approach (ALADDIN). ALADDIN employs a battery of random forest classifiers to select, individually for each compound of interest, from an ensemble of protein structures, the single most suitable protein structure for docking. ALADDIN outperformed the best single-structure docking runs, ensemble docking and a similarity-based docking approach on three out of four investigated targets, with up to 0.15, 0.11 and 0.16 higher area under the receiver operating characteristic curve (AUC) values, respectively. Only in the case of cytochrome P450 3A4, ALADDIN, like any of the other tested approaches, failed to obtain decent performance. ALADDIN can be particularly useful for structure-based virtual screening of malleable proteins, including kinases, some viral enzymes and anti-targets.

Combined approach of homology modeling, molecular dynamics, and docking: computer-aided drug discovery

With the continuous development in software, algorithms, and increase in computer speed, the field of computer-aided drug design has been witnessing reduction in the time and cost of the drug designing process. Structure based drug design (SBDD), which is based on the 3D structure of the enzyme, is helping in proposing novel inhibitors. Although a number of crystal structures are available in various repositories, there are various proteins whose experimental crystallization is difficult. In such cases, homology modeling, along with the combined application of MD and docking, helps in establishing a reliable 3D structure that can be used for SBDD. In this review, we have reported recent works, which have employed these three techniques for generating structures and further proposing novel inhibitors, for cytoplasmic proteins, membrane proteins, and metal containing proteins. Also, we have discussed these techniques in brief in terms of the theory involved and the various software employed. Hence, this review can give a brief idea about using these tools specifically for a particular problem.

Ligand-Binding-Site Structure Refinement Using Molecular Dynamics with Restraints Derived from Predicted Binding Site Templates

Accurate modeling of ligand-binding-site structures plays a critical role in structure-based virtual screening. However, the structures of the ligand-binding site in most predicted protein models are generally of low quality and need refinements. In this work, we present a ligand-binding-site structure refinement protocol using molecular dynamics simulation with restraints derived from predicted binding site templates. Our benchmark validation shows great performance for 40 diverse sets of proteins from the Astex list. The ligand-binding sites on modeled protein structures are consistently refined using our method with an average Cα RMSD improvement of 0.90 Å. Comparison of ligand binding modes from ligand docking to initial unrefined and refined structures shows an average of 1.97 Å RMSD improvement in the refined structures. These results demonstrate a promising new method of structure refinement for protein ligand-binding-site structures.

Emerging Screening Approaches in the Development of Nrf2-Keap1 Protein-Protein Interaction Inhibitors

Due to role of the Keap1–Nrf2 protein–protein interaction (PPI) in protecting cells from oxidative stress, the development of small molecule inhibitors that inhibit this interaction has arisen as a viable approach to combat maladies caused by oxidative stress, such as cancers, neurodegenerative disease and diabetes. To obtain specific and genuine Keap1–Nrf2 inhibitors, many efforts have been made towards developing new screening approaches. However, there is no inhibitor for this target entering the clinic for the treatment of human diseases. New strategies to identify novel bioactive compounds from large molecular databases and accelerate the developmental process of the clinical application of Keap1–Nrf2 protein–protein interaction inhibitors are greatly needed. In this review, we have summarized virtual screening and other methods for discovering new lead compounds against the Keap1–Nrf2 protein–protein interaction. We also discuss the advantages and limitations of different strategies, and the potential of this PPI as a drug target in disease therapy.

AMMOS2: a web server for protein-ligand-water complexes refinement via molecular mechanics

AMMOS2 is an interactive web server for efficient computational refinement of protein-small organic molecule complexes. The AMMOS2 protocol employs atomic-level energy minimization of a large number of experimental or modeled protein-ligand complexes. The web server is based on the previously developed standalone software AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening). AMMOS utilizes the physics-based force field AMMP sp4 and performs optimization of protein-ligand interactions at five levels of flexibility of the protein receptor. The new version 2 of AMMOS implemented in the AMMOS2 web server allows the users to include explicit water molecules and individual metal ions in the protein-ligand complexes during minimization. The web server provides comprehensive analysis of computed energies and interactive visualization of refined protein-ligand complexes. The ligands are ranked by the minimized binding energies allowing the users to perform additional analysis for drug discovery or chemical biology projects. The web server has been extensively tested on 21 diverse protein-ligand complexes. AMMOS2 minimization shows consistent improvement over the initial complex structures in terms of minimized protein-ligand binding energies and water positions optimization. The AMMOS2 web server is freely available without any registration requirement at the URL: http://drugmod.rpbs.univ-paris-diderot.fr/ammosHome.php.

Determinants of Oligonucleotide Selectivity of APOBEC3B

APOBEC3B (A3B) is a prominent source of mutation in many cancers. To date, it has been difficult to capture the native protein-DNA interactions that confer A3B's substrate specificity by crystallography due to the highly dynamic nature of wild-type A3B active site. We use computational tools to restore a recent crystal structure of a DNA-bound A3B C-terminal domain mutant construct to its wild type sequence, and run molecular dynamics simulations to study its substrate recognition mechanisms. Analysis of these simulations reveal dynamics of the native A3Bctd-oligonucleotide interactions, including the experimentally inaccessible loop 1-oligonucleotide interactions. A second series of simulations in which the target cytosine nucleotide was computationally mutated from a deoxyribose to a ribose show a change in sugar ring pucker, leading to a rearrangement of the binding site and revealing a potential intermediate in the binding pathway. Finally, apo simulations of A3B, starting from the DNA-bound open state, experience a rapid and consistent closure of the binding site, reaching conformations incompatible with substrate binding. This study reveals a more realistic and dynamic view of the wild type A3B binding site and provides novel insights for structure-guided design efforts for A3B.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

Discovering New Casein Kinase 1d Inhibitors with an Innovative Molecular Dynamics Enabled Virtual Screening Workflow

The value of including protein flexibility in structure-based drug design (SBDD) is widely documented, and currently, molecular dynamics (MD) simulations represent a powerful tool to investigate protein dynamics. Yet, the inclusion of MD-derived information in pre-existing SBDD workflows is still far from trivial. We recently published an integrated MD-FLAP (Fingerprints for Ligands and Proteins) approach combining MD, clustering and Linear Discriminant Analysis (LDA) for enhancing accuracy, efficacy, and for protein conformational selection in virtual screening (VS) campaigns. Here we prospectively applied the MD-FLAP workflow to discover novel chemotypes inhibiting the Casein Kinase 1 delta (CSNK1D) enzyme. We first obtained a VS model able to separate active from inactive compounds, with a global AUC of 0.9 and a partial ROC enrichment at 0.5% of 0.18, and use it to mine the internal Pfizer screening database. Seven active molecules sharing a phenyl-indazole scaffold, not yet reported among CSNK1D inhibitors, were found. The most potent inhibitor showed an IC₅₀ of 134 nM.

Virtual Screening Meets Deep Learning

BACKGROUND

Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.

OBJECTIVE

This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.