Docking-based virtual screening studies aiming at the covalent inhibition of SARS-CoV-2 MPro by targeting the cysteine 145

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 which has infected millions of people worldwide. The main protease of SARS-CoV-2 (M^Pro) has been recognized as a key target for the development of antiviral compounds. Taking advantage of the X-ray crystal complex with reversible covalent inhibitors interacting with the catalytic cysteine 145 (Cys145), we explored flexible docking studies to select alternative compounds able to target this residue as covalent inhibitors. First, docking studies of three known electrophilic compounds led to results consistent with co-crystallized data validating the method for SARS-CoV-2 M^Pro covalent inhibition. Then, libraries of soft electrophiles (overall 41 757 compounds) were submitted to docking-based virtual screening resulting in the identification of 17 molecules having their electrophilic group close to the Cys145 residue. We also investigated flexible docking studies of a focused approved covalent drugs library including 32 compounds with various electrophilic functional groups. Among them, the calculations resulted in the identification of four compounds, namely dimethylfumarate, fosfomycin, ibrutinib and saxagliptin, able first, to bind to the active site of the protein and second, to form a covalent bond with the catalytic cysteine.     

An anchor-dependent molecular docking process for docking small flexible molecules into rigid protein receptors

A molecular docking method designated as ADDock, anchor- dependent molecular docking process for docking small flexible molecules into rigid protein receptors, is presented in this article. ADDock makes the bond connection lists for atoms based on anchors chosen for building molecular structures for docking small flexible molecules or ligands into rigid active sites of protein receptors. ADDock employs an extended version of piecewise linear potential for scoring the docked structures. Since no translational motion for small molecules is implemented during the docking process, ADDock searches the best docking result by systematically changing the anchors chosen, which are usually the single-edge connected nodes or terminal hydrogen atoms of ligands. ADDock takes intact ligand structures generated during the docking process for computing the docked scores; therefore, no energy minimization is required in the evaluation phase of docking. The docking accuracy by ADDock for 92 receptor-ligand complexes docked is 91.3%. All these complexes have been docked by other groups using other docking methods. The receptor-ligand steric interaction energies computed by ADDock for some sets of active and inactive compounds selected and docked into the same receptor active sites are apparently separated. These results show that based on the steric interaction energies computed between the docked structures and receptor active sites, ADDock is able to separate active from inactive compounds for both being docked into the same receptor.     

Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists

Identification of novel compound classes for a drug target is a challenging task for cheminformatics and drug design when considerable research has already been undertaken and many potent lead structures have been identified, which leaves limited unclaimed chemical space for innovation. We validated and successfully applied different state-of-the-art techniques for virtual screening (Bayesian machine learning, automated molecular docking, pharmacophore search, pharmacophore QSAR and shape analysis) of 4.6 million unique and readily available chemical structures to identify promising new and competitive antagonists of the strychnine-insensitive Glycine binding site (Glycine_B site) of the NMDA receptor. The novelty of the identified virtual hits was assessed by scaffold analysis, putting a strong emphasis on novelty detection. The resulting hits were tested in vitro and several novel, active compounds were identified. While the majority of the computational methods tested were able to partially discriminate actives from structurally similar decoy molecules, the methods differed substantially in their prospective applicability in terms of novelty detection. The results demonstrate that although there is no single best computational method, it is most worthwhile to follow this concept of focused compound library design and screening, as there still can new bioactive compounds be found that possess hitherto unexplored scaffolds and interesting variations of known chemotypes.     

Optimization of high throughput virtual screening by combining shape-matching and docking methods

Receptor flexibility is a critical issue in structure-based virtual screening methods. Although a multiple-receptor conformation docking is an efficient way to account for receptor flexibility, it is still too slow for large molecular libraries. It was reported that a fast ligand-centric, shape-based virtual screening was more consistent for hit enrichment than a typical single-receptor conformation docking. Thus, we designed a "distributed docking" method that improves virtual high throughput screening by combining a shape-matching method with a multiple-receptor conformation docking. Database compounds are classified in advance based on shape similarities to one of the crystal ligands complexed with the target protein. This classification enables us to pick the appropriate receptor conformation for a single-receptor conformation docking of a given compound, thereby avoiding time-consuming multiple docking. In particular, this approach utilizes cross-docking scores of known ligands to all available receptor structures in order to optimize the algorithm. The present virtual screening method was tested for reidentification of known PPARgamma and p38 MAP kinase active compounds. We demonstrate that this method improves the enrichment while maintaining the computation speed of a typical single-receptor conformation docking.     

Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets

Virtual screening by molecular docking has become a widely used approach to lead discovery in the pharmaceutical industry when a high-resolution structure of the biological target of interest is available. The performance of three widely used docking programs (Glide, GOLD, and DOCK) for virtual database screening is studied when they are applied to the same protein target and ligand set. Comparisons of the docking programs and scoring functions using a large and diverse data set of pharmaceutically interesting targets and active compounds are carried out. We focus on the problem of docking and scoring flexible compounds which are sterically capable of docking into a rigid conformation of the receptor. The Glide XP methodology is shown to consistently yield enrichments superior to the two alternative methods, while GOLD outperforms DOCK on average. The study also shows that docking into multiple receptor structures can decrease the docking error in screening a diverse set of active compounds.     

Combinatorial library-based design with Basis Products

Uncovering useful lead compounds from a vast virtual library of synthesizable compounds continues to be of tremendous interest to pharmaceutical researchers. Here we present the concept of Basis Products (BPs), a new and broadly applicable method for achieving efficient selections from a combinatorial library. By definition, Basis Products are a strategically selected subset of compounds from a potentially very large combinatorial library, and any compound in a combinatorial library can represented by its BPs. In this article we will show how to use BP docking scores to find the top compounds of a combinatorial library. Compared with the brute-force docking of an entire virtual library, docking with BPs are much more efficient because of the substantial size reduction, saving both time and resources. We will also demonstrate how BPs can be used for property-based combinatorial library designs. Furthermore, BPs can also be considered as fragments carrying chemistry knowledge, hence they can potentially be used in combination with any fragment-based design method. Therefore, BPs can be used to integrate combinatorial design with structure-based design and/or fragment-based design. Other potential applications of BPs include lead hopping and consensus core building, which we will describe briefly as well in this report.     

A novel frequency distribution selection method for efficient plate layout of a diverse combinatorial library.

A deterministic method (frequency distribution method) for selecting compounds from a partitioned virtual combinatorial library for efficient synthesis is presented here. The method is based on reagent frequency analysis and can be applied to any library of molecules distributed in any given partitioned chemical space (cluster, cell-based, etc.). Compound selection by reagent frequency distribution can produce a unique, diverse set of molecules that adequately represents the library while requiring the least amount of compounds to be synthesized and minimizing the number of different reagents that must be used. This method also provides a practical solution to the configuration of plate layout. Because the method essentially identifies "expensive" regions in the chemical space to synthesize for a desired diversity or similarity coverage, decisions concerning the necessity to synthesize these compounds can be addressed. Minimum compound generation and efficient plate layout results in savings both in time of synthesis and cost of materials. This method always results in a discrete solution, which can be used for any given library size as well as any combination of reagents and is also readily adaptable to robotic automation.     

The in silico identification of potent anti-cancer agents by targeting the ATP binding site of the N-domain of HSP90

To identify new HSP90 inhibitors, the ATP binding site of the N-domain of HSP90 was targeted by molecular docking of a library of 23,129,083 compounds (from the ZINC database) to the ATP binding site of the N-domain of HSP90. Structure-based virtual screen (SBVS) was performed using idock software on the istar web platform. Based on idock binding energies, 40 molecules were considered as HSP90 inhibitors. In the next step, the 40 molecules and the compound AT13387 (Onalespib) were docked to the XJX binding site using AutoDock Vina software. By comparing the binding energies of the 40 molecules selected with compound AT13387, 26 molecules were selected. By applying the rule of five, eight molecules were selected as hit compounds. The interactions of these eight compounds with the XJX binding site were obtained and investigated, and two-dimensional interaction maps were provided for the others. Finally, computing the toxicity of these compounds with the ProTox-II webserver shows that three compounds, namely ZINC89453765, ZINC23918431 and ZINC12414793, can be considered as good HSP90 inhibitors. These compounds are inactive for nuclear receptor signalling and stress response pathways including heat shock response, so do not have the limitations of common HSP90 inhibitors. They are also inactive for hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity and cytotoxicity.     

An efficient in silico screening method based on the protein-compound affinity matrix and its application to the design of a focused library for cytochrome P450 (CYP) ligands

A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds. Each compound was depicted as a point in the PCA space. Compounds that were close to the known active compounds were selected as candidate hit compounds. A machine-learning technique optimized the docking scores of the protein-compound affinity matrix to maximize the database enrichment of the known active compounds, providing an optimized focused library.     

HIV蛋白酶抑制剂——利迪链菌素的分子对接研究

用分子对接的方法, 对利迪链菌素的抗HIV蛋白酶活性进行了研究. 为了更准确地反映利迪链菌素分子与酶蛋白结合的情况, 充分考虑受体活性部位的柔性, 采用了FlexX(初步对接)和Flexidock(精确对接)分两步将配体与受体进行对接. 在初步对接中, 设计了不同的受体活性部位来考察是否有结合水分子参与抑制剂与酶的结合. 对一种作用方式已知的非肽类HIV蛋白酶抑制剂Aha006进行的对接研究显示, 分子模拟的结果与实际情况吻合得较好, 证明了本文所采用的方法的可靠性. 利迪链菌素与蛋白酶活性部位的对接结果显示, 配体分子与受体之间的结合没有结合水分子的参与, 两者通过5对氢键作用结合成为稳定的复合物. 利迪链菌素占据结合腔, 覆盖了蛋白酶的活性三联体Asp25-Thr26-Gly27, 从而起到抑制其生物活性的作用.     

A critical evaluation of several global optimization algorithms for the purpose of molecular docking

Several global optimization algorithms were applied to the problem of molecular docking: random walk and Metropolis Monte Carlo Simulated Annealing as references, and Stochastic Approximation with Smoothing (SAS), and Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) as new methodologies. Of particular interest is whether any of these algorithms could be used to dock a database of typical small molecules in a reasonable amount of time. To address this question, each algorithm was used to dock four small molecules presenting a wide range of sizes, degrees of flexibility, and types of interactions. Of the algorithms tested, only stochastic approximation with smoothing appeared to be sufficiently fast and reliable to be useful for database searches. This algorithm can reliably dock relatively small and fairly rigid molecules in a few seconds, and larger and more flexible molecules in a few minutes. The remaining algorithms tested were able to reliably dock the small and fairly rigid molecules, but showed little or no reliability when docking large or flexible molecules. In addition, to decrease the error in the typical grid-based energy evaluations a new form of interpolation, logarithmic interpolation, is proposed. This interpolation scheme is shown to both quantitatively reduce the numerical error and practically to improve the docking results. © 1999 John Wiley & Sons, Inc. J Comput Chem 20: 1740–1751, 1999     

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 Å for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.     

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.     

Virtual screening and rational drug design method using structure generation system based on 3D-QSAR and docking

An efficient virtual and rational drug design method is presented. It combines virtual bioactive compound generation with 3D-QSAR model and docking. Using this method, it is possible to generate a lot of highly diverse molecules and find virtual active lead compounds. The method was validated by the study of a set of anti-tumor drugs. With the constraints of pharmacophore obtained by DISCO implemented in SYBYL 6.8, 97 virtual bioactive compounds were generated, and their anti-tumor activities were predicted by CoMFA. Eight structures with high activity were selected and screened by the 3D-QSAR model. The most active generated structure was further investigated by modifying its structure in order to increase the activity. A comparative docking study with telomeric receptor was carried out, and the results showed that the generated structures could form more stable complexes with receptor than the reference compound selected from experimental data. This investigation showed that the proposed method was a feasible way for rational drug design with high screening efficiency.     

Functional analysis of repositioned anilide derivatives as anticancer compounds

Off the different types cancers 40% of the population have been observed to be affected by leukemia. Contemporary therapeutics is focusing on generation of new synthetic analogues that can exert maximum positive physiological effect with minimum dosage and negligible deleterious side effects. New generation pharmacists are focusing on such promising effects of Imatinib (a potential anti-cancer drug molecule), Dasatinib, Pelitinib and Nilotinib. The present research study focuses on novel synthesized anilides derivative against BCR-ABL kinase as potential anti-leukemic agent. Validation of the compounds by molecular docking with specific BCR-ABL kinase confirmed their activity. Toxicity prediction of these compounds helped to identify sustainability as therapeutic molecules. The IC₅₀ values were calculated (211 ug, 175 ug, 272ug for compounds A, B, C resp.) and the mode of cell death was gauged by DNA laddering assay. The cells were observed to be induced for programmed cell death. By validating and in-vivo testing of three synthesized compounds, the compound B was observed to be more stable thermodynamically with a potentially vital active site and appears to be a promising anti-leukemic factor. The present research thus lays a preliminary platform in world of pharmaceutics, where these new analogues appear to be efficient, target specific and less toxic molecules.     

Ligand-protein docking with water molecules

The presence of water molecules plays an important role in the accuracy of ligand-protein docking predictions. Comprehensive docking simulations have been performed on a large set of ligand-protein complexes whose crystal structures contain water molecules in their binding sites. Only those water molecules found in the immediate vicinity of both the ligand and the protein were considered. We have investigated whether prior optimization of the orientation of water molecules in either the presence or absence of the bound ligand has any effect on the accuracy of docking predictions. We have observed a statistically significant overall increase in accuracy when water molecules are included during docking simulations and have found this to be independent of the method of optimization of the orientation of water molecules. These results confirm the importance of including water molecules whenever possible in a ligand-protein docking simulation. Our findings also reveal that prior optimization of the orientation of water molecules, in the absence of any bound ligand, does not have a detrimental effect on the improved accuracy of ligand-protein docking. This is important, given the use of docking simulations to predict the binding modes of new ligands or drug molecules.     

Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling

Cytochrome P450 2D6 (CYP2D6) is used to develop an approach for predicting affinity and relevant binding conformation(s) for highly flexible binding sites. The approach combines the use of docking scores and compound properties as attributes in building a neural network (NN) model. It begins by identifying segments of CYP2D6 that are important for binding specificity, based on structural variability among diverse CYP enzymes. A family of distinct, low-energy conformations of CYP2D6 are generated using simulated annealing (SA) and a collection of 82 compounds with known CYP2D6 affinities are docked. Interestingly, docking poses are observed on the backside of the heme as well as in the known active site. Docking scores for the active site binders, along with compound-specific attributes, are used to train a neural network model to properly bin compounds as strong binders, moderate binders, or nonbinders. Attribute selection is used to preselect the most important scores and compound-specific attributes for the model. A prediction accuracy of 85+/-6% is achieved. Dominant attributes include docking scores for three of the 20 conformations in the ensemble as well as the compound's formal charge, number of aromatic rings, and AlogP. Although compound properties were highly predictive attributes (12% improvement over baseline) in the NN-based prediction of CYP2D6 binders, their combined use with docking score attributes is synergistic (net increase of 23% above baseline). Beyond prediction of affinity, attribute selection provides a way to identify the most relevant protein conformation(s), in terms of binding competence. In the case of CYP2D6, three out of the ensemble of 20 SA-generated structures are found to be the most predictive for binding.     

Structural Insights and Docking Analysis of Adamantane-Linked 1,2,4-Triazole Derivatives as Potential 11β-HSD1 Inhibitors

The solid-state structural analysis and docking studies of three adamantane-linked 1,2,4-triazole derivatives are presented. Crystal structure analyses revealed that compound 2 crystallizes in the triclinic P-1 space group, while compounds 1 and 3 crystallize in the same monoclinic P2₁/c space group. Since the only difference between them is the para substitution on the aryl group, the electronic nature of these NO₂ and halogen groups seems to have no influence over the formation of the solid. However, a probable correlation with the size of the groups is not discarded due to the similar intermolecular disposition between the NO₂/Cl substituted molecules. Despite the similarities, CE-B3LYP energy model calculations show that pairwise interaction energies vary between them, and therefore the total packing energy is affected. HOMO-LUMO calculated energies show that the NO₂ group influences the reactivity properties characterizing the molecule as soft and with the best disposition to accept electrons. Further, in silico studies predicted that the compounds might be able to inhibit the 11β-HSD1 enzyme, which is implicated in obesity and diabetes. Self- and cross-docking experiments revealed that a number of non-native 11β-HSD1 inhibitors were able to accurately dock within the 11β-HSD1 X-ray structure 4C7J. The molecular docking of the adamantane-linked 1,2,4-triazoles have similar predicted binding affinity scores compared to the 4C7J native ligand 4YQ. However, they were unable to form interactions with key active site residues. Based on these docking results, a series of potentially improved compounds were designed using computer aided drug design tools. The docking results of the new compounds showed similar predicted 11β-HSD1 binding affinity scores as well as interactions to a known potent 11β-HSD1 inhibitor.