Survey of public domain software for docking simulations and virtual screening

HawkDock version 2: an updated web server to predict and analyze the structures of protein-protein complexes

Protein-protein interactions (PPIs) are fundamental to cellular functions, yet predicting and analyzing their 3D structures remains a critical and computationally demanding challenge. To address this, the HawkDock web server was developed as an integrated computational platform for predicting and analyzing protein-protein complexes. Over the past 6 years, HawkDock has successfully processed >234 000 computational tasks. In this study, an updated version of HawkDock was developed with the following advancements: (1) a deep learning-based flexible docking method, GeoDock, has been integrated to improve docking accuracy, particularly for apo-protein structures; (2) the VD-MM/GBSA method, which outperforms conventional MM/GBSA approaches in predicting binding affinities, has been implemented; (3) a new Mutation Analysis Module has been added to systematically evaluate the energetic impacts of amino acid mutations on protein-protein binding; (4) the server has been migrated to a high-performance cluster with Amber upgraded to version 24. Here, we describe the general protocol of HawkDock2, with a particular focus on its new features related to flexible docking, VD-MM/GBSA affinity prediction, and amino acid residue mutations. Comprehensive validation studies have demonstrated the reliability and effectiveness of these new features. HawkDock2 will remain freely accessible to all users at http://cadd.zju.edu.cn/hawkdock/.

Identification of lurasidone as a potent inhibitor of severe fever with thrombocytopenia syndrome virus by targeting the viral nucleoprotein

Introduction

Severe fever with thrombocytopenia syndrome virus (SFTSV) is an emerging tick-borne bunyavirus that causes acute febrile illness with thrombocytopenia and a high mortality rate in humans. Currently, no specific antiviral agents have been approved for the prevention or treatment of SFTSV infection. The viral nucleoprotein (NP) is a critical component involved in viral RNA replication and transcription, representing a promising target for antiviral drug development.

Methods

We performed a structure-based virtual screening of the FDA-approved drug library using AutoDock Vina, aiming to identify potential inhibitors targeting the RNA-binding pocket of SFTSV NP. Promising candidates were further evaluated for antiviral activity in vitro.

Results

Among the screened compounds, lurasidone exhibited strong antiviral activity against SFTSV, with an IC50 value of 4.552 μM and a selectivity index (SI) greater than 10, indicating favorable antiviral potency and low cytotoxicity. Mechanistic investigations suggest that lurasidone may exert its inhibitory effect by directly binding to the NP, thereby interfering with viral genome replication.

Conclusion

This study identifies lurasidone as a potential antiviral candidate targeting SFTSV NP and provides a theoretical basis for the repurposing of FDA-approved drugs against emerging viral infections. These findings offer new insights into therapeutic strategies for the treatment of SFTSV.

Prediction of aptamer affinity using an artificial intelligence approach

Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesized. Even if the SELEX approach has been improved a lot, it is frequently challenging and time-consuming to identify aptamers experimentally. In particular, structure-based methods are the most used in computer-aided design and development of aptamers. For this purpose, numerous web-based platforms have been suggested for the purpose of forecasting the secondary structure and 3D configurations of RNAs and DNAs. Also, molecular docking and molecular dynamics (MD), which are commonly utilized in protein compound selection by structural information, are suitable for aptamer selection. On the other hand, from a large number of sequences, artificial intelligence (AI) may be able to quickly discover the possible aptamer candidates. Conversely, sophisticated machine and deep-learning (DL) models have demonstrated efficacy in forecasting the binding properties between ligands and targets during drug discovery; as such, they may provide a reliable and precise method for forecasting the binding of aptamers to targets. This research looks at advancements in AI pipelines and strategies for aptamer binding ability prediction, such as machine and deep learning, as well as structure-based approaches, molecular dynamics and molecular docking simulation methods.

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

Design and Prediction of Aptamers Assisted by In Silico Methods

An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding affinity. Aptamers are developed through the process of systematic evolution of ligands by exponential enrichment (SELEX), which is repeated to increase the binding power and specificity. However, the SELEX process is time-consuming, and the characterization of aptamer candidates selected through it requires additional effort. Here, we describe in silico methods in order to suggest the most efficient way to develop aptamers and minimize the laborious effort required to screen and optimise aptamers. We investigated several methods for the estimation of aptamer-target molecule binding through conformational structure prediction, molecular docking, and molecular dynamic simulation. In addition, examples of machine learning and deep learning technologies used to predict the binding of targets and ligands in the development of new drugs are introduced. This review will be helpful in the development and application of in silico aptamer screening and characterization.

Investigating Derivatives of Tanshinone IIA Sulfonate Sodium and Chloroxine for Their Inhibition Activities against the SARS-CoV-2 Papain-like Protease

SARS-CoV-2 has caused a global pandemic of COVID-19, posing a huge threat to public health. The SARS-CoV-2 papain-like cysteine protease (PLpro) plays a significant role in virus replication and host immune regulation, which is a promising antiviral drug target. Several potential inhibitors have been identified in vitro. However, the detailed mechanism of action and structure-activity relationship require further studies. Here, we investigated the structure-activity relationships of the series of derivatives of tanshinone IIA sulfonate sodium (TSS) and chloroxine based on biochemical analysis and molecular dynamics simulation. We found that compound 7, a derivative of chloroxine, can disrupt PLpro-ISG15 interaction and exhibits an antiviral effect for SARS-CoV-2 variants (wild type, delta, and omicron) at the low micromolar level. These studies confirmed that inhibiting PLpro-ISG15 interaction and, thus, restoring the host's innate immunity are effective methods for fighting against viral infection.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Identification of Concomitant Inhibitors against Glutamine Synthetase and Isocitrate Lyase in Mycobacterium tuberculosis from Natural Sources

Tuberculosis (T.B.) is a disease that occurs due to infection by the bacterium, Mycobacterium tuberculosis (Mtb), which is responsible for millions of deaths every year. Due to the emergence of multidrug and extensive drug-resistant Mtb strains, there is an urgent need to develop more powerful drugs for inclusion in the current tuberculosis treatment regime. In this study, 1778 molecules from four medicinal plants, Azadirachta indica, Camellia sinensis, Adhatoda vasica, and Ginkgo biloba, were selected and docked against two chosen drug targets, namely, Glutamine Synthetase (G.S.) and Isocitrate Lyase (I.C.L.). Molecular Docking was performed using the Glide module of the Schrӧdinger suite to identify the best-performing ligands; the complexes formed by the best-performing ligands were further investigated for their binding stability via Molecular Dynamics Simulation of 100 ns. The present study suggests that Azadiradione from Azadirachta indica possesses the potential to inhibit Glutamine Synthetase and Isocitrate Lyase of M. tuberculosis concomitantly. The excellent docking score of the ligand and the stability of receptor-ligand complexes, coupled with the complete pharmacokinetic profile of Azadiradione, support the proposal of the small molecule, Azadiradione as a novel antitubercular agent. Further, wet lab analysis of Azadiradione may lead to the possible discovery of a novel antitubercular drug.

Comparative Evaluation of the Effects of Legacy and New Generation Perfluoralkyl Substances (PFAS) on Thyroid Cells In Vitro

BACKGROUND

Per- and poly-fluorinated alkyl substances (PFAS) are environment-persitent emerging endocrine disrupting chemicals raising health concerns worldwide. Exposure to PFAS has been associated with the imbalance of thyroid hormones. However, available studies addressing the cell mechanism underlying thyroid disrupting feature of legacy PFAS, such as perfluoro-octanoic acid (PFOA), perfluoro-octane-sulfonic acid (PFOS), and the new generation substitutes, such as C6O4, are still lacking. In this study the potential disrupting effect of PFOA, PFOS, and C6O4 on a murine thyroid cell model was assessed.

METHODS

A rat FRTL-5 cell line was used as the normal thyroid follicular cell model. Cell iodide-uptake, induced by thyroid stimulating hormone (TSH), was used to assess the functional impact of PFAS exposure on cell function. Tetrazolium salt-based cell viability assay and merocyanine 540-based cell staining were used to address the possible involvement of cell toxicity and membrane biophysical properties on altered cell function. The possible direct interaction of PFAS with TSH-receptor (TSH-R) was investigated by computer-based molecular docking and analysis of molecular dynamics. Evaluation of intracellular cAMP levels and gene expression analysis were used to validate the direct impairment of TSH-R-mediated downstream events upon PFAS exposure.

RESULTS

Different from PFOS or C6O4, exposure to PFOA at a concentration ≥ 10 ng/mL was associated with significant impairment of the iodide uptake upon TSH stimulation (respectively: basal 100.0 ± 19.0%, CTRL + TSH 188.9 ± 7.8%, PFOA 10 ng/mL + TSH 120.4 ± 20.9%, p= 0.030 vs CTRL + TSH; PFOA 100 ng/mL + TSH 115,6 ± 12,3% p= 0.017 vs CTRL + TSH). No impairment of cell viability or membrane stability was observed. Computational analysis showed a possible direct differential interaction of C6O4, PFOA, and PFOS on a same binding site of the extracellular domain of TSH-R. Finally, exposure to PFOA was associated with a significant reduction of downstream intracellular cAMP levels and both sodium-iodide transporter and thyroperoxidase gene expression upon TSH-R stimulation.

CONCLUSIONS

Our data suggest that legacy and new generation PFAS can differentially influence TSH dependent signaling pathways through the direct interaction with TSH-R.

Chemical Compounds, Antioxidant Activities, and Inhibitory Activities Against Xanthine Oxidase of the Essential Oils From the Three Varieties of Sunflower (Helianthus annuus L.) Receptacles

Background/Aim: Essential oils of sunflower receptacles (SEOs) have antibacterial and antioxidant potential. However, the differences of biological activities from the different varieties of sunflowers have not been studied till now. The purpose of this study was to compare the differences of chemical compounds, antioxidant activities, and inhibitory activities against xanthine oxidase (XO) of SEOs from the three varieties of sunflowers including LD5009, SH363, and S606.

Methods: SEOs were extracted by using the optimal extraction conditions selected by response surface methodology (RSM). Chemical compounds of SEOs were identified from the three varieties of sunflowers by gas chromatography-mass spectrometry (GC-MS). Antioxidant activities of SEOs were detected by 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), and iron ion reduction ability. Inhibitory activities of SEOs against XO were measured by using UV spectrophotometer. XO inhibitors were selected from the main chemical compounds of SEOs by the high-throughput selections and molecular simulation docking.

Results: The extraction yields of SEOs from LD5009, SH363, and S606 were 0.176, 0.319, and 0.580%, respectively. A total of 101 chemical compounds of SEOs were identified from the three varieties of sunflowers. In addition, the results of inhibitory activities against XO showed that SEOs can reduce uric acid significantly. Eupatoriochromene may be the most important chemical compounds of SEOs for reducing uric acid. The results of antioxidant activities and inhibitory activities against XO showed that SEOs of LD5009 had the strongest antioxidant and XO inhibitory activities. The Pearson correlation coefficient (r > 0.95) showed that γ-terpinene, (E)-citral, and L-Bornyl acetate were highly correlated with the antioxidant activities and XO inhibitory ability.

Conclusion: SEOs had antioxidant activities and XO inhibitory ability. It would provide more scientific information for utilization and selection of varieties of sunflowers, which would increase the food quality of sunflowers and incomes of farmers.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

BACKGROUND

CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity.

OBJECTIVE

This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2.

METHOD

We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs.

RESULTS

Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina.

CONCLUSION

All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.

Engineering a High-Affinity PD-1 Peptide for Optimized Immune Cell-Mediated Tumor Therapy

Purpose

The purpose of this study was to optimize a peptide (nABP284) that binds to PD-1 by a computer-based protocol in order to increase its affinity. Then, this study aimed to determine the inhibitory effects of this peptide on cancer immune escape by coculturing improving cytokine-induced killer (ICIK) cells with cancer cells.

Materials and Methods

nABP284 that binds to PD-1 was identified by phage display technology in our previous study. AutoDock and PyMOL were used to optimize the sequence of nABP284 to design a new peptide (nABPD1). Immunofluorescence was used to demonstrate that the peptides bound to PD-1. Surface plasmon resonance (SPR) was used to measure the binding affinity of the peptides. The blocking effect of the peptides on PD-1 was evaluated by a neutralization experiment with human recombinant PD-L1 protein. The inhibition of activated lymphocytes by cancer cells was simulated by coculturing of human acute T lymphocytic leukemia cells (Jurkat T cells) with human tongue squamous cell carcinoma cells (Cal27 cells). The anticancer activities were determined by coculturing ICIK cells with Cal27 cells in vitro.

Results

A high-affinity peptide (nABPD1, KD=11.9 nM) for PD-1 was obtained by optimizing the nABP284 peptide (KD=11.8 µM). nABPD1 showed better efficacy than nABP284 in terms of increasing the secretion of IL-2 by Jurkat T cells and enhancing the in vitro antitumor activity of ICIK cells.

Conclusion

nABPD1 possesses higher affinity for PD-1 than nABP284, which significantly enhances its ability to block the PD-1/PD-L1 interaction and to increase ICIK cell-mediated antitumor activity by armoring ICIK cells.

Artificial Intelligence in Aptamer-Target Binding Prediction

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer-target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer-target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.

IODVA1, a guanidinobenzimidazole derivative, targets Rac activity and Ras-driven cancer models

We report the synthesis and preliminary characterization of IODVA1, a potent small molecule that is active in xenograft mouse models of Ras-driven lung and breast cancers. In an effort to inhibit oncogenic Ras signaling, we combined in silico screening with inhibition of proliferation and colony formation of Ras-driven cells. NSC124205 fulfilled all criteria. HPLC analysis revealed that NSC124205 was a mixture of at least three compounds, from which IODVA1 was determined to be the active component. IODVA1 decreased 2D and 3D cell proliferation, cell spreading and ruffle and lamellipodia formation through downregulation of Rac activity. IODVA1 significantly impaired xenograft tumor growth of Ras-driven cancer cells with no observable toxicity. Immuno-histochemistry analysis of tumor sections suggests that cell death occurs by increased apoptosis. Our data suggest that IODVA1 targets Rac signaling to induce death of Ras-transformed cells. Therefore, IODVA1 holds promise as an anti-tumor therapeutic agent.

FeptideDB: A web application for new bioactive peptides from food protein

BACKGROUND

Bioactive peptides derived from food are important sources for alternative medicine and possess therapeutic activity. Several biochemical methods have been achieved to isolate bioactive peptides from food, which are tedious and time consuming. In silico methods are an alternative process to reduce cost and time with respect to bioactive peptide production. In this paper, FeptideDB was used to collect bioactive peptide (BP) data from both published research articles and available bioactive peptide databases. FeptideDB was developed to assist in forecasting bioactive peptides from food by combining peptide cleavage tools and database matching. Furthermore, this application was able to predict the potential of cleaved peptides from 'enzyme digestion module' to identify new ACE (angiotensin converting enzyme) inhibitors using an automatic molecular docking approach.

RESULTS

The FeptideDB web application contains tools for generating all possible peptides cleaved from input protein by various available enzymes. This database was also used for analysis and visualization to assist in bioactive peptide discovery. One module of FeptideDB has the ability to create 3-dimensional peptide structures to further predict inhibitors for the target protein, ACE (angiotensin converting enzyme).

CONCLUSIONS

FeptideDB is freely available to researchers who are interested in exploring bioactive peptides. The FeptideDB interface is easy to use, allowing users to rapidly retrieve data based on desired search criteria. FeptideDB is freely available at http://www4g.biotec.or.th/FeptideDB/. Ultimately, FeptideDB is a computational aid for assessing peptide bioactivities.

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.

re-TAMD: exploring interactions between H3 peptide and YEATS domain using enhanced sampling

BACKGROUND

Analysis of preferred binding regions of a ligand on a protein is important for detecting cryptic binding pockets and improving the ligand selectivity.

RESULT

The enhanced sampling approach TAMD has been adapted to allow a ligand to unbind from its native binding site and explore the protein surface. This so-called re-TAMD procedure was then used to explore the interaction between the N terminal peptide of histone H3 and the YEATS domain. Depending on the length of the peptide, several regions of the protein surface were explored. The peptide conformations sampled during the re-TAMD correspond to peptide free diffusion around the protein surface.

CONCLUSIONS

The re-TAMD approach permitted to get information on the relative influence of different regions of the N terminal peptide of H3 on the interaction between H3 and YEATS.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

The Performance of Several Docking Programs at Reproducing Protein-Macrolide-Like Crystal Structures

The accuracy of five docking programs at reproducing crystallographic structures of complexes of 8 macrolides and 12 related macrocyclic structures, all with their corresponding receptors, was evaluated. Self-docking calculations indicated excellent performance in all cases (mean RMSD values ≤ 1.0) and confirmed the speed of AutoDock Vina. Afterwards, the lowest-energy conformer of each molecule and all the conformers lying 0–10 kcal/mol above it (as given by Macrocycle, from MacroModel 10.0) were subjected to standard docking calculations. While each docking method has its own merits, the observed speed of the programs was as follows: Glide 6.6 > AutoDock Vina 1.1.2 > DOCK 6.5 >> AutoDock 4.2.6 > AutoDock 3.0.5. For most of the complexes, the five methods predicted quite correct poses of ligands at the binding sites, but the lower RMSD values for the poses of highest affinity were in the order: Glide 6.6 ≈ AutoDock Vina ≈ DOCK 6.5 > AutoDock 4.2.6 >> AutoDock 3.0.5. By choosing the poses closest to the crystal structure the order was: AutoDock Vina > Glide 6.6 ≈ DOCK 6.5 ≥ AutoDock 4.2.6 >> AutoDock 3.0.5. Re-scoring (AutoDock 4.2.6//AutoDock Vina, Amber Score and MM-GBSA) improved the agreement between the calculated and experimental data. For all intents and purposes, these three methods are equally reliable.

Development of a sugar-binding residue prediction system from protein sequences using support vector machine

Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (https://doi.org/10.5281/zenodo.61513).

A Pareto Algorithm for Efficient De Novo Design of Multi-functional Molecules

We have introduced a Pareto sorting algorithm into Synopsis, a de novo design program that generates synthesizable molecules with desirable properties. We give a detailed description of the algorithm and illustrate its working in 2 different de novo design settings: the design of putative dual and selective FGFR and VEGFR inhibitors, and the successful design of organic structure determining agents (OSDAs) for the synthesis of zeolites. We show that the introduction of Pareto sorting not only enables the simultaneous optimization of multiple properties but also greatly improves the performance of the algorithm to generate molecules with hard-to-meet constraints. This in turn allows us to suggest approaches to address the problem of false positive hits in de novo structure based drug design by introducing structural and physicochemical constraints in the designed molecules, and by forcing essential interactions between these molecules and their target receptor.

Combined rational design and a high throughput screening platform for identifying chemical inhibitors of a Ras-activating enzyme

The Ras family small GTPases regulate multiple cellular processes, including cell growth, survival, movement, and gene expression, and are intimately involved in cancer pathogenesis. Activation of these small GTPases is catalyzed by a special class of enzymes, termed guanine nucleotide exchange factors (GEFs). Herein, we developed a small molecule screening platform for identifying lead hits targeting a Ras GEF enzyme, SOS1. We employed an ensemble structure-based virtual screening approach in combination with a multiple tier high throughput experimental screen utilizing two complementary fluorescent guanine nucleotide exchange assays to identify small molecule inhibitors of GEF catalytic activity toward Ras. From a library of 350,000 compounds, we selected a set of 418 candidate compounds predicted to disrupt the GEF-Ras interaction, of which dual wavelength GDP dissociation and GTP-loading experimental screening identified two chemically distinct small molecule inhibitors. Subsequent biochemical validations indicate that they are capable of dose-dependently inhibiting GEF catalytic activity, binding to SOS1 with micromolar affinity, and disrupting GEF-Ras interaction. Mutagenesis studies in conjunction with structure-activity relationship studies mapped both compounds to different sites in the catalytic pocket, and both inhibited Ras signaling in cells. The unique screening platform established here for targeting Ras GEF enzymes could be broadly useful for identifying lead inhibitors for a variety of small GTPase-activating GEF reactions.

Rational design of small molecule inhibitors targeting the Ras GEF, SOS1

Ras GTPases regulate intracellular signaling involved in cell proliferation. Elevated Ras signaling activity has been associated with human cancers. Ras activation is catalyzed by guanine nucleotide exchange factors (GEFs), of which SOS1 is a major member that transduces receptor tyrosine kinase signaling to Ras. We have developed a rational approach coupling virtual screening with experimental screening in identifying small-molecule inhibitors targeting the catalytic site of SOS1 and SOS1-regulated Ras activity. A lead inhibitor, NSC-658497, was found to bind to SOS1, competitively suppress SOS1-Ras interaction, and dose-dependently inhibit SOS1 GEF activity. Mutagenesis and structure-activity relationship studies map the NSC-658497 site of action to the SOS1 catalytic site, and define the chemical moieties in the inhibitor essential for the activity. NSC-658497 showed dose-dependent efficacy in inhibiting Ras, downstream signaling activities, and associated cell proliferation. These studies establish a proof of principle for rational design of small-molecule inhibitors targeting Ras GEF enzymatic activity.

Endocrine disruption of oestrogen action and female reproductive tract cancers

Endocrine disrupting chemicals (EDC) are ubiquitous and persistent compounds that have the capacity to interfere with normal endocrine homoeostasis. The female reproductive tract is exquisitely sensitive to the action of sex steroids, and oestrogens play a key role in normal reproductive function. Malignancies of the female reproductive tract are the fourth most common cancer in women, with endometrial cancer accounting for most cases. Established risk factors for development of endometrial cancer include high BMI and exposure to oestrogens or synthetic compounds such as tamoxifen. Studies on cell and animal models have provided evidence that many EDC can bind oestrogen receptors and highlighted early life exposure as a window of risk for adverse lifelong effects on the reproductive system. The most robust evidence for a link between early life exposure to EDC and adverse reproductive health has come from studies on women who were exposed in utero to diethylstilbestrol. Demonstration that EDC can alter expression of members of the HOX gene cluster highlights one pathway that might be vulnerable to their actions. In summary, evidence for a direct link between EDC exposure and cancers of the reproductive system is currently incomplete. It will be challenging to attribute causality to any single EDC when exposure and development of malignancy may be separated by many years and influenced by lifestyle factors such as diet (a source of phytoestrogens) and adiposity. This review considers some of the evidence collected to date.

Computational tools for designing smart libraries

Traditional directed evolution experiments are often time-, labor- and cost-intensive because they involve repeated rounds of random mutagenesis and the selection or screening of large mutant libraries. The efficiency of directed evolution experiments can be significantly improved by targeting mutagenesis to a limited number of hot-spot positions and/or selecting a limited set of substitutions. The design of such "smart" libraries can be greatly facilitated by in silico analyses and predictions. Here we provide an overview of computational tools applicable for (a) the identification of hot-spots for engineering enzyme properties, and (b) the evaluation of predicted hot-spots and selection of suitable amino acids for substitutions. The selected tools do not require any specific expertise and can easily be implemented by the wider scientific community.

[Medicine from the computer]

Small molecules can have a significant effect on human metabolic processes. Computational drug design aims at constructing specialized small molecules that selectively and efficiently address specific proteins. The basic ideas of computational molecular design are presented and it will be shown how a virtual protein can be computer designed. This virtual protein can be used to predict the binding affinity of given small molecules without having to synthesize them in a laboratory. Modern computational drug design goes far beyond the lock and key principle. Possible future developments are discussed and a current successful example of computational drug design in the field of painkiller medication is demonstrated.

Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches

We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds toward the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in the top 10% among all the participants. In parallel, MedusaDock, designed to predict reliable docking poses, was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock- and QSAR-predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus.

[Medicine from the computer]

Small molecules can have a significant effect on human metabolic processes. Computational drug design aims at constructing specialized small molecules that selectively and efficiently address specific proteins. The basic ideas of computational molecular design are presented and it will be shown how a virtual protein can be computer designed. This virtual protein can be used to predict the binding affinity of given small molecules without having to synthesize them in a laboratory. Modern computational drug design goes far beyond the lock and key principle. Possible future developments are discussed and a current successful example of computational drug design in the field of painkiller medication is demonstrated.

Inhibition of histo-blood group antigen binding as a novel strategy to block norovirus infections

Noroviruses (NoVs) are the most important viral pathogens that cause epidemic acute gastroenteritis. NoVs recognize human histo-blood group antigens (HBGAs) as receptors or attachment factors. The elucidation of crystal structures of the HBGA-binding interfaces of a number of human NoVs representing different HBGA binding patterns opens a new strategy for the development of antiviral compounds against NoVs through rational drug design and computer-aided virtual screening methods. In this study, docking simulations and virtual screening were used to identify hit compounds targeting the A and B antigens binding sites on the surface of the capsid P protein of a GII.4 NoV (VA387). Following validation by re-docking of the A and B ligands, these structural models and AutoDock suite of programs were used to screen a large drug-like compound library (derived from ZINC library) for inhibitors blocking GII.4 binding to HBGAs. After screening >2 million compounds using multistage protocol, 160 hit compounds with best predicted binding affinities and representing a number of distinct chemical classes have been selected for subsequent experimental validation. Twenty of the 160 compounds were found to be able to block the VA387 P dimers binding to the A and/or B HBGAs at an IC₅₀<40.0 µM, with top 5 compounds blocking the HBGA binding at an IC₅₀<10.0 µM in both oligosaccharide- and saliva-based blocking assays. Interestingly, 4 of the top-5 compounds shared the basic structure of cyclopenta [a] dimethyl phenanthren, indicating a promising structural template for further improvement by rational design.

One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade

Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.

On setting up and assessing docking simulations for virtual screening

Small molecule docking and virtual screening of candidate compounds have become an integral part of drug discovery pipelines, complementing and streamlining experimental efforts in that regard. In this chapter, we describe specific software packages and protocols that can be used to efficiently set up a computational screening using a library of compounds and a docking program. We also discuss consensus- and clustering-based approaches that can be used to assess the results, and potentially re-rank the hits. While docking programs share many common features, they may require tailored implementation of virtual screening pipelines for specific computing platforms. Here, we primarily focus on solutions for several public domain packages that are widely used in the context of drug development.