Gypsum-DL: an open-source program for preparing small-molecule libraries for structure-based virtual screening

MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design

Generative models are undergoing rapid research and application to de novo drug design. To facilitate their application and evaluation, we present MolScore. MolScore already contains many drug-design-relevant scoring functions commonly used in benchmarks such as, molecular similarity, molecular docking, predictive models, synthesizability, and more. In addition, providing performance metrics to evaluate generative model performance based on the chemistry generated. With this unification of functionality, MolScore re-implements commonly used benchmarks in the field (such as GuacaMol, MOSES, and MolOpt). Moreover, new benchmarks can be created trivially. We demonstrate this by testing a chemical language model with reinforcement learning on three new tasks of increasing complexity related to the design of 5-HT2a ligands that utilise either molecular descriptors, 266 pre-trained QSAR models, or dual molecular docking. Lastly, MolScore can be integrated into an existing Python script with just three lines of code. This framework is a step towards unifying generative model application and evaluation as applied to drug design for both practitioners and researchers. The framework can be found on GitHub and downloaded directly from the Python Package Index.Scientific ContributionMolScore is an open-source platform to facilitate generative molecular design and evaluation thereof for application in drug design. This platform takes important steps towards unifying existing benchmarks, providing a platform to share new benchmarks, and improves customisation, flexibility and usability for practitioners over existing solutions.

MolModa: accessible and secure molecular docking in a web browser

Molecular docking advances early-stage drug discovery by predicting the geometries and affinities of small-molecule compounds bound to drug-target receptors, predictions that researchers can leverage in prioritizing drug candidates for experimental testing. Unfortunately, existing docking tools often suffer from poor usability, data security, and maintainability, limiting broader adoption. Additionally, the complexity of the docking process, which requires users to execute a series of specialized steps, often poses a substantial barrier for non-expert users. Here, we introduce MolModa, a secure, accessible environment where users can perform molecular docking entirely in their web browsers. We provide two case studies that illustrate how MolModa provides valuable biological insights. We further compare MolModa to other docking tools to highlight its strengths and limitations. MolModa is available free of charge for academic and commercial use, without login or registration, at https://durrantlab.com/molmoda.

Dioxinodehydroeckol: A Potential Neuroprotective Marine Compound Identified by In Silico Screening for the Treatment and Management of Multiple Brain Disorders

Neurodegenerative disorders such as Alzheimer's disease (AD), Glioblastoma multiforme (GBM), Amyotrophic lateral sclerosis (ALS), and Parkinson's disease (PD) are some of the most prevalent neurodegenerative disorders in humans. Even after a variety of advanced therapies, prognosis of all these disorders is not favorable, with survival rates of 14-20 months only. To further improve the prognosis of these disorders, it is imperative to discover new compounds which will target effector proteins involved in these disorders. In this study, we have focused on in silico screening of marine compounds against multiple target proteins involved in AD, GBM, ALS, and PD. Fifty marine-origin compounds were selected from literature, out of which, thirty compounds passed ADMET parameters. Ligand docking was performed after ADMET analysis for AD, GBM, ALS, and PD-associated proteins in which four protein targets Keap1, Ephrin A2, JAK3 Kinase domain, and METTL3-METTL14 N6-methyladenosine methyltransferase (MTA70) were found to be binding strongly with the screened compound Dioxinodehydroeckol (DHE). Molecular dynamics simulations were performed at 100 ns with triplicate runs to validate the docking score and assess the dynamics of DHE interactions with each target protein. The results indicated Dioxinodehydroeckol, a novel marine compound, to be a putative inhibitor among all the screened molecules, which might be effective against multiple target proteins involved in neurological disorders, requiring further in vitro and in vivo validations.

Enzymatic hydrolysis of single-use bioplastic items by improved recombinant yeast strains

Single-use bioplastic items pose new challenges for a circular plastics economy as they require different processing than petroleum-based plastics items. Microbial and enzymatic recycling approaches could address some of the pitfalls created by the influx of bioplastic waste. In this study, the recombinant expression of a cutinase-like-enzyme (CLE1) was improved in the yeast Saccharomyces cerevisiae to efficiently hydrolyse several commercial single-use bioplastic items constituting blends of poly(lactic acid), poly(1,4-butylene adipate-co-terephthalate), poly(butylene succinate) and mineral fillers. The hydrolysis process was optimised in controlled bioreactor configurations to deliver substantial monomer concentrations and, ultimately, 29 to 78% weight loss. Product inhibition studies and molecular docking provided insights into potential bottlenecks of the enzymatic hydrolysis process, while FT-IR analysis showed the preferential breakdown of specific polymers in blended commercial bioplastic items. This work constitutes a step towards implementing enzymatic hydrolysis as a circular economy approach for the valorisation of end-of-life single-use bioplastic items.

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.

In silico identification of five binding sites on the SARS-CoV-2 spike protein and selection of seven ligands for such sites

To contribute to the development of products capable of complexing with the SARS-CoV-2 spike protein, and thus preventing the virus from entering the host cell, this work aimed at discovering binding sites in the whole protein structure, as well as selecting substances capable of binding efficiently to such sites. Initially, the three-dimensional structure of the protein, with all receptor binding domains in the closed state, underwent blind docking with 38 substances potentially capable of binding to this protein according to the literature. This allowed the identification of five binding sites. Then, those substances with more affinities for these sites underwent pharmacophoric search in the ZINC15 database. The 14,329 substances selected from ZINC15 were subjected to docking to the five selected sites of the spike protein. The ligands with more affinities for the protein sites, as well as the selected sites themselves, were used in the de novo design of new ligands that were also docked to the binding sites of the protein. The best ligands, regardless of their origins, were used to form complexes with the spike protein, which were subsequently used in molecular dynamics simulations and calculations of ligands affinities to the protein through the molecular mechanics/Poisson-Boltzmann surface area method (MMPBSA). Seven substances with good affinities to the spike protein (-12.9 to -20.6 kcal/mol), satisfactory druggability (Bioavailability score: 0.17 to 0.55), and low acute toxicity to mice (LD50: 751 to 1421 mg/kg) were selected as potentially useful for the future development of new products to manage COVID-19 infections.Communicated by Ramaswamy H. Sarma.

High potential for biomass-degrading CAZymes revealed by pine forest soil metagenomics

The undisturbed environment in Netarhat, with its high levels of accumulated lignocellulosic biomass, presents an opportunity to identify microbes for biomass digestion. This study focuses on the bioprospecting of native soil microbes from the Netarhat forest in Jharkhand, India, with the potential for lignocellulosic substrate digestion. These biocatalysts could help overcome the bottleneck of biomass saccharification and reduce the overall cost of biofuel production, replacing harmful fossil fuels. The study used metagenomic analysis of pine forest soil via whole genome shotgun sequencing, revealing that most of the reads matched with the bacterial species, very low percentage of reads (0.1%) belongs to fungal species, with 13% of unclassified reads. Actinobacteria were found to be predominant among the bacterial species. MetaErg annotation identified 11,830 protein family genes and 2 metabolic marker genes in the soil samples. Based on the Carbohydrate Active EnZyme (CAZy) database, 3,996 carbohydrate enzyme families were identified, with family Glycosyl hydrolase (GH) dominating with 1,704 genes. Most observed GH families in the study were GH0, 3, 5, 6. 9, 12. 13, 15, 16, 39, 43, 57, and 97. Modelling analysis of a representative GH 43 gene suggested a strong affinity for cellulose than xylan. This study highlights the lignocellulosic digestion potential of the native microfauna of the lesser-known pine forest of Netarhat.Communicated by Ramaswamy H. Sarma.

CBPDdb: a curated database of compounds derived from Coumarin-Benzothiazole-Pyrazole

The present article describes the building of a small-molecule web server, CBPDdb, employing R-shiny. For the generation of the web server, three compounds were chosen, namely coumarin, benzothiazole and pyrazole, and their derivatives were curated from the literature. The two-dimensional (2D) structures were drawn using ChemDraw, and the .sdf file was created employing Discovery Studio Visualizer v2017. These compounds were read on the R-shiny app using ChemmineR, and the dataframe consisting of a total of 1146 compounds was generated and manipulated employing the dplyr package. The web server is provided with JSME 2D sketcher. The descriptors of the compounds are obtained using propOB with a filter. The users can download the filtered data in the .csv and .sdf formats, and the entire dataset of a compound can be downloaded in .sdf format. This web server facilitates the researchers to screen plausible inhibitors for different diseases. Additionally, the method used in building the web server can be adapted for developing other small-molecule databases (web servers) in RStudio. Database URL: https://srampogu.shinyapps.io/CBPDdb_Revised/.

A perspective on the sharing of docking data

Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Ligand Binding Properties of Odorant-Binding Protein OBP5 from Mus musculus

Odorant-binding proteins (OBPs) are abundant soluble proteins secreted in the nasal mucus of a variety of species that are believed to be involved in the transport of odorants toward olfactory receptors. In this study, we report the functional characterization of mouse OBP5 (mOBP5). mOBP5 was recombinantly expressed as a hexahistidine-tagged protein in bacteria and purified using metal affinity chromatography. The oligomeric state and secondary structure composition of mOBP5 were investigated using gel filtration and circular dichroism spectroscopy. Fluorescent experiments revealed that mOBP5 interacts with the fluorescent probe N-phenyl naphthylamine (NPN) with micromolar affinity. Competitive binding experiments with 40 odorants indicated that mOBP5 binds a restricted number of odorants with good affinity. Isothermal titration calorimetry (ITC) confirmed that mOBP5 binds these compounds with association constants in the low micromolar range. Finally, protein homology modeling and molecular docking analysis indicated the amino acid residues of mOBP5 that determine its binding properties.

Aminated Quinolinequinones as Privileged Scaffolds for Antibacterial Agents: Synthesis, In Vitro Evaluation, and Putative Mode of Action

Our previous studies have revealed that the aminated 1,4-quinone scaffold can be used for the development of novel antibacterial and/or antifungal agents. In this study, the aminated quinolinequinones (AQQ1-9) were designed, synthesized, and evaluated for their antimicrobial activity against a panel of seven bacterial strains (three Gram-positive and four Gram-negative bacteria) and three fungal strains. The structure-activity relationship (SAR) for the QQs was also summarized. The antibacterial activity results indicated that the two aminated QQs (AQQ6 and AQQ9) were active against Enterococcus faecalis (ATCC 29212) with a MIC value of 78.12 μg/mL. Besides, the two aminated QQs (AQQ8 and AQQ9) were active against Staphylococcus aureus (ATCC 29213) with MIC values of 4.88 and 2.44 μg/mL, respectively. The most potent aminated QQs (AQQ8 and AQQ9) were identified as promising lead molecules to further explore their mode of action. The selected QQs (AQQ8 and AQQ9) were further evaluated in vitro to assess their potential antimicrobial activity against each of 20 clinically obtained methicillin-resistant S. aureus isolates, antibiofilm activity, and bactericidal activity using time-kill curve assay. We found that the molecules prevented adhesion of over 50% of the cells in the biofilm. Molecular docking studies were performed to predict the predominant binding mode(s) of the ligands. We believe that the molecules need further investigation, especially against infections involving biofilm-forming microbes.

First-in-Class Allosteric Inhibitors of DNMT3A Disrupt Protein-Protein Interactions and Induce Acute Myeloid Leukemia Cell Differentiation

We previously identified two structurally related pyrazolone (compound 1) and pyridazine (compound 2) allosteric inhibitors of DNMT3A through screening of a small chemical library. Here, we show that these compounds bind and disrupt protein-protein interactions (PPIs) at the DNMT3A tetramer interface. This disruption is observed with distinct partner proteins and occurs even when the complexes are acting on DNA, which better reflects the cellular context. Compound 2 induces differentiation of distinct myeloid leukemia cell lines including cells with mutated DNMT3A R882. To date, small molecules targeting DNMT3A are limited to competitive inhibitors of AdoMet or DNA and display extreme toxicity. Our work is the first to identify small molecules with a mechanism of inhibition involving the disruption of PPIs with DNMT3A. Ongoing optimization of compounds 1 and 2 provides a promising basis to induce myeloid differentiation and treatment of diseases that display aberrant PPIs with DNMT3A, such as acute myeloid leukemia.

Structure-Based Identification and Biological Characterization of New NAPRT Inhibitors

NAPRT, the rate-limiting enzyme of the Preiss–Handler NAD biosynthetic pathway, has emerged as a key biomarker for the clinical success of NAMPT inhibitors in cancer treatment. Previous studies found that high protein levels of NAPRT conferred resistance to NAMPT inhibition in several tumor types whereas the simultaneous blockade of NAMPT and NAPRT results in marked anti-tumor effects. While research has mainly focused on NAMPT inhibitors, the few available NAPRT inhibitors (NAPRTi) have a low affinity for the enzyme and have been scarcely characterized. In this work, a collection of diverse compounds was screened in silico against the NAPRT structure, and the selected hits were tested through cell-based assays in the NAPRT-proficient OVCAR-5 ovarian cell line and on the recombinant hNAPRT. We found different chemotypes that efficiently inhibit the enzyme in the micromolar range concentration and for which direct engagement with the target was verified by differential scanning fluorimetry. Of note, the therapeutic potential of these compounds was evidenced by a synergistic interaction between the NAMPT inhibitor FK866 and the new NAPRTi in terms of decreasing OVCAR-5 intracellular NAD levels and cell viability. For example, compound IM29 can potentiate the effect of FK866 of more than two-fold in reducing intracellular NAD levels. These results pave the way for the development of a new generation of human NAPRTi with anticancer activity.

Identification of NAPRT Inhibitors with Anti-Cancer Properties by In Silico Drug Discovery

Depriving cancer cells of sufficient NAD levels, mainly through interfering with their NAD-producing capacity, has been conceived as a promising anti-cancer strategy. Numerous inhibitors of the NAD-producing enzyme, nicotinamide phosphoribosyltransferase (NAMPT), have been developed over the past two decades. However, their limited anti-cancer activity in clinical trials raised the possibility that cancer cells may also exploit alternative NAD-producing enzymes. Recent studies show the relevance of nicotinic acid phosphoribosyltransferase (NAPRT), the rate-limiting enzyme of the Preiss–Handler NAD-production pathway for a large group of human cancers. We demonstrated that the NAPRT inhibitor 2-hydroxynicotinic acid (2-HNA) cooperates with the NAMPT inhibitor FK866 in killing NAPRT-proficient cancer cells that were otherwise insensitive to FK866 alone. Despite this emerging relevance of NAPRT as a potential target in cancer therapy, very few NAPRT inhibitors exist. Starting from a high-throughput virtual screening approach, we were able to identify and annotate two additional chemical scaffolds that function as NAPRT inhibitors. These compounds show comparable anti-cancer activity to 2-HNA and improved predicted aqueous solubility, in addition to demonstrating favorable drug-like profiles.

Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease

We present several workflows for protein-ligand docking and free energy calculation for use in the workflow management system Galaxy. The workflows are composed of several widely used open-source tools, including rDock and GROMACS, and can be executed on public infrastructure using either Galaxy’s graphical interface or the command line. We demonstrate the utility of the workflows by running a high-throughput virtual screening of around 50000 compounds against the SARS-CoV-2 main protease, a system which has been the subject of intense study in the last year.

Complex of HIV-1 Integrase with Cellular Ku Protein: Interaction Interface and Search for Inhibitors

The interaction of HIV-1 integrase and the cellular Ku70 protein is necessary for HIV replication due to its positive effect on post-integration DNA repair. We have previously described in detail the Ku70 binding site within integrase. However, the integrase binding site in Ku70 remained poorly characterized. Here, using a peptide fishing assay and site-directed mutagenesis, we have identified residues I72, S73, and I76 of Ku70 as key for integrase binding. The molecular dynamics studies have revealed a possible way for IN to bind to Ku70, which is consistent with experimental data. According to this model, residues I72 and I76 of Ku70 form a "leucine zipper" with integrase residues, and, therefore, their concealment by low-molecular-weight compounds should impede the Ku70 interaction with integrase. We have identified such compounds by molecular docking and have confirmed their capacity to inhibit the formation of the integrase complex with Ku70. Our data demonstrate that the site of IN binding within Ku70 identified in the present work may be used for further search for inhibitors of the integrase binding to Ku70.

Adaptive laboratory evolution in S. cerevisiae highlights role of transcription factors in fungal xenobiotic resistance

In vitro evolution and whole genome analysis were used to comprehensively identify the genetic determinants of chemical resistance in Saccharomyces cerevisiae. Sequence analysis identified many genes contributing to the resistance phenotype as well as numerous amino acids in potential targets that may play a role in compound binding. Our work shows that compound-target pairs can be conserved across multiple species. The set of 25 most frequently mutated genes was enriched for transcription factors, and for almost 25 percent of the compounds, resistance was mediated by one of 100 independently derived, gain-of-function SNVs found in a 170 amino acid domain in the two Zn₂C₆ transcription factors YRR1 and YRM1 (p < 1 × 10⁻¹⁰⁰). This remarkable enrichment for transcription factors as drug resistance genes highlights their important role in the evolution of antifungal xenobiotic resistance and underscores the challenge to develop antifungal treatments that maintain potency.

Ottilie et al. employ an experimental evolution approach to investigate the role of transcription factors in yeast chemical resistance. Most emergent mutations in resistant strains were enriched in transcription factor coding genes, highlighting their importance in drug resistance.

Identification of Corosolic and Oleanolic Acids as Molecules Antagonizing the Human RORγT Nuclear Receptor Using the Calculated Fingerprints of the Molecular Similarity

RORγT is a protein product of the RORC gene belonging to the nuclear receptor subfamily of retinoic-acid-receptor-related orphan receptors (RORs). RORγT is preferentially expressed in Th17 lymphocytes and drives their differentiation from naive CD4+ cells and is involved in the regulation of the expression of numerous Th17-specific cytokines, such as IL-17. Because Th17 cells are implicated in the pathology of autoimmune diseases (e.g., psoriasis, inflammatory bowel disease, multiple sclerosis), RORγT, whose activity is regulated by ligands, has been recognized as a drug target in potential therapies against these diseases. The identification of such ligands is time-consuming and usually requires the screening of chemical libraries. Herein, using a Tanimoto similarity search, we found corosolic acid and other pentacyclic tritepenes in the library we previously screened as compounds highly similar to the RORγT inverse agonist ursolic acid. Furthermore, using gene reporter assays and Th17 lymphocytes, we distinguished compounds that exert stronger biological effects (ursolic, corosolic, and oleanolic acid) from those that are ineffective (asiatic and maslinic acids), providing evidence that such combinatorial methodology (in silico and experimental) might help wet screenings to achieve more accurate results, eliminating false negatives.

BINANA 2: Characterizing Receptor/Ligand Interactions in Python and JavaScript

BINding ANAlyzer (BINANA) is an algorithm for identifying and characterizing receptor/ligand interactions and other factors that contribute to binding. We recently updated BINANA to make the algorithm more accessible to a broader audience. We have also ported the Python3 codebase to JavaScript, thus enabling BINANA analysis in the web browser. As proof of principle, we created a web-browser application so students and chemical-biology researchers can quickly visualize receptor/ligand complexes and their unique binding interactions.

Screening for Inhibitors of Main Protease in SARS-CoV-2: In Silico and In Vitro Approach Avoiding Peptidyl Secondary Amides

In addition to vaccines, antiviral drugs are essential for suppressing COVID-19. Although several inhibitor candidates were reported for SARS-CoV-2 main protease, most are highly polar peptidomimetics with poor oral bioavailability and cell membrane permeability. Here, we conducted structure-based virtual screening and in vitro assays to obtain hit compounds belonging to a new chemical space, excluding peptidyl secondary amides. In total, 180 compounds were subjected to the primary assay at 20 μM, and nine compounds with inhibition rates of >5% were obtained. The IC50 of six compounds was determined in dose-response experiments, with the values on the order of 10-4 M. Although nitro groups were enriched in the substructure of the hit compounds, they did not significantly contribute to the binding interaction in the predicted docking poses. Physicochemical properties prediction showed good oral absorption. These new scaffolds are promising candidates for future optimization.

Insect RDL Receptor Models for Virtual Screening: Impact of the Template Conformational State in Pentameric Ligand-Gated Ion Channels

The RDL receptor is one of the most relevant protein targets for insecticide molecules. It belongs to the pentameric ligand-gated ion channel (pLGIC) family. Given that the experimental structures of pLGICs are difficult to obtain, homology modeling has been extensively used for these proteins, particularly for the RDL receptor. However, no detailed assessments of the usefulness of homology models for virtual screening (VS) have been carried out for pLGICs. The aim of this study was to evaluate which are the determinant factors for a good VS performance using RDL homology models, specially analyzing the impact of the template conformational state. Fifteen RDL homology models were obtained based on different pLGIC templates representing the closed, open, and desensitized states. A retrospective VS process was performed on each model, and their performance in the prioritization of active ligands was assessed. In addition, the three best-performing models among each of the conformations were subjected to molecular dynamics simulations (MDS) in complex with a representative active ligand. The models showed variations in their VS performance parameters that were related to the structural properties of the binding site. VS performance tended to improve in more constricted binding cavities. The best performance was obtained with a model based on a template in the closed conformation. MDS confirmed that the closed model was the one that best represented the interactions with an active ligand. These results imply that different templates should be evaluated and the structural variations between their channel conformational states should be specially examined, providing guidelines for the application of homology modeling for VS in other proteins of the pLGIC family.

Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking

Screening already approved drugs for activity against a novel pathogen can be an important part of global rapid-response strategies in pandemics. Such high-throughput repurposing screens have already identified several existing drugs with potential to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifically against this pathogen requires unambiguous identification of their corresponding targets, something the high-throughput screens are not typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom protein structures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plurality of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method with known drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentially suitable for repurposing to the inverse docking procedure. The most common preferential targets were the human enzymes TMPRSS2 and PIKfyve, followed by the viral enzymes Helicase and PLpro. All compounds that selected TMPRSS2 are known serine protease inhibitors, and those that selected PIKfyve are known tyrosine kinase inhibitors. Detailed structural analysis of the docking poses revealed important insights into why these selections arose, and could potentially lead to more rational design of new drugs against these targets.

Automated Exploration of Prebiotic Chemical Reaction Space: Progress and Perspectives

Prebiotic chemistry often involves the study of complex systems of chemical reactions that form large networks with a large number of diverse species. Such complex systems may have given rise to emergent phenomena that ultimately led to the origin of life on Earth. The environmental conditions and processes involved in this emergence may not be fully recapitulable, making it difficult for experimentalists to study prebiotic systems in laboratory simulations. Computational chemistry offers efficient ways to study such chemical systems and identify the ones most likely to display complex properties associated with life. Here, we review tools and techniques for modelling prebiotic chemical reaction networks and outline possible ways to identify self-replicating features that are central to many origin-of-life models.

Dual targeting of higher-order DNA structures by azacryptands induces DNA junction-mediated DNA damage in cancer cells

DNA is intrinsically dynamic and folds transiently into alternative higher-order structures such as G-quadruplexes (G4s) and three-way DNA junctions (TWJs). G4s and TWJs can be stabilised by small molecules (ligands) that have high chemotherapeutic potential, either as standalone DNA damaging agents or combined in synthetic lethality strategies. While previous approaches have claimed to use ligands that specifically target either G4s or TWJs, we report here on a new approach in which ligands targeting both TWJs and G4s in vitro demonstrate cellular effects distinct from that of G4 ligands, and attributable to TWJ targeting. The DNA binding modes of these new, dual TWJ-/G4-ligands were studied by a panel of in vitro methods and theoretical simulations, and their cellular properties by extensive cell-based assays. We show here that cytotoxic activity of TWJ-/G4-ligands is mitigated by the DNA damage response (DDR) and DNA topoisomerase 2 (TOP2), making them different from typical G4-ligands, and implying a pivotal role of TWJs in cells. We designed and used a clickable ligand, TrisNP-α, to provide unique insights into the TWJ landscape in cells and its modulation upon co-treatments. This wealth of data was exploited to design an efficient synthetic lethality strategy combining dual ligands with clinically relevant DDR inhibitors.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

In silico Targeting, inhibition and analysis of polyketide synthase enzyme in Aspergillus ssp

Aflatoxins are toxic and carcinogenic components produced by some Aspergillus species such as Aspergillus flavus. Polyketide synthases enzyme (PKS) plays a central role in aflatoxin s biosynthesis of in Aspergillus flavus, especially the product template (PT) domain, which controls the aldol cyclization of the polyketide forerunner during the biosynthesis of the aflatoxin pathway process. Here, we apply the in silico approaches to validate 623 natural components obtained from the South African Natural Compound Database (SANCDB), to distinguish the PT domain s prospected inhibitors. From the 623 compounds, docking results showed that there are 330 different compounds with energy binding lower than the natural substrate (palmitic acid or PLM) of the Product Templet domain (PT). Three factors were selected to determine the best 10 inhibiting components; 1) energy binding, 2) the strengthen chemical interactions, 3) the drug-likeness. The top ten inhibiting components are kraussianone 6, kraussianone 1, neodiospyrin, clionamine D, bromotopsentin, isodiospyrin, spongotine A, kraussianone 3, 14β-Hydroxybufa-3,5,20,22-tetraenolide and kraussianone 7. The chemical interactions between 3HRQ domain and the natural substrate in the active site amino acids are highly similar to the 3HRQ with the top ten components, but the main differences are in the binding energy which is the best in the top ten ligands. Those ten components give successful inhibition with PT domain which will lead to the formula to be used for inhibition and control aflatoxin contamination of agriculture crop yields and lessen the degree of harming and sicknesses that are coming about because of acquiring measures of aflatoxin.

LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates

Structure-based virtual screening (VS) uses computer docking to prioritize candidate small-molecule ligands for subsequent experimental testing. Docking programs evaluate molecular binding in part by predicting the geometry with which a given compound might bind a target receptor (e.g., the docked "pose" relative to a protein target). Candidate ligands predicted to participate in the same intermolecular interactions typical of known ligands (or ligands that bind related proteins) are arguably more likely to be true binders. Some docking programs allow users to apply constraints during the docking process with the goal of prioritizing these critical interactions. But these programs often have restrictive and/or expensive licenses, and many popular open-source docking programs (e.g., AutoDock Vina) lack this important functionality. We present LigGrep, a free, open-source program that addresses this limitation. As input, LigGrep accepts a protein receptor file, a directory containing many docked-compound files, and a list of user-specified filters describing critical receptor/ligand interactions. LigGrep evaluates each docked pose and outputs the names of the compounds with poses that pass all filters. To demonstrate utility, we show that LigGrep can improve the hit rates of test VS targeting H. sapiens poly(ADPribose) polymerase 1 (HsPARP1), H. sapiens peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (HsPin1p), and S. cerevisiae hexokinase-2 (ScHxk2p). We hope that LigGrep will be a useful tool for the computational biology community. A copy is available free of charge at http://durrantlab.com/liggrep/ .

AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4.

ProteinVR: Web-based molecular visualization in virtual reality

Protein structure determines biological function. Accurately conceptualizing 3D protein/ligand structures is thus vital to scientific research and education. Virtual reality (VR) enables protein visualization in stereoscopic 3D, but many VR molecular-visualization programs are expensive and challenging to use; work only on specific VR headsets; rely on complicated model-preparation software; and/or require the user to install separate programs or plugins. Here we introduce ProteinVR, a web-based application that works on various VR setups and operating systems. ProteinVR displays molecular structures within 3D environments that give useful biological context and allow users to situate themselves in 3D space. Our web-based implementation is ideal for hypothesis generation and education in research and large-classroom settings. We release ProteinVR under the open-source BSD-3-Clause license. A copy of the program is available free of charge from http://durrantlab.com/protein-vr/, and a working version can be accessed at http://durrantlab.com/pvr/.

Proteins are microscopic machines that help maintain, defend, and regulate cells. Properly understanding the three-dimensional structures of these machines–as well as the small molecules that interact with them–can advance scientific fields ranging from basic molecular biology to drug discovery. Virtual reality (VR) is a powerful tool for studying protein structures. But many current systems for viewing molecules in VR, though effective, have challenging usability limitations. We have created a new web application called ProteinVR that overcomes these challenges. ProteinVR enables VR molecular visualization in users’ browsers, without requiring them to install a separate program or plugin. It runs on a broad range of desktop, laptop, and mobile devices. For users without VR headsets, ProteinVR leverages mobile-device orientation sensors or video-game-style keyboard navigation to provide an immersive experience. We release ProteinVR as open-source software and have posted a working version at http://durrantlab.com/pvr/.