RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

An in silico drug repurposing approach to identify HDAC1 inhibitors against glioblastoma

Despite considerable improvement in therapy and diagnosis, brain tumors remain a global public health concern. Among all brain tumors, 80% are due to Glioblastoma. The average survival rate of a patient once diagnosed with glioblastoma is 15 months. Lately, the role of peptidase enzymes, especially Neprilysin, a neutral endopeptidase, is gaining attention for its role in tumor growth regulation. Neprilysin expressions are positively correlated with several tumors including GBM and reduced expression of NEP protein is associated with the pathogenesis of multiple tumors. One of the main reasons for NEP protein downregulation is the action of Histone deacetylase (HDAC) enzymes, especially HDAC1. Additionally, studies have reported that increased levels of HDAC1 are responsible for downregulating NEP gene expression. Hence, HDAC1 inhibition can be a good target to elevate NEP levels, which can be a good therapeutic approach to GBM. This study utilizes the computational drug repurposing tool, Schrodinger Maestro to identify HDAC1 inhibitors from the ZINC15 database.1379 FDA-approved drugs from the ZINC15 database were screened through molecular docking. Based on docking score and ligand-protein interaction, the top ten molecules were selected which were then subjected to binding energy calculation and molecular dynamics (MD) simulations. The three most active drugs from the MD simulations- ZINC22010649 (Panobinostat), ZINC4392649 (Tasimelteon) and ZINC1673 (Melphalan), were tested on C6 and U87 MG glioblastoma cells for cytotoxicity and HDAC1 protein levels using western blot analysis. Among the three drugs, Panobinostat exhibited potent cytotoxic action and showed a significant reduction in the HDAC1 protein levels.Communicated by Ramaswamy H. Sarma.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Machine Learning Enables Prediction of Pyrrolysyl-tRNA Synthetase Substrate Specificity

Knowledge about the substrate scope for a given enzyme is informative for elucidating biochemical pathways and also for expanding applications of the enzyme. However, no general methods are available to accurately predict the substrate specificity of an enzyme. Pyrrolysyl-tRNA synthetase (PylRS) is a powerful tool for incorporating various noncanonical amino acids (NCAAs) into proteins, which enabled us to probe, image, rationally engineer, and evolve protein structure and function. However, the incorporation of a new NCAA typically requires the selection of large libraries of PylRS with randomized mutations at active sites, and this process requires multiple rounds of selection for each new substrate. Therefore, a single aminoacyl-tRNA synthetase with broad substrate promiscuity is ideal to facilitate widespread applications of the genetic NCAA incorporation technique. Herein, machine learning models were developed to predict the substrate specificity of PylRS to accept novel NCAAs that could be incorporated into proteins by three PylRS mutants. The models were built from a training set of 285 unique enzyme-substrate pairs of three PylRS mutants including IFRS, BtaRS, and MFRS against 95 NCAAs. The best BaggingTree (BT) model was then used for virtually screening a NCAAs library containing 1474 phenylalanine, tyrosine, tryptophan, and alanine analogues, and 156 NCAAs were predicted to be accepted by at least one of the three PylRS mutants. Then, 27 NCAAs including 24 positive and 3 negative substrates were experimentally tested for their activities, and 20 of the 24 positive substrates showed weak or strong activity and were accepted by at least one PylRS mutant, among which 11 NCAAs were never reported to be incorporated into proteins before. Three negative substrates did not show any activity. Experimental results suggested that the BT model provides a three-class classification accuracy of 0.69 and a binary classification accuracy of 0.86. This study expanded the substrate scope of three PylRS variants and provided a framework for developing machine learning models to predict substrate specificity of other PylRS variants.

Quantifying Functional-Group-like Structural Fragments in Molecules and Its Applications in Drug Design

A functional group in a molecule is a structural fragment consisting of a few atoms or a single atom that imparts reactivity to a molecule. Hence, defining functional groups is crucial in chemistry to predict the properties and reactivities of molecules. However, there is no established method in the literature for defining functional groups based on reactivity parameters. In this work, we addressed this issue by designing a set of predefined structural fragments along with reactivity parameters like electron conjugation and ring strain. This approach uses bond orders and atom connectivities to quantify the presence of these fragments within an organic molecule based on a given input molecular coordinate. To assess the effectiveness of this approach, we performed a case study to show the benefits of using these newly designed structural fragments instead of traditional fingerprint-based methods for grouping potential COX1/COX2 inhibitors by screening an approved drug library against aspirin molecule. The structural fragment-based model for ternary classification of rat oral LD₅₀ of chemicals showed performance similar to the fingerprint-based models. In evaluating the regression model performance for aqueous solubility, log(S), predictions, our approach outperformed the fingerprint-based model.

Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.

Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions

New drug production, from target identification to marketing approval, takes over 12 years and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the urgent need for more powerful computational methods for drug discovery. Here, we review the computational approaches to predicting protein-ligand interactions in the context of drug discovery, focusing on methods using artificial intelligence (AI). We begin with a brief introduction to proteins (targets), ligands (e.g. drugs) and their interactions for nonexperts. Next, we review databases that are commonly used in the domain of protein-ligand interactions. Finally, we survey and analyze the machine learning (ML) approaches implemented to predict protein-ligand binding sites, ligand-binding affinity and binding pose (conformation) including both classical ML algorithms and recent deep learning methods. After exploring the correlation between these three aspects of protein-ligand interaction, it has been proposed that they should be studied in unison. We anticipate that our review will aid exploration and development of more accurate ML-based prediction strategies for studying protein-ligand interactions.

Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities

New chemical entities (NCEs) such as small molecules and antibody-drug conjugates have strong binding affinity for biological targets, which provide deep insights into structure-specific interactions for the design of future drugs. As structures of drugs increase in complexity, the importance of computational predictions comes into sharp focus. Knowledge of various computational tools enables us to predict the molecular properties, toxicity, and biological efficacy of the drugs and help the medicinal chemists to discover new drugs more efficiently. Newly approved drugs have higher affinities for proteins and nucleic acids and are applied for the treatment of human diseases. We have carried out the computational studies of 21 such NCEs, specifically small molecules and antibody-drug conjugates, and studied the biological efficacy of these drugs. Their bioactivity score and molecular and pharmacokinetic properties were evaluated using online computer software programs, viz., Molinspiration and Osiris Property Explorer. The SwissTargetPrediction tool was used for the efficient prediction of protein targets for the NCEs. The results indicated higher stability for the drug complexes due to a larger HOMO-LUMO gap. A high electrophilicity index reflects good electrophilic behavior and high reactivity of the drugs. Lipinski's ''rule of five'' indicated that most of the drug complexes are likely to be orally active. These drugs also showed non-mutagenic, non-tumorigenic, non-irritant, and non-effective reproductive behavior. We hope that these studies will provide an insight into molecular recognition and definitely help the medicinal chemists to design new drugs in future.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Building intuition for binding free energy calculations: Bound state definition, restraints, and symmetry

The theory behind computation of absolute binding free energies using explicit-solvent molecular simulations is well-established, yet somewhat complex, with counter-intuitive aspects. This leads to frequent frustration, common misconceptions, and sometimes erroneous numerical treatment. To improve this, we present the main practically relevant segments of the theory with constant reference to physical intuition. We pinpoint the role of the implicit or explicit definition of the bound state (or the binding site) to make a robust link between an experimental measurement and a computational result. We clarify the role of symmetry and discuss cases where symmetry number corrections have been misinterpreted. In particular, we argue that symmetry corrections as classically presented are a source of confusion and could be advantageously replaced by restraint free energy contributions. We establish that contrary to a common intuition, partial or missing sampling of some modes of symmetric bound states does not affect the calculated decoupling free energies. Finally, we review these questions and pitfalls in the context of a few common practical situations: binding to a symmetric receptor (equivalent binding sites), binding of a symmetric ligand (equivalent poses), and formation of a symmetric complex, in the case of homodimerization.