Aid Drug Discovery Research Articles

Molecules targeting a novel homotrimer cavity of Spike protein attenuate replication of SARS-CoV-2

The SARS-CoV-2 Spike glycoprotein (S) utilizes a unique trimeric conformation to interact with the ACE2 receptor on host cells, making it a prime target for inhibitors that block viral entry. We have previously identified a novel proteinaceous cavity within the Spike protein homotrimer that could serve as a binding site for small molecules. However, it is not known whether these molecules would inhibit, stimulate, or have no effect on viral replication. To address this, we employed structural-based screening to identify small molecules that dock into the trimer cavity and assessed their impact on viral replication. Our findings show that a cohort of identified small molecules binding to the Spike trimer cavity effectively reduces the replication of various SARS-CoV-2 variants. These molecules exhibited inhibitory effects on B.1 (European original, D614G, EDB2) and B.1.617.2 (δ) variants, while showing moderate activity against the B.1.1.7 (α) variant. We further categorized these molecules into distinct groups based on their structural similarities. Our experiments demonstrated a dose-dependent viral replication inhibitory activity of these compounds, with some, like BCC0040453 exhibiting no adverse effects on cell viability even at high concentrations. Further investigation revealed that pre-incubating virions with compounds like BCC0031216 at different temperatures significantly inhibited viral replication, suggesting their specificity towards the S protein. Overall, our study highlights the inhibitory impact of a diverse set of chemical molecules on the biological activity of the Spike protein. These findings provide valuable insights into the role of the trimer cavity in the viral replication cycle and aid drug discovery programs aimed at targeting the coronavirus family.

From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics

Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.

Computer-Aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review.

Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family.

The Flaviviridae family consists of single-stranded positive-sense RNA viruses, which contains the genera Flavivirus, Hepacivirus, Pegivirus, and Pestivirus. Currently, there is an outbreak of viral diseases caused by this family affecting millions of people worldwide, leading to significant morbidity and mortality rates. Advances in computational chemistry have greatly facilitated the discovery of novel drugs and treatments for diseases associated with this family. Chemoinformatic techniques, such as the perturbation theory machine learning method, have played a crucial role in developing new approaches based on ML models that can effectively aid drug discovery. The IFPTML models have shown its capability to handle, classify, and process large data sets with high specificity. The results obtained from different models indicates that this methodology is proficient in processing the data, resulting in a reduction of the false positive rate by 4.25%, along with an accuracy of 83% and reliability of 92%. These values suggest that the model can serve as a computational tool in assisting drug discovery efforts and the development of new treatments against Flaviviridae family diseases.

Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.

A knowledge-guided pre-training framework for improving molecular representation learning

Learning effective molecular feature representation to facilitate molecular property prediction is of great significance for drug discovery. Recently, there has been a surge of interest in pre-training graph neural networks (GNNs) via self-supervised learning techniques to overcome the challenge of data scarcity in molecular property prediction. However, current self-supervised learning-based methods suffer from two main obstacles: the lack of a well-defined self-supervised learning strategy and the limited capacity of GNNs. Here, we propose Knowledge-guided Pre-training of Graph Transformer (KPGT), a self-supervised learning framework to alleviate the aforementioned issues and provide generalizable and robust molecular representations. The KPGT framework integrates a graph transformer specifically designed for molecular graphs and a knowledge-guided pre-training strategy, to fully capture both structural and semantic knowledge of molecules. Through extensive computational tests on 63 datasets, KPGT exhibits superior performance in predicting molecular properties across various domains. Moreover, the practical applicability of KPGT in drug discovery has been validated by identifying potential inhibitors of two antitumor targets: hematopoietic progenitor kinase 1 (HPK1) and fibroblast growth factor receptor 1 (FGFR1). Overall, KPGT can provide a powerful and useful tool for advancing the artificial intelligence (AI)-aided drug discovery process.

Atom-swap chemistry could aid drug discovery

Atom-swap chemistry could aid drug discovery

Assessing the structural dynamics of the glucose-6-phosphate dehydrogenase dimer interface using molecular dynamics simulation and ligand screening using computer aided drug discovery

ABSTRACTGlucose-6-phosphate-dehydrogenase deficiency is the most common enzymopathy. Current therapies for G6PD deficiency are unable to treat a broad range of pathogenic variants. In this study, we assess the structural dynamics of six G6PD variants using molecular dynamics simulation to correlate their genotypic and phenotypic attributes. G6PD multimerisation is highly influenced by its ligands G6P and NADP, where the former disrupts dimer formation, and the latter facilitates tetramerisation. Results of our simulation demonstrate that the WT and a relatively stable variant (G131V), were found to have greater NADP binding occupancy and hydrogen bonds between βN sheet of each monomeric subunit, thereby increasing the stability of the dimer interface. G6PD protein structures with high structural integrity at the dimer interface were found to be compact, characterised by low radius of gyration values, and increased surface area or high solvent-accessible surface area at the tetramer salt bridge residues. Using mutational clustering methods, a critical G6PD region at the βK–βL loop was identified and may serve as a potential target for treatment. We further extend this study to identify chemical compounds that induce modulatory effects on the protein using computer aided drug discovery which warrant further studies and future testing.

Characterization of DNA Damage Repair Related Signature and Molecular Feature in Low-Grade Gliomas to Aid Chemotherapy and Drug Discovery.

DNA damage repair (DDR) related genes are associated with the development, progression, aggressiveness, and heterogeneity of low-grade gliomas (LGG). However, the precise role of DDR in LGG prognosis and molecular subtypes remains to be elucidated. We analyzed 477 and 594 LGG samples from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) to develop a prognostic model using the random forest algorithm and Cox regression. Independent prognostic factors were incorporated into a nomogram, and its performance was assessed using receiver operating characteristic and calibration curves. We also used Connectivity Map analysis to identify potential small molecule drugs targeting DDR. Molecular subtypes based on DDR were identified by consensus cluster analysis, and the clinical characteristics, mutation landscape, immune tumor microenvironment, and drug sensitivity of patients with different subtypes in the TCGA and CGGA datasets were further compared. The Boruta algorithm was used to select features from the differentially expressed genes between clusters to generate DDR scores. Results were further validated in the Glioma Longitudinal AnalySiS consortium dataset. Statistical analysis and tests were implemented using R software version 4.0.2. We developed a prognostic model containing six DDR-related genes, which served as a potential independent prognostic indicator in LGG across three datasets. The area under the curve (AUC) values for 1-, 3-, and 5-year survival in the TCGA dataset were 0.901, 0.832, and 0.771, respectively. The nomogram demonstrated high accuracy in predicting 1-, 3-, and 5-year survival, with AUC values greater than 0.8. Additionally, we identified and validated two molecular subtypes based on DDR genes. These subtypes exhibited significant differences in somatic mutations, clinical prognosis, and immune cell infiltration. One subtype showed higher immune and stromal scores, worse prognosis, and increased sensitivity to common chemotherapeutic agents. Finally, we established a DDR score which served as another promising prognostic predictor for LGG. The prognostic model and molecular subtypes based on DDR genes can help in more detailed classification and provide insights for personalized management of LGG and clinical drug development.

COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.

Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (COMO), a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases and disease data to aid drug discovery. COMO can be installed as a Docker Image or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for model construction and a streamlined approach for predicting repurposable drugs, COMO enables researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease where metabolic inhibition is relevant. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies. The source code of the COMO pipeline is available at https://github.com/HelikarLab/COMO. The Docker image can be pulled at https://github.com/HelikarLab/COMO/pkgs/container/como.

Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls.

Building and analyzing knowledge graphs (KGs) to aid drug discovery is a topical area of research. A salient feature of KGs is their ability to combine many heterogeneous data sources in a format that facilitates discovering connections. The utility of KGs has been exemplified in areas such as drug repurposing, with insights made through manual exploration and modeling of the data. In this chapter, we discuss promises and pitfalls of using natural language processing (NLP) to mine "unstructured text"- typically from scientific literature- as a data source for KGs. This draws on our experience of initially parsing "structured" data sources-such as ChEMBL-as the basis for data within a KG, and then enriching or expanding upon them using NLP. The fundamental promise of NLP for KGs is the automated extraction of data from millions of documents-a task practically impossible to do via human curation alone. However, there are many potential pitfalls in NLP-KG pipelines, such as incorrect named entity recognition and ontology linking, all of which could ultimately lead to erroneous inferences and conclusions.

Building polarization into protein-inhibitor binding dynamics in rational drug design for rheumatoid arthritis

Standard force field-based simulations to accomplish structure-based evaluations of lead molecules is a powerful tool. Combining protein fragmentation into tractable sub-systems with continuum solvation method is envisaged to enable quantum mechanics-based electronic structure calculations of macromolecules in their realistic environment. This along with incorporation of many-body polarization effect in molecular dynamics simulations may augment an accurate description of electrostatics of protein-inhibitor systems for effective drug design. Rheumatoid arthritis (RA) is a complex autoimmune disorder plagued by the ceiling effect of current targeted therapies, encouraging identification of new druggable targets and corresponding drug design to tackle the refractory form of disease. In this study, polarization-inclusive force field approach has been used to model protein solvation and ligand binding for ‘Mitogen-activated protein kinase’ (MAP3K8), a regulatory node of notable pharmacological relevance in RA synovial biology. For MAP3K8 inhibitors belonging to different scaffold series, the calculations illustrated differential electrostatic contribution to their relative binding affinities and successfully explained examples from available structure-activity relationship studies. Results from this study exemplified i) the advantage of this approach in reliably ranking inhibitors having close nanomolar range activities for the same target; and ii) its prospective application in lead molecule identification aiding drug discovery efforts in RA. Communicated by Ramaswamy H. Sarma

SARM1, an Enzyme Involved in Axon Degeneration, Catalyzes Multiple Activities through a Ternary Complex Mechanism.

Sterile alpha and toll/interleukin receptor (TIR) motif containing protein 1 (SARM1) is an NAD+ hydrolase and cyclase involved in axonal degeneration. In addition to NAD+ hydrolysis and cyclization, SARM1 catalyzes a base exchange reaction between nicotinic acid (NA) and NADP+ to generate NAADP, which is a potent calcium signaling molecule. Herein, we describe efforts to characterize the hydrolysis, cyclization, and base exchange activities of TIR-1, the Caenorhabditis elegans ortholog of SARM1; TIR-1 also catalyzes NAD(P)+ hydrolysis and/or cyclization and regulates axonal degeneration in worms. We show that the catalytic domain of TIR-1 undergoes a liquid-to-solid phase transition that regulates not only the hydrolysis and cyclization reactions but also the base exchange reaction. We define the substrate specificities of the reactions, demonstrate that cyclization and base exchange reactions occur within the same pH range, and establish that TIR-1 uses a ternary complex mechanism. Overall, our findings will aid drug discovery efforts and provide insight into the mechanism of recently described inhibitors.

Computer aided drug discovery (CADD) of a thieno[2,3-d]pyrimidine derivative as a new EGFR inhibitor targeting the ribose pocket

Depending on the pharmacophoric characteristics of EGFR inhibitors, a new thieno[2,3-d]pyrimidine derivative has been developed. Firstly, the potential inhibitory effect of the designed compound against EGFR has been proven by docking experiments that showed correct binding modes and excellent binding energies of −98.44 and −88.00 kcal/mol, against EGFR wild-type and mutant type, respectively. Furthermore, MD simulations studies confirmed the precise energetic, conformational, and dynamic alterations that occurred after binding to EGFR. The correct binding was also confirmed by essential dynamics studies. To further investigate the general drug-like properties of the developed candidate, in silico ADME and toxicity studies have also been carried out. The thieno[2,3-d]pyrimidine derivative was synthesized following the earlier promising findings. Fascinatingly, the synthesized compound (4) showed promising inhibitory effects against EGFRWT and EGFRT790M with IC50 values of 25.8 and 182.3 nM, respectively. Also, it exhibited anticancer potentialities against A549 and MCF-7cell lines with IC50 values of 13.06 and 20.13 µM, respectively. Interestingly, these strong activities were combined with selectivity indices of 2.8 and 1.8 against the two cancer cell lines, respectively. Further investigations indicated the ability of compound 4 to arrest the cancer cells’ growth at the G2/M phase and to increase early and late apoptosis percentages from 2.52% and 2.80 to 17.99% and 16.72%, respectively. Additionally, it was observed that compound 4 markedly increased the levels of caspase-3 and caspase-9 by 4 and 3-fold compared to the control cells. Moreover, it up-regulated the level of BAX by 3-fold and down-regulated the level of Bcl-2 by 3-fold affording a BAX/Bcl-2 ratio of 9. Communicated by Ramaswamy H. Sarma

Study of Steric Hindrance of Ketone Ester Reduction Chemistry

Drug Discovery Research (DDR) is a process by which new potential therapeutic entities are recognized. Lead discovery and Computer aided drug discovery are two most popular methods in DDR. However, chemical modification such as development of the chemical linker gains its popularity in various therapeutic classes. In this poster, we are proposing four chemical steps starting from commercially available amino acids and their chemical routes for synthesizing linkers. We hypothesize that the more sterically hindered R group on Amino Acid will result in an increase in diastereomer ratio (DR) in reduction chemistry. In this poster we will discuss the detailed chemical reaction methods, their data, and how it’s being proposed as a potential anti-cancer agent.

Mapping rheumatoid arthritis susceptibility through integrative bioinformatics and genomics

Rheumatoid arthritis (RA) is an autoimmune disease that influences several organs and tissues, especially the synovial joints, and is associated with multiple genetic and environmental factors. Numerous databases provide information on the relationship between a specific gene and the disease pathogenesis. However, it is important to further prioritize biological risk genes for downstream development and validation. This study aims to map RA-association genetic variation using genome-wide association study (GWAS) databases and prioritize influential genes in RA pathogenesis based on functional annotations. These functional annotations include missense/nonsense mutations, cis-expression quantitative trait locus (cis-eQTL), overlap knockout mouse phenotype (KMP), protein-protein interaction (PPI), molecular pathway analysis (MPA), and primary immunodeficiency (PID). 119 genetic variants mapped had a potential high risk for RA based on functional scoring. The top eight risk genes of RA are TYK2 and IFNGR2, followed by TNFRSF1A, IL12RB1 and CD40, C5, NCF2, and IL6R . These candidate genes are potential biomarkers for RA that can aid drug discovery and disease diagnosis.

Differential Protein Citrullination in Human ER- and ER+ Tumor and Adjacent Healthy Breast Tissue.

Post-translational modification of arginine to citrulline is catalyzed by members of the peptidylarginine deiminase (PAD) family. Dysregulation of this catalysis is a significant driver of the pathogenesis of numerous inflammatory diseases, including cancer. However, dysregulation of PAD activity has not been examined in breast cancer with respect to hormone receptor status. In this study, we measured PAD enzyme levels using Western blotting and investigated protein citrullination using a mass spectrometry-based proteomics approach in primary estrogen receptor negative (ER-) or positive (ER+) breast tumor and matched adjacent normal tissue. Our findings reveal 72 and 41 citrullinated proteins in ER- tumor and adjacent healthy tissue, respectively, where 20 of these proteins are common between the two groups. We detected 64 and 49 citrullinated proteins in ER+ tumor and adjacent healthy tissue, respectively, where 32 proteins are common. Interestingly, upon comparison of ER- and ER+ tumor tissue, only 32 citrullinated proteins are shared between the two and the rest are unique to the tumor's receptor status. Using the STRING database for protein-protein interaction network analysis, these proteins are involved in protein-folding events (i.e., heat shock proteins) in ER- samples and blood-clotting events (i.e., fibulin) in ER+ samples. Constituents of the extracellular matrix structure (i.e., collagen and fibrinogen) were found in both. Herein, we establish evidence that supports the role of this unique post-translational modification in breast cancer biology. Finally, to aid drug discovery against citrullination, we developed a liquid chromatography-ultraviolet method to measure PAD enzymatic activity and optimized glucagon-like peptide II to quantitatively measure the ability of PADs to citrullinate its substrate.

Phytochemicals based computer aided drug discovery for huntington’s disease to identify a potential management option

Phytochemicals based computer aided drug discovery for huntington’s disease to identify a potential management option

IEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction.

Artificial intelligence has become more prevalent in broad fields, including drug discovery, in which the process is costly and time-consuming when conducted through wet experiments. As a result, drug repurposing, which tries to utilize approved and low-risk drugs for a new purpose, becomes more attractive. However, screening candidates from many drugs for specific protein targets is still expensive and tedious. This study aims to leverage computational resources to aid drug discovery by utilizing drug-protein interaction data and estimating their interaction strength, so-called binding affinity. Our estimation approach addresses multiple challenges encountered in the field. First, we employed a graph-based deep learning technique to overcome the limitations of drug compounds represented in string format by incorporating background knowledge of node and edge information as separate multi-dimensional features. Second, we tackled the complexities associated with extracting the representation and structure of proteins by utilizing a pre-trained model for feature extraction. Also, we employed graph operations over the 1D representation of a protein sequence to overcome the fixed-length problem typically encountered in language model tasks. In addition, we conducted a comparative analysis with a baseline model that creates a protein graph from a contact map prediction model, giving valuable insights into the performance and effectiveness of our proposed method. We evaluated the performance of our model using the same benchmark datasets with a variety of matrices as other previous work, and the results show that our model achieved the best prediction results while requiring no contact map information compared to other graph-based methods.

Computer aided drug designing in development of herbal drug

Natural products have been an essential component of sustaining civilizations due to their medicinal properties. Serendipitous past discoveries of bioactive natural products have served as a model for the creation of analogs with desired physicochemical features. There are many bioactive natural compounds with medicinal potential in nature, some of which are inaccessible to standard techniques of study. In the case of natural products, the value of computational techniques as flexible instruments for aiding drug discovery and development has been acknowledged for decades without exception. The importance of computer-aided drug design of phyto-components and how researchers still rely on these computational tools for the quick discovery of attractive drug candidate molecules. We also discussed molecular docking phytochemicals like flavonoids, alkaloids and terpenoids. The identification of preclinical drug candidate molecules and the structural elucidation of pharmacological drug targets have hastened the development of both structure-based and ligand-based drugs.