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de Azevedo WF, Quiroga R, Villarreal MA, da Silveira NJF, Bitencourt-Ferreira G, da Silva AD, Veit-Acosta M, Oliveira PR, Tutone M, Biziukova N, Poroikov V, Tarasova O, Baud S. SAnDReS 2.0: Development of machine-learning models to explore the scoring function space. J Comput Chem 2024; 45:2333-2346. [PMID: 38900052 DOI: 10.1002/jcc.27449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/04/2024] [Accepted: 06/02/2024] [Indexed: 06/21/2024]
Abstract
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.
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Affiliation(s)
| | - Rodrigo Quiroga
- Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), CONICET-Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | - Marcos Ariel Villarreal
- Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), CONICET-Departamento de Química Teórica y Computacional, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, Argentina
| | | | | | - Amauri Duarte da Silva
- Programa de Pós-Graduação em Tecnologias da Informação e Gestão em Saúde, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, Brazil
| | | | | | - Marco Tutone
- Dipartimento di Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), Università di Palermo, Palermo, Italy
| | | | | | | | - Stéphaine Baud
- Laboratoire SiRMa, UMR CNRS/URCA 7369, UFR Sciences Exactes et Naturelles, Université de Reims Champagne-Ardenne, CNRS, MEDYC, Reims, France
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2
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Nieto-Fabregat F, Lenza MP, Marseglia A, Di Carluccio C, Molinaro A, Silipo A, Marchetti R. Computational toolbox for the analysis of protein-glycan interactions. Beilstein J Org Chem 2024; 20:2084-2107. [PMID: 39189002 PMCID: PMC11346309 DOI: 10.3762/bjoc.20.180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 08/01/2024] [Indexed: 08/28/2024] Open
Abstract
Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.
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Affiliation(s)
- Ferran Nieto-Fabregat
- Department of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy
| | - Maria Pia Lenza
- Department of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy
| | - Angela Marseglia
- Department of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy
| | - Cristina Di Carluccio
- Department of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy
| | - Antonio Molinaro
- Department of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy
| | - Alba Silipo
- Department of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy
| | - Roberta Marchetti
- Department of Chemical Sciences, University of Naples Federico II, Via Cinthia 4, 80126, Italy
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Zhou Y, Jiang Y, Chen SJ. SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening. J Chem Theory Comput 2024. [PMID: 39150889 DOI: 10.1021/acs.jctc.4c00681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2024]
Abstract
The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.
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Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, University of Missouri-Columbia, Columbia, Missouri 65211-7010, United States
| | - Yangwei Jiang
- Department of Physics and Astronomy, University of Missouri-Columbia, Columbia, Missouri 65211-7010, United States
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri-Columbia, Columbia, Missouri 65211-7010, United States
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Wang R, Gao C, Yu M, Song J, Feng Z, Wang R, Pan H, Liu H, Li W, Fan X. Mechanistic prediction and validation of Brevilin A Therapeutic effects in Lung Cancer. BMC Complement Med Ther 2024; 24:214. [PMID: 38840248 PMCID: PMC11151568 DOI: 10.1186/s12906-024-04516-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 05/22/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Traditional Chinese medicine (TCM) has been found widespread application in neoplasm treatment, yielding promising therapeutic candidates. Previous studies have revealed the anti-cancer properties of Brevilin A, a naturally occurring sesquiterpene lactone derived from Centipeda minima (L.) A.Br. (C. minima), a TCM herb, specifically against lung cancer. However, the underlying mechanisms of its effects remain elusive. This study employs network pharmacology and experimental analyses to unravel the molecular mechanisms of Brevilin A in lung cancer. METHODS The Batman-TCM, Swiss Target Prediction, Pharmmapper, SuperPred, and BindingDB databases were screened to identify Brevilin A targets. Lung cancer-related targets were sourced from GEO, Genecards, OMIM, TTD, and Drugbank databases. Utilizing Cytoscape software, a protein-protein interaction (PPI) network was established. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), and gene-pathway correlation analysis were conducted using R software. To validate network pharmacology results, molecular docking, molecular dynamics simulations, and in vitro experiments were performed. RESULTS We identified 599 Brevilin A-associated targets and 3864 lung cancer-related targets, with 155 overlapping genes considered as candidate targets for Brevilin A against lung cancer. The PPI network highlighted STAT3, TNF, HIF1A, PTEN, ESR1, and MTOR as potential therapeutic targets. GO and KEGG analyses revealed 2893 enriched GO terms and 157 enriched KEGG pathways, including the PI3K-Akt signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. GSEA demonstrated a close association between hub genes and lung cancer. Gene-pathway correlation analysis indicated significant associations between hub genes and the cellular response to hypoxia pathway. Molecular docking and dynamics simulations confirmed Brevilin A's interaction with PTEN and HIF1A, respectively. In vitro experiments demonstrated Brevilin A-induced dose- and time-dependent cell death in A549 cells. Notably, Brevilin A treatment significantly reduced HIF-1α mRNA expression while increasing PTEN mRNA levels. CONCLUSIONS This study demonstrates that Brevilin A exerts anti-cancer effects in treating lung cancer through a multi-target and multi-pathway manner, with the HIF pathway potentially being involved. These results lay a theoretical foundation for the prospective clinical application of Brevilin A.
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Affiliation(s)
- Ruixue Wang
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Cuiyun Gao
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai, Shandong, China
| | - Meng Yu
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jialing Song
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China
- School of Rehabilitation Medicine, Binzhou Medical University, Yantai, Shandong, China
| | - Zhenzhen Feng
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Ruyu Wang
- School of clinical medicine, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China
| | - Huafeng Pan
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Haimeng Liu
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
| | - Wei Li
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
| | - Xiangzhen Fan
- Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
- Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
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Momin YH, Yeligar VC, Saralaya MG, Dharmamoorthy G, Mallikarjuna BP, Jadhav ST, Das K, Almuqbil M, Ahmad F, Rabbani SI, Asdaq SMB. Computational investigation of 2, 4-Di Tert Butyl Phenol as alpha amylase inhibitor isolated from Coccinia grandis (L.) Voigt using molecular docking, and ADMET parameters. Comput Biol Chem 2024; 110:108087. [PMID: 38718496 DOI: 10.1016/j.compbiolchem.2024.108087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/10/2024] [Accepted: 04/24/2024] [Indexed: 05/27/2024]
Abstract
INTRODUCTION Diabetes Mellitus is the metabolic disorder most prevalent globally, accounting for a substantial morbidity rate. The conventional drugs available for the management of diabetes are either expensive or lack the required efficacy. The purpose of this research is to isolate and characterize an active phytoconstituent from Coccinia grandis and assess its anti-diabetic properties. METHODS AND MATERIALS Stems of Coccinia grandis are subjected to successive extraction and isolation. The isolated compound by column chromatography was characterized by FTIR (fourier-transform infrared), 1 H NMR (proton nuclear magnetic resonance), and Mass spectroscopy. The antidiabetic potential of the isolated compound was evaluated by in-vitro alpha-amylase inhibitory activity. Further, the compound was subjected to molecular docking studies to study its interaction with the human pancreatic alpha-amylase (Molegro Virtual Docker) as well to determine the pharmacokinetic and toxicity profile using computational techniques (OSIRIS property explorer, Swiss ADME, pkCSM, and PreADMET). RESULTS The characterization of the compound suggests the structure to be 2,4-ditertiary butyl phenol. The in-vitro alpha-amylase inhibitory study indicated a concentration-dependent inhibition and the IC50 (median lethal dose) value of the isolated compound was found to be 64.36 μg/ml. The docking study with the A chain of receptor 5EMY yielded a favorable docking score of -81.48 Kcal mol-1, suggesting that the compound binds to the receptor with high affinity through electrostatic, hydrophobic, and hydrogen bonds. Furthermore, the silico ADME analysis of the compound revealed improved metabolism, a skin permeability of -3.87 cm/s, gastrointestinal absorption of 95.48 %, and a total clearance of 0.984 log ml min-1 kg-1. In silico toxicity analysis also predicted cutaneous irritations but no carcinogenicity, mutagenicity, or hepatotoxicity. CONCLUSION The data suggested that the isolated compound (2, 4-tertiary butyl phenol) has the potential to inhibit the alpha-amylase activity and possess optimal ADME properties as well as tolerable side effects.
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Affiliation(s)
- Yasmin H Momin
- Department of Pharmaceutical Chemistry, Annasaheb Dange College of B.Pharmacy, Ashta, MH 416301, India
| | - V C Yeligar
- Department of Pharmaceutical Chemistry, S Krishna Institute of Pharmacy, #39, Karad, Malkapur, Maharashtra 415539, India
| | - M G Saralaya
- Department of Pharmaceutical Chemistry, Annasaheb Dange College of B.Pharmacy, Ashta, MH 416301, India
| | - G Dharmamoorthy
- Department of Pharmaceutical Chemistry, MB School of Pharmaceutical Sciences, Mohan Babu University, Tirupati 517102, India
| | - B P Mallikarjuna
- Department of Pharmaceutical Chemistry, MB School of Pharmaceutical Sciences, Mohan Babu University, Tirupati 517102, India.
| | - S T Jadhav
- Department of Pharmaceutics, Rajarambapu College of Pharmacy, Kasegaon, Maharashtra 517102, India
| | - Kuntal Das
- Dept of Pharmacognosy, Mallige College of Pharmacy, #71, Silvepura, Chikkabanavara Post, Bengaluru 560090, India.
| | - Mansour Almuqbil
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia.
| | - Fuzail Ahmad
- Department of Respiratory Therapy, College of Applied Sciences, AlMareefa University, Diriya, Riyadh, Saudi Arabia.
| | - Syed Imam Rabbani
- Department of Pharmacology and Toxicology, College of Pharmacy, Qassim University, Buraydah 51452, Saudi Arabia
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Zhou Y, Chen SJ. Advances in machine-learning approaches to RNA-targeted drug design. ARTIFICIAL INTELLIGENCE CHEMISTRY 2024; 2:100053. [PMID: 38434217 PMCID: PMC10904028 DOI: 10.1016/j.aichem.2024.100053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.
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Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211-7010, USA
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
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Wang L, He X, Ji B, Han F, Niu T, Cai L, Zhai J, Hao D, Wang J. Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction. Biomolecules 2024; 14:648. [PMID: 38927052 PMCID: PMC11201553 DOI: 10.3390/biom14060648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Structure-based virtual screening utilizes molecular docking to explore and analyze ligand-macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In this study, we introduced a novel protocol that combines our in-house geometry optimization algorithm, the conjugate gradient with backtracking line search (CG-BS), which is capable of restraining and constraining rotatable torsional angles and other geometric parameters with a highly accurate machine learning potential, ANI-2x, renowned for its precise molecular energy predictions reassembling the wB97X/6-31G(d) model. By integrating this protocol with binding pose prediction using the Glide, we conducted additional structural optimization and potential energy prediction on 11 small molecule-macromolecule and 12 peptide-macromolecule systems. We observed that ANI-2x/CG-BS greatly improved the docking power, not only optimizing binding poses more effectively, particularly when the RMSD of the predicted binding pose by Glide exceeded around 5 Å, but also achieving a 26% higher success rate in identifying those native-like binding poses at the top rank compared to Glide docking. As for the scoring and ranking powers, ANI-2x/CG-BS demonstrated an enhanced performance in predicting and ranking hundreds or thousands of ligands over Glide docking. For example, Pearson's and Spearman's correlation coefficients remarkedly increased from 0.24 and 0.14 with Glide docking to 0.85 and 0.69, respectively, with the addition of ANI-2x/CG-BS for optimizing and ranking small molecules binding to the bacterial ribosomal aminoacyl-tRNA receptor. These results suggest that ANI-2x/CG-BS holds considerable potential for being integrated into virtual screening pipelines due to its enhanced docking performance.
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Affiliation(s)
- Luxuan Wang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Xibing He
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Beihong Ji
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Fengyang Han
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Taoyu Niu
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Lianjin Cai
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Jingchen Zhai
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
| | - Dongxiao Hao
- School of Electronics and Information Engineering, Ankang University, Ankang 725000, China
| | - Junmei Wang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.W.); (X.H.); (B.J.); (F.H.); (T.N.); (L.C.); (J.Z.)
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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Rayka M, Mirzaei M, Mohammad Latifi A. An ensemble-based approach to estimate confidence of predicted protein-ligand binding affinity values. Mol Inform 2024; 43:e202300292. [PMID: 38358080 DOI: 10.1002/minf.202300292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/22/2024] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequently, it is crucial to report a measure of confidence and quantify the uncertainty in the model's predictions during test time. Here, we adopt the conformal prediction technique to evaluate the confidence of a prediction for each member of the core set of the CASF 2016 benchmark. The conformal prediction technique requires a diverse ensemble of predictors for uncertainty estimation. To this end, we introduce ENS-Score as an ensemble predictor, which includes 30 models with different protein-ligand representation approaches and achieves Pearson's correlation of 0.842 on the core set of the CASF 2016 benchmark. Also, we comprehensively investigate the residual error of each data point to assess the normality behavior of the distribution of the residual errors and their correlation to the structural features of the ligands, such as hydrophobic interactions and halogen bonding. In the end, we provide a local host web application to facilitate the usage of ENS-Score. All codes to repeat results are provided at https://github.com/miladrayka/ENS_Score.
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Affiliation(s)
- Milad Rayka
- Applied Biotechnology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Morteza Mirzaei
- Applied Biotechnology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Ali Mohammad Latifi
- Applied Biotechnology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
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10
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Kravchenko A, de Vries SJ, Smaïl-Tabbone M, Chauvot de Beauchene I. HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses. BMC Bioinformatics 2024; 25:129. [PMID: 38532339 DOI: 10.1186/s12859-024-05733-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 03/06/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA'TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement. RESULTS Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA-protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.
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Affiliation(s)
- Anna Kravchenko
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France
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11
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McCorkindale W, Filep M, London N, Lee AA, King-Smith E. Deconvoluting low yield from weak potency in direct-to-biology workflows with machine learning. RSC Med Chem 2024; 15:1015-1021. [PMID: 38516605 PMCID: PMC10953487 DOI: 10.1039/d3md00719g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/12/2024] [Indexed: 03/23/2024] Open
Abstract
High throughput and rapid biological evaluation of small molecules is an essential factor in drug discovery and development. Direct-to-biology (D2B), whereby compound purification is foregone, has emerged as a viable technique in time efficient screening, specifically for PROTAC design and biological evaluation. However, one notable limitation is the prerequisite of high yielding reactions to ensure the desired compound is indeed the compound responsible for biological activity. Herein, we report a machine learning based yield-assay deconfounder capable of deconvoluting low yield from low potency to identify false negatives. We validated this approach by identifying promising SARS-CoV-2 main protease inhibitors with nanomolar activity that rivaled potency observed from the standard D2B workflow. Furthermore, we show how our framework can be utilized in a broad, in silico screen to produce compounds of similar potency as a D2B assay.
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Affiliation(s)
| | - Mihajlo Filep
- Department of Chemical and Structural Biology, The Weizmann Institute of Science Israel
| | - Nir London
- Department of Chemical and Structural Biology, The Weizmann Institute of Science Israel
| | - Alpha A Lee
- Cavendish Laboratory, University of Cambridge UK
| | - Emma King-Smith
- Yusuf Hamied Department of Chemistry, University of Cambridge UK
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12
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Smith MD, Darryl Quarles L, Demerdash O, Smith JC. Drugging the entire human proteome: Are we there yet? Drug Discov Today 2024; 29:103891. [PMID: 38246414 DOI: 10.1016/j.drudis.2024.103891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 01/23/2024]
Abstract
Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.
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Affiliation(s)
- Micholas Dean Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA
| | - L Darryl Quarles
- Departments of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; ORRxD LLC, 3404 Olney Drive, Durham, NC 27705, USA
| | - Omar Demerdash
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Jeremy C Smith
- University of Tennessee/Oak Ridge National Laboratory Center for Molecular Biophysics, Oak Ridge, TN 37830, USA; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37996, USA.
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13
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Charbe NB, Zacconi FC, Kowthavarapu VK, Gupta C, Palakurthi SS, Satheeshkumar R, Lokwani DK, Tambuwala MM, Palakurthi S. Targeting Allosteric Site of PCSK9 Enzyme for the Identification of Small Molecule Inhibitors: An In Silico Drug Repurposing Study. Biomedicines 2024; 12:286. [PMID: 38397888 PMCID: PMC10887305 DOI: 10.3390/biomedicines12020286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/29/2023] [Accepted: 01/23/2024] [Indexed: 02/25/2024] Open
Abstract
The primary cause of atherosclerotic cardiovascular disease (ASCVD) is elevated levels of low-density lipoprotein cholesterol (LDL-C). Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a crucial role in this process by binding to the LDL receptor (LDL-R) domain, leading to reduced influx of LDL-C and decreased LDL-R cell surface presentation on hepatocytes, resulting higher circulating levels of LDL-C. As a consequence, PCSK9 has been identified as a crucial target for drug development against dyslipidemia and hypercholesterolemia, aiming to lower plasma LDL-C levels. This research endeavors to identify promising inhibitory candidates that target the allosteric site of PCSK9 through an in silico approach. To start with, the FDA-approved Drug Library from Selleckchem was selected and virtually screened by docking studies using Glide extra-precision (XP) docking mode and Smina software (Version 1.1.2). Subsequently, rescoring of 100 drug compounds showing good average docking scores were performed using Gnina software (Version 1.0) to generate CNN Score and CNN binding affinity. Among the drug compounds, amikacin, bestatin, and natamycin were found to exhibit higher docking scores and CNN affinities against the PCSK9 enzyme. Molecular dynamics simulations further confirmed that these drug molecules established the stable protein-ligand complexes when compared to the apo structure of PCSK9 and the complex with the co-crystallized ligand structure. Moreover, the MM-GBSA calculations revealed binding free energy values ranging from -84.22 to -76.39 kcal/mol, which were found comparable to those obtained for the co-crystallized ligand structure. In conclusion, these identified drug molecules have the potential to serve as inhibitors PCSK9 enzyme and these finding could pave the way for the development of new PCSK9 inhibitory drugs in future in vitro research.
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Affiliation(s)
- Nitin Bharat Charbe
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics (Lake Nona), University of Florida, Orlando, FL 32827, USA; (V.K.K.); (C.G.)
| | - Flavia C. Zacconi
- Facultad de Química y de Farmacia, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile;
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile
| | - Venkata Krishna Kowthavarapu
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics (Lake Nona), University of Florida, Orlando, FL 32827, USA; (V.K.K.); (C.G.)
| | - Churni Gupta
- Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics (Lake Nona), University of Florida, Orlando, FL 32827, USA; (V.K.K.); (C.G.)
| | - Sushesh Srivatsa Palakurthi
- Department of Pharmaceutical Sciences, Rangel College of Pharmacy, Texas A&M University, Kingsville, TX 78363, USA; (S.S.P.); (R.S.); (S.P.)
| | - Rajendran Satheeshkumar
- Department of Pharmaceutical Sciences, Rangel College of Pharmacy, Texas A&M University, Kingsville, TX 78363, USA; (S.S.P.); (R.S.); (S.P.)
| | - Deepak K. Lokwani
- Department of Pharmaceutical Chemistry, Rajarshi Shahu College of Pharmacy, Buldana 443001, India;
| | - Murtaza M. Tambuwala
- Lincoln Medical School, University of Lincoln, Brayford Pool Campus, Lincoln LN6 7TS, UK
| | - Srinath Palakurthi
- Department of Pharmaceutical Sciences, Rangel College of Pharmacy, Texas A&M University, Kingsville, TX 78363, USA; (S.S.P.); (R.S.); (S.P.)
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14
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Voitsitskyi T, Bdzhola V, Stratiichuk R, Koleiev I, Ostrovsky Z, Vozniak V, Khropachov I, Henitsoi P, Popryho L, Zhytar R, Yesylevskyy S, Nafiiev A, Starosyla S. Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets. RSC Adv 2024; 14:1341-1353. [PMID: 38174256 PMCID: PMC10763617 DOI: 10.1039/d3ra08147h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
This study introduces the PocketCFDM generative diffusion model, aimed at improving the prediction of small molecule poses in the protein binding pockets. The model utilizes a novel data augmentation technique, involving the creation of numerous artificial binding pockets that mimic the statistical patterns of non-bond interactions found in actual protein-ligand complexes. An algorithmic method was developed to assess and replicate these interaction patterns in the artificial binding pockets built around small molecule conformers. It is shown that the integration of artificial binding pockets into the training process significantly enhanced the model's performance. Notably, PocketCFDM surpassed DiffDock in terms of non-bond interaction and steric clash numbers, and the inference speed. Future developments and optimizations of the model are discussed. The inference code and final model weights of PocketCFDM are accessible publicly via the GitHub repository: https://github.com/vtarasv/pocket-cfdm.git.
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Affiliation(s)
- Taras Voitsitskyi
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine 46 Nauky Ave. Kyiv 03038 Ukraine
| | - Volodymyr Bdzhola
- Institute of Molecular Biology and Genetics of The National Academy of Sciences of Ukraine 150 Zabolotnogo Str. Kyiv 03143 Ukraine
| | - Roman Stratiichuk
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Department of Biophysics and Medical Informatics, Educational and Scientific Centre "Institute of Biology and Medicine", Taras Shevchenko Kyiv National University 64 Volodymyrska Str. Kyiv 01601 Ukraine
| | - Ihor Koleiev
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine 46 Nauky Ave. Kyiv 03038 Ukraine
| | | | | | | | | | | | - Roman Zhytar
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
| | - Semen Yesylevskyy
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences Prague 6 CZ-166 10 Czech Republic
- Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine 46 Nauky Ave. Kyiv 03038 Ukraine
- Department of Physical Chemistry, Faculty of Science, Palacký University Olomouc 17 listopadu 12 Olomouc 771 46 Czech Republic
| | - Alan Nafiiev
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
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15
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Talevi A. Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects. Methods Mol Biol 2024; 2714:1-20. [PMID: 37676590 DOI: 10.1007/978-1-0716-3441-7_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.
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Affiliation(s)
- Alan Talevi
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata (UNLP), La Plata, Argentina.
- Argentinean National Council of Scientific and Technical Research (CONICET), La Plata, Argentina.
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16
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Zha J, He J, Wu C, Zhang M, Liu X, Zhang J. Designing drugs and chemical probes with the dualsteric approach. Chem Soc Rev 2023; 52:8651-8677. [PMID: 37990599 DOI: 10.1039/d3cs00650f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Traditionally, drugs are monovalent, targeting only one site on the protein surface. This includes orthosteric and allosteric drugs, which bind the protein at orthosteric and allosteric sites, respectively. Orthosteric drugs are good in potency, whereas allosteric drugs have better selectivity and are solutions to classically undruggable targets. However, it would be difficult to simultaneously reach high potency and selectivity when targeting only one site. Also, both kinds of monovalent drugs suffer from mutation-caused drug resistance. To overcome these obstacles, dualsteric modulators have been proposed in the past twenty years. Compared to orthosteric or allosteric drugs, dualsteric modulators are bivalent (or bitopic) with two pharmacophores. Each of the two pharmacophores bind the protein at the orthosteric and an allosteric site, which could bring the modulator with special properties beyond monovalent drugs. In this study, we comprehensively review the current development of dualsteric modulators. Our main effort reason and illustrate the aims to apply the dualsteric approach, including a "double win" of potency and selectivity, overcoming mutation-caused drug resistance, developments of function-biased modulators, and design of partial agonists. Moreover, the strengths of the dualsteric technique also led to its application outside pharmacy, including the design of highly sensitive fluorescent tracers and usage as molecular rulers. Besides, we also introduced drug targets, designing strategies, and validation methods of dualsteric modulators. Finally, we detail the conclusions and perspectives.
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Affiliation(s)
- Jinyin Zha
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, China.
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jixiao He
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chengwei Wu
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mingyang Zhang
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyi Liu
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, China.
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region, China.
- State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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17
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Cai L, Han F, Ji B, He X, Wang L, Niu T, Zhai J, Wang J. In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling. Molecules 2023; 28:8034. [PMID: 38138524 PMCID: PMC10745665 DOI: 10.3390/molecules28248034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/30/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
The "Long-COVID syndrome" has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<-6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.
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Affiliation(s)
| | | | | | | | | | | | | | - Junmei Wang
- School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; (L.C.); (F.H.); (B.J.); (X.H.); (L.W.); (T.N.); (J.Z.)
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18
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Tang J, Jin J, Huang Z, An F, Huang C, Jiang W. The discovery of subunit-selective GluN1/GluN2B NMDAR antagonist via pharmacophere-based virtual screening. Exp Biol Med (Maywood) 2023; 248:2560-2577. [PMID: 38282535 PMCID: PMC10854469 DOI: 10.1177/15353702231220666] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/05/2024] [Indexed: 01/30/2024] Open
Abstract
The incidence and mortality rates of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are gradually increasing worldwide. Numerous studies have demonstrated that N-methyl-D-aspartic acid receptor (NMDAR)-mediated excitotoxicity contributes to neurodegenerative diseases. Ifenprodil, a subtype-selective NMDAR antagonist, showed strong therapeutic potential. However, it suffers from low oral bioavailability and off-target side effects. In this study, natural compounds were identified for selective inhibition of GluN1/GluN2B NMDAR of human. We obtained a set of natural compounds (n = 81,366) from COCONUT, TIPdb, PAMDB, CMNPD, YMDB, and NPAtlas databases, and then virtually screened by an ifenprodil-structure-based pharmacophore model and molecular docking. The top 100 compounds were selected for binding affinity prediction via batch drug-target affinity (BatchDTA). Then, the top 50 compounds were analyzed by absorption, distribution, metabolism, excretion, toxicity (ADMET). Molecular dynamics involving molecular mechanics/position-Boltzmann surface area (MM-PBSA) calculation were performed to further screening. The top 15 compounds with strong binding affinity and ifenprodil, a proven GluN2B-selective NMDAR antagonist, were subjected to molecular dynamic simulations (100 ns), root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), H-bonds, solvent accessible surface area (SASA), principal component analysis (PCA), and binding free energy calculations. Based on these analyses, one possible lead compound carrying positive charges (CNP0099440) was identified, with great binding affinity and less off-target activity by contrast to ifenprodil. CNP0099440 has great potential to be a GluN1/GluN2B NMDAR antagonist candidate and can be further detected via in vitro and in vivo experiments.
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Affiliation(s)
- Jialing Tang
- Department of Biochemistry and Molecular Biology, College of Basic Medical Sciences, Naval Medical University, Shanghai 200433, China
| | - Ju Jin
- Department of Biochemistry and Molecular Biology, College of Basic Medical Sciences, Naval Medical University, Shanghai 200433, China
| | - Zhihong Huang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China
| | - Faliang An
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Caiguo Huang
- Department of Biochemistry and Molecular Biology, College of Basic Medical Sciences, Naval Medical University, Shanghai 200433, China
| | - Wenli Jiang
- Department of Biochemistry and Molecular Biology, College of Basic Medical Sciences, Naval Medical University, Shanghai 200433, China
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19
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Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. J Chem Theory Comput 2023; 19:7478-7495. [PMID: 37883810 PMCID: PMC10653122 DOI: 10.1021/acs.jctc.3c00814] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.
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Affiliation(s)
- Song Xia
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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20
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Reshma A, Tamilanban T, Chitra V, Subramaniyan V, Gupta G, Fuloria NK, Sekar M, Fuloria S, Sahu R, Narayanan J, Chakravarthy S, Selvaraj S. Anti-obesity effects of olivetol in adult zebrafish model induced by short-term high-fat diet. Sci Rep 2023; 13:18449. [PMID: 37891223 PMCID: PMC10611697 DOI: 10.1038/s41598-023-44462-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Obesity is a complex disease caused by various factors, and synthetic drugs used to treat it can have side effects. Natural compounds, such as olivetol, could be a promising alternative. Olivetol is a substance found in certain lichen species and has anti-inflammatory and anti-cancer properties. In this study, researchers conducted in-silico molecular docking studies and found that olivetol had significant binding affinity with receptors involved in obesity. They also investigated the effects of olivetol on a diet-induced obese zebrafish model and found that high doses of olivetol reduced excessive fat accumulation and triglyceride and lipid accumulation. The low dose of olivetol showed a significant reduction in liver enzymes' levels. However, the high dose of olivetol resulted in a significant increase in HMG-CoA levels. These results suggest that olivetol may be a promising anti-obesity agent for the treatment of hyperlipidemia-related disorders, but further research is necessary to understand its full effects on the body.
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Affiliation(s)
- Andukuri Reshma
- Department of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India
| | - T Tamilanban
- Department of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.
| | - V Chitra
- Department of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia.
- Centre for Global Health Research, Saveetha Medical College and Hospital, Saveetha University, Chennai, 602105, India.
| | - Gaurav Gupta
- School of Pharmacy, Graphic Era Hill University, Dehradun, 248007, India
- School of Pharmacy, Suresh Gyan Vihar University, Jagatpura, Jaipur, 302017, India
| | | | - Mahendran Sekar
- School of Pharmacy, Monash University Malaysia , Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia
| | - Shivkanya Fuloria
- Faculty of Pharmacy, AIMST University, 08100, Bedong, Kedah, Malaysia
| | | | - J Narayanan
- Department of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India
| | - Srikumar Chakravarthy
- SEGi University, Jalan Teknologi, Taman Sains Selangor, Kota Damansara, PJU 5, 47810, Petaling Jaya, Selangor, Malaysia
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21
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Shiota K, Akutsu T. Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction. BIOINFORMATICS ADVANCES 2023; 3:vbad155. [PMID: 37928345 PMCID: PMC10625475 DOI: 10.1093/bioadv/vbad155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/20/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Motivation Extended connectivity interaction features (ECIF) is a method developed to predict protein-ligand binding affinity, allowing for detailed atomic representation. It performed very well in terms of Comparative Assessment of Scoring Functions 2016 (CASF-2016) scoring power. However, ECIF has the limitation of not being able to adequately account for interatomic distances. Results To investigate what kind of distance representation is effective for P-L binding affinity prediction, we have developed two algorithms that improved ECIF's feature extraction method to take distance into account. One is multi-shelled ECIF, which takes into account the distance between atoms by dividing the distance between atoms into multiple layers. The other is weighted ECIF, which weights the importance of interactions according to the distance between atoms. A comparison of these two methods shows that multi-shelled ECIF outperforms weighted ECIF and the original ECIF, achieving a CASF-2016 scoring power Pearson correlation coefficient of 0.877. Availability and implementation All the codes and data are available on GitHub (https://github.com/koji11235/MSECIFv2).
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Affiliation(s)
- Koji Shiota
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto 606-8501, Japan
| | - Tatsuya Akutsu
- Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Kyoto 606-8501, Japan
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22
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Pan S, Xia L, Xu L, Li Z. SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features. BMC Bioinformatics 2023; 24:334. [PMID: 37679724 PMCID: PMC10485962 DOI: 10.1186/s12859-023-05460-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/31/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Drug-target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction. In order to solve the problem of fusion of substructure information of drug molecular graphs and utilize multi-scale information of protein, a self-supervised pre-training model based on substructure extraction and multi-scale features is proposed in this paper. RESULTS For drug molecules, the model obtains substructure information through the method of probability matrix, and the contrastive learning method is implemented on the graph-level representation and subgraph-level representation to pre-train the graph encoder for downstream tasks. For targets, a BiLSTM method that integrates multi-scale features is used to capture long-distance relationships in the amino acid sequence. The experimental results showed that our model achieved better performance for DTA prediction. CONCLUSIONS The proposed model improves the performance of the DTA prediction, which provides a novel strategy based on substructure extraction and multi-scale features.
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Affiliation(s)
- Shourun Pan
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Leiming Xia
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Lei Xu
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Zhen Li
- College of Computer Science and Technology, Qingdao University, Qingdao, China.
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23
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Abstract
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.
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Affiliation(s)
- Christoph Gorgulla
- Harvard Medical School and Physics Department, Harvard University, Boston, Massachusetts, USA;
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Current affiliation: Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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24
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Mohanty M, Mohanty PS. Molecular docking in organic, inorganic, and hybrid systems: a tutorial review. MONATSHEFTE FUR CHEMIE 2023; 154:1-25. [PMID: 37361694 PMCID: PMC10243279 DOI: 10.1007/s00706-023-03076-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 05/08/2023] [Indexed: 06/28/2023]
Abstract
Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review. Graphical abstract
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Affiliation(s)
- Madhuchhanda Mohanty
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, 751024 India
| | - Priti S. Mohanty
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, 751024 India
- School of Chemical Technology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, 751024 India
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25
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Pliushcheuskaya P, Künze G. Recent Advances in Computer-Aided Structure-Based Drug Design on Ion Channels. Int J Mol Sci 2023; 24:ijms24119226. [PMID: 37298178 DOI: 10.3390/ijms24119226] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/16/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023] Open
Abstract
Ion channels play important roles in fundamental biological processes, such as electric signaling in cells, muscle contraction, hormone secretion, and regulation of the immune response. Targeting ion channels with drugs represents a treatment option for neurological and cardiovascular diseases, muscular degradation disorders, and pathologies related to disturbed pain sensation. While there are more than 300 different ion channels in the human organism, drugs have been developed only for some of them and currently available drugs lack selectivity. Computational approaches are an indispensable tool for drug discovery and can speed up, especially, the early development stages of lead identification and optimization. The number of molecular structures of ion channels has considerably increased over the last ten years, providing new opportunities for structure-based drug development. This review summarizes important knowledge about ion channel classification, structure, mechanisms, and pathology with the main focus on recent developments in the field of computer-aided, structure-based drug design on ion channels. We highlight studies that link structural data with modeling and chemoinformatic approaches for the identification and characterization of new molecules targeting ion channels. These approaches hold great potential to advance research on ion channel drugs in the future.
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Affiliation(s)
- Palina Pliushcheuskaya
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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26
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Radwan AA, Alanazi FK, Raish M. Design and synthesis of multi-functional small-molecule based inhibitors of amyloid-β aggregation: Molecular modeling and in vitro evaluation. PLoS One 2023; 18:e0286195. [PMID: 37228136 DOI: 10.1371/journal.pone.0286195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/10/2023] [Indexed: 05/27/2023] Open
Abstract
Amyloid-β1-42 (Aβ42) peptide aggregate formation in the brain plays a crucial role in the onset and progression of Alzheimer's disease. According to published research, the Aβ monomer's amino acid residues KLVFF (16-20) self-associate to create antiparallel β-sheet fibrils. Small compounds can prevent self-assembly and destroy Aβ fibrils by attaching to the Aβ16-20 regions of Aβ42. To enhance biological characteristics and binding affinity to the amyloid beta peptide, β-sheet breaker small molecules can be developed and modified with various scaffolds. In the current study, a novel series of 2,3-disubstitutedbenzofuran derivatives was designed and created by fusing the benzofuran core of a known iron chelator, neuroprotective, and neurorestorative agent, like VK-28, with a motif found in the structure of a known muscarinic inhibitor and amyloid binding agent, like SKF-64346. Measurements of the binding affinity and in vitro aggregation inhibition of the Aβ42 peptide were made using the thioflavin T (ThT) test. Using AutoDock 4.2 software, molecular docking studies of the synthesized compounds were performed on the monomer and fibril structures of amyloid beta peptide. The compounds 8a-8g exhibited strong binding energy and affinity to Aβ fibrils as well as a 50%-67% reduction of the growth of Aβ aggregation. Finally, the positive traits of our recently synthesized compounds make them excellent candidates for additional in vivo testing as a "β-sheet breaking agent."
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Affiliation(s)
- Awwad A Radwan
- Kayyali Chair for Pharmaceutical Industries, Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Fars K Alanazi
- Kayyali Chair for Pharmaceutical Industries, Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mohammad Raish
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
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27
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Usman M, Ayub A, Habib S, Rana MS, Rehman Z, Zohaib A, Jamal SB, Jaiswal AK, Andrade BS, de Carvalho Azevedo V, Faheem M, Javed A. Vaccinomics Approach for Multi-Epitope Vaccine Design against Group A Rotavirus Using VP4 and VP7 Proteins. Vaccines (Basel) 2023; 11:726. [PMID: 37112638 PMCID: PMC10144065 DOI: 10.3390/vaccines11040726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/15/2023] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
Rotavirus A is the most common cause of Acute Gastroenteritis globally among children <5 years of age. Due to a segmented genome, there is a high frequency of genetic reassortment and interspecies transmission which has resulted in the emergence of novel genotypes. There are concerns that monovalent (Rotarix: GlaxoSmithKline Biologicals, Rixensart, Belgium) and pentavalent (RotaTeq: MERCK & Co., Inc., Kenilworth, NJ, USA) vaccines may be less effective against non-vaccine strains, which clearly shows the demand for the design of a vaccine that is equally effective against all circulating genotypes. In the present study, a multivalent vaccine was designed from VP4 and VP7 proteins of RVA. Epitopes were screened for antigenicity, allergenicity, homology with humans and anti-inflammatory properties. The vaccine contains four B-cell, three CTL and three HTL epitopes joined via linkers and an N-terminal RGD motif adjuvant. The 3D structure was predicted and refined preceding its docking with integrin. Immune simulation displayed promising results both in Asia and worldwide. In the MD simulation, the RMSD value varied from 0.2 to 1.6 nm while the minimum integrin amino acid fluctuation (0.05-0.1 nm) was observed with its respective ligand. Codon optimization was performed with an adenovirus vector in a mammalian expression system. The population coverage analysis showed 99.0% and 98.47% in South Asia and worldwide, respectively. These computational findings show potential against all RVA genotypes; however, in-vitro/in-vivo screening is essential to devise a meticulous conclusion.
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Affiliation(s)
- Muhammad Usman
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad 44000, Pakistan
- Department of Virology, National Institute of Health, Islamabad 45500, Pakistan
| | - Aaima Ayub
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Sabahat Habib
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | | | - Zaira Rehman
- Department of Virology, National Institute of Health, Islamabad 45500, Pakistan
| | - Ali Zohaib
- Department of Microbiology, The Islamia University of Bahawalpur, Baghdad-ul-Jadeed Campus, Bahawalpur 63100, Pakistan
| | - Syed Babar Jamal
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi 46000, Pakistan (M.F.)
| | - Arun Kumar Jaiswal
- Laboratory of Cellular and Molecular Genetics (LGCM), PG Program in Bioinformatics, Department of Genetics, Ecology, and Evolution, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte 31270-901, Brazil
| | - Bruno Silva Andrade
- Laboratory of Bioinformatics and Computational Chemistry, State University of Southwest of Bahia, Bahia 45083-900, Brazil
| | - Vasco de Carvalho Azevedo
- Laboratory of Cellular and Molecular Genetics (LGCM), PG Program in Bioinformatics, Department of Genetics, Ecology, and Evolution, Institute of Biological Sciences, Federal University of Minas Gerais (UFMG), Belo Horizonte 31270-901, Brazil
| | - Muhammad Faheem
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi 46000, Pakistan (M.F.)
| | - Aneela Javed
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad 44000, Pakistan
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28
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Grasso D, Galderisi S, Santucci A, Bernini A. Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology. Int J Mol Sci 2023; 24:ijms24065819. [PMID: 36982893 PMCID: PMC10054308 DOI: 10.3390/ijms24065819] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/09/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.
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Affiliation(s)
- Daniela Grasso
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Silvia Galderisi
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Andrea Bernini
- Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
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29
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Rayka M, Firouzi R. GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features. Mol Inform 2023; 42:e2200135. [PMID: 36722733 DOI: 10.1002/minf.202200135] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 02/02/2023]
Abstract
In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructing a new scoring function, a function that can predict the binding score for a generated protein-ligand pose during docking procedure or a crystal complex, based on machine and deep learning has become an active research area in computer-aided drug design. GB-Score is a state-of-the-art machine learning-based scoring function that utilizes distance-weighted interatomic contact features, PDBbind-v2019 general set, and Gradient Boosting Trees algorithm to the binding affinity prediction. The distance-weighted interatomic contact featurization method used the distance between different ligand and protein atom types for numerical representation of the protein-ligand complex. GB-Score attains Pearson's correlation 0.862 and RMSE 1.190 on the CASF-2016 benchmark test in the scoring power metric. GB-Score's codes are freely available on the web at https://github.com/miladrayka/GB_Score.
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Affiliation(s)
- Milad Rayka
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
| | - Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
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30
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Cheohen CFDAR, Esteves MEA, da Fonseca TS, Leal CM, Assis FDLF, Campos MF, Rebelo RS, Allonso D, Leitão GG, da Silva ML, Leitão SG. In silico screening of phenylethanoid glycosides, a class of pharmacologically active compounds as natural inhibitors of SARS-CoV-2 proteases. Comput Struct Biotechnol J 2023; 21:1461-1472. [PMID: 36817956 PMCID: PMC9920770 DOI: 10.1016/j.csbj.2023.02.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 02/09/2023] [Accepted: 02/09/2023] [Indexed: 02/13/2023] Open
Abstract
Since the advent of Covid-19, several natural products have been investigated regarding their in silico interactions with SARS-CoV-2 proteases - 3CLpro and PLpro, two of the most important pharmacological targets for antiviral development. Phenylethanoid glycosides (PG) are a class of natural products present in important medicinal plants and a drug containing this group of active ingredients has been successfully used in the treatment of Covid-19 in China. Thus, a dataset with 567 derivatives of this class was built from reviews published between 1994 and 2020, and their interaction against both SARS-CoV-2 proteases was investigated. The virtual screening was performed by filtering the PGs through the evaluation of scores based on the AutoDock Vina, GOLD/ChemPLP, and GOLD/GoldScore evaluation functions. The bRO5 pharmacokinetic parameters of the PGs ranked in the previous step were analyzed and their interaction with key amino acid residues of the 3CLpro and PLpro enzymes was evaluated. Ninety-eight compounds were identified by computational approaches against PLpro and 80 PGs against 3CLpro. Of these, four interacted with key catalytic residues of PLpro, which is an indicative of inhibitory activity, and three compounds interacted with catalytic key residues of 3CLpro. Of these, five PGs occur in plants of the Traditional Chinese Medicine (TCM), while two are components of plants/formulations currently used in the Covid-19 protocols in China. The data presented here show the potential of PGs as selective inhibitors of SARS-CoV-2 3CLpro and PLpro.
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Affiliation(s)
- Caio Felipe de Araujo Ribas Cheohen
- Programa de Pós-graduação Multicêntrico em Ciências Fisiológicas, Centro de Ciências da Saúde, Instituto de Biodiversidade e Sustentabilidade NUPEM, Universidade Federal do Rio de Janeiro, Macaé, RJ 27965045, Brazil
| | - Maria Eduarda Alves Esteves
- Programa de Pós-graduação em Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, Manguinhos, Rio de Janeiro, RJ 21041361, Brazil
| | - Thamirys Silva da Fonseca
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil,Programa de Pós-graduação em Biotecnologia Vegetal e Bioprocessos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Carla Monteiro Leal
- Programa de Pós-graduação em Biotecnologia Vegetal e Bioprocessos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Fernanda de Lemos Fernandes Assis
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Mariana Freire Campos
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil,Programa de Pós-graduação em Biotecnologia Vegetal e Bioprocessos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Raianne Soares Rebelo
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Diego Allonso
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Gilda Guimarães Leitão
- Instituto de Pesquisas de Produtos Naturais, Centro de Ciências da Saúde, Bl. H, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil
| | - Manuela Leal da Silva
- Programa de Pós-graduação Multicêntrico em Ciências Fisiológicas, Centro de Ciências da Saúde, Instituto de Biodiversidade e Sustentabilidade NUPEM, Universidade Federal do Rio de Janeiro, Macaé, RJ 27965045, Brazil,Programa de Pós-graduação em Biologia Computacional e Sistemas, Instituto Oswaldo Cruz, Manguinhos, Rio de Janeiro, RJ 21041361, Brazil,Corresponding author at: Programa de Pós-graduação Multicêntrico em Ciências Fisiológicas, Centro de Ciências da Saúde, Instituto de Biodiversidade e Sustentabilidade NUPEM, Universidade Federal do Rio de Janeiro, Macaé, RJ 27965045, Brazil.
| | - Suzana Guimarães Leitão
- Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil,Programa de Pós-graduação em Biotecnologia Vegetal e Bioprocessos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil,Corresponding author at: Faculdade de Farmácia, Centro de Ciências da Saúde, Bl. A 2º andar, Ilha do Fundão, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ 21941902, Brazil.
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31
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Qin Y, Li Q, Fan L, Ning X, Wei X, You C. Biomanufacturing by In Vitro Biotransformation (ivBT) Using Purified Cascade Multi-enzymes. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2023; 186:1-27. [PMID: 37455283 DOI: 10.1007/10_2023_231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
In vitro biotransformation (ivBT) refers to the use of an artificial biological reaction system that employs purified enzymes for the one-pot conversion of low-cost materials into biocommodities such as ethanol, organic acids, and amino acids. Unshackled from cell growth and metabolism, ivBT exhibits distinct advantages compared with metabolic engineering, including but not limited to high engineering flexibility, ease of operation, fast reaction rate, high product yields, and good scalability. These characteristics position ivBT as a promising next-generation biomanufacturing platform. Nevertheless, challenges persist in the enhancement of bulk enzyme preparation methods, the acquisition of enzymes with superior catalytic properties, and the development of sophisticated approaches for pathway design and system optimization. In alignment with the workflow of ivBT development, this chapter presents a systematic introduction to pathway design, enzyme mining and engineering, system construction, and system optimization. The chapter also proffers perspectives on ivBT development.
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Affiliation(s)
- Yanmei Qin
- University of Chinese Academy of Sciences, Beijing, China
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Qiangzi Li
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Lin Fan
- University of Chinese Academy of Sciences, Beijing, China
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- University of Chinese Academy of Sciences Sino-Danish College, Beijing, China
| | - Xiao Ning
- University of Chinese Academy of Sciences, Beijing, China
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Xinlei Wei
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
- National Technology Innovation Center of Synthetic Biology, Tianjin, China.
| | - Chun You
- In Vitro Synthetic Biology Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
- National Technology Innovation Center of Synthetic Biology, Tianjin, China.
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32
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Gbore DJ, Zakari S, Yusuf L. In silico studies of bioactive compounds from Alpinia officinarum as inhibitors of Zika virus protease. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
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33
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Blanes-Mira C, Fernández-Aguado P, de Andrés-López J, Fernández-Carvajal A, Ferrer-Montiel A, Fernández-Ballester G. Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening. Molecules 2022; 28:molecules28010175. [PMID: 36615367 PMCID: PMC9821981 DOI: 10.3390/molecules28010175] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.
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Assessing How Residual Errors of Scoring Functions Correlate to Ligand Structural Features. Int J Mol Sci 2022; 23:ijms232315018. [PMID: 36499344 PMCID: PMC9739603 DOI: 10.3390/ijms232315018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 12/02/2022] Open
Abstract
Scoring functions (SFs) are ubiquitous tools for early stage drug discovery. However, their accuracy currently remains quite moderate. Despite a number of successful target-specific SFs appearing recently, up until now, no ideas on how to systematically improve the general scope of SFs have been formulated. In this work, we hypothesized that the specific features of ligands, corresponding to interactions well appreciated by medicinal chemists (e.g., hydrogen bonds, hydrophobic and aromatic interactions), might be responsible, in part, for the remaining SF errors. The latter provides direction to efforts aimed at the rational and systematic improvement of SF accuracy. In this proof-of-concept work, we took a CASF-2016 coreset of 285 ligands as a basis for comparison and calculated the values of scores for a representative panel of SFs (including AutoDock 4.2, AutoDock Vina, X-Score, NNScore2.0, ΔVina RF20, and DSX). The residual error of linear correlation of each SF value, with the experimental values of affinity and activity, was then analyzed in terms of its correlation with the presence of the fragments responsible for certain medicinal chemistry defined interactions. We showed that, despite the fact that SFs generally perform reasonably, there is room for improvement in terms of better parameterization of interactions involving certain fragments in ligands. Thus, this approach opens a potential way for the systematic improvement of SFs without their significant complication. However, the straightforward application of the proposed approach is limited by the scarcity of reliable available data for ligand-receptor complexes, which is a common problem in the field.
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