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Bilkay M, Kanbes-Dindar C, Bozal-Palabiyik B, Eren G, Satana Kara HE, Uslu B. Spectroscopic, electrochemical, and molecular docking studies of the interaction between the antihistamine drug desloratadine and dsDNA. Anal Biochem 2024; 694:115622. [PMID: 39025196 DOI: 10.1016/j.ab.2024.115622] [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: 04/13/2024] [Revised: 06/10/2024] [Accepted: 07/15/2024] [Indexed: 07/20/2024]
Abstract
Through the utilization of fluorescence spectroscopy, electrochemical, and molecular docking methods, this research investigates the interaction between the antihistamine drug desloratadine and calf thymus double-stranded DNA (ct-dsDNA). Deoxyguanosine (dGuo) and deoxyadenosine (dAdo) oxidation signals were diminished by incubation with varying concentrations of desloratadine, as determined by differential pulse voltammetry (DPV). This change was ascribed to desloratadine's binding mechanism to ct-dsDNA. The binding constant (Kb) between desloratadine and ct-dsDNA was determined to be 2.2 × 105 M-1 throughout electrochemical experiments. In order to further develop our comprehension of the interaction mechanism between desloratadine and ct-dsDNA, a series of spectroscopic experiments and molecular docking simulations were conducted. The Kb value was found to be 8.85 × 104 M-1 at a temperature of 25 °C by the use of fluorescence spectroscopic techniques. In summary, the utilization of electrochemical and spectroscopic techniques, alongside molecular docking investigations, has led to the prediction that desloratadine has the capability to interact with ct-dsDNA by groove binding.
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Affiliation(s)
- Mehmetcan Bilkay
- Gazi University, Faculty of Pharmacy, Department of Analytical Chemistry, 06330, Ankara, Turkey
| | - Cigdem Kanbes-Dindar
- Ankara University, Faculty of Pharmacy, Department of Analytical Chemistry, 06560, Ankara, Turkey
| | - Burcin Bozal-Palabiyik
- Ankara University, Faculty of Pharmacy, Department of Analytical Chemistry, 06560, Ankara, Turkey
| | - Gokcen Eren
- Gazi University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, 06330, Ankara, Turkey
| | - Hayriye Eda Satana Kara
- Gazi University, Faculty of Pharmacy, Department of Analytical Chemistry, 06330, Ankara, Turkey.
| | - Bengi Uslu
- Ankara University, Faculty of Pharmacy, Department of Analytical Chemistry, 06560, Ankara, Turkey.
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2
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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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3
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Prat A, Abdel Aty H, Bastas O, Kamuntavičius G, Paquet T, Norvaišas P, Gasparotto P, Tal R. HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery. J Chem Inf Model 2024; 64:5817-5831. [PMID: 39037942 DOI: 10.1021/acs.jcim.4c00481] [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: 07/24/2024]
Abstract
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein-ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's r = 0.86, RMSE = 1.15, Top-1 = 0.95). We introduced a novel approach for interaction profiling, aimed at detecting potential biases within both the model and data sets. This approach not only enhanced interpretability but also reinforced the impartiality of our methodology. Finally, we demonstrated HydraScreen's ability to generalize effectively across novel proteins and ligands through a temporal split. We also provide insights into potential avenues for future development aimed at enhancing the robustness of machine learning scoring functions. HydraScreen (accessible at http://hydrascreen.ro5.ai/paper) provides a user-friendly GUI and a public API, facilitating the easy-access assessment of protein-ligand complexes.
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Affiliation(s)
- Alvaro Prat
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Hisham Abdel Aty
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Orestis Bastas
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | | | - Tanya Paquet
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Povilas Norvaišas
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Piero Gasparotto
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
| | - Roy Tal
- AI Chemistry, Ro5 2801 Gateway Drive, Irving, 75063 Texas, United States
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4
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Shaimardanov AR, Shulga DA, Palyulin VA. Do electrostatic interactions make a difference in physics-based AutoDock4 scoring function? J Comput Chem 2024; 45:1806-1820. [PMID: 38661234 DOI: 10.1002/jcc.27373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/21/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024]
Abstract
Physics-based scoring function AutoDock4 is one of the most successfully applied tools in the area of structure-based drug design. However, current scoring functions are still far from being perfect. In a recent work highlighting the strengths and deficiencies of current scoring functions, we discovered that the residual error of ΔGbind predictions made by AutoDock4 is highly correlated to the presence of formally charged fragments in a ligand. In this work, we study how the use of the high-quality atomic charges, applied for contemporary force fields calculation, affects the quality of the experimental ΔGbind prediction by means of AutoDock4. We initially expected that the previously found discrepancy could be attributed to the Gasteiger charges used within AutoDock4. We show that AutoDock4 is, surprisingly, not sensitive to the charges used, and the use of QC-derived atomic charges does not lead to any statistical improvements. We also briefly discuss the role of the explicit empirical hydrogen bond term along with the electrostatic term.
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Affiliation(s)
- Arslan R Shaimardanov
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Dmitry A Shulga
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russian Federation
| | - Vladimir A Palyulin
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russian Federation
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5
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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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6
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Luginina AP, Khnykin AN, Khorn PA, Moiseeva OV, Safronova NA, Pospelov VA, Dashevskii DE, Belousov AS, Borschevskiy VI, Mishin AV. Rational Design of Drugs Targeting G-Protein-Coupled Receptors: Ligand Search and Screening. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:958-972. [PMID: 38880655 DOI: 10.1134/s0006297924050158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/22/2024] [Accepted: 02/23/2024] [Indexed: 06/18/2024]
Abstract
G protein-coupled receptors (GPCRs) are transmembrane proteins that participate in many physiological processes and represent major pharmacological targets. Recent advances in structural biology of GPCRs have enabled the development of drugs based on the receptor structure (structure-based drug design, SBDD). SBDD utilizes information about the receptor-ligand complex to search for suitable compounds, thus expanding the chemical space of possible receptor ligands without the need for experimental screening. The review describes the use of structure-based virtual screening (SBVS) for GPCR ligands and approaches for the functional testing of potential drug compounds, as well as discusses recent advances and successful examples in the application of SBDD for the identification of GPCR ligands.
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Affiliation(s)
- Aleksandra P Luginina
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Andrey N Khnykin
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Polina A Khorn
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Olga V Moiseeva
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
- Skryabin Institute of Biochemistry and Physiology of Microorganisms, Russian Academy of Sciences, Pushchino, Moscow Region, 142290, Russia
| | - Nadezhda A Safronova
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Vladimir A Pospelov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Dmitrii E Dashevskii
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Anatolii S Belousov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia
| | - Valentin I Borschevskiy
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia.
- Frank Laboratory of Neutron Physics, Joint Institute for Nuclear Research, Dubna, Moscow Region, 141980, Russia
| | - Alexey V Mishin
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, 141701, Russia.
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7
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Qu X, Dong L, Luo D, Si Y, Wang B. Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2024; 64:2263-2274. [PMID: 37433009 DOI: 10.1021/acs.jcim.3c00567] [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: 07/13/2023]
Abstract
Water network rearrangement from the ligand-unbound state to the ligand-bound state is known to have significant effects on the protein-ligand binding interactions, but most of the current machine learning-based scoring functions overlook these effects. In this study, we endeavor to construct a comprehensive and realistic deep learning model by incorporating water network information into both ligand-unbound and -bound states. In particular, extended connectivity interaction features were integrated into graph representation, and graph transformer operator was employed to extract features of the ligand-unbound and -bound states. Through these efforts, we developed a water network-augmented two-state model called ECIFGraph::HM-Holo-Apo. Our new model exhibits satisfactory performance in terms of scoring, ranking, docking, screening, and reverse screening power tests on the CASF-2016 benchmark. In addition, it can achieve superior performance in large-scale docking-based virtual screening tests on the DEKOIS2.0 data set. Our study highlights that the use of a water network-augmented two-state model can be an effective strategy to bolster the robustness and applicability of machine learning-based scoring functions, particularly for targets with hydrophilic or solvent-exposed binding pockets.
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Affiliation(s)
- Xiaoyang Qu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Yubing Si
- College of Chemistry, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, P. R. China
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8
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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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9
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Luo D, Liu D, Qu X, Dong L, Wang B. Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning. J Chem Inf Model 2024; 64:1892-1906. [PMID: 38441880 DOI: 10.1021/acs.jcim.3c01961] [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: 03/26/2024]
Abstract
Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.
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Affiliation(s)
- Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Dandan Liu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Xiaoyang Qu
- School of Pharmacy and Medical Technology, Putian University, Putian 351100, P. R. China
- Key Laboratory of Pharmaceutical Analysis and Laboratory Medicine (Putian University), Fujian Province University, Putian 351100, P. R. China
| | - Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, P. R. China
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10
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Oselusi SO, Dube P, Odugbemi AI, Akinyede KA, Ilori TL, Egieyeh E, Sibuyi NR, Meyer M, Madiehe AM, Wyckoff GJ, Egieyeh SA. The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials. Comput Biol Med 2024; 169:107927. [PMID: 38184864 DOI: 10.1016/j.compbiomed.2024.107927] [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: 09/06/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.
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Affiliation(s)
- Samson O Oselusi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Phumuzile Dube
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Adeshina I Odugbemi
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, 7535, South Africa
| | - Kolajo A Akinyede
- Department of Science Technology, Biochemistry Unit, The Federal Polytechnic P.M.B.5351, Ado Ekiti, 360231, Nigeria
| | - Tosin L Ilori
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Elizabeth Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Nicole Rs Sibuyi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Mervin Meyer
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Abram M Madiehe
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Gerald J Wyckoff
- School of Pharmacy, Division of Pharmacology and Pharmaceutical Sciences, University of Missouri, Kansas City, MO, 64110-2446, United States
| | - Samuel A Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa.
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11
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Wang H. Prediction of protein-ligand binding affinity via deep learning models. Brief Bioinform 2024; 25:bbae081. [PMID: 38446737 PMCID: PMC10939342 DOI: 10.1093/bib/bbae081] [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: 11/27/2023] [Revised: 01/31/2024] [Indexed: 03/08/2024] Open
Abstract
Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.
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Affiliation(s)
- Huiwen Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China
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12
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Chakrabarti M, Tan YS, Balius TE. Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK. Methods Mol Biol 2024; 2797:67-90. [PMID: 38570453 DOI: 10.1007/978-1-0716-3822-4_6] [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: 04/05/2024]
Abstract
Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.
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Affiliation(s)
- Mayukh Chakrabarti
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Y Stanley Tan
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Trent E Balius
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
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13
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Jarończyk M, Abagyan R, Totrov M. Software and Databases for Protein-Protein Docking. Methods Mol Biol 2024; 2780:129-138. [PMID: 38987467 DOI: 10.1007/978-1-0716-3985-6_8] [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: 07/12/2024]
Abstract
Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.
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Affiliation(s)
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
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14
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Ashour A, Sherif AE, El-Sayed SM, Kim JY, Jang DS, Anvari A, Farahat AA, Ibrahim SRM, Mohamed GA, Ainousah BE, Aljohani RF, Al-Hejaili RR, Khoja RH, Hassan AHE, Zaki AA. Tetraenone A: A New β-Ionone Derivative from Tetraena aegyptia. Metabolites 2023; 13:1202. [PMID: 38132884 PMCID: PMC10744760 DOI: 10.3390/metabo13121202] [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: 11/05/2023] [Revised: 11/15/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023] Open
Abstract
In this study, the chemical investigation of Tetraena aegyptia (Zygophyllaceae) led to the identification of a new megastigmene derivative, tetraenone A ((2S, 5R, 6R, 7E)-2-hydroxy-5,6-dihydro-β-ionone) (1), along with (3S, 5R, 6S, 7E)-3-hydroxy-5,6-epoxy-5,6-dihydro-β-ionone- (2), 3,4-dihydroxy-cinnamyl alcohol-4-glucoside (3), 3β,19α-dihydroxy-ursan-28-oic acid (4), quinovic acid (5), p-coumaric acid (6), and ferulic acid (7), for the first time. The chemical structures of 1-7 were confirmed by analysis of their 1D and 2D NMR and HRESIMS spectra and by their comparison with the relevant literature. The absolute configurations of 1 and 2 were assigned based on NOESY interactions and ECD spectra. Conformational analysis showed that 1 existed exclusively in one of the two theoretically possible chair conformers with a predominant s-trans configuration for the 3-oxobut-1-en-1-yl group with the ring, while the half-chair conformer had a pseudo-axial hydroxy group that was predominant over the other half-chair conformation. Boat conformations were not among the most stable conformations, and the s-trans isomerism was in favor of s-cis configuration. In silico investigation revealed that 1 and 2 had more favorable binding interactions with Mpro rather than with TMPRSS2. Accordingly, molecular dynamic simulations were performed on the complexes of compounds 1 and 2 with Mpro to explore the stability of their interaction with the target protein structure. Compounds 1 and 2 might offer a possible starting point for developing covalent inhibitors of Mpro of SARS-CoV-2.
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Affiliation(s)
- Ahmed Ashour
- Department of Pharmacognosy, Faculty of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
- Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt;
| | - Asmaa E. Sherif
- Department of Pharmacognosy, Faculty of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
- Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt;
| | - Selwan M. El-Sayed
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt;
| | - Ji-Young Kim
- Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea; (J.-Y.K.); (D.S.J.)
| | - Dae Sik Jang
- Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul 02447, Republic of Korea; (J.-Y.K.); (D.S.J.)
| | - Abtin Anvari
- Master of Pharmaceutical Sciences Program, California Northstate University, 9700 W Taron Dr., Elk Grove, CA 95757, USA; (A.A.); (A.A.F.)
| | - Abdelbasset A. Farahat
- Master of Pharmaceutical Sciences Program, California Northstate University, 9700 W Taron Dr., Elk Grove, CA 95757, USA; (A.A.); (A.A.F.)
- Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Sabrin R. M. Ibrahim
- Preparatory Year Program, Department of Chemistry, Batterjee Medical College, Jeddah 21442, Saudi Arabia;
- Department of Pharmacognosy, Faculty of Pharmacy, Assiut University, Assiut 71526, Egypt
| | - Gamal A. Mohamed
- Department of Natural Products and Alternative Medicine, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Bayan E. Ainousah
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, Umm Al-Qura University, Makkah 21955, Saudi Arabia;
| | - Raghad F. Aljohani
- College of Pharmacy, Taibah University, Medina 42353, Saudi Arabia; (R.F.A.); (R.R.A.-H.); (R.H.K.)
| | - Razan R. Al-Hejaili
- College of Pharmacy, Taibah University, Medina 42353, Saudi Arabia; (R.F.A.); (R.R.A.-H.); (R.H.K.)
| | - Rahaf H. Khoja
- College of Pharmacy, Taibah University, Medina 42353, Saudi Arabia; (R.F.A.); (R.R.A.-H.); (R.H.K.)
| | - Ahmed H. E. Hassan
- Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed A. Zaki
- Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt;
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA
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15
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Li G, Yuan Y, Zhang R. Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction. Comput Biol Chem 2023; 107:107972. [PMID: 37883905 DOI: 10.1016/j.compbiolchem.2023.107972] [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: 08/23/2023] [Revised: 10/07/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
Accurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional neural network (CNN) to effectively capture both local and global molecular information. Additionally, attention mechanisms can be incorporated to automatically learn and adjust the level of attention given to local and global information, thereby enhancing the performance of the model. To achieve this, we propose the PLAsformer approach, which encodes local and global information of molecules using 3DCNN and BiGRU with attention mechanism, respectively. This approach enhances the model's ability to encode comprehensive local and global molecular information. PLAsformer achieved a Pearson's correlation coefficient of 0.812 and a Root Mean Square Error (RMSE) of 1.284 when comparing experimental and predicted affinity on the PDBBind-2016 dataset. These results surpass the current state-of-the-art methods for binding affinity prediction. The high accuracy of PLAsformer's predictive performance, along with its excellent generalization ability, is clearly demonstrated by these findings.
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Affiliation(s)
- Gaili Li
- School of Information science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Yongna Yuan
- School of Information science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Ruisheng Zhang
- School of Information science and Engineering, Lanzhou University, Lanzhou 730000, China.
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16
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Li Y, Fan Z, Rao J, Chen Z, Chu Q, Zheng M, Li X. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). MEDICAL REVIEW (2021) 2023; 3:465-486. [PMID: 38282802 PMCID: PMC10808869 DOI: 10.1515/mr-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/30/2023] [Indexed: 01/30/2024]
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
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Affiliation(s)
- Yanbei Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyi Chen
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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17
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Janela T, Bajorath J. Anatomy of Potency Predictions Focusing on Structural Analogues with Increasing Potency Differences Including Activity Cliffs. J Chem Inf Model 2023; 63:7032-7044. [PMID: 37943257 DOI: 10.1021/acs.jcim.3c01530] [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: 11/10/2023]
Abstract
Potency predictions are popular in compound design and optimization but are complicated by intrinsic limitations. Moreover, even for nonlinear methods, activity cliffs (ACs, formed by structural analogues with large potency differences) represent challenging test cases for compound potency predictions. We have devised a new test system for potency predictions, including AC compounds, that is based on partitioned matched molecular pairs (MMP) and makes it possible to monitor prediction accuracy at the level of analogue pairs with increasing potency differences. The results of systematic predictions using different machine learning and control methods on MMP-based data sets revealed increasing prediction errors when potency differences between corresponding training and test compounds increased, including large prediction errors for AC compounds. At the global level, these prediction errors were not apparent due to the statistical dominance of analogue pairs with small potency differences. Test compounds from such pairs were accurately predicted and determined the observed global prediction accuracy. Shapley value analysis, an explainable artificial intelligence approach, was applied to identify structural features determining potency predictions using different methods. The analysis revealed that numerical predictions of different regression models were determined by features that were shared by MMP partner compounds or absent in these compounds, with opposing effects. These findings provided another rationale for accurate predictions of similar potency values for structural analogues and failures in predicting the potency of AC compounds.
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Affiliation(s)
- Tiago Janela
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany
- Lamarr Institute for Machine Learning and Artificial Intelligence, Rheinische Friedrich-Wilhelms-Universität Bonn, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany
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18
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Hou Y, Bai Y, Lu C, Wang Q, Wang Z, Gao J, Xu H. Applying molecular docking to pesticides. PEST MANAGEMENT SCIENCE 2023; 79:4140-4152. [PMID: 37547967 DOI: 10.1002/ps.7700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/17/2023] [Accepted: 08/05/2023] [Indexed: 08/08/2023]
Abstract
Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Yang Hou
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yuqian Bai
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Chang Lu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Qiuchan Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Zishi Wang
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Jinsheng Gao
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Hongliang Xu
- Engineering Research Center of Pesticide of Heilongjiang Province, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
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19
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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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20
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Shoaib TH, Abdelmoniem N, Mukhtar RM, Alqhtani AT, Alalawi AL, Alawaji R, Althubyani MS, Mohamed SGA, Mohamed GA, Ibrahim SRM, Hussein HGA, Alzain AA. Molecular Docking and Molecular Dynamics Studies Reveal the Anticancer Potential of Medicinal-Plant-Derived Lignans as MDM2-P53 Interaction Inhibitors. Molecules 2023; 28:6665. [PMID: 37764441 PMCID: PMC10536213 DOI: 10.3390/molecules28186665] [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: 08/27/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
The interaction between the tumor suppressor protein p53 and its negative regulator, the MDM2 oncogenic protein, has gained significant attention in cancer drug discovery. In this study, 120 lignans reported from Ferula sinkiangensis and Justicia procumbens were assessed for docking simulations on the active pocket of the MDM2 crystal structure bound to Nutlin-3a. The docking analysis identified nine compounds with higher docking scores than the co-crystallized reference. Subsequent AMDET profiling revealed satisfactory pharmacokinetic and safety parameters for these natural products. Three compounds, namely, justin A, 6-hydroxy justicidin A, and 6'-hydroxy justicidin B, were selected for further investigation due to their strong binding affinities of -7.526 kcal/mol, -7.438 kcal/mol, and -7.240 kcal/mol, respectively, which surpassed the binding affinity of the reference inhibitor Nutlin-3a (-6.830 kcal/mol). To assess the stability and reliability of the binding of the candidate hits, a molecular dynamics simulation was performed over a duration of 100 ns. Remarkably, the thorough analysis demonstrated that all the hits exhibited stable molecular dynamics profiles. Based on their effective binding to MDM2, favorable pharmacokinetic properties, and molecular dynamics behavior, these compounds represent a promising starting point for further refinement. Nevertheless, it is essential to synthesize the suggested compounds and evaluate their activity through in vitro and in vivo experiments.
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Affiliation(s)
- Tagyedeen H. Shoaib
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani 21111, Sudan; (T.H.S.); (N.A.); (R.M.M.)
| | - Nihal Abdelmoniem
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani 21111, Sudan; (T.H.S.); (N.A.); (R.M.M.)
| | - Rua M. Mukhtar
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani 21111, Sudan; (T.H.S.); (N.A.); (R.M.M.)
| | - Amal Th. Alqhtani
- Pharmaceutical Care Services, Madinah Cardiac Center, MOH, Al Madinah Al Munawwarah 11176, Saudi Arabia; (A.T.A.); (M.S.A.)
| | - Abdullah L. Alalawi
- Pharmaceutical Care Services, King Salman Medical City, MOH, Al Madinah Al Munawwarah 11176, Saudi Arabia;
| | - Razan Alawaji
- Department of Pharmacology and Toxicology, College of Pharmacy, Qassim University, Qassim 51452, Saudi Arabia;
| | - Mashael S. Althubyani
- Pharmaceutical Care Services, Madinah Cardiac Center, MOH, Al Madinah Al Munawwarah 11176, Saudi Arabia; (A.T.A.); (M.S.A.)
| | - Shaimaa G. A. Mohamed
- Faculty of Dentistry, British University, El Sherouk City, Suez Desert Road, Cairo 11837, Egypt;
| | - Gamal A. Mohamed
- Department of Natural Products and Alternative Medicine, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Sabrin R. M. Ibrahim
- Preparatory Year Program, Department of Chemistry, Batterjee Medical College, Jeddah 21442, Saudi Arabia
- Department of Pharmacognosy, Faculty of Pharmacy, Assiut University, Assiut 71526, Egypt
| | - Hazem G. A. Hussein
- Preparatory Year Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia;
| | - Abdulrahim A. Alzain
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani 21111, Sudan; (T.H.S.); (N.A.); (R.M.M.)
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21
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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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22
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Shen C, Zhang X, Hsieh CY, Deng Y, Wang D, Xu L, Wu J, Li D, Kang Y, Hou T, Pan P. A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers. Chem Sci 2023; 14:8129-8146. [PMID: 37538816 PMCID: PMC10395315 DOI: 10.1039/d3sc02044d] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/03/2023] [Indexed: 08/05/2023] Open
Abstract
Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions are only applicable to a specific task, e.g., binding affinity prediction, binding pose prediction or virtual screening, suggesting that the development of a scoring function with balanced performance in all critical tasks remains a grand challenge. To this end, we propose a novel parameterization strategy by introducing an adjustable binding affinity term that represents the correlation between the predicted outcomes and experimental data into the training of mixture density network. The resulting residue-atom distance likelihood potential not only retains the superior docking and screening power over all the other state-of-the-art approaches, but also achieves a remarkable improvement in scoring and ranking performance. We emphatically explore the impacts of several key elements on prediction accuracy as well as the task preference, and demonstrate that the performance of scoring/ranking and docking/screening tasks of a certain model could be well balanced through an appropriate manner. Overall, our study highlights the potential utility of our innovative parameterization strategy as well as the resulting scoring framework in future structure-based drug design.
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Affiliation(s)
- Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
- State Key Lab of CAD&CG, Zhejiang University Hangzhou 310058 Zhejiang China
- School of Public Health, Zhejiang University Hangzhou 310058 Zhejiang China
- CarbonSilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yafeng Deng
- CarbonSilicon AI Technology Co., Ltd Hangzhou 310018 Zhejiang China
| | - Dong Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology Changzhou 213001 China
| | - Jian Wu
- School of Public Health, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Dan Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
- State Key Lab of CAD&CG, Zhejiang University Hangzhou 310058 Zhejiang China
| | - Peichen Pan
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China
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23
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Abdel-Rehim A, Orhobor O, Hang L, Ni H, King RD. Protein-ligand binding affinity prediction exploiting sequence constituent homology. Bioinformatics 2023; 39:btad502. [PMID: 37572302 PMCID: PMC10463547 DOI: 10.1093/bioinformatics/btad502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 07/10/2023] [Accepted: 08/11/2023] [Indexed: 08/14/2023] Open
Abstract
MOTIVATION Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand. RESULTS The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset. AVAILABILITY AND IMPLEMENTATION Code and data uploaded to https://github.com/abbiAR/PLBAffinity.
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Affiliation(s)
- Abbi Abdel-Rehim
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom
| | | | - Lou Hang
- Department of Mathematics, University College London, London WC1H 0AY, United Kingdom
| | - Hao Ni
- Department of Mathematics, University College London, London WC1H 0AY, United Kingdom
- The Alan Turing Institute, London NW1 2DB, United Kingdom
| | - Ross D King
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, United Kingdom
- The Alan Turing Institute, London NW1 2DB, United Kingdom
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
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24
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Purisima EO, Corbeil CR, Gaudreault F, Wei W, Deprez C, Sulea T. Solvated interaction energy: from small-molecule to antibody drug design. Front Mol Biosci 2023; 10:1210576. [PMID: 37351549 PMCID: PMC10282643 DOI: 10.3389/fmolb.2023.1210576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 05/26/2023] [Indexed: 06/24/2023] Open
Abstract
Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15 years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH.
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25
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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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26
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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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27
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Chen H, Bajorath J. Designing highly potent compounds using a chemical language model. Sci Rep 2023; 13:7412. [PMID: 37150793 PMCID: PMC10164739 DOI: 10.1038/s41598-023-34683-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 05/05/2023] [Indexed: 05/09/2023] Open
Abstract
Compound potency prediction is a major task in medicinal chemistry and drug design. Inspired by the concept of activity cliffs (which encode large differences in potency between similar active compounds), we have devised a new methodology for predicting potent compounds from weakly potent input molecules. Therefore, a chemical language model was implemented consisting of a conditional transformer architecture for compound design guided by observed potency differences. The model was evaluated using a newly generated compound test system enabling a rigorous assessment of its performance. It was shown to predict known potent compounds from different activity classes not encountered during training. Moreover, the model was capable of creating highly potent compounds that were structurally distinct from input molecules. It also produced many novel candidate compounds not included in test sets. Taken together, the findings confirmed the ability of the new methodology to generate structurally diverse highly potent compounds.
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Affiliation(s)
- Hengwei Chen
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany.
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28
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Das A, Bank S, Chatterjee S, Paul N, Sarkar K, Chatterjee A, Chakraborty S, Banerjee C, Majumdar A, Das M, Ghosh S. Bifenthrin disrupts cytochrome c oxidase activity and reduces mitochondrial DNA copy number through oxidative damage in pool barb (Puntius sophore). CHEMOSPHERE 2023; 332:138848. [PMID: 37156291 DOI: 10.1016/j.chemosphere.2023.138848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/19/2023] [Accepted: 05/02/2023] [Indexed: 05/10/2023]
Abstract
Bifenthrin (BF), a synthetic pyrethroid is used worldwide for both agricultural and non-agricultural purposes due to its high insecticidal activity and low toxicity in mammals. However, its improper usage implies a possible risk to aquatic life. The Study was aimed to correlate the association of BF toxicity with mitochondrial DNA copy number variation in edible fish Punitus sophore. The 96-h LC 50 of BF in P. sophore was 3.4 μg/L, fish was treated with sub-lethal doses (0.34 μg/L,0.68 μg/L) of BF for 15 days. The activity and expression level of cytochrome c oxidase (Mt-COI) were measured to assess mitochondrial dysfunction caused by BF. Results showed BF reduced the level of Mt-COI mRNA in treated groups, hindered complex IV activity and increased ROS generation leading to oxidative damage. mtDNAcn was decreased in the muscle, brain and liver after BF treatment. Furthermore, BF induced neurotoxicity in brain and muscle cells through the inhibition of AchE activity. The treated groups showed elevated level of malondialdehyde (MDA) and an imbalance of antioxidant enzymes activity. Molecular docking and simulation analysis also predicted that BF binds to the active sites of the enzyme and restricts the fluctuation of active sites' residues. Hence, outcome of the study suggests reduction of mtDNAcn could be a potential biomarker to assess Bifenthrin induced toxicity in aquatic ecosystem.
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Affiliation(s)
- Anwesha Das
- Department of Zoology, Ballygunge Science College, University of Calcutta, Kolkata, 700019, West Bengal, India.
| | - Sarbashri Bank
- Department of Zoology, Ballygunge Science College, University of Calcutta, Kolkata, 700019, West Bengal, India.
| | - Srilagna Chatterjee
- Department of Zoology, Ballygunge Science College, University of Calcutta, Kolkata, 700019, West Bengal, India.
| | - Nirvika Paul
- Department of Zoology, Ballygunge Science College, University of Calcutta, Kolkata, 700019, West Bengal, India.
| | - Kunal Sarkar
- Department of Zoology, Ballygunge Science College, University of Calcutta, Kolkata, 700019, West Bengal, India.
| | - Arindam Chatterjee
- Department of Zoology, Ballygunge Science College, University of Calcutta, Kolkata, 700019, West Bengal, India.
| | - Santanu Chakraborty
- Department of Zoology, Ballygunge Science College, University of Calcutta, Kolkata, 700019, West Bengal, India.
| | - Chaitali Banerjee
- Department of Zoology, Vidyasagar College for Women, Kolkata, 700006, West Bengal, India.
| | - Anasuya Majumdar
- Department of Zoology, Vidyasagar College for Women, Kolkata, 700006, West Bengal, India.
| | - Madhusudan Das
- Department of Zoology, Ballygunge Science College, University of Calcutta, Kolkata, 700019, West Bengal, India.
| | - Sudakshina Ghosh
- Department of Zoology, Vidyasagar College for Women, Kolkata, 700006, West Bengal, India.
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29
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Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023; 28:molecules28093906. [PMID: 37175316 PMCID: PMC10180087 DOI: 10.3390/molecules28093906] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023] Open
Abstract
The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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30
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Firouzi R, Sowlati-Hashjin S, Chávez-García C, Ashouri M, Karimi-Jafari MH, Karttunen M. Identification of Catechins' Binding Sites in Monomeric A β42 through Ensemble Docking and MD Simulations. Int J Mol Sci 2023; 24:ijms24098161. [PMID: 37175868 PMCID: PMC10179585 DOI: 10.3390/ijms24098161] [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: 03/14/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
The assembly of the amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aβ monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aβ42. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ42. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aβ. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aβ.
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Affiliation(s)
- Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran 1496813151, Iran
| | | | - Cecilia Chávez-García
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Mitra Ashouri
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran P.O. Box 14155-6619, Iran
| | - Mohammad Hossein Karimi-Jafari
- Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran P.O. Box 14155-6619, Iran
| | - Mikko Karttunen
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
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31
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Duan H, Zhang R, Yuan L, Liu Y, Asikaer A, Liu Y, Shen Y. Exploring the therapeutic mechanisms of Gleditsiae Spina acting on pancreatic cancer via network pharmacology, molecular docking and molecular dynamics simulation. RSC Adv 2023; 13:13971-13984. [PMID: 37181515 PMCID: PMC10167735 DOI: 10.1039/d3ra01761c] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 04/19/2023] [Indexed: 05/16/2023] Open
Abstract
Pancreatic cancer is one of the most aggressive tumors and also has a low survival rate. The dried spines of Gleditsia sinensis Lam are known as "Gleditsiae Spina" and they mostly contain flavonoids, phenolic acids, terpenoids, steroids, and other chemical components. In this study, the potential active components and molecular mechanisms of Gleditsiae Spina for treating pancreatic cancer were systematically revealed by network pharmacology, molecular docking and molecular dynamics simulations (MDs). RAC-alpha serine/threonine-protein kinase (AKT1), cellular tumor antigen p53 (TP53), tumor necrosis factor α (TNFα), interleukin-6 (IL6) and vascular endothelial growth factor A (VEGFA) were common targets of Gleditsiae Spina, human cytomegalovirus infection signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and MAPK signaling pathway were critical pathways of fisetin, eriodyctiol, kaempferol and quercetin in the treatment of pancreatic cancer. Molecular dynamics simulations (MDs) results showed that eriodyctiol and kaempferol have long-term stable hydrogen bonds and high binding free energy for TP53 (-23.64 ± 0.03 kcal mol-1 and -30.54 ± 0.02 kcal mol-1, respectively). Collectively, our findings identify active components and potential targets in Gleditsiae Spina for the treatment of pancreatic cancer, which may help to explore leading compounds and potential drugs for pancreatic cancer.
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Affiliation(s)
- Hongtao Duan
- School of Pharmacy and Bioengineering, Chongqing University of Technology Chongqing 405400 China
| | - Rui Zhang
- Department of Pharmacy, Guizhou Provincial People's Hospital 550002 Guiyang China
| | - Lu Yuan
- School of Pharmacy and Bioengineering, Chongqing University of Technology Chongqing 405400 China
| | - Yiyuan Liu
- School of Pharmacy and Bioengineering, Chongqing University of Technology Chongqing 405400 China
| | - Aiminuer Asikaer
- School of Pharmacy and Bioengineering, Chongqing University of Technology Chongqing 405400 China
| | - Yang Liu
- Department of Hepatobiliary Surgery II, Guizhou Provincial People's Hospital 550002 Guiyang China
| | - Yan Shen
- School of Pharmacy and Bioengineering, Chongqing University of Technology Chongqing 405400 China
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32
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Edris A, Abdelrahman M, Osman W, Sherif AE, Ashour A, Garelnabi EAE, Ibrahim SRM, Bafail R, Samman WA, Ghazawi KF, Mohamed GA, Alzain AA. Design of Novel Letrozole Analogues Targeting Aromatase for Breast Cancer: Molecular Docking, Molecular Dynamics, and Theoretical Studies on Gold Nanoparticles. Metabolites 2023; 13:metabo13050583. [PMID: 37233624 DOI: 10.3390/metabo13050583] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/10/2023] [Accepted: 04/21/2023] [Indexed: 05/27/2023] Open
Abstract
The use of aromatase inhibitors is an established therapy for estrogen-dependent breast cancer in postmenopausal women. However, the only commercially available aromatase inhibitor, letrozole, is not highly selective; in addition to aromatase, it has an affinity for binding to desmolase, an enzyme involved in steroidogenesis, which explains the main side effects. Therefore, we designed new compounds based on the structure of letrozole. More than five thousand compounds were constructed based on the letrozole structure. Then, these compounds were screened for their binding ability toward the target protein, aromatase. Quantum docking, Glide docking, and ADME studies showed 14 new molecules with docking scores of ≤-7 kcal/mol, compared to the docking score of -4.109 kcal/mol of the reference, letrozole. Moreover, molecular dynamics (MD) and post-MD MM-GBSA calculations were calculated for the top three compounds, and the results supported in their interaction's stability. Finally, the density-functional theory (DFT) study applied to the top compound to study the interaction with gold nanoparticles revealed the most stable position for the interaction with the gold nanoparticles. The results of this study confirmed that these newly designed compounds could be useful starting points for lead optimization. Further in vitro and in vivo studies are recommended for these compounds to verify these promising results experimentally.
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Affiliation(s)
- Alaa Edris
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani 21111, Sudan
| | - Mohammed Abdelrahman
- Department of Pharmaceutics, Faculty of Pharmacy, University of Gezira, Wad Madani 21111, Sudan
| | - Wadah Osman
- Department of Pharmacognosy, Faculty of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-kharj 11942, Saudi Arabia
- Department of Pharmacognosy, Faculty of Pharmacy, University of Khartoum, Al-Qasr Ave, Khartoum 11111, Sudan
| | - Asmaa E Sherif
- Department of Pharmacognosy, Faculty of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-kharj 11942, Saudi Arabia
- Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Ashour
- Department of Pharmacognosy, Faculty of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-kharj 11942, Saudi Arabia
- Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura 35516, Egypt
| | - Elrashied A E Garelnabi
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Khartoum, Al-Qasr Ave, Khartoum 11111, Sudan
| | - Sabrin R M Ibrahim
- Preparatory Year Program, Department of Chemistry, Batterjee Medical College, Jeddah 21442, Saudi Arabia
- Department of Pharmacognosy, Faculty of Pharmacy, Assiut University, Assiut 71526, Egypt
| | - Rawan Bafail
- Department of Pharmaceutics and Pharmaceutical Technology, College of Pharmacy, Taibah University, Medina 42353, Saudi Arabia
| | - Waad A Samman
- Department of Pharmacology and Toxicology, College of Pharmacy, Taibah University, Medina 30078, Saudi Arabia
| | - Kholoud F Ghazawi
- Clinical Pharmacy Department, College of Pharmacy, Umm Al-Qura University, Makkah 24382, Saudi Arabia
| | - Gamal A Mohamed
- Department of Natural Products and Alternative Medicine, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Abdulrahim A Alzain
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani 21111, Sudan
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33
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Ferraz MVF, Neto JCS, Lins RD, Teixeira ES. An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties. Phys Chem Chem Phys 2023; 25:7257-7267. [PMID: 36810523 DOI: 10.1039/d2cp05644e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol-1 to 2.45 kcal mol-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.
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Affiliation(s)
- Matheus V F Ferraz
- Department of Virology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, FIOCRUZ, Recife, PE, Brazil.,Department of Fundamental Chemistry, Federal University of Pernambuco, UFPE, Recife, PE, Brazil.,Heidelberg Institute for Theoretical Studies, HITS, Heidelberg, Germany
| | - José C S Neto
- Recife Center for Advanced Studies and Systems, CESAR, Recife, PE, Brazil.
| | - Roberto D Lins
- Department of Virology, Aggeu Magalhães Institute, Oswaldo Cruz Foundation, FIOCRUZ, Recife, PE, Brazil.,Department of Fundamental Chemistry, Federal University of Pernambuco, UFPE, Recife, PE, Brazil
| | - Erico S Teixeira
- Recife Center for Advanced Studies and Systems, CESAR, Recife, PE, Brazil.
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34
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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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35
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Szwabowski GL, Baker DL, Parrill AL. Application of computational methods for class A GPCR Ligand discovery. J Mol Graph Model 2023; 121:108434. [PMID: 36841204 DOI: 10.1016/j.jmgm.2023.108434] [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: 10/03/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/22/2023]
Abstract
G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.
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Affiliation(s)
| | - Daniel L Baker
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA
| | - Abby L Parrill
- Department of Chemistry, The University of Memphis, Memphis, TN, 38152, USA.
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36
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Predicting Potent Compounds Using a Conditional Variational Autoencoder Based upon a New Structure-Potency Fingerprint. Biomolecules 2023; 13:biom13020393. [PMID: 36830761 PMCID: PMC9953226 DOI: 10.3390/biom13020393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/07/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Prediction of the potency of bioactive compounds generally relies on linear or nonlinear quantitative structure-activity relationship (QSAR) models. Nonlinear models are generated using machine learning methods. We introduce a novel approach for potency prediction that depends on a newly designed molecular fingerprint (FP) representation. This structure-potency fingerprint (SPFP) combines different modules accounting for the structural features of active compounds and their potency values in a single bit string, hence unifying structure and potency representation. This encoding enables the derivation of a conditional variational autoencoder (CVAE) using SPFPs of training compounds and apply the model to predict the SPFP potency module of test compounds using only their structure module as input. The SPFP-CVAE approach correctly predicts the potency values of compounds belonging to different activity classes with an accuracy comparable to support vector regression (SVR), representing the state-of-the-art in the field. In addition, highly potent compounds are predicted with very similar accuracy as SVR and deep neural networks.
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37
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Liu R, Liu X, Wu J. Persistent Path-Spectral (PPS) Based Machine Learning for Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2023; 63:1066-1075. [PMID: 36647267 DOI: 10.1021/acs.jcim.2c01251] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Molecular descriptors are essential to quantitative structure activity/property relationship (QSAR/QSPR) models and machine learning models. Here we propose persistent path-spectral (PPS), PPS-based molecular descriptors, and PPS-based machine learning model for the prediction of the protein-ligand binding affinity, for the first time. For the graph, simplicial complex, and hypergraph representation of molecular structures and interactions, the path-Laplacian can be constructed and the derived path-spectral naturally gives a quantitative description of molecules. Further, by introducing the filtration process of the representation, the persistent path-spectral can be derived, which gives a multiscale characterization of molecules. Molecular descriptors from the persistent path-spectral attributes then are combined with the machine learning model, in particular, the gradient boosting tree, to form our PPS-ML model. We test our model on three most commonly used data sets, i.e., PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016, and our model can achieve competitive results.
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Affiliation(s)
- Ran Liu
- Hebei Normal University, Shijiazhuang, Hebei050024, China.,Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408, China
| | - Xiang Liu
- Chern Institute of Mathematics, Nankai University, Tianjin, 300071, China
| | - Jie Wu
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408, China
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38
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Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions. Molecules 2023; 28:molecules28041661. [PMID: 36838647 PMCID: PMC9966217 DOI: 10.3390/molecules28041661] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/05/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.
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39
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Matalińska J, Lipiński PFJ. Docking is not enough: 17-trifluoromethylphenyl trinor PGF2α is only a very weak ligand of neurokinin-1 receptor. Exp Mol Pathol 2023; 129:104849. [PMID: 36526011 DOI: 10.1016/j.yexmp.2022.104849] [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: 09/22/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
17-trifluoromethylphenyl trinor prostaglandin F2α (17-CF3PTPGF2α) was reported recently to exhibit in vitro and in vivo anticancer activity. Based solely on the results of in silico molecular docking, it was claimed that this compound is NK1 receptor (NK1R) antagonist and that its activity is through this receptor. In this contribution we show that 17-CF3PTPGF2α is only a very weak NK1R ligand (IC50 > 200 μM). In connection with that we discuss the issue of this compound's molecular target. Finally, we briefly narrate on the proper use of molecular docking in biomedical research.
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Affiliation(s)
- Joanna Matalińska
- Department of Neuropeptides, Mossakowski Medical Research Institute Polish Academy of Sciences, 5 Pawińskiego Street, PL 02-106, Warsaw, Poland.
| | - Piotr F J Lipiński
- Department of Neuropeptides, Mossakowski Medical Research Institute Polish Academy of Sciences, 5 Pawińskiego Street, PL 02-106, Warsaw, Poland.
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40
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Vemula D, Jayasurya P, Sushmitha V, Kumar YN, Bhandari V. CADD, AI and ML in drug discovery: A comprehensive review. Eur J Pharm Sci 2023; 181:106324. [PMID: 36347444 DOI: 10.1016/j.ejps.2022.106324] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 11/03/2022] [Indexed: 11/06/2022]
Abstract
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.
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Affiliation(s)
- Divya Vemula
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | - Perka Jayasurya
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | - Varthiya Sushmitha
- National Institute of Pharmaceutical Education and Research- Hyderabad, India
| | | | - Vasundhra Bhandari
- National Institute of Pharmaceutical Education and Research- Hyderabad, India.
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41
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Ghosh R, Thomas DS, Arcot J. Molecular Recognition Patterns between Vitamin B12 and Proteins Explored through STD-NMR and In Silico Studies. Foods 2023; 12:foods12030575. [PMID: 36766105 PMCID: PMC9914923 DOI: 10.3390/foods12030575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/23/2023] [Accepted: 01/23/2023] [Indexed: 02/03/2023] Open
Abstract
Ligand-receptor molecular recognition is the basis of biological processes. The Saturation Transfer Difference-NMR (STD-NMR) technique has been recently used to gain qualitative and quantitative information about physiological interactions at an atomic resolution. The molecular recognition patterns between the cyanocobalamin (CNBL)/aqua cobalamin (OHBL) and different plant and animal proteins were investigated via STD-NMR supplemented by molecular docking. This study demonstrates that myoglobin has the highest binding affinity and that gluten has the lowest affinity. Casein also shows a higher binding affinity for cyanocobalamin when compared with that of plant-based proteins. STD-NMR results showed the moderate binding capability of casein with both CNBL and OHBL. Computer simulation confirmed the recognition mode in theory and was compared with the experiments. This work is beneficial for understanding the binding affinity and biological action of cyanocobalamin and will attract researchers to use NMR technology to link the chemical and physiological properties of nutrients.
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Affiliation(s)
- Ruchira Ghosh
- Food and Health, School of Chemical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
| | | | - Jayashree Arcot
- Food and Health, School of Chemical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia
- Correspondence: ; Tel.: +61-293855360
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42
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Rzęsikowska K, Kalinowska-Tłuścik J, Krawczuk A. Hierarchical analysis of the target-based scoring function modification for the example of selected class A GPCRs. Phys Chem Chem Phys 2023; 25:3513-3520. [PMID: 36637161 DOI: 10.1039/d2cp04671g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Computational methods, especially molecular docking-based calculations, have become indispensable in the modern drug discovery workflow. The constantly increasing chemical space requires fast, robust but most of all highly predictive methods to search for new bioactive agents. Thus, the scoring function (SF) is a useful and broadly applied energy-based element of docking software, allowing quick and effective evaluation of a ligand's propensity to bind to selected protein targets. Despite many spectacular successes of molecular docking applications in virtual screening (VS), the obtained results are often far from ideal, leading to incorrect selection of hit molecules and poor pose prediction. In our study we focused on docking calculation for the selected class A G-protein coupled receptors (GPCRs), with experimentally determined 3D structures and a sufficient set of known ligands with affinity values reported in the ChEMBL database. Our goal is to investigate how much the energy-based scoring function for this particular target class changes when changing from the default to the re-estimated weighting scheme on the specified energy terms in the SF definition. Additionally, we want to verify if indeed more accurate results are obtained when considering different levels of the biological hierarchy, namely: the whole class A GPCRs, sub-subfamilies, or just the individual proteins while applying default or specifically designed weighting coefficients. The performed calculation and evaluation factor values suggest a significant improvement of docking results for the designed SF definition. This individual approach improves the accuracy of binding affinity prediction and active compound recognition. The designed scoring function for classes, sub-subfamilies, or proteins leads to a significant improvement of molecular docking performance, especially at the level of individual proteins. Our results show that to increase the efficiency and predictive power of molecular docking calculations applied in classical VS, the strategy based on the individual approach for scoring function definition for selected proteins should be considered.
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Affiliation(s)
- Katarzyna Rzęsikowska
- Department of Crystal Chemistry and Crystal Physics, Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387 Kraków, Poland.
| | - Justyna Kalinowska-Tłuścik
- Department of Crystal Chemistry and Crystal Physics, Faculty of Chemistry, Jagiellonian University, Gronostajowa 2, 30-387 Kraków, Poland.
| | - Anna Krawczuk
- Institute of Inorganic Chemistry, Georg-August University, Tammannstrasse 4, 37077, Goettingen, Germany.
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43
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Xia C, Feng SH, Xia Y, Pan X, Shen HB. Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network. Brief Bioinform 2023; 24:6982728. [PMID: 36627113 DOI: 10.1093/bib/bbac603] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/01/2022] [Accepted: 12/08/2022] [Indexed: 01/12/2023] Open
Abstract
Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinity of a protein-ligand complex with the known bound structure because of the potential preference of scoring system. In recent years, deep learning (DL) techniques have been applied to SFs without sophisticated feature engineering. Nevertheless, existing methods cannot model the differential contribution of atoms in various regions of proteins, and the relationship between atom properties and intermolecular distance is also not fully explored. We propose a novel empirical graph neural network for accurate protein-ligand binding affinity prediction (EGNA). Graphs of protein, ligand and their interactions are constructed based on different regions of each bound complex. Proteins and ligands are effectively represented by graph convolutional layers, enabling the EGNA to capture interaction patterns precisely by simulating empirical SFs. The contributions of different factors on binding affinity can thus be transparently investigated. EGNA is compared with the state-of-the-art machine learning-based SFs on two widely used benchmark data sets. The results demonstrate the superiority of EGNA and its good generalization capability.
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Affiliation(s)
- Chunqiu Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| | - Shi-Hao Feng
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| | - Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China
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44
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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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45
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Basnet S, Marahatha R, Shrestha A, Bhattarai S, Katuwal S, Sharma KR, Marasini BP, Dahal SR, Basnyat RC, Patching SG, Parajuli N. In Vitro and In Silico Studies for the Identification of Potent Metabolites of Some High-Altitude Medicinal Plants from Nepal Inhibiting SARS-CoV-2 Spike Protein. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248957. [PMID: 36558090 PMCID: PMC9786757 DOI: 10.3390/molecules27248957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/01/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
Despite ongoing vaccination programs against COVID-19 around the world, cases of infection are still rising with new variants. This infers that an effective antiviral drug against COVID-19 is crucial along with vaccinations to decrease cases. A potential target of such antivirals could be the membrane components of the causative pathogen, SARS-CoV-2, for instance spike (S) protein. In our research, we have deployed in vitro screening of crude extracts of seven ethnomedicinal plants against the spike receptor-binding domain (S1-RBD) of SARS-CoV-2 using an enzyme-linked immunosorbent assay (ELISA). Following encouraging in vitro results for Tinospora cordifolia, in silico studies were conducted for the 14 reported antiviral secondary metabolites isolated from T. cordifolia-a species widely cultivated and used as an antiviral drug in the Himalayan country of Nepal-using Genetic Optimization for Ligand Docking (GOLD), Molecular Operating Environment (MOE), and BIOVIA Discovery Studio. The molecular docking and binding energy study revealed that cordifolioside-A had a higher binding affinity and was the most effective in binding to the competitive site of the spike protein. Molecular dynamics (MD) simulation studies using GROMACS 5.4.1 further assayed the interaction between the potent compound and binding sites of the spike protein. It revealed that cordifolioside-A demonstrated better binding affinity and stability, and resulted in a conformational change in S1-RBD, hence hindering the activities of the protein. In addition, ADMET analysis of the secondary metabolites from T. cordifolia revealed promising pharmacokinetic properties. Our study thus recommends that certain secondary metabolites of T. cordifolia are possible medicinal candidates against SARS-CoV-2.
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Affiliation(s)
- Saroj Basnet
- Center for Drug Design and Molecular Simulation Division, Kathmandu 44600, Nepal
| | - Rishab Marahatha
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
- Department of Chemistry, Oklahoma State University, Still Water, OK 74078, USA
| | - Asmita Shrestha
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
| | - Salyan Bhattarai
- Paraza Pharma, Inc., 2525 Avenue Marie-Curie, Montreal, QC H4S 2E1, Canada
| | - Saurav Katuwal
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
| | - Khaga Raj Sharma
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
| | | | - Salik Ram Dahal
- Department of Chemistry, Oklahoma State University, Still Water, OK 74078, USA
- Oakridge National Laboratory, Bethel Valley Rd, Oak Ridge, TN 37830, USA
| | - Ram Chandra Basnyat
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
| | - Simon G. Patching
- Independent Researcher, Leeds LS2 9JT, UK
- Correspondence: (S.G.P.); (N.P.)
| | - Niranjan Parajuli
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
- Correspondence: (S.G.P.); (N.P.)
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46
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Xu M, Shen C, Yang J, Wang Q, Huang N. Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening. ACS OMEGA 2022; 7:39417-39428. [PMID: 36340123 PMCID: PMC9632257 DOI: 10.1021/acsomega.2c05826] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.
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Affiliation(s)
- Min Xu
- College
of Life Sciences, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
| | - Cheng Shen
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
- Graduate
School of Peking Union Medical College, Chinese Academy of Medical Sciences, No. 9, Dongdan Santiao, Dongcheng District, Beijing 100730, China
| | - Jincai Yang
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
| | - Qing Wang
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
- School
of Pharmaceutical Science and Technology, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin 300072, China
| | - Niu Huang
- National
Institute of Biological Sciences, 7 Science Park Road, Zhongguancun Life Science
Park, Beijing 102206, China
- Tsinghua
Institute of Multidisciplinary Biomedical Research, Tsinghua University, 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
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47
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Liu J, Xia KL, Wu J, Yau SST, Wei GW. Biomolecular Topology: Modelling and Analysis. ACTA MATHEMATICA SINICA, ENGLISH SERIES 2022; 38:1901-1938. [PMID: 36407804 PMCID: PMC9640850 DOI: 10.1007/s10114-022-2326-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 07/12/2022] [Indexed: 05/25/2023]
Abstract
With the great advancement of experimental tools, a tremendous amount of biomolecular data has been generated and accumulated in various databases. The high dimensionality, structural complexity, the nonlinearity, and entanglements of biomolecular data, ranging from DNA knots, RNA secondary structures, protein folding configurations, chromosomes, DNA origami, molecular assembly, to others at the macromolecular level, pose a severe challenge in their analysis and characterization. In the past few decades, mathematical concepts, models, algorithms, and tools from algebraic topology, combinatorial topology, computational topology, and topological data analysis, have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge. In this work, we introduce biomolecular topology, which concerns the topological problems and models originated from the biomolecular systems. More specifically, the biomolecular topology encompasses topological structures, properties and relations that are emerged from biomolecular structures, dynamics, interactions, and functions. We discuss the various types of biomolecular topology from structures (of proteins, DNAs, and RNAs), protein folding, and protein assembly. A brief discussion of databanks (and databases), theoretical models, and computational algorithms, is presented. Further, we systematically review related topological models, including graphs, simplicial complexes, persistent homology, persistent Laplacians, de Rham-Hodge theory, Yau-Hausdorff distance, and the topology-based machine learning models.
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Affiliation(s)
- Jian Liu
- School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, 050024 P. R. China
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
| | - Ke-Lin Xia
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, 639798 Singapore
| | - Jie Wu
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084 P. R. China
| | - Stephen Shing-Toung Yau
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, 101408 P. R. China
- Department of Mathematical Sciences, Tsinghua University, Beijing, 100084 P. R. China
| | - Guo-Wei Wei
- Department of Mathematics & Department of Biochemistry and Molecular Biology & Department of Electrical and Computer Engineering, Michigan State University, Wells Hall 619 Red Cedar Road, East Lansing, MI 48824-1027 USA
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Zhao Z, Bourne PE. Harnessing systematic protein-ligand interaction fingerprints for drug discovery. Drug Discov Today 2022; 27:103319. [PMID: 35850431 DOI: 10.1016/j.drudis.2022.07.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 12/15/2022]
Abstract
Determining protein-ligand interaction characteristics and mechanisms is crucial to the drug discovery process. Here, we review recent progress and successful applications of a systematic protein-ligand interaction fingerprint (IFP) approach for investigating proteome-wide protein-ligand interactions for drug development. Specifically, we review the use of this IFP approach for revealing polypharmacology across the kinome, predicting promising targets from which to design allosteric inhibitors and covalent kinase inhibitors, uncovering the binding mechanisms of drugs of interest, and demonstrating resistant mechanisms of specific drugs. Together, we demonstrate that the IFP strategy is efficient and practical for drug design research for protein kinases as targets and is extensible to other protein families.
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Affiliation(s)
- Zheng Zhao
- School of Data Science and Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA.
| | - Philip E Bourne
- School of Data Science and Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA.
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Chen R, Li X, Yang Y, Song X, Wang C, Qiao D. Prediction of protein-protein interaction sites in intrinsically disordered proteins. Front Mol Biosci 2022; 9:985022. [PMID: 36250006 PMCID: PMC9567019 DOI: 10.3389/fmolb.2022.985022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/27/2022] [Indexed: 11/25/2022] Open
Abstract
Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles.
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Affiliation(s)
- Ranran Chen
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Xinlu Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Yaqing Yang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Xixi Song
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Cheng Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
- *Correspondence: Cheng Wang, ; Dongdong Qiao,
| | - Dongdong Qiao
- Shandong Mental Health Center, Shandong University, Jinan, China
- *Correspondence: Cheng Wang, ; Dongdong Qiao,
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Dutkiewicz Z. Computational methods for calculation of protein-ligand binding affinities in structure-based drug design. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2020-0034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Abstract
Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.
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Affiliation(s)
- Zbigniew Dutkiewicz
- Department of Chemical Technology of Drugs , Poznan University of Medical Sciences , ul. Grunwaldzka 6 , 60-780 Poznań , Poznan , 60-780, Poland
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