1
|
Zhang Y, Li S, Meng K, Sun S. Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction. J Chem Inf Model 2024; 64:1456-1472. [PMID: 38385768 DOI: 10.1021/acs.jcim.3c01841] [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: 02/23/2024]
Abstract
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.
Collapse
Affiliation(s)
- Yunjiang Zhang
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Shuyuan Li
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Kong Meng
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Shaorui Sun
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| |
Collapse
|
2
|
Menchon G, Maveyraud L, Czaplicki G. Molecular Dynamics as a Tool for Virtual Ligand Screening. Methods Mol Biol 2024; 2714:33-83. [PMID: 37676592 DOI: 10.1007/978-1-0716-3441-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).
Collapse
Affiliation(s)
- Grégory Menchon
- Inserm U1242, Oncogenesis, Stress and Signaling (OSS), Université de Rennes 1, Rennes, France
| | - Laurent Maveyraud
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France
| | - Georges Czaplicki
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France.
| |
Collapse
|
3
|
Feng H, Wang F, Li N, Xu Q, Zheng G, Sun X, Hu M, Li X, Xing G, Zhang G. Use of tree-based machine learning methods to screen affinitive peptides based on docking data. Mol Inform 2023; 42:e202300143. [PMID: 37696773 DOI: 10.1002/minf.202300143] [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: 06/13/2023] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 09/13/2023]
Abstract
Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.
Collapse
Affiliation(s)
- Hua Feng
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Fangyu Wang
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Ning Li
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, China
| | - Qian Xu
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Guanming Zheng
- Public Health and Preventive Medicine Teaching and Research Center, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Xuefeng Sun
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Man Hu
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Xuewu Li
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Guangxu Xing
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Gaiping Zhang
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Longhu Modern Immunology Laboratory, Zhengzhou, China
- School of Advanced Agricultural sciences, Peking University, Beijing, China
- Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, Jiangsu, China
| |
Collapse
|
4
|
Du L, Geng C, Zeng Q, Huang T, Tang J, Chu Y, Zhao K. Dockey: a modern integrated tool for large-scale molecular docking and virtual screening. Brief Bioinform 2023; 24:7034216. [PMID: 36764832 DOI: 10.1093/bib/bbad047] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/12/2023] Open
Abstract
Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.
Collapse
Affiliation(s)
- Lianming Du
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
- Institute for Advanced Study, Chengdu University, Chengdu 610106, China
| | - Chaoyue Geng
- College of Food and Biological Engineering, Chengdu University, Chengdu 610106, China
| | - Qianglin Zeng
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
| | - Ting Huang
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
| | - Jie Tang
- College of Food and Biological Engineering, Chengdu University, Chengdu 610106, China
| | - Yiwen Chu
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
| | - Kelei Zhao
- Antibiotics Research and Re-evaluation Key Laboratory of Sichuan Province, School of Pharmacy, Chengdu University, Chengdu 610106, China
| |
Collapse
|
5
|
Harikrishna AS, Venkitasamy K. Identification of novel human nicotinamide N-methyltransferase inhibitors: a structure-based pharmacophore modeling and molecular dynamics approach. J Biomol Struct Dyn 2023; 41:14638-14650. [PMID: 36856058 DOI: 10.1080/07391102.2023.2183714] [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: 09/08/2022] [Accepted: 02/18/2023] [Indexed: 03/02/2023]
Abstract
Human nicotinamide N-methyltransferase (hNNMT) is a cytosolic enzyme associated in the phase-II metabolism, belonging to the S-adenosyl-L-methionine (SAM)-dependent methyltransferases family. Overexpression of hNNMT was observed in diseases such as metabolic disorders and different types of cancers, which suggest NNMT as a prospective therapeutic target. In this study we propose a structure-based pharmacophore model to understand the structural features responsible for the pharmacological activity. The generated model was validated using the ROC curve (AUC), goodness of hit score (GH), specificity, sensitivity and enrichment factor (EF). The pharmacophore was employed to retrieve active molecules from the ZINC database, followed by virtual-screening and molecular docking. Six molecules with the best pharmfit score, binding energy and ADMET properties were identified in this study. A 150 ns molecular dynamics simulation was performed on the selected molecules complexed with hNNMT protein to validate the results. The molecules ZINC35464499, ZINC13311192, ZINC31159282, ZINC14650833, ZINC14819515 and ZINC00303881 were identified, which could be act as the potential hNNMT inhibitors and can also be used as direct hits for developing novel hNNMT antagonists.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- A S Harikrishna
- Chemical Biology Laboratory, Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India
| | - Kesavan Venkitasamy
- Chemical Biology Laboratory, Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India
| |
Collapse
|
6
|
Qu X, Dong L, Zhang J, Si Y, Wang B. Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules. J Chem Inf Model 2022; 62:4369-4379. [PMID: 36083808 DOI: 10.1021/acs.jcim.2c00916] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein-ligand interfaces, are expected to improve the prediction performance for diverse SFs.
Collapse
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 and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), 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 and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| | - Jinyan Zhang
- 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 and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), 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 and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005 P. R. China
| |
Collapse
|
7
|
Li L, Dai S, Liu JY, Wu W, Zhao QX, Wang X, Wang N, Xu ZH. Antagonistic Effect and In Vitro Activity of Dauricine on Glucagon Receptor. JOURNAL OF NATURAL PRODUCTS 2022; 85:2035-2043. [PMID: 35834753 DOI: 10.1021/acs.jnatprod.2c00446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Abnormal increases in glucagon (GCG) are the primary cause of type II diabetes mellitus. When GCG interacts with a glucagon receptor (GCGR), GCG can increase the blood glucose level. In this paper, a compound that could interfere with the binding of GCG and GCGR to inhibit the increase of blood glucose was investigated. First, molecular docking was used to conduct preliminary screening of compounds whose active components could combine with GCGR by AutoDock Vina. The binding of the receptor-ligand complex was analyzed by PyMOL. Results showed that dauricine could tightly bind to the receptor pocket. Second, the plasmid pcDNA3.1(+)-GCGR containing the target gene was transfected into HEK293 cells for expression, which was the cell model established to screen GCGR antagonist. Dauricine, the lead compound of glucagon receptor antagonist (GRA), was screened using the GRA screening model in vitro. Finally, using [Des-His1, Glu9]-Glucagon amide as the positive control, flow cytometry was used to express the antagonistic effect of the compound. Consequently, dauricine can antagonize the GCGR.
Collapse
Affiliation(s)
- Li Li
- College of Science, Xihua University, Chengdu 610039, China
| | - Shuang Dai
- College of Science, Xihua University, Chengdu 610039, China
| | - Jing-Ya Liu
- College of Science, Xihua University, Chengdu 610039, China
| | - Wei Wu
- College of Science, Xihua University, Chengdu 610039, China
| | - Qian-Xi Zhao
- College of Science, Xihua University, Chengdu 610039, China
| | - Xin Wang
- College of Science, Xihua University, Chengdu 610039, China
| | - Na Wang
- College of Science, Xihua University, Chengdu 610039, China
| | - Zhi-Hong Xu
- College of Science, Xihua University, Chengdu 610039, China
| |
Collapse
|
8
|
Artificial intelligence in virtual screening: models versus experiments. Drug Discov Today 2022; 27:1913-1923. [PMID: 35597513 DOI: 10.1016/j.drudis.2022.05.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/08/2022] [Accepted: 05/12/2022] [Indexed: 12/22/2022]
Abstract
A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.
Collapse
|
9
|
Tianma Formula Alleviates Dementia via ACER2-Mediated Sphingolipid Signaling Pathway Involving A β. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2021:6029237. [PMID: 35069753 PMCID: PMC8357478 DOI: 10.1155/2021/6029237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 07/06/2021] [Indexed: 12/14/2022]
Abstract
Objective To reveal the molecular mechanism of the antagonistic effect of traditional Chinese medicine Tianma formula (TF) on dementia including vascular dementia (VaD) and Alzheimer's disease (AD) and to provide a scientific basis for the study of traditional Chinese medicine for prevention and treatment of dementia. Method The TF was derived from the concerted application of traditional Chinese medicine. We detected the pharmacological effect of TF in VaD rats. The molecular mechanism of TF was examined by APP/PS1 mice in vivo, Caenorhabditis elegans (C. elegans) in vitro, ELISA, pathological assay via HE staining, and transcriptome. Based on RNA-seq analysis in VaD rats, the differentially expressed genes (DEGs) were identified and then verified by quantitative PCR (qPCR) and ELISA. The molecular mechanisms of TF on dementia were further confirmed by network pharmacology and molecular docking finally. Results The Morris water maze showed that TF could improve the cognitive memory function of the VaD rats. The ELISA and histological analysis suggested that TF could protect the hippocampus via reducing tau and IL-6 levels and increasing SYN expression. Meanwhile, it could protect the neurological function by alleviating Aβ deposition in APP/PS1 mice and C. elegans. In the RNA-seq analysis, 3 sphingolipid metabolism pathway-related genes, ADORA3, FCER1G, and ACER2, and another 5 nerve-related genes in 45 key DEGs were identified, so it indicated that the protection mechanism of TF was mainly associated with the sphingolipid metabolism pathway. In the qPCR assay, compared with the model group, the mRNA expressions of the 8 genes mentioned above were upregulated, and these results were consistent with RNA-seq. The protein and mRNA levels of ACER2 were also upregulated. Also, the results of network pharmacology analysis and molecular docking were consistent with those of RNA-seq analysis. Conclusion TF alleviates dementia by reducing Aβ deposition via the ACER2-mediated sphingolipid signaling pathway.
Collapse
|
10
|
Meng J, Zhang L, Wang L, Li S, Xie D, Zhang Y, Liu H. TSSF-hERG: A machine-learning-based hERG potassium channel-specific scoring function for chemical cardiotoxicity prediction. Toxicology 2021; 464:153018. [PMID: 34757159 DOI: 10.1016/j.tox.2021.153018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 10/15/2021] [Accepted: 10/26/2021] [Indexed: 11/27/2022]
Abstract
The human ether-à-go-go-related gene (hERG) encodes the Kv11.1 voltage-gated potassium ion (K+) channel that conducts the rapidly activating delayed rectifier current (IKr) in cardiomyocytes to regulate the repolarization process. Some drugs, as blockers of hERG potassium channels, cannot be marketed due to prolonged QT intervals, as well known as cardiotoxicity. Predetermining the binding affinity values between drugs and hERG through in silico methods can greatly reduce the time and cost required for experimental verification. In this study, we collected 9,215 compounds with AutoDock Vina's docking structures as training set, and collected compounds from four references as test sets. A series of models for predicting the binding affinities of hERG blockers were built based on five machine learning algorithms and combinations of interaction features and ligand features. The model built by support vector regression (SVR) using the combination of all features achieved the best performance on both tenfold cross-validation and external verification, which was selected and named as TSSF-hERG (target-specific scoring function for hERG). TSSF-hERG is more accurate than the classic scoring function of AutoDock Vina and the machine-learning-based generic scoring function RF-Score, with a Pearson's correlation coefficient (Rp) of 0.765, a Spearman's rank correlation coefficient (Rs) of 0.757, a root-mean-square error (RMSE) of 0.585 in a tenfold cross-validation study. All results demonstrated that TSSF-hERG would be useful for improving the power of binding affinity prediction between hERG and compounds, which can be further used for prediction or virtual screening of the hERG-related cardiotoxicity of drug candidates.
Collapse
Affiliation(s)
- Jinhui Meng
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China; Technology Innovation Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China
| | - Lianxin Wang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Shimeng Li
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Di Xie
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Yuxi Zhang
- School of Life Science, Liaoning University, Shenyang, 110036, China
| | - Hongsheng Liu
- Technology Innovation Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China; School of Pharmacy, Liaoning University, Shenyang, 110036, China.
| |
Collapse
|
11
|
Xiong G, Shen C, Yang Z, Jiang D, Liu S, Lu A, Chen X, Hou T, Cao D. Featurization strategies for protein–ligand interactions and their applications in scoring function development. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1567] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Guoli Xiong
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
| | - Chao Shen
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences Zhejiang University Hangzhou China
| | - Ziyi Yang
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
| | - Dejun Jiang
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences Zhejiang University Hangzhou China
- College of Computer Science and Technology Zhejiang University Hangzhou China
| | - Shao Liu
- Department of Pharmacy Xiangya Hospital, Central South University Changsha China
| | - Aiping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis Xiangya Hospital, Central South University Changsha China
| | - Tingjun Hou
- Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences Zhejiang University Hangzhou China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong SAR China
| |
Collapse
|