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Lee S, Jang B, Hwang J, Lee Y, Cho S, Yang H, Yun JH, Shin DH, Lee W, Oh ES. Everolimus exerts anticancer effects through inhibiting the interaction of matrix metalloproteinase-7 with syndecan-2 in colon cancer cells. Am J Physiol Cell Physiol 2024; 326:C1067-C1079. [PMID: 38314724 DOI: 10.1152/ajpcell.00669.2023] [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: 12/05/2023] [Revised: 01/28/2024] [Accepted: 01/28/2024] [Indexed: 02/07/2024]
Abstract
Previous work showed that matrix metalloproteinase-7 (MMP-7) regulates colon cancer activities through an interaction with syndecan-2 (SDC-2) and SDC-2-derived peptide that disrupts this interaction and exhibits anticancer activity in colon cancer. Here, to identify potential anticancer agents, a library of 1,379 Food and Drug Administration (FDA)-approved drugs that interact with the MMP-7 prodomain were virtually screened by protein-ligand docking score analysis using the GalaxyDock3 program. Among five candidates selected based on their structures and total energy values for interacting with the MMP-7 prodomain, the known mechanistic target of rapamycin kinase (mTOR) inhibitor, everolimus, showed the highest binding affinity and the strongest ability to disrupt the interaction of the MMP-7 prodomain with the SDC-2 extracellular domain in vitro. Everolimus treatment of the HCT116 human colon cancer cell line did not affect the mRNA expression levels of MMP-7 and SDC-2 but reduced the adhesion of cells to MMP-7 prodomain-coated plates and the cell-surface localization of MMP-7. Thus, everolimus appears to inhibit the interaction between MMP-7 and SDC-2. Everolimus treatment of HCT116 cells also reduced their gelatin-degradation activity and anticancer activities, including colony formation. Interestingly, cells treated with sirolimus, another mTOR inhibitor, triggered less gelatin-degradation activity, suggesting that this inhibitory effect of everolimus was not due to inhibition of the mTOR pathway. Consistently, everolimus inhibited the colony-forming ability of mTOR-resistant HT29 cells. Together, these data suggest that, in addition to inhibiting mTOR signaling, everolimus exerts anticancer activity by interfering with the interaction of MMP-7 and SDC-2, and could be a useful therapeutic anticancer drug for colon cancer.NEW & NOTEWORTHY The utility of cancer therapeutics targeting the proteolytic activities of MMPs is limited because MMPs are widely distributed throughout the body and involved in many different aspects of cell functions. This work specifically targets the activation of MMP-7 through its interaction with syndecan-2. Notably, everolimus, a known mTOR inhibitor, blocked this interaction, demonstrating a novel role for everolimus in inhibiting mTOR signaling and impairing the interaction of MMP-7 with syndecan-2 in colon cancer.
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Affiliation(s)
- Seohyeon Lee
- Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Bohee Jang
- Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Jisun Hwang
- Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Yejin Lee
- Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Subin Cho
- Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Hyeonju Yang
- Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Ji-Hye Yun
- PCG-Biotech, Ltd. Yonsei Engineering Research Park 114A, Yonsei University, Seoul, Republic of Korea
- Center for Genome Engineering, Institute for Basic Science, Daejeon, Republic of Korea
| | - Dong Hae Shin
- College of Pharmacy, Ewha Womans University, Seoul, Republic of Korea
| | - Weontae Lee
- PCG-Biotech, Ltd. Yonsei Engineering Research Park 114A, Yonsei University, Seoul, Republic of Korea
| | - Eok-Soo Oh
- Department of Life Sciences, Ewha Womans University, Seoul, Republic of Korea
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2
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Ugurlu SY, McDonald D, Lei H, Jones AM, Li S, Tong HY, Butler MS, He S. Cobdock: an accurate and practical machine learning-based consensus blind docking method. J Cheminform 2024; 16:5. [PMID: 38212855 PMCID: PMC10785400 DOI: 10.1186/s13321-023-00793-x] [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: 03/27/2023] [Accepted: 12/10/2023] [Indexed: 01/13/2024] Open
Abstract
Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.
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Affiliation(s)
- Sadettin Y Ugurlu
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | | | - Huangshu Lei
- YaoPharma Co. Ltd., 100 Xingguang Avenue, Renhe Town, Yubei District, Chongqing, 401121, People's Republic of China
| | - Alan M Jones
- School of Pharmacy, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Shu Li
- Centre for Artificial Intelligence Driven Drug Discovery, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao, 5HV2+CP8, China
| | - Henry Y Tong
- Centre for Artificial Intelligence Driven Drug Discovery, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao, 5HV2+CP8, China
| | | | - Shan He
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
- AIA Insights Ltd, Birmingham, UK.
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Krupa MA, Krupa P. Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking. Methods Mol Biol 2024; 2780:27-41. [PMID: 38987462 DOI: 10.1007/978-1-0716-3985-6_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: 07/12/2024]
Abstract
Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.
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Affiliation(s)
- Magdalena A Krupa
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
| | - Paweł Krupa
- Institute of Physics, Polish Academy of Sciences, Warsaw, Poland.
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4
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Zhang X, Shen C, Wang T, Deng Y, Kang Y, Li D, Hou T, Pan P. ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions. Brief Bioinform 2023; 24:bbad295. [PMID: 37738401 DOI: 10.1093/bib/bbad295] [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: 06/21/2023] [Revised: 07/17/2023] [Accepted: 07/31/2023] [Indexed: 09/24/2023] Open
Abstract
Cracking the entangling code of protein-ligand interaction (PLI) is of great importance to structure-based drug design and discovery. Different physical and biochemical representations can be used to describe PLI such as energy terms and interaction fingerprints, which can be analyzed by machine learning (ML) algorithms to create ML-based scoring functions (MLSFs). Here, we propose the ML-based PLI capturer (ML-PLIC), a web platform that automatically characterizes PLI and generates MLSFs to identify the potential binders of a specific protein target through virtual screening (VS). ML-PLIC comprises five modules, including Docking for ligand docking, Descriptors for PLI generation, Modeling for MLSF training, Screening for VS and Pipeline for the integration of the aforementioned functions. We validated the MLSFs constructed by ML-PLIC in three benchmark datasets (Directory of Useful Decoys-Enhanced, Active as Decoys and TocoDecoy), demonstrating accuracy outperforming traditional docking tools and competitive performance to the deep learning-based SF, and provided a case study of the Serine/threonine-protein kinase WEE1 in which MLSFs were developed by using the ML-based VS pipeline in ML-PLIC. Underpinning the latest version of ML-PLIC is a powerful platform that incorporates physical and biological knowledge about PLI, leveraging PLI characterization and MLSF generation into the design of structure-based VS pipeline. The ML-PLIC web platform is now freely available at http://cadd.zju.edu.cn/plic/.
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Affiliation(s)
- Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Tianyue Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, 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
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, 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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5
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Zhang X, Shen C, Jiang D, Zhang J, Ye Q, Xu L, Hou T, Pan P, Kang Y. TB-IECS: an accurate machine learning-based scoring function for virtual screening. J Cheminform 2023; 15:63. [PMID: 37403155 DOI: 10.1186/s13321-023-00731-x] [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: 02/07/2023] [Accepted: 06/18/2023] [Indexed: 07/06/2023] Open
Abstract
Machine learning-based scoring functions (MLSFs) have shown potential for improving virtual screening capabilities over classical scoring functions (SFs). Due to the high computational cost in the process of feature generation, the numbers of descriptors used in MLSFs and the characterization of protein-ligand interactions are always limited, which may affect the overall accuracy and efficiency. Here, we propose a new SF called TB-IECS (theory-based interaction energy component score), which combines energy terms from Smina and NNScore version 2, and utilizes the eXtreme Gradient Boosting (XGBoost) algorithm for model training. In this study, the energy terms decomposed from 15 traditional SFs were firstly categorized based on their formulas and physicochemical principles, and 324 feature combinations were generated accordingly. Five best feature combinations were selected for further evaluation of the model performance in regard to the selection of feature vectors with various length, interaction types and ML algorithms. The virtual screening power of TB-IECS was assessed on the datasets of DUD-E and LIT-PCBA, as well as seven target-specific datasets from the ChemDiv database. The results showed that TB-IECS outperformed classical SFs including Glide SP and Dock, and effectively balanced the efficiency and accuracy for practical virtual screening.
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Affiliation(s)
- Xujun Zhang
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Jintu Zhang
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
| | - Qing Ye
- 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
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, 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.
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of, Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
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6
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Lee C, Yang J, Kwon S, Seok C. GalaxyDock2-HEME: Protein-ligand docking for heme proteins. J Comput Chem 2023; 44:1369-1380. [PMID: 36809651 DOI: 10.1002/jcc.27092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/20/2023] [Accepted: 02/08/2023] [Indexed: 02/23/2023]
Abstract
Prediction of protein-ligand binding poses is an essential component for understanding protein-ligand interactions and computer-aided drug design. Various proteins involve prosthetic groups such as heme for their functions, and adequate consideration of the prosthetic groups is vital for protein-ligand docking. Here, we extend the GalaxyDock2 protein-ligand docking algorithm to handle ligand docking to heme proteins. Docking to heme proteins involves increased complexity because the interaction of heme iron and ligand has covalent nature. GalaxyDock2-HEME, a new protein-ligand docking program for heme proteins, has been developed based on GalaxyDock2 by adding an orientation-dependent scoring term to describe heme iron-ligand coordination interaction. This new docking program performs better than other noncommercial docking programs such as EADock with MMBP, AutoDock Vina, PLANTS, LeDock, and GalaxyDock2 on a heme protein-ligand docking benchmark set in which ligands are known to bind iron. In addition, docking results on two other sets of heme protein-ligand complexes in which ligands do not bind iron show that GalaxyDock2-HEME does not have a high bias toward iron binding compared to other docking programs. This implies that the new docking program can distinguish iron binders from noniron binders for heme proteins.
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Affiliation(s)
- Changsoo Lee
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Galux Inc, Gwanak-gu, Seoul, Republic of Korea
| | - Sohee Kwon
- Galux Inc, Gwanak-gu, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea.,Galux Inc, Gwanak-gu, Seoul, Republic of Korea
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7
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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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Panagiotopoulos AA, Kalyvianaki K, Tsodoulou PK, Darivianaki MN, Dellis D, Notas G, Daskalakis V, Theodoropoulos PA, Panagiotidis CΑ, Castanas E, Kampa M. Recognition motifs for importin 4 [(L)PPRS(G/P)P] and importin 5 [KP(K/Y)LV] binding, identified by bio-informatic simulation and experimental in vitro validation. Comput Struct Biotechnol J 2022; 20:5952-5961. [PMID: 36382187 PMCID: PMC9646746 DOI: 10.1016/j.csbj.2022.10.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 01/21/2023] Open
Abstract
Nuclear translocation of large proteins is mediated through karyopherins, carrier proteins recognizing specific motifs of cargo proteins, known as nuclear localization signals (NLS). However, only few NLS signals have been reported until now. In the present work, NLS signals for Importins 4 and 5 were identified through an unsupervised in silico approach, followed by experimental in vitro validation. The sequences LPPRS(G/P)P and KP(K/Y)LV were identified and are proposed as recognition motifs for Importins 4 and 5 binding, respectively. They are involved in the trafficking of important proteins into the nucleus. These sequences were validated in the breast cancer cell line T47D, which expresses both Importins 4 and 5. Elucidating the complex relationships of the nuclear transporters and their cargo proteins is very important in better understanding the mechanism of nuclear transport of proteins and laying the foundation for the development of novel therapeutics, targeting specific importins.
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Affiliation(s)
| | - Konstantina Kalyvianaki
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71013, Greece
| | - Paraskevi K. Tsodoulou
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Maria N. Darivianaki
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Dimitris Dellis
- National Infrastructures for Research and Technology, Athens 11523, Greece
| | - George Notas
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71013, Greece
| | - Vangelis Daskalakis
- Department of Chemical Engineering, Cyprus University of Technology, Limassol, Cyprus
| | | | - Christos Α. Panagiotidis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Elias Castanas
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71013, Greece,Corresponding authors.
| | - Marilena Kampa
- Laboratory of Experimental Endocrinology, School of Medicine, University of Crete, 71013, Greece,Corresponding authors.
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9
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Shin SH, Oh SM, Yoon Park JH, Lee KW, Yang H. OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets. BMC Bioinformatics 2022; 23:218. [PMID: 35672685 PMCID: PMC9175487 DOI: 10.1186/s12859-022-04752-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 05/25/2022] [Indexed: 11/22/2022] Open
Abstract
Background Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typically interact with multiple target proteins to elicit their effects. Various machine learning models have been developed to predict multi-target modulating NCs with desired physiological effects. However, due to deficiencies with existing chemical-protein interaction datasets, which are mostly single-labeled and limited, the existing models struggle to predict new chemical-protein interactions. New techniques are needed to overcome these limitations. Results We propose a novel NC discovery model called OptNCMiner that offers various advantages. The model is trained via end-to-end learning with a feature extraction step implemented, and it predicts multi-target modulating NCs through multi-label learning. In addition, it offers a few-shot learning approach to predict NC-protein interactions using a small training dataset. OptNCMiner achieved better prediction performance in terms of recall than conventional classification models. It was tested for the prediction of NC-protein interactions using small datasets and for a use case scenario to identify multi-target modulating NCs for type 2 diabetes mellitus complications. Conclusions OptNCMiner identifies NCs that modulate multiple target proteins, which facilitates the discovery and the understanding of biological activity of novel NCs with desirable health benefits.
Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04752-5.
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Affiliation(s)
- Seo Hyun Shin
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Seung Man Oh
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea
| | - Jung Han Yoon Park
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ki Won Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, 08826, Republic of Korea. .,Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea. .,Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Hee Yang
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
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Hudson PS, Aviat F, Meana-Pañeda R, Warrensford L, Pollard BC, Prasad S, Jones MR, Woodcock HL, Brooks BR. Obtaining QM/MM binding free energies in the SAMPL8 drugs of abuse challenge: indirect approaches. J Comput Aided Mol Des 2022; 36:263-277. [PMID: 35597880 PMCID: PMC9148874 DOI: 10.1007/s10822-022-00443-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 02/17/2022] [Indexed: 11/28/2022]
Abstract
Accurately predicting free energy differences is essential in realizing the full potential of rational drug design. Unfortunately, high levels of accuracy often require computationally expensive QM/MM Hamiltonians. Fortuitously, the cost of employing QM/MM approaches in rigorous free energy simulation can be reduced through the use of the so-called “indirect” approach to QM/MM free energies, in which the need for QM/MM simulations is avoided via a QM/MM “correction” at the classical endpoints of interest. Herein, we focus on the computation of QM/MM binding free energies in the context of the SAMPL8 Drugs of Abuse host–guest challenge. Of the 5 QM/MM correction coupled with force-matching submissions, PM6-D3H4/MM ranked submission proved the best overall QM/MM entry, with an RMSE from experimental results of 2.43 kcal/mol (best in ranked submissions), a Pearson’s correlation of 0.78 (second-best in ranked submissions), and a Kendall \documentclass[12pt]{minimal}
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\begin{document}$$\tau$$\end{document}τ correlation of 0.52 (best in ranked submissions).
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Affiliation(s)
- Phillip S Hudson
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA.
| | - Félix Aviat
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Rubén Meana-Pañeda
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Luke Warrensford
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Benjamin C Pollard
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Samarjeet Prasad
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - Michael R Jones
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
| | - H Lee Woodcock
- Department of Chemistry, University of South Florida, Tampa, FL, 33620, USA
| | - Bernard R Brooks
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20852, USA
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11
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Fundamental considerations in drug design. COMPUTER AIDED DRUG DESIGN (CADD): FROM LIGAND-BASED METHODS TO STRUCTURE-BASED APPROACHES 2022:17-55. [PMCID: PMC9212230 DOI: 10.1016/b978-0-323-90608-1.00005-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.
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12
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Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction. J Comput Aided Mol Des 2021; 35:1095-1123. [PMID: 34708263 DOI: 10.1007/s10822-021-00423-4] [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: 05/03/2021] [Accepted: 10/11/2021] [Indexed: 10/20/2022]
Abstract
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein-ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. In this work, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.
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13
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Masoudi-Sobhanzadeh Y, Jafari B, Parvizpour S, Pourseif MM, Omidi Y. A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset. Comput Biol Med 2021; 138:104896. [PMID: 34601392 DOI: 10.1016/j.compbiomed.2021.104896] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 09/22/2021] [Accepted: 09/22/2021] [Indexed: 01/03/2023]
Abstract
Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.
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Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behzad Jafari
- Department of Medicinal Chemistry, Faculty of Pharmacy, Urmia University of Medical Sciences, Urmia, Iran
| | - Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammad M Pourseif
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, College of Pharmacy, Nova Southeastern University, Florida, 33328, USA.
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14
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Fluorescence Spectroscopic Analysis of ppGpp Binding to cAMP Receptor Protein and Histone-Like Nucleoid Structuring Protein. Int J Mol Sci 2021; 22:ijms22157871. [PMID: 34360641 PMCID: PMC8346002 DOI: 10.3390/ijms22157871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/02/2021] [Accepted: 07/20/2021] [Indexed: 11/17/2022] Open
Abstract
The cyclic AMP receptor protein (CRP) is one of the best-known transcription factors, regulating about 400 genes. The histone-like nucleoid structuring protein (H-NS) is one of the nucleoid-forming proteins and is responsible for DNA packaging and gene repression in prokaryotes. In this study, the binding of ppGpp to CRP and H-NS was determined by fluorescence spectroscopy. CRP from Escherichia coli exhibited intrinsic fluorescence at 341 nm when excited at 280 nm. The fluorescence intensity decreased in the presence of ppGpp. The dissociation constant of 35 ± 3 µM suggests that ppGpp binds to CRP with a similar affinity to cAMP. H-NS also shows intrinsic fluorescence at 329 nm. The fluorescence intensity was decreased by various ligands and the calculated dissociation constant for ppGpp was 80 ± 11 µM, which suggests that the binding site was occupied fully by ppGpp under starvation conditions. This study suggests the modulatory effects of ppGpp in gene expression regulated by CRP and H-NS. The method described here may be applicable to many other proteins.
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15
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Zhang W, Meng Q, Tang J, Guo F. Exploring effectiveness of ab-initio protein-protein docking methods on a novel antibacterial protein complex dataset. Brief Bioinform 2021; 22:6265196. [PMID: 33959764 DOI: 10.1093/bib/bbab150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/12/2021] [Accepted: 03/27/2021] [Indexed: 12/27/2022] Open
Abstract
Diseases caused by bacterial infections become a critical problem in public heath. Antibiotic, the traditional treatment, gradually loses their effectiveness due to the resistance. Meanwhile, antibacterial proteins attract more attention because of broad spectrum and little harm to host cells. Therefore, exploring new effective antibacterial proteins is urgent and necessary. In this paper, we are committed to evaluating the effectiveness of ab-initio docking methods in antibacterial protein-protein docking. For this purpose, we constructed a three-dimensional (3D) structure dataset of antibacterial protein complex, called APCset, which contained $19$ protein complexes whose receptors or ligands are homologous to antibacterial peptides from Antimicrobial Peptide Database. Then we selected five representative ab-initio protein-protein docking tools including ZDOCK3.0.2, FRODOCK3.0, ATTRACT, PatchDock and Rosetta to identify these complexes' structure, whose performance differences were obtained by analyzing from five aspects, including top/best pose, first hit, success rate, average hit count and running time. Finally, according to different requirements, we assessed and recommended relatively efficient protein-protein docking tools. In terms of computational efficiency and performance, ZDOCK was more suitable as preferred computational tool, with average running time of $6.144$ minutes, average Fnat of best pose of $0.953$ and average rank of best pose of $4.158$. Meanwhile, ZDOCK still yielded better performance on Benchmark 5.0, which proved ZDOCK was effective in performing docking on large-scale dataset. Our survey can offer insights into the research on the treatment of bacterial infections by utilizing the appropriate docking methods.
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Affiliation(s)
- Wei Zhang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Qiaozhen Meng
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S.,Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
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16
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Park H, Zhou G, Baek M, Baker D, DiMaio F. Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein-Ligand Docking. J Chem Theory Comput 2021; 17:2000-2010. [PMID: 33577321 PMCID: PMC8218654 DOI: 10.1021/acs.jctc.0c01184] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Accurate and rapid calculation of protein-small molecule interaction free energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here, we describe a new approach in which small molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures. We optimize parameters by requiring that the experimentally determined molecular lattice arrangements have lower energy than all alternative lattice arrangements. Thousands of independent crystal lattice-prediction simulations were run on each of 1386 small molecule crystal structures, and energy function parameters of an implicit solvent energy model were optimized, so native crystal lattice arrangements had the lowest energy. The resulting energy model was implemented in Rosetta, together with a rapid genetic algorithm docking method employing grid-based scoring and receptor flexibility. The success rate of bound structure recapitulation in cross-docking on 1112 complexes was improved by more than 10% over previously published methods, with solutions within <1 Å in over half of the cases. Our results demonstrate that small molecule crystal structures are a rich source of information for guiding molecular force field development, and the improved Rosetta energy function should increase accuracy in a wide range of small molecule structure prediction and design studies.
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Affiliation(s)
- Hahnbeom Park
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Guangfeng Zhou
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - Minkyung Baek
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
| | - David Baker
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, United States
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
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17
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Yu J, Yang J, Seok C, Song WJ. Symmetry-related residues as promising hotspots for the evolution of de novo oligomeric enzymes. Chem Sci 2021; 12:5091-5101. [PMID: 34168770 PMCID: PMC8179601 DOI: 10.1039/d0sc06823c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Directed evolution has provided us with great opportunities and prospects in the synthesis of tailor-made proteins. It, however, often requires at least mid to high throughput screening, necessitating more effective strategies for laboratory evolution. We herein demonstrate that protein symmetry can be a versatile criterion for searching for promising hotspots for the directed evolution of de novo oligomeric enzymes. The randomization of symmetry-related residues located at the rotational axes of artificial metallo-β-lactamase yields drastic effects on catalytic activities, whereas that of non-symmetry-related, yet, proximal residues to the active site results in negligible perturbations. Structural and biochemical analysis of the positive hits indicates that seemingly trivial mutations at symmetry-related spots yield significant alterations in overall structures, metal-coordination geometry, and chemical environments of active sites. Our work implicates that numerous artificially designed and natural oligomeric proteins might have evolutionary advantages of propagating beneficial mutations using their global symmetry. Symmetry-related residues located at the rotational axes can be promising hotspots for the evolution of de novo oligomeric enzymes even though they are distantly located from the active site pocket.![]()
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Affiliation(s)
- Jaeseung Yu
- Department of Chemistry, College of Natural Sciences, Seoul National University Seoul 08826 Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, College of Natural Sciences, Seoul National University Seoul 08826 Republic of Korea
| | - Chaok Seok
- Department of Chemistry, College of Natural Sciences, Seoul National University Seoul 08826 Republic of Korea
| | - Woon Ju Song
- Department of Chemistry, College of Natural Sciences, Seoul National University Seoul 08826 Republic of Korea
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18
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Chen G, Seukep AJ, Guo M. Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs. Mar Drugs 2020; 18:md18110545. [PMID: 33143025 PMCID: PMC7692358 DOI: 10.3390/md18110545] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/27/2020] [Accepted: 10/28/2020] [Indexed: 12/28/2022] Open
Abstract
Marine drugs have long been used and exhibit unique advantages in clinical practices. Among the marine drugs that have been approved by the Food and Drug Administration (FDA), the protein–ligand interactions, such as cytarabine–DNA polymerase, vidarabine–adenylyl cyclase, and eribulin–tubulin complexes, are the important mechanisms of action for their efficacy. However, the complex and multi-targeted components in marine medicinal resources, their bio-active chemical basis, and mechanisms of action have posed huge challenges in the discovery and development of marine drugs so far, which need to be systematically investigated in-depth. Molecular docking could effectively predict the binding mode and binding energy of the protein–ligand complexes and has become a major method of computer-aided drug design (CADD), hence this powerful tool has been widely used in many aspects of the research on marine drugs. This review introduces the basic principles and software of the molecular docking and further summarizes the applications of this method in marine drug discovery and design, including the early virtual screening in the drug discovery stage, drug target discovery, potential mechanisms of action, and the prediction of drug metabolism. In addition, this review would also discuss and prospect the problems of molecular docking, in order to provide more theoretical basis for clinical practices and new marine drug research and development.
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Affiliation(s)
- Guilin Chen
- Key Laboratory of Plant Germplasm Enhancement & Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; (G.C.); (A.J.S.)
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- Innovation Academy for Drug Discovery and Development, Chinese Academy of Sciences, Shanghai 201203, China
| | - Armel Jackson Seukep
- Key Laboratory of Plant Germplasm Enhancement & Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; (G.C.); (A.J.S.)
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- Innovation Academy for Drug Discovery and Development, Chinese Academy of Sciences, Shanghai 201203, China
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Buea, P.O. Box 63 Buea, Cameroon
| | - Mingquan Guo
- Key Laboratory of Plant Germplasm Enhancement & Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China; (G.C.); (A.J.S.)
- Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China
- Innovation Academy for Drug Discovery and Development, Chinese Academy of Sciences, Shanghai 201203, China
- Correspondence: ; Tel.: +86-27-8770-0850
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19
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Yang J, Kwon S, Bae SH, Park KM, Yoon C, Lee JH, Seok C. GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds. J Chem Inf Model 2020; 60:3246-3254. [PMID: 32401021 DOI: 10.1021/acs.jcim.0c00104] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Computational techniques for predicting interactions of proteins and druglike molecules have often been used to search for compounds that bind a given protein with high affinity. More recently, such tools have also been applied to the reverse procedure of searching protein targets for a given compound. Among methods for predicting protein-ligand interactions, ligand-based methods relying on similarity to ligands of known interactions are effective only when similar protein-ligand interactions are known. Receptor-based methods predicting protein-ligand interactions by molecular docking are effective only when high-accuracy receptor structures and binding sites are available. Moreover, the computational cost of molecular docking tends to be too high to be applied to the entire protein structure database. In this paper, an effective target prediction method, which combines ligand similarity-based and receptor structure-based approaches, is introduced. In this method, protein-ligand docking is performed after efficient structure- and similarity-based screening. The enriched protein target database by predicted binding ligands and sites allows detection of protein targets with previously unknown ligand interactions. The method, called GalaxySagittarius, is freely available as a web server at http://galaxy.seoklab.org/sagittarius.
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Affiliation(s)
- Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Sang-Hun Bae
- Dr. Noah Biotech, Gwanggyo Ace Tower 2, 256 Changyoung-daero, Yeongtong-gu, Suwon 16229, Republic of Korea
| | - Kyoung Mii Park
- Dr. Noah Biotech, Gwanggyo Ace Tower 2, 256 Changyoung-daero, Yeongtong-gu, Suwon 16229, Republic of Korea
| | - Changsik Yoon
- Dr. Noah Biotech, Gwanggyo Ace Tower 2, 256 Changyoung-daero, Yeongtong-gu, Suwon 16229, Republic of Korea
| | - Ji-Hyun Lee
- Dr. Noah Biotech, Gwanggyo Ace Tower 2, 256 Changyoung-daero, Yeongtong-gu, Suwon 16229, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
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20
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Aldehyde-alcohol dehydrogenase undergoes structural transition to form extended spirosomes for substrate channeling. Commun Biol 2020; 3:298. [PMID: 32523125 PMCID: PMC7286902 DOI: 10.1038/s42003-020-1030-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 05/26/2020] [Indexed: 11/09/2022] Open
Abstract
Aldehyde-alcohol dehydrogenase (AdhE) is an enzyme responsible for converting acetyl-CoA to ethanol via acetaldehyde using NADH. AdhE is composed of two catalytic domains of aldehyde dehydrogenase (ALDH) and alcohol dehydrogenase (ADH), and forms a spirosome architecture critical for AdhE activity. Here, we present the atomic resolution (3.43 Å) cryo-EM structure of AdhE spirosomes in an extended conformation. The cryo-EM structure shows that AdhE spirosomes undergo a structural transition from compact to extended forms, which may result from cofactor binding. This transition leads to access to a substrate channel between ALDH and ADH active sites. Furthermore, prevention of this structural transition by crosslinking hampers the activity of AdhE, suggesting that the structural transition is important for AdhE activity. This work provides a mechanistic understanding of the regulation mechanisms of AdhE activity via structural transition, and a platform to modulate AdhE activity for developing antibiotics and for facilitating biofuel production.
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21
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Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem 2020; 8:343. [PMID: 32411671 PMCID: PMC7200080 DOI: 10.3389/fchem.2020.00343] [Citation(s) in RCA: 209] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 04/01/2020] [Indexed: 12/15/2022] Open
Abstract
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.
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Affiliation(s)
- Eduardo Habib Bechelane Maia
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil.,Federal Center for Technological Education of Minas Gerais-CEFET-MG, Belo Horizonte, Brazil
| | - Letícia Cristina Assis
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| | | | | | - Alex Gutterres Taranto
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
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22
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Dos Santos Maia M, Soares Rodrigues GC, Silva Cavalcanti AB, Scotti L, Scotti MT. Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry. Mini Rev Med Chem 2020; 20:1322-1340. [PMID: 32013847 DOI: 10.2174/1389557520666200204121129] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/31/2019] [Accepted: 11/04/2019] [Indexed: 02/08/2023]
Abstract
The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.
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Affiliation(s)
- Mayara Dos Santos Maia
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Gabriela Cristina Soares Rodrigues
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Andreza Barbosa Silva Cavalcanti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Luciana Scotti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
| | - Marcus Tullius Scotti
- Program of Natural and Synthetic Bioactive Products (PgPNSB), Health Sciences Center, Federal University of Paraiba, Joao Pessoa-PB, Brazil
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23
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Shen C, Hu Y, Wang Z, Zhang X, Zhong H, Wang G, Yao X, Xu L, Cao D, Hou T. Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions. Brief Bioinform 2020; 22:497-514. [PMID: 31982914 DOI: 10.1093/bib/bbz173] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/10/2019] [Accepted: 11/21/2019] [Indexed: 01/12/2023] Open
Abstract
How to accurately estimate protein-ligand binding affinity remains a key challenge in computer-aided drug design (CADD). In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well with experimentally measured biological activities. In the past few years, machine learning (ML)-based SFs have gradually emerged as potential alternatives and outperformed classical SFs in a series of studies. In this study, to better recognize the potential of classical SFs, we have conducted a comparative assessment of 25 commonly used SFs. Accordingly, the scoring power was systematically estimated by using the state-of-the-art ML methods that replaced the original multiple linear regression method to refit individual energy terms. The results show that the newly-developed ML-based SFs consistently performed better than classical ones. In particular, gradient boosting decision tree (GBDT) and random forest (RF) achieved the best predictions in most cases. The newly-developed ML-based SFs were also tested on another benchmark modified from PDBbind v2007, and the impacts of structural and sequence similarities were evaluated. The results indicated that the superiority of the ML-based SFs could be fully guaranteed when sufficient similar targets were contained in the training set. Moreover, the effect of the combinations of features from multiple SFs was explored, and the results indicated that combining NNscore2.0 with one to four other classical SFs could yield the best scoring power. However, it was not applicable to derive a generic target-specific SF or SF combination.
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24
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Syrlybaeva RR, Talipov MR. CBSF: A New Empirical Scoring Function for Docking Parameterized by Weights of Neural Network. COMPUTATIONAL AND MATHEMATICAL BIOPHYSICS 2019. [DOI: 10.1515/cmb-2019-0009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
A new CBSF empirical scoring function for the estimation of binding energies between proteins and small molecules is proposed in this report. The final score is obtained as a sum of three energy terms calculated using descriptors based on a simple counting of the interacting protein-ligand atomic pairs. All the required weighting coefficients for this method were derived from a pretrained neural network. The proposed method demonstrates a high accuracy and reproduces binding energies of protein-ligand complexes from the CASF-2016 test set with a standard deviation of 2.063 kcal/mol (1.511 log units) and an average error of 1.682 kcal/mol (1.232 log units). Thus, CBSF has a significant potential for the development of rapid and accurate estimates of the protein-ligand interaction energies.
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Affiliation(s)
- Raulia R. Syrlybaeva
- Department of Chemistry and Biochemistry , New Mexico State University , Las Cruces, New Mexico 88003 , United States ; College of Pharmacy , University of Georgia , Athens , Georgia 30602 , United States
| | - Marat R. Talipov
- Department of Chemistry and Biochemistry , New Mexico State University , Las Cruces , New Mexico 88003 , United States
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25
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Hoang HN, Tran TT, Jung C. The Activation of Glycerol Dehydrogenase fromEscherichia coliby ppGpp. B KOREAN CHEM SOC 2019. [DOI: 10.1002/bkcs.11932] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Huyen Nga Hoang
- Department of Molecular MedicineChonnam National University, Graduate school Gwangju South Korea
| | - Thanh Tuyen Tran
- Department of Molecular MedicineChonnam National University, Graduate school Gwangju South Korea
| | - Che‐Hun Jung
- Department of Molecular MedicineChonnam National University, Graduate school Gwangju South Korea
- Department of ChemistryChonnam National University, Graduate school Gwangju South Korea
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26
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Park T, Woo H, Baek M, Yang J, Seok C. Structure prediction of biological assemblies using GALAXY in CAPRI rounds 38-45. Proteins 2019; 88:1009-1017. [PMID: 31774573 DOI: 10.1002/prot.25859] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 11/11/2019] [Accepted: 11/23/2019] [Indexed: 12/12/2022]
Abstract
We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested. Template-based methods depend heavily on the availability of proper templates and template-target similarity, and template-target difference is responsible for inaccuracy of template-based models. Inaccurate template-based models could be improved by our structure refinement and loop modeling methods based on physics-based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics-based methods for such problems is still to come.
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Affiliation(s)
- Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
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27
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Moman E, Grishina MA, Potemkin VA. Nonparametric chemical descriptors for the calculation of ligand-biopolymer affinities with machine-learning scoring functions. J Comput Aided Mol Des 2019; 33:943-953. [PMID: 31728812 DOI: 10.1007/s10822-019-00248-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 11/04/2019] [Indexed: 12/20/2022]
Abstract
The computational prediction of ligand-biopolymer affinities is a crucial endeavor in modern drug discovery and one that still poses major challenges. The choice of the appropriate computational method often reveals itself as a trade-off between accuracy and speed, with mathematical devices referred to as scoring functions being the fastest. Among the many shortcomings of scoring functions there is the lack of universal applicability to every molecular system. This is so largely due to their reliance on atom type perception and/or parametrization. This article proposes the use of nonparametric Model of Effective Radii of Atoms descriptors that can be readily computed for the entire Periodic Table and demonstrate that, in combination with machine learning algorithms, they can yield competitive performances and chemically meaningful insights.
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Affiliation(s)
- Edelmiro Moman
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080.
| | - Maria A Grishina
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080
| | - Vladimir A Potemkin
- South Ural State University, 20A Tchaikovsky Street, Chelyabinsk, Russian Federation, 454080
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28
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Yang J, Baek M, Seok C. GalaxyDock3: Protein–ligand docking that considers the full ligand conformational flexibility. J Comput Chem 2019; 40:2739-2748. [DOI: 10.1002/jcc.26050] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 06/18/2019] [Accepted: 07/29/2019] [Indexed: 12/27/2022]
Affiliation(s)
- Jinsol Yang
- Department of ChemistrySeoul National University Seoul 151‐747 Republic of Korea
| | - Minkyung Baek
- Department of ChemistrySeoul National University Seoul 151‐747 Republic of Korea
| | - Chaok Seok
- Department of ChemistrySeoul National University Seoul 151‐747 Republic of Korea
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29
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DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state. J Cheminform 2019; 11:52. [PMID: 31392430 PMCID: PMC6686496 DOI: 10.1186/s13321-019-0373-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/27/2019] [Indexed: 12/14/2022] Open
Abstract
Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2.![]()
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30
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Li J, Fu A, Zhang L. An Overview of Scoring Functions Used for Protein-Ligand Interactions in Molecular Docking. Interdiscip Sci 2019; 11:320-328. [PMID: 30877639 DOI: 10.1007/s12539-019-00327-w] [Citation(s) in RCA: 185] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Revised: 02/06/2019] [Accepted: 03/06/2019] [Indexed: 12/17/2022]
Abstract
Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.
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Affiliation(s)
- Jin Li
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China.,School of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China
| | - Ailing Fu
- College of Pharmaceutical Sciences, Southwest University, Chongqing, 400715, China
| | - Le Zhang
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China. .,College of Computer Science, Sichuan University, Chengdu, 610065, China. .,Medical Big Data Center, Sichuan University, Chengdu, 610065, China. .,Zdmedical, Information Polytron Technologies Inc Chongqing, Chongqing, 401320, China.
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31
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Abstract
Quantification of noncovalent interactions is the key for the understanding of binding mechanisms, of biological systems, for the design of drugs, their delivery and for the design of receptors for separations, sensors, actuators, or smart materials.
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32
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Ngo TD, Oh C, Mizar P, Baek M, Park KS, Nguyen L, Byeon H, Yoon S, Ryu Y, Ryu BH, Kim TD, Yang JW, Seok C, Lee SS, Kim KK. Structural Basis for the Enantioselectivity of Esterase Est-Y29 toward (S)-Ketoprofen. ACS Catal 2018. [DOI: 10.1021/acscatal.8b02797] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Tri Duc Ngo
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Changsuk Oh
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Pushpak Mizar
- Chemistry, Highfield Campus, University of Southampton, Southampton SO17 1BJ, U.K
| | - Minkyung Baek
- Department of Chemistry, College of Natural Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Kwang-su Park
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Lan Nguyen
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - Huimyoung Byeon
- Department of Energy Science, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sangyoung Yoon
- Department of Applied Chemistry & Biological Engineering, Ajou University, Suwon 16499, Republic of Korea
| | - Yeonwoo Ryu
- Department of Applied Chemistry & Biological Engineering, Ajou University, Suwon 16499, Republic of Korea
| | - Bum Han Ryu
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
| | - T. Doohun Kim
- Department of Chemistry, College of Natural Sciences, Sookmyung Women’s University, Seoul 04310, Republic of Korea
| | - Jung Woon Yang
- Department of Energy Science, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, College of Natural Sciences, Seoul National University, Seoul 08826, Republic of Korea
| | - Seung Seo Lee
- Chemistry, Highfield Campus, University of Southampton, Southampton SO17 1BJ, U.K
| | - Kyeong Kyu Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, Republic of Korea
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33
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Guedes IA, Pereira FSS, Dardenne LE. Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges. Front Pharmacol 2018; 9:1089. [PMID: 30319422 PMCID: PMC6165880 DOI: 10.3389/fphar.2018.01089] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 09/07/2018] [Indexed: 12/19/2022] Open
Abstract
Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.
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Affiliation(s)
- Isabella A Guedes
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Felipe S S Pereira
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
| | - Laurent E Dardenne
- Grupo de Modelagem Molecular em Sistemas Biológicos, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
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34
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Ren X, Shi YS, Zhang Y, Liu B, Zhang LH, Peng YB, Zeng R. Novel Consensus Docking Strategy to Improve Ligand Pose Prediction. J Chem Inf Model 2018; 58:1662-1668. [PMID: 30044626 DOI: 10.1021/acs.jcim.8b00329] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Molecular docking, which mainly includes pose prediction and binding affinity calculation, has become an important tool for assisting structure-based drug design. Correctly predicting the ligand binding pose to a protein target enables the estimation of binding free energy using various tools. Previous studies have shown that the consensus method can be used to improve the docking performance with respect to compound scoring and pose prediction. In this report, a novel consensus docking strategy was proposed, which uses a dynamic benchmark data set selection to determine the best program combinations to improve the docking success rate. Using the complexes from PDBbind as a benchmark data set, a 4.9% enhancement in success rate was achieved compared with the best program.
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Affiliation(s)
- Xiaodong Ren
- Department of Pharmacy , Guizhou Provincial People's Hospital , Guiyang 550002 , P.R. China
| | - Yu-Sheng Shi
- Key Laboratory of Biotechnology and Bioresources Utilization, Educational of Minister, College of Life Science , Dalian Nationalities University , Dalian 116600 , P.R. China
| | - Yan Zhang
- Jiamusi College , Heilongjiang University of Chinese Medicine , Jiamusi 154007 , P.R. China
| | - Bin Liu
- Jiamusi College , Heilongjiang University of Chinese Medicine , Jiamusi 154007 , P.R. China
| | - Li-Hong Zhang
- Jiamusi College , Heilongjiang University of Chinese Medicine , Jiamusi 154007 , P.R. China
| | - Yu-Bo Peng
- Jiamusi College , Heilongjiang University of Chinese Medicine , Jiamusi 154007 , P.R. China
| | - Rui Zeng
- College of Pharmacy , Southwest University for Nationalities , Chengdu 610041 , P.R. China
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35
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Gaillard T. Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark. J Chem Inf Model 2018; 58:1697-1706. [DOI: 10.1021/acs.jcim.8b00312] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Thomas Gaillard
- Laboratoire de Biochimie (CNRS UMR7654), Department of Biology, Ecole Polytechnique, 91128 Palaiseau, France
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