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Ugurlu SY, McDonald D, Enisoglu R, Zhu Z, He S. MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank aggregation. Sci Rep 2025; 15:5545. [PMID: 39953061 PMCID: PMC11829001 DOI: 10.1038/s41598-024-83558-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Accepted: 12/16/2024] [Indexed: 02/17/2025] Open
Abstract
Proteolysis-targeting chimaeras (PROTACs), which induce proteolysis by recruiting an E3 ligase to dock into a target protein, are acquiring popularity as a novel pharmacological modality because of the unique features of PROTAC, including high potency, low dosage, and effective on undruggable targets. While PROTACs are promising prospects as chemical probes and therapeutic agents, their discovery usually necessitates the synthesis of numerous analogues to explore variations on the chemical linker structure exhaustively. Without extensive trial and error, it is unknown how to link the two protein-recruiting moieties to facilitate the formation of a productive ternary complex. Although molecular docking-based and optimization pipelines have been designed to predict ternary complexes, guiding rational PROTAC design, they have suffered from limited predictive performance in the quality of the ternary structure and their ranks. Here, MEGA PROTAC has been designed to enhance the performance in quality and ranking of ternary structures. MEGA PROTAC employs MEGADOCK to execute docking for protein-protein complexes (PPCs). The docking establishes an initial exploration area for PPCs. A sequential filtration strategy combined with rank aggregation is employed to choose a subset of PPCs for grid search. Once candidate PPCs are selected, a grid search method is used separately for translation and rotation. The remaining proteins have been grouped into clusters, and MEGA PROTAC further filters these clusters based on the energy score of the proteins within each cluster. MEGA PROTAC utilises rank aggregation to choose the best clusters and then employs MEGADOCK to dock PROTAC into the selected PPCs, forming a ternary structure. Finally, MEGA PROTAC was tested on 22 cases to compare with the state-of-the-art method, Bayesian optimisation for ternary complex prediction (BOTCP). MEGA PROTAC outperformed BOTCP on 16 test cases out of 22 cases, achieving a higher maximum DockQ score with an 18% higher mean and 35% higher median. Also, MEGA PROTAC exhibited 75% superior ranks and a reduced cluster number for maximum DockQ score compared to BOTCP. Also, MEGA PROTAC outperforms BOTCP by achieving a twofold improvement in locating the first acceptable DockQ scores, with a more significant proportion of near-native structures within the detected cluster.
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Affiliation(s)
- Sadettin Y Ugurlu
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | | | - Ramazan Enisoglu
- School of Science and Technology, City St George's, University of London, Northampton Square, London, EC1V 0HB, UK
| | - Zexuan Zhu
- National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China
| | - Shan He
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
- AIA Insights Ltd, Birmingham, UK.
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Eom H, Park S, Cho K, Lee J, Kim H, Kim S, Yang J, Han YH, Lee J, Seok C, Lee M, Song W, Steinegger M. Discovery of highly active kynureninases for cancer immunotherapy through protein language model. Nucleic Acids Res 2025; 53:gkae1245. [PMID: 39777462 PMCID: PMC11704957 DOI: 10.1093/nar/gkae1245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 11/16/2024] [Accepted: 12/05/2024] [Indexed: 01/11/2025] Open
Abstract
Tailor-made enzymes empower a wide range of versatile applications, although searching for the desirable enzymes often requires high throughput screening and thus poses significant challenges. In this study, we employed homology searches and protein language models to discover and prioritize enzymes by their kinetic parameters. We aimed to discover kynureninases as a potentially versatile therapeutic enzyme, which hydrolyses L-kynurenine, a potent immunosuppressive metabolite, to overcome the immunosuppressive tumor microenvironment in anticancer therapy. Subsequently, we experimentally validated the efficacy of four top-ranked kynureninases under in vitro and in vivo conditions. Our findings revealed a catalytically most active one with a nearly twofold increase in turnover number over the prior best and a 3.4-fold reduction in tumor weight in mouse model comparisons. Consequently, our approach holds promise for the targeted quantitative enzyme discovery and selection suitable for specific applications with higher accuracy, significantly broadening the scope of enzyme utilization. A web-executable version of our workflow is available at seekrank.steineggerlab.com and our code is available as free open-source software at github.com/steineggerlab/SeekRank.
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Affiliation(s)
- Hyunuk Eom
- Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Sukhwan Park
- School of Biological Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Kye Soo Cho
- Galux Inc, 1837 Nambusunhwan-ro, Gwanak-gu, Seoul 08738, Republic of Korea
| | - Jihyeon Lee
- Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Hyunbin Kim
- School of Biological Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Stephanie Kim
- School of Biological Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jinsol Yang
- Galux Inc, 1837 Nambusunhwan-ro, Gwanak-gu, Seoul 08738, Republic of Korea
| | - Young-Hyun Han
- Galux Inc, 1837 Nambusunhwan-ro, Gwanak-gu, Seoul 08738, Republic of Korea
| | - Juyong Lee
- Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- School of Pharmacy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- Arontier Co., 241 Gangnam-daero, Seocho-gu, Seoul 06735, Republic of Korea
| | - Chaok Seok
- Artificial Intelligence Institute, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- Galux Inc, 1837 Nambusunhwan-ro, Gwanak-gu, Seoul 08738, Republic of Korea
| | - Myeong Sup Lee
- Department of Biomedical Sciences, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
- Galux Inc, 1837 Nambusunhwan-ro, Gwanak-gu, Seoul 08738, Republic of Korea
| | - Woon Ju Song
- Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- Artificial Intelligence Institute, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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3
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Zhang X, Zhang M, Li Y, Deng P. Identification of Potential Selective PAK4 Inhibitors Through Shape and Protein Conformation Ensemble Screening and Electrostatic-Surface-Matching Optimization. Curr Issues Mol Biol 2025; 47:29. [PMID: 39852144 PMCID: PMC11764389 DOI: 10.3390/cimb47010029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2024] [Revised: 12/31/2024] [Accepted: 01/03/2025] [Indexed: 01/26/2025] Open
Abstract
P21-activated kinase 4 (PAK4) plays a crucial role in the proliferation and metastasis of various cancers. However, developing selective PAK4 inhibitors remains challenging due to the high homology within the PAK family. Therefore, developing highly selective PAK4 inhibitors is critical to overcoming the limitations of existing inhibitors. We analyzed the structural differences in the binding pockets of PAK1 and PAK4 by combining cross-docking and molecular dynamics simulations to identify key binding regions and unique structural features of PAK4. We then performed screening using shape and protein conformation ensembles, followed by a re-evaluation of the docking results with deep-learning-driven GNINA to identify the candidate molecule, STOCK7S-56165. Based on this, we applied a fragment-replacement strategy under electrostatic-surface-matching conditions to obtain Compd 26. This optimization significantly improved electrostatic interactions and reduced binding energy, highlighting its potential for selectivity. Our findings provide a novel approach for developing selective PAK4 inhibitors and lay the theoretical foundation for future anticancer drug design.
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Affiliation(s)
- Xiaoxuan Zhang
- College of Pharmacy, Chongqing Medical University, Chongqing 400016, China; (X.Z.); (M.Z.); (Y.L.)
- Chongqing Research Center for Pharmaceutical Engineering, Chongqing 400016, China
| | - Meile Zhang
- College of Pharmacy, Chongqing Medical University, Chongqing 400016, China; (X.Z.); (M.Z.); (Y.L.)
- Chongqing Research Center for Pharmaceutical Engineering, Chongqing 400016, China
| | - Yihao Li
- College of Pharmacy, Chongqing Medical University, Chongqing 400016, China; (X.Z.); (M.Z.); (Y.L.)
- Chongqing Research Center for Pharmaceutical Engineering, Chongqing 400016, China
| | - Ping Deng
- College of Pharmacy, Chongqing Medical University, Chongqing 400016, China; (X.Z.); (M.Z.); (Y.L.)
- Chongqing Research Center for Pharmaceutical Engineering, Chongqing 400016, China
- Chongqing Key Research Laboratory for Quality Evaluation and Safety Research of APIs, Chongqing 400016, China
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Kim H, Ryu S, Jung N, Yang J, Seok C. CSearch: chemical space search via virtual synthesis and global optimization. J Cheminform 2024; 16:137. [PMID: 39639340 PMCID: PMC11622599 DOI: 10.1186/s13321-024-00936-8] [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: 07/09/2024] [Accepted: 11/22/2024] [Indexed: 12/07/2024] Open
Abstract
The two key components of computational molecular design are virtually generating molecules and predicting the properties of these generated molecules. This study focuses on an effective method for molecular generation through virtual synthesis and global optimization of a given objective function. Using a pre-trained graph neural network (GNN) objective function to approximate the docking energies of compounds for four target receptors, we generated highly optimized compounds with 300-400 times less computational effort compared to virtual compound library screening. These optimized compounds exhibit similar synthesizability and diversity to known binders with high potency and are notably novel compared to library chemicals or known ligands. This method, called CSearch, can be effectively utilized to generate chemicals optimized for a given objective function. With the GNN function approximating docking energies, CSearch generated molecules with predicted binding poses to the target receptors similar to known inhibitors, demonstrating its effectiveness in producing drug-like binders.Scientific Contribution We have developed a method for effectively exploring the chemical space of drug-like molecules using a global optimization algorithm with fragment-based virtual synthesis. The compounds generated using this method optimize the given objective function efficiently and are synthesizable like commercial library compounds. Furthermore, they are diverse, novel drug-like molecules with properties similar to known inhibitors for target receptors.
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Affiliation(s)
- Hakjean Kim
- Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea
| | | | - Nuri Jung
- Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea
| | | | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea.
- Galux Inc, Seoul, 08738, Republic of Korea.
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Ortega-Vallbona R, Palomino-Schätzlein M, Tolosa L, Benfenati E, Ecker GF, Gozalbes R, Serrano-Candelas E. Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study. Int J Mol Sci 2024; 25:11154. [PMID: 39456937 PMCID: PMC11508863 DOI: 10.3390/ijms252011154] [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: 09/20/2024] [Revised: 10/10/2024] [Accepted: 10/11/2024] [Indexed: 10/28/2024] Open
Abstract
The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure-activity relationship models, quantitative structure-activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks. Emphasizing the regulatory acceptance of each technique, we examine how these methodologies can be integrated to provide a comprehensive understanding of chemical toxicity. This review highlights the transformative impact of in silico techniques in toxicology, proposing guidelines for their application in evidence gathering for developing and filling data gaps in adverse outcome pathway networks. These guidelines can be applied to other cases, advancing the field of toxicological risk assessment.
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Affiliation(s)
- Rita Ortega-Vallbona
- ProtoQSAR S.L., Calle Nicolás Copérnico 6, Parque Tecnológico de Valencia, 46980 Paterna, Spain; (R.O.-V.); (M.P.-S.); (R.G.)
| | - Martina Palomino-Schätzlein
- ProtoQSAR S.L., Calle Nicolás Copérnico 6, Parque Tecnológico de Valencia, 46980 Paterna, Spain; (R.O.-V.); (M.P.-S.); (R.G.)
| | - Laia Tolosa
- Unidad de Hepatología Experimental, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Av. Fernando Abril Martorell 106, 46026 Valencia, Spain;
- Biomedical Research Networking Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, C/Monforte de Lemos, 28029 Madrid, Spain
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy;
| | - Gerhard F. Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek Platz 2, 1090 Wien, Austria;
| | - Rafael Gozalbes
- ProtoQSAR S.L., Calle Nicolás Copérnico 6, Parque Tecnológico de Valencia, 46980 Paterna, Spain; (R.O.-V.); (M.P.-S.); (R.G.)
- MolDrug AI Systems S.L., Olimpia Arozena Torres 45, 46108 Valencia, Spain
| | - Eva Serrano-Candelas
- ProtoQSAR S.L., Calle Nicolás Copérnico 6, Parque Tecnológico de Valencia, 46980 Paterna, Spain; (R.O.-V.); (M.P.-S.); (R.G.)
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6
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Kabier M, Gambacorta N, Trisciuzzi D, Kumar S, Nicolotti O, Mathew B. MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations. J Comput Chem 2024; 45:1980-1986. [PMID: 38703357 DOI: 10.1002/jcc.27390] [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: 03/01/2024] [Revised: 04/12/2024] [Accepted: 04/19/2024] [Indexed: 05/06/2024]
Abstract
Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.
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Affiliation(s)
- Muzammil Kabier
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Nicola Gambacorta
- Division of Medical Genetics, IRCSS Foundation-Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia), Foggia, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Sunil Kumar
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Bijo Mathew
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
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7
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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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8
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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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9
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Choi J. Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner. iScience 2023; 26:106911. [PMID: 37305691 PMCID: PMC10250834 DOI: 10.1016/j.isci.2023.106911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 04/28/2023] [Accepted: 05/12/2023] [Indexed: 06/13/2023] Open
Abstract
Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.
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Affiliation(s)
- Juyoung Choi
- Department of Life Science, Sogang University, Seoul 04017, South Korea
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10
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Lee S, Kim S, Lee GR, Kwon S, Woo H, Seok C, Park H. Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction. Comput Struct Biotechnol J 2022; 21:158-167. [PMID: 36544468 PMCID: PMC9747351 DOI: 10.1016/j.csbj.2022.11.057] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/27/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022] Open
Abstract
While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios.
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Key Words
- AF, AlphaFold
- CAPRI, critical assessment of predicted interactions, DOF, Degree-of-freedom
- DL, deep learning
- Deep learning
- Drug discovery
- GALD, Rosetta GA LigandDock
- GD3, GalaxyDock3
- GDT, global distance test
- GPCR
- Ligand docking
- MD, molecular dynamics
- Protein structure prediction
- RMSD, root-mean-squared deviation
- SBDD, Structure-based drug design
- TBM, template-based modeling or template-based model
- p-lDDT, predicted local distance difference test
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Affiliation(s)
- Sumin Lee
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Republic of Korea
| | - Seeun Kim
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Gyu Rie Lee
- Department of Biochemistry, University of Washington, WA, USA
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Hahnbeom Park
- Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
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11
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Kwon S, Seok C. CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking. Comput Struct Biotechnol J 2022; 21:1-10. [PMID: 36514334 PMCID: PMC9719078 DOI: 10.1016/j.csbj.2022.11.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 11/27/2022] Open
Abstract
Structure prediction of protein-ligand complexes, called protein-ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein-ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein-ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.
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Affiliation(s)
- Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
- Galux Inc, Seoul 08738, South Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 08826, South Korea
- Galux Inc, Seoul 08738, South Korea
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12
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Sim J, Kwon S, Seok C. HProteome-BSite: predicted binding sites and ligands in human 3D proteome. Nucleic Acids Res 2022; 51:D403-D408. [PMID: 36243970 PMCID: PMC9825455 DOI: 10.1093/nar/gkac873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/20/2022] [Accepted: 09/29/2022] [Indexed: 01/29/2023] Open
Abstract
Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome. The model structures for human proteins from the AlphaFold Protein Structure Database were processed to structural domains of high confidence to maximize the coverage and reliability of interaction prediction. For ligand binding site prediction, an updated version of a template-based method GalaxySite was used. A high-level performance of the updated GalaxySite was confirmed. HProteome-BSite covers 80.74% of the UniProt entries in the AlphaFold human 3D proteome. Predicted binding sites and binding poses of potential ligands are provided for effective applications to further functional studies and drug discovery. The HProteome-BSite database is available at https://galaxy.seoklab.org/hproteome-bsite/database and is free and open to all users.
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Affiliation(s)
- Jiho Sim
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea,Galux Inc, Gwanak-gu, Seoul 08738, Republic of Korea
| | - Chaok Seok
- To whom correspondence should be addressed. Tel: +82 2 880 9197; Fax: +82 2 889 1568;
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13
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Heo K, Lee JW, Jang Y, Kwon S, Lee J, Seok C, Ha NC, Seok YJ. A pGpG-specific phosphodiesterase regulates cyclic di-GMP signaling in Vibrio cholerae. J Biol Chem 2022; 298:101626. [PMID: 35074425 PMCID: PMC8861645 DOI: 10.1016/j.jbc.2022.101626] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 01/18/2022] [Accepted: 01/19/2022] [Indexed: 11/10/2022] Open
Abstract
The bacterial second messenger bis-(3′-5′)-cyclic diguanylate monophosphate (c-di-GMP) controls various cellular processes, including motility, toxin production, and biofilm formation. c-di-GMP is enzymatically synthesized by GGDEF domain–containing diguanylate cyclases and degraded by HD-GYP domain–containing phosphodiesterases (PDEs) to 2 GMP or by EAL domain–containing PDE-As to 5ʹ-phosphoguanylyl-(3ʹ,5ʹ)-guanosine (pGpG). Since excess pGpG feedback inhibits PDE-A activity and thereby can lead to the uncontrolled accumulation of c-di-GMP, a PDE that degrades pGpG to 2 GMP (PDE-B) has been presumed to exist. To date, the only enzyme known to hydrolyze pGpG is oligoribonuclease Orn, which degrades all kinds of oligoribonucleotides. Here, we identified a pGpG-specific PDE, which we named PggH, using biochemical approaches in the gram-negative bacteria Vibrio cholerae. Biochemical experiments revealed that PggH exhibited specific PDE activity only toward pGpG, thus differing from the previously reported Orn. Furthermore, the high-resolution structure of PggH revealed the basis for its PDE activity and narrow substrate specificity. Finally, we propose that PggH could modulate the activities of PDE-As and the intracellular concentration of c-di-GMP, resulting in phenotypic changes including in biofilm formation.
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Affiliation(s)
- Kyoo Heo
- School of Biological Sciences and Institute of Microbiology, Seoul National University, Seoul, Republic of Korea
| | - Jae-Woo Lee
- School of Biological Sciences and Institute of Microbiology, Seoul National University, Seoul, Republic of Korea
| | - Yongdae Jang
- Department of Agricultural Biotechnology, Research Institute for Agriculture and Life Sciences, Center for Food and Bioconvergence, Seoul National University, Seoul, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jaehun Lee
- School of Biological Sciences and Institute of Microbiology, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Nam-Chul Ha
- Department of Agricultural Biotechnology, Research Institute for Agriculture and Life Sciences, Center for Food and Bioconvergence, Seoul National University, Seoul, Republic of Korea.
| | - Yeong-Jae Seok
- School of Biological Sciences and Institute of Microbiology, Seoul National University, Seoul, Republic of Korea.
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14
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Byun J, Lee J. Identifying the Hot Spot Residues of the SARS-CoV-2 Main Protease Using MM-PBSA and Multiple Force Fields. Life (Basel) 2021; 12:54. [PMID: 35054447 PMCID: PMC8779590 DOI: 10.3390/life12010054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/23/2021] [Accepted: 12/27/2021] [Indexed: 01/03/2023] Open
Abstract
In this study, we investigated the binding affinities between the main protease of SARS-CoV-2 virus (Mpro) and its various ligands to identify the hot spot residues of the protease. To benchmark the influence of various force fields on hot spot residue identification and binding free energy calculation, we performed MD simulations followed by MM-PBSA analysis with three different force fields: CHARMM36, AMBER99SB, and GROMOS54a7. We performed MD simulations with 100 ns for 11 protein-ligand complexes. From the series of MD simulations and MM-PBSA calculations, it is identified that the MM-PBSA estimations using different force fields are weakly correlated to each other. From a comparison between the force fields, AMBER99SB and GROMOS54a7 results are fairly correlated while CHARMM36 results show weak or almost no correlations with the others. Our results suggest that MM-PBSA analysis results strongly depend on force fields and should be interpreted carefully. Additionally, we identified the hot spot residues of Mpro, which play critical roles in ligand binding through energy decomposition analysis. It is identified that the residues of the S4 subsite of the binding site, N142, M165, and R188, contribute strongly to ligand binding. In addition, the terminal residues, D295, R298, and Q299 are identified to have attractive interactions with ligands via electrostatic and solvation energy. We believe that our findings will help facilitate developing the novel inhibitors of SARS-CoV-2.
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Affiliation(s)
| | - Juyong Lee
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, Chuncheon 24341, Korea;
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15
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Serçinoğlu O, Bereketoglu C, Olsson PE, Pradhan A. In silico and in vitro assessment of androgen receptor antagonists. Comput Biol Chem 2021; 92:107490. [PMID: 33932781 DOI: 10.1016/j.compbiolchem.2021.107490] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 04/20/2021] [Accepted: 04/20/2021] [Indexed: 11/25/2022]
Abstract
There is a growing concern for male reproductive health as studies suggest that there is a sharp increase in prostate cancer and other fertility related problems. Apart from lifestyle, pollutants are also known to negatively affect the reproductive system. In addition to many other compounds that have been shown to alter androgen signaling, several environmental pollutants are known to disrupt androgen signaling via binding to androgen receptor (AR) or indirectly affecting the androgen synthesis. We analyzed here the molecular mechanism of the interaction between the human AR Ligand Binding Domain (hAR-LBD) and two environmental pollutants, linuron (a herbicide) and procymidone (a pesticide), and compared with the steroid agonist dihydrotestosterone (DHT) and well-known hAR antagonists bicalutamide and enzalutamide. Using molecular docking and dynamics simulations, we showed that the co-activator interaction site of the hAR-LBD is disrupted in different ways by different ligands. Binding free energies of the ligands were also ordered in increasing order as follows: linuron, procymidone, DHT, bicalutamide, and enzalutamide. These data were confirmed by in vitro assays. Reporter assay with MDA-kb2 cells showed that linuron, procymidone, bicalutamide and enzalutamide can inhibit androgen mediated activation of luciferase activity. Gene expression analysis further showed that these compounds can inhibit the expression of prostate specific antigen (PSA) and microseminoprotein beta (MSMB) in prostate cell line LNCaP. Comparative analysis showed that procymidone is more potent than linuron in inhibiting AR activity. Furthermore, procymidone at 10 μM dose showed equivalent and higher activity to AR inhibitor enzalutamide and bicalutamide respectively.
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Affiliation(s)
- Onur Serçinoğlu
- Department of Bioengineering, Faculty of Engineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Ceyhun Bereketoglu
- Iskenderun Technical University, Faculty of Engineering and Natural Sciences, Department of Biomedical Engineering, Hatay, Turkey
| | - Per-Erik Olsson
- Biology, The Life Science Center, School of Science and Technology, Örebro University, SE-701 82, Örebro, Sweden
| | - Ajay Pradhan
- Biology, The Life Science Center, School of Science and Technology, Örebro University, SE-701 82, Örebro, Sweden.
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16
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Wang K, Zhou R, Li Y, Li M. DeepDTAF: a deep learning method to predict protein-ligand binding affinity. Brief Bioinform 2021; 22:6214647. [PMID: 33834190 DOI: 10.1093/bib/bbab072] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/27/2021] [Accepted: 02/14/2021] [Indexed: 01/10/2023] Open
Abstract
Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many computational methods have been proposed to predict binding affinity, most of which usually require protein 3D structures that are not often available. Therefore, new methods that can fully take advantage of sequence-level features are greatly needed to predict protein-ligand binding affinity and accelerate the drug discovery process. We developed a novel deep learning approach, named DeepDTAF, to predict the protein-ligand binding affinity. DeepDTAF was constructed by integrating local and global contextual features. More specifically, the protein-binding pocket, which possesses some special properties for directly binding the ligand, was firstly used as the local input feature for protein-ligand binding affinity prediction. Furthermore, dilated convolution was used to capture multiscale long-range interactions. We compared DeepDTAF with the recent state-of-art methods and analyzed the effectiveness of different parts of our model, the significant accuracy improvement showed that DeepDTAF was a reliable tool for affinity prediction. The resource codes and data are available at https: //github.com/KailiWang1/DeepDTAF.
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Affiliation(s)
| | - Renyi Zhou
- School of Computer Science and Engineering, Central South University, China
| | - Yaohang Li
- Department of Computer Science at Old Dominion University, Norfolk, USA
| | - Min Li
- School of Computer Science and Engineering, Central South University, China
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17
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Bartocci A, Gillet N, Jiang T, Szczepaniak F, Dumont E. Molecular Dynamics Approach for Capturing Calixarene-Protein Interactions: The Case of Cytochrome C. J Phys Chem B 2020; 124:11371-11378. [PMID: 33270456 DOI: 10.1021/acs.jpcb.0c08482] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Functionalized supramolecular cages are of growing importance in biology and biochemistry. They have recently been proposed as efficient auxiliaries to obtain high-resolution cocrystallized proteins. Here, we propose a molecular dynamics investigation of the supramolecular association of sulfonated calix-[8]-arenes to cytochrome c starting from initially distant proteins and ligands. We characterize two main binding sites for the sulfonated calixarene on the cytochrome c surface which are in perfect agreement with the previous experiments with regard to the structure (comparison with the X-ray structure PDB 6GD8) and the binding free energies [comparison between the molecular mechanics Poisson-Boltzmann surface area analysis and the isothermal titration calorimetry measurements]. The per-residue decomposition of the interaction energies reveals the detailed picture of this electrostatically driven association and notably the role of arginine R13 as a bridging residue between the two main anchoring sites. In addition, the analysis of the residue behavior by means of a supervised machine learning protocol unveils the formation of a hydrogen bond network far from the binding sites, increasing the rigidity of the protein. This study paves the way toward an automated procedure to predict the supramolecular protein-cage association, with the possibility of a computational screening of new promising derivatives for controlled protein assembly and protein surface recognition processes.
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Affiliation(s)
- Alessio Bartocci
- Univ Lyon, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard Lyon 1, Laboratoire de Chimie, F-69342 Lyon, France
| | - Natacha Gillet
- Univ Lyon, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard Lyon 1, Laboratoire de Chimie, F-69342 Lyon, France
| | - Tao Jiang
- Univ Lyon, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard Lyon 1, Laboratoire de Chimie, F-69342 Lyon, France
| | - Florence Szczepaniak
- Univ Lyon, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard Lyon 1, Laboratoire de Chimie, F-69342 Lyon, France
| | - Elise Dumont
- Univ Lyon, ENS de Lyon, CNRS UMR 5182, Université Claude Bernard Lyon 1, Laboratoire de Chimie, F-69342 Lyon, France.,Institut Universitaire de France, 5 Rue Descartes, 75005 Paris, France
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18
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Sarfaraz S, Muneer I, Liu H. Combining fragment docking with graph theory to improve ligand docking for homology model structures. J Comput Aided Mol Des 2020; 34:1237-1259. [PMID: 33034007 PMCID: PMC7544562 DOI: 10.1007/s10822-020-00345-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 09/24/2020] [Indexed: 11/30/2022]
Abstract
Computational protein–ligand docking is well-known to be prone to inaccuracies in input receptor structures, and it is challenging to obtain good docking results with computationally predicted receptor structures (e.g. through homology modeling). Here we introduce a fragment-based docking method and test if it reduces requirements on the accuracy of an input receptor structures relative to non-fragment docking approaches. In this method, small rigid fragments are docked first using AutoDock Vina to generate a large number of favorably docked poses spanning the receptor binding pocket. Then a graph theory maximum clique algorithm is applied to find combined sets of docked poses of different fragment types onto which the complete ligand can be properly aligned. On the basis of these alignments, possible binding poses of complete ligand are determined. This docking method is first tested for bound docking on a series of Cytochrome P450 (CYP450) enzyme–substrate complexes, in which experimentally determined receptor structures are used. For all complexes tested, ligand poses of less than 1 Å root mean square deviations (RMSD) from the actual binding positions can be recovered. Then the method is tested for unbound docking with modeled receptor structures for a number of protein–ligand complexes from different families including the very recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease. For all complexes, poses with RMSD less than 3 Å from actual binding positions can be recovered. Our results suggest that for docking with approximately modeled receptor structures, fragment-based methods can be more effective than common complete ligand docking approaches.
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Affiliation(s)
- Sara Sarfaraz
- School of life sciences, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Iqra Muneer
- School of life sciences, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Haiyan Liu
- School of life sciences, University of Science and Technology of China, Hefei, 230026, Anhui, China.
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19
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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: 0.8] [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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