1
|
Meng Q, Guo F, Wang E, Tang J. ComDock: A novel approach for protein-protein docking with an efficient fusing strategy. Comput Biol Med 2023; 167:107660. [PMID: 37944303 DOI: 10.1016/j.compbiomed.2023.107660] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/08/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
Protein-protein interaction plays an important role in studying the mechanism of protein functions from the structural perspective. Molecular docking is a powerful approach to detect protein-protein complexes using computational tools, due to the high cost and time-consuming of the traditional experimental methods. Among existing technologies, the template-based method utilizes the structural information of known homologous 3D complexes as available and reliable templates to achieve high accuracy and low computational complexity. However, the performance of the template-based method depends on the quality and quantity of templates. When insufficient or even no templates, the ab initio docking method is necessary and largely enriches the docking conformations. Therefore, it's a feasible strategy to fuse the effectivity of the template-based model and the universality of ab initio model to improve the docking performance. In this study, we construct a new, diverse, comprehensive template library derived from PDB, containing 77,685 complexes. We propose a template-based method (named TemDock), which retrieves the evolutionary relationship between the target sequence and samples in the template library and transfers similar structural information. Then, the target structure is built by superposing on the homologous template complex with TM-align. Moreover, we develop a consensus-based method (named ComDock) to integrate our TemDock and an existing ab initio method (ZDOCK). On 105 targets with templates from Benchmark 5.0, the TemDock and ComDock achieve a success rate of 68.57 % and 71.43 % in the top 10 conformations, respectively. Compared with the HDOCK, ComDock obtains better I-RMSD of hit configurations on 9 targets and more hit models in the top 100 conformations. As an efficient method for protein-protein docking, the ComDock is expected to study protein-protein recognition and reveal the various biological passways that are critical for developing drug discovery. The final results are stored at https://github.com/guofei-tju/mqz_ComDock_docking.
Collapse
Affiliation(s)
- Qiaozhen Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, China.
| | - Ercheng Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Zhejiang Laboratory, Hangzhou, Zhejiang, China.
| | - Jijun Tang
- Shenzhen Institute of Advanced Technology of Chinese Academy of Sciences, Shenzhen, China.
| |
Collapse
|
2
|
Hacisuleyman A, Erman B. Fine tuning rigid body docking results using the Dreiding force field: A computational study of 36 known nanobody-protein complexes. Proteins 2023; 91:1417-1426. [PMID: 37232507 DOI: 10.1002/prot.26529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023]
Abstract
This paper aims to understand the binding strategies of a nanobody-protein pair by studying known complexes. Rigid body protein-ligand docking programs produce several complexes, called decoys, which are good candidates with high scores of shape complementarity, electrostatic interactions, desolvation, buried surface area, and Lennard-Jones potentials. However, the decoy that corresponds to the native structure is not known. We studied 36 nanobody-protein complexes from the single domain antibody database, sd-Ab DB, http://www.sdab-db.ca/. For each structure, a large number of decoys are generated using the Fast Fourier Transform algorithm of the software ZDOCK. The decoys were ranked according to their target protein-nanobody interaction energies, calculated by using the Dreiding Force Field, with rank 1 having the lowest interaction energy. Out of 36 protein data bank (PDB) structures, 25 true structures were predicted as rank 1. Eleven of the remaining structures required Ångstrom size rigid body translations of the nanobody relative to the protein to match the given PDB structure. After the translation, the Dreiding interaction (DI) energies of all complexes decreased and became rank 1. In one case, rigid body rotations as well as translations of the nanobody were required for matching the crystal structure. We used a Monte Carlo algorithm that randomly translates and rotates the nanobody of a decoy and calculates the DI energy. Results show that rigid body translations and the DI energy are sufficient for determining the correct binding location and pose of ZDOCK created decoys. A survey of the sd-Ab DB showed that each nanobody makes at least one salt bridge with its partner protein, indicating that salt bridge formation is an essential strategy in nanobody-protein recognition. Based on the analysis of the 36 crystal structures and evidence from existing literature, we propose a set of principles that could be used in the design of nanobodies.
Collapse
Affiliation(s)
- Aysima Hacisuleyman
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Burak Erman
- Chemical and Biological Engineering, Koc University, Istanbul, Turkey
| |
Collapse
|
3
|
Yang C, Yang Z, Tong K, Wang J, Yang W, Yu R, Jiang F, Ji Y. Homology modeling and molecular docking simulation of martentoxin as a specific inhibitor of the BK channel. Ann Transl Med 2022; 10:71. [PMID: 35282126 PMCID: PMC8848368 DOI: 10.21037/atm-21-6967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 01/13/2022] [Indexed: 11/18/2022]
Abstract
Background Large conductance calcium-activated potassium channel (BK channel) is gated by both voltage and calcium ions and is widely distributed in excitable and nonexcitable cells. BK channel plays an important role in epilepsy and other diseases, but BK channel subtype-specific drugs are still extremely rare. Martentoxin was previously isolated from the venom of members of Scorpionidae and shown to be composed of 37 amino acids. Research has shown that the pharmacological selectivity of martentoxin to the BK channel is higher than that to other potassium channels. Therefore, it is of great significance to study the mechanism of interaction between martentoxin and BK channels. Methods The three-dimensional structure of BK channel pore region was constructed by homologous modeling method, and the key amino acid sites of BK channel interaction with martentoxin were analyzed by protein-protein docking, molecular dynamic simulation and virtual alanine mutation. Results Based on homologous modeling of BK channel pore structure and protein-protein docking analysis, Phe1, Lys28 and Arg35 of martentoxin were found to be key amino acids in toxin BK channel interaction. Conclusions This study reveals the structural basis of martentoxin interaction with BK channel. These results will contribute to the design of BK channel specific blockers based on the structure of martentoxin.
Collapse
Affiliation(s)
- Chao Yang
- Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences (Xinhua Hospital Chongming Branch), Shanghai, China
| | - Zihao Yang
- College of Life Sciences and Food Engineering, Huaiyin Institute of Technology, Huai'an, China
| | - Kuiyuan Tong
- College of Life Sciences and Food Engineering, Huaiyin Institute of Technology, Huai'an, China
| | - Jiawei Wang
- School of Life and Medicine Sciences, Shanghai University, Shanghai, China
| | - Wanli Yang
- Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences (Xinhua Hospital Chongming Branch), Shanghai, China
| | - Ruihua Yu
- Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences (Xinhua Hospital Chongming Branch), Shanghai, China
| | - Feng Jiang
- Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences (Xinhua Hospital Chongming Branch), Shanghai, China
| | - Yonghua Ji
- Translational Institute for Cancer Pain, Chongming Hospital Affiliated to Shanghai University of Medicine and Health Sciences (Xinhua Hospital Chongming Branch), Shanghai, China.,School of Life and Medicine Sciences, Shanghai University, Shanghai, China
| |
Collapse
|
4
|
Li HX, Yang WY, Li LP, Zhou H, Li WY, Ma Y, Wang RL. Molecular dynamics study of CDC25B R492L mutant causing the activity decrease of CDC25B. J Mol Graph Model 2021; 109:108030. [PMID: 34509094 DOI: 10.1016/j.jmgm.2021.108030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/01/2021] [Accepted: 09/05/2021] [Indexed: 11/25/2022]
Abstract
Cell division cycle 25B (CDC25B) was responsible for regulating the various stages of cell division in the cell cycle. R492L was one of the common types of CDC25B mutants. Researches showed that compared to CDC25BWT, CDC25BR492L mutant had a ∼100-fold reduction in the rate constant for forming phosphatase intermediate (k2). However, the molecular basis of how the CDC25BR492L mutant influenced the process of binding between CDC25B and CDK2/CyclinA was not yet known. Therefore, the optimizations of three-dimensional structure of the CDC25BWT-CDK2/CyclinA system and the CDC25BR492L-CDK2/CyclinA system were constructed by ZDOCK and RDOCK, and five methods were employed to verify the reasonability of the docking structure. Then the molecular dynamics simulations on the two systems were performed to explore the reason why CDC25BR492L mutant caused the weak interactions between CDC25BR492L and CDK2/CyclinA, respectively. The remote docking site (Arg488-Tyr497) and the second active site (Lys538-Arg544) of CDC25B were observed to have high fluctuations in the CDC25BR492L-CDK2/CyclinA system with post-analysis, where the high fluctuation of these two regions resulted in weak interactions between CD25B and CDK2. In addition, Asp38-Glu42 and Asp206-Asp210 of CDK2 showed the slightly descending fluctuation, and CDK2 revealed an enhanced the self-interaction, which made CDK2 keep a relatively stable state in the CDC25BR492L-CDK2/CyclinA system. Finally, Leu492 of CDC25B was speculated to be the key residue, which had great effects on the binding between CDC25BR492L and CDK2 in the CDC25BR492L-CDK2/CyclinA system. Consequently, overall analyses appeared in this study ultimately offered a helpful understanding of the weak interactions between CDC25BR492L and CDK2.
Collapse
Affiliation(s)
- Hao-Xin Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Wen-Yu Yang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, People's Republic of China
| | - Li-Peng Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Hui Zhou
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Wei-Ya Li
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China
| | - Ying Ma
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China.
| | - Run-Ling Wang
- Tianjin Key Laboratory of Technologies Enabling Development of Clinical Therapeutics and Diagnostics, School of Pharmacy, Tianjin Medical University, Tianjin, 300070, People's Republic of China.
| |
Collapse
|
5
|
Abstract
T cell receptor (TCR) recognition of peptides presented by major histocompatibility complex (MHC) molecules is a fundamental process in the adaptive immune system. An understanding of this recognition process at the molecular level is crucial for TCR based therapeutics and vaccine design. The broad nature of TCR diversity and cross-reactivity presents a challenge for traditional structural resolution. Computational modelling of TCR-pMHC complexes offers an efficient alternative. This study compares the ability of four general-purpose docking platforms (ClusPro, LightDock, ZDOCK and HADDOCK) to make use of varying levels of binding interface information for accurate TCR-pMHC modelling. Each platform was tested on an expanded benchmark set of 44 TCR-pMHC docking cases. In general, HADDOCK is shown to be the best performer. Docking strategy guidance is provided to obtain the best models for each platform for future research. The TCR-pMHC docking cases used in this study can be downloaded from https://github.com/innate2adaptive/ExpandedBenchmark.
Collapse
Affiliation(s)
- Thomas Peacock
- Division of Infection and Immunity, University College London, London, United Kingdom.,The UCL Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), Department Computer Science, University College London, London, United Kingdom
| | - Benny Chain
- Division of Infection and Immunity, University College London, London, United Kingdom
| |
Collapse
|
6
|
Parate S, Rampogu S, Lee G, Hong JC, Lee KW. Exploring the Binding Interaction of Raf Kinase Inhibitory Protein With the N-Terminal of C-Raf Through Molecular Docking and Molecular Dynamics Simulation. Front Mol Biosci 2021; 8:655035. [PMID: 34124147 PMCID: PMC8194344 DOI: 10.3389/fmolb.2021.655035] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/14/2021] [Indexed: 12/12/2022] Open
Abstract
Protein-protein interactions are indispensable physiological processes regulating several biological functions. Despite the availability of structural information on protein-protein complexes, deciphering their complex topology remains an outstanding challenge. Raf kinase inhibitory protein (RKIP) has gained substantial attention as a favorable molecular target for numerous pathologies including cancer and Alzheimer’s disease. RKIP interferes with the RAF/MEK/ERK signaling cascade by endogenously binding with C-Raf (Raf-1 kinase) and preventing its activation. In the current investigation, the binding of RKIP with C-Raf was explored by knowledge-based protein-protein docking web-servers including HADDOCK and ZDOCK and a consensus binding mode of C-Raf/RKIP structural complex was obtained. Molecular dynamics (MD) simulations were further performed in an explicit solvent to sample the conformations for when RKIP binds to C-Raf. Some of the conserved interface residues were mutated to alanine, phenylalanine and leucine and the impact of mutations was estimated by additional MD simulations and MM/PBSA analysis for the wild-type (WT) and constructed mutant complexes. Substantial decrease in binding free energy was observed for the mutant complexes as compared to the binding free energy of WT C-Raf/RKIP structural complex. Furthermore, a considerable increase in average backbone root mean square deviation and fluctuation was perceived for the mutant complexes. Moreover, per-residue energy contribution analysis of the equilibrated simulation trajectory by HawkDock and ANCHOR web-servers was conducted to characterize the key residues for the complex formation. One residue each from C-Raf (Arg398) and RKIP (Lys80) were identified as the druggable “hot spots” constituting the core of the binding interface and corroborated by additional long-time scale (300 ns) MD simulation of Arg398Ala mutant complex. A notable conformational change in Arg398Ala mutant occurred near the mutation site as compared to the equilibrated C-Raf/RKIP native state conformation and an essential hydrogen bonding interaction was lost. The thirteen binding sites assimilated from the overall analysis were mapped onto the complex as surface and divided into active and allosteric binding sites, depending on their location at the interface. The acquired information on the predicted 3D structural complex and the detected sites aid as promising targets in designing novel inhibitors to block the C-Raf/RKIP interaction.
Collapse
Affiliation(s)
- Shraddha Parate
- Division of Life Sciences, Division of Applied Life Science (BK21 Plus), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Korea
| | - Shailima Rampogu
- Division of Life Sciences, Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Department of Bio and Medical Big Data (BK21 Four Program), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Korea
| | - Gihwan Lee
- Division of Life Sciences, Division of Applied Life Science (BK21 Plus), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Korea
| | - Jong Chan Hong
- Division of Life Sciences, Division of Applied Life Science (BK21 Plus), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Korea
| | - Keun Woo Lee
- Division of Life Sciences, Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Department of Bio and Medical Big Data (BK21 Four Program), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Korea
| |
Collapse
|
7
|
Liu B, Zhang Z, Lu S, He Q, Deng N, Meng H, Pan C, Li H, Liu M, Huang A, Shen F. In-silico analysis of ligand-receptor binding patterns of α-MMC, TCS and MAP30 protein to LRP1 receptor. J Mol Graph Model 2020; 98:107619. [PMID: 32311663 DOI: 10.1016/j.jmgm.2020.107619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 03/13/2020] [Accepted: 04/03/2020] [Indexed: 11/27/2022]
Abstract
Alpha-momorcharin (α-MMC), trichosanthin (TCS), and momordica anti-HIV protein of 30 kD (MAP30) are potential anti-tumor drug candidates but have cytotoxicity to normal cells. The binding of these proteins to LRP1 receptor and the subsequent endocytosis are essential to their cytotoxicity, but this binding process remains largely unknown. This study, in-silico analysis of the binding patterns, was conducted via the protein-protein docking software, ZDOCK 3.0.2 package, to better understand the binding process. Specifically, α-MMC, TCS and MAP30 were selected and bound to binding subunits CR56 and CR17 of LRP1. After docking, the 10 best docking solutions are retained based on the default ZDOCK scores and used for structural assessment. Our results showed that, α-MMC bound to LRP1 stably at the amino acid residues 1-20, at which 8 residues formed 21 hydrogen bonds with 15 residues of CR56 and 10 residues formed 15 hydrogen bonds with 12 residues of CR17. In contrast, TCS and MAP30 bound mainly to LRP1 at the residues 1-57/79-150 and residues 58-102, respectively, which were functional domains of TCS and MAP30. Since residues 1-20 are outside the functional domain of α-MMC, α-MMC is considered more suitable to attenuate by mutating the receptor binding site. Thus, our analysis lays the foundation for future genetic engineering work on α-MMC, and makes important contributions to its potential clinical use in cancer treatment.
Collapse
Affiliation(s)
- Bin Liu
- Department of Clinical Pathology, WestChina-Frontier Pharma Tech Co., Ltd., Chengdu, 610043, PR China
| | - Zhonglin Zhang
- School of Pharmacy, Chengdu Medical College, Chengdu, 610500, PR China
| | - Shiyong Lu
- Maternal and Child Health Hospital of Qingbaijiang, Chengdu, 610500, PR China
| | - Qianchuan He
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Nianhua Deng
- School of Laboratory Medicine, Chengdu Medical College, Chengdu, 610500, PR China
| | - Hao Meng
- Beijing Computing Center, Beijing Academy of Science and Technology, Beijing, 100094, China; Beijing Beike Deyuan Bio-Pharm Technology Co., Ltd., Beijing, 100094, China
| | - Chenling Pan
- Beijing Computing Center, Beijing Academy of Science and Technology, Beijing, 100094, China; Beijing Beike Deyuan Bio-Pharm Technology Co., Ltd., Beijing, 100094, China
| | - Huanhuan Li
- Beijing Computing Center, Beijing Academy of Science and Technology, Beijing, 100094, China; Beijing Beike Deyuan Bio-Pharm Technology Co., Ltd., Beijing, 100094, China
| | - Mengling Liu
- School of Pharmacy, Chengdu Medical College, Chengdu, 610500, PR China
| | - Axiu Huang
- School of Laboratory Medicine, Chengdu Medical College, Chengdu, 610500, PR China
| | - Fubing Shen
- School of Laboratory Medicine, Chengdu Medical College, Chengdu, 610500, PR China.
| |
Collapse
|
8
|
Ambrosetti F, Jiménez-García B, Roel-Touris J, Bonvin AMJJ. Modeling Antibody-Antigen Complexes by Information-Driven Docking. Structure 2019; 28:119-129.e2. [PMID: 31727476 DOI: 10.1016/j.str.2019.10.011] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 07/03/2019] [Accepted: 10/18/2019] [Indexed: 10/25/2022]
Abstract
Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial for improving our ability to design efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for these complexes. We investigate here how information about complementarity-determining regions and binding epitopes can be used to drive the modeling process, and present a comparative study of four different docking software suites (ClusPro, LightDock, ZDOCK, and HADDOCK) providing specific options for antibody-antigen modeling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models in both the presence and absence of information about the epitope on the antigen.
Collapse
Affiliation(s)
- Francesco Ambrosetti
- Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00184 Rome, Italy; Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Brian Jiménez-García
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.
| |
Collapse
|
9
|
Agrawal P, Singh H, Srivastava HK, Singh S, Kishore G, Raghava GPS. Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinformatics 2019; 19:426. [PMID: 30717654 PMCID: PMC7394329 DOI: 10.1186/s12859-018-2449-y] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 10/29/2018] [Indexed: 11/10/2022] Open
Abstract
Background Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD. Result Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively. Conclusion The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/). Electronic supplementary material The online version of this article (10.1186/s12859-018-2449-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Piyush Agrawal
- Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India.,CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | - Harinder Singh
- CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | | | - Sandeep Singh
- CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | - Gaurav Kishore
- CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India
| | - Gajendra P S Raghava
- Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India. .,CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India.
| |
Collapse
|
10
|
Vreven T, Schweppe DK, Chavez JD, Weisbrod CR, Shibata S, Zheng C, Bruce JE, Weng Z. Integrating Cross-Linking Experiments with Ab Initio Protein-Protein Docking. J Mol Biol 2018; 430:1814-1828. [PMID: 29665372 DOI: 10.1016/j.jmb.2018.04.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 03/19/2018] [Accepted: 04/10/2018] [Indexed: 12/23/2022]
Abstract
Ab initio protein-protein docking algorithms often rely on experimental data to identify the most likely complex structure. We integrated protein-protein docking with the experimental data of chemical cross-linking followed by mass spectrometry. We tested our approach using 19 cases that resulted from an exhaustive search of the Protein Data Bank for protein complexes with cross-links identified in our experiments. We implemented cross-links as constraints based on Euclidean distance or void-volume distance. For most test cases, the rank of the top-scoring near-native prediction was improved by at least twofold compared with docking without the cross-link information, and the success rate for the top 5 predictions nearly tripled. Our results demonstrate the delicate balance between retaining correct predictions and eliminating false positives. Several test cases had multiple components with distinct interfaces, and we present an approach for assigning cross-links to the interfaces. Employing the symmetry information for these cases further improved the performance of complex structure prediction.
Collapse
Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| | - Devin K Schweppe
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Juan D Chavez
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Chad R Weisbrod
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Sayaka Shibata
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Chunxiang Zheng
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - James E Bruce
- Department of Chemistry and Department of Genome Sciences, University of Washington, Seattle, WA 98109, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
| |
Collapse
|
11
|
Li HL, Ma Y, Ma Y, Li Y, Chen XB, Dong WL, Wang RL. The design of novel inhibitors for treating cancer by targeting CDC25B through disruption of CDC25B-CDK2/Cyclin A interaction using computational approaches. Oncotarget 2017; 8:33225-40. [PMID: 28402259 DOI: 10.18632/oncotarget.16600] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Accepted: 03/17/2017] [Indexed: 01/28/2023] Open
Abstract
Cell division cycle 25B is a key cell cycle regulator and widely considered as potent clinical drug target for cancers. This research focused on identifying potential compounds in theory which are able to disrupt transient interactions between CDC25B and its CDK2/Cyclin A substrate.By using the method of ZDOCK and RDOCK, the most optimized 3D structure of CDK2/Cyclin A in complex with CDC25B was constructed and validated using two methods: 1) the superimposition of proteins; 2) analysis of the hydrogen bond distances of Arg 488(N1)-Asp 206(OD1), Arg 492(NE)-Asp 206(OD1), Arg 492(N1)-Asp 206(OD2) and Tyr 497(NE)-Asp 210(OD1). A series of new compounds was gained through searching the fragment database derived from ZINC based on the known inhibitor-compound 7 by the means of "replace fragment" technique. The compounds acquired via meeting the requirements of the absorption, distribution, metabolism, and excretion (ADME) predictions. Finally, 12 compounds with better binding affinity were identified. The comp#1, as a representative, was selected to be synthesized and assayed for their CDC25B inhibitory activities. The comp#1 exhibited mild inhibitory activities against human CDC25B with IC50 values at about 39.02 μM. Molecular Dynamic (MD) simulation revealed that the new inhibitor-comp#1 had favorable conformations for binding to CDC25B and disturbing the interactions between CDC25B and CDK2/Cyclin A.
Collapse
|
12
|
Abstract
Applying atomistic computational modeling to drug discovery has proven to be a hugely successful approach, allowing drug-receptor interactions to be predicted and drugs to be optimized for potency, selectivity, and safety. However, when it comes to predicting protein-protein interactions and to rationally designing regulators of these interactions, computational tools often fail. Here, we report one of the rare instances where state-of-the-art computer simulations, guided by experiment, were able to correctly predict one of the most sophisticated protein-protein interactions. We revisit our previous discovery of the complex of human PD-1 with the ligand PD-L1 and compare our earlier findings with the recently published crystal structure of the same complex. Side-by-side comparison of the model of the complex with its crystal structure reveals outstanding agreement and suggests that our protein-protein prediction workflow could be applied to similar problems.
Collapse
Affiliation(s)
- Marawan Ahmed
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Khaled Barakat
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada. .,Li Ka Shing Institute of Virology, University of Alberta, Edmonton, Alberta, Canada. .,Li Ka Shing Applied Virology Institute, University of Alberta, Edmonton, Alberta, Canada.
| |
Collapse
|
13
|
Kingsley LJ, Esquivel-Rodríguez J, Yang Y, Kihara D, Lill MA. Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations. J Comput Chem 2016; 37:1861-5. [PMID: 27232548 DOI: 10.1002/jcc.24412] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Revised: 05/02/2016] [Accepted: 05/06/2016] [Indexed: 11/09/2022]
Abstract
Crystallization of protein-protein complexes can often be problematic and therefore computational structural models are often relied on. Such models are often generated using protein-protein docking algorithms, where one of the main challenges is selecting which of several thousand potential predictions represents the most near-native complex. We have developed a novel technique that involves the use of steered molecular dynamics (sMD) and umbrella sampling to identify near-native complexes among protein-protein docking predictions. Using this technique, we have found a strong correlation between our predictions and the interface RMSD (iRMSD) in ten diverse test systems. On two of the systems, we investigated if the prediction results could be further improved using potential of mean force calculations. We demonstrated that a near-native (<2.0 Å iRMSD) structure could be identified in the top-1 ranked position for both systems. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Laura J Kingsley
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, 575 Stadium Mall Drivem, West Lafayette, Indiana, 47907
| | - Juan Esquivel-Rodríguez
- Department of Computer Science, Purdue University, 305 North University Street, West Lafayette, Indiana, 47907
| | - Ying Yang
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, 575 Stadium Mall Drivem, West Lafayette, Indiana, 47907
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, 305 North University Street, West Lafayette, Indiana, 47907.,Department of Biological Sciences, Purdue University, 249 South Martin Jischke Drive, West Lafayette, Indiana, 47907
| | - Markus A Lill
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, 575 Stadium Mall Drivem, West Lafayette, Indiana, 47907
| |
Collapse
|
14
|
Abstract
We compared the performance of template-free (docking) and template-based methods for the prediction of protein-protein complex structures. We found similar performance for a template-based method based on threading (COTH) and another template-based method based on structural alignment (PRISM). The template-based methods showed similar performance to a docking method (ZDOCK) when the latter was allowed one prediction for each complex, but when the same number of predictions was allowed for each method, the docking approach outperformed template-based approaches. We identified strengths and weaknesses in each method. Template-based approaches were better able to handle complexes that involved conformational changes upon binding. Furthermore, the threading-based and docking methods were better than the structural-alignment-based method for enzyme-inhibitor complex prediction. Finally, we show that the near-native (correct) predictions were generally not shared by the various approaches, suggesting that integrating their results could be the superior strategy.
Collapse
Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, ASC-5th floor room 1069, 368 Plantation St., Worcester, MA 01605, USA.
| | | | | | | |
Collapse
|