1
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Kousaka S, Ishikawa T. Quantum Chemistry-Based Protein-Protein Docking without Empirical Parameters. J Chem Theory Comput 2024; 20:5164-5175. [PMID: 38845143 DOI: 10.1021/acs.jctc.4c00531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
This study developed a novel protein-protein docking approach based on quantum chemistry. To judge the appropriateness of complex structures, we introduced two criterion values, EV1 and EV2, computed using the fragment molecular orbital method without any empirical parameters. These criterion values enable us to search complex structures in which patterns of the electrostatic potential of the two proteins are optimally aligned at their interface. The performance of our method was validated using 53 complexes in a benchmark set provided for protein-protein docking. When employing bound state structures, docking success rates reached 64% for EV1 and 76% for EV2. On the other hand, when employing unbound state structures, docking success rates reached 13% for EV1 and 17% for EV2.
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Affiliation(s)
- Sumire Kousaka
- Department of Chemistry, Biotechnology, and Chemical Engineering, Graduate School of Science and Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan
| | - Takeshi Ishikawa
- Department of Chemistry, Biotechnology, and Chemical Engineering, Graduate School of Science and Engineering, Kagoshima University, 1-21-40 Korimoto, Kagoshima 890-0065, Japan
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2
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Cheng H, Wang GG, Chen L, Wang R. A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking and protein-peptide docking. Comput Biol Med 2024; 168:107727. [PMID: 38029532 DOI: 10.1016/j.compbiomed.2023.107727] [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: 08/13/2023] [Revised: 09/28/2023] [Accepted: 11/15/2023] [Indexed: 12/01/2023]
Abstract
Multi-objective optimization problems (MOPs) are characterized as optimization problems in which multiple conflicting objective functions are optimized simultaneously. To solve MOPs, some algorithms used machine learning models to drive the evolutionary algorithms, leading to the design of a variety of model-based evolutionary algorithms. However, model collapse occurs during the generation of candidate solutions, which results in local optima and poor diversity in model-based evolutionary algorithms. To address this problem, we propose a dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA), where the dual-populations coordinate and cooperate to generate high-quality solutions, thus improving the performance of the evolutionary algorithm. We compare the proposed algorithm with the 7 state-of-the-art algorithms on 20 multi-objective benchmark functions. Experimental results indicate that DGMOEA achieves significant results in solving MOPs, where the metrics IGD and HV outperform the other comparative algorithms on 15 and 18 out of 20 benchmarks, respectively. Our algorithm is evaluated on the LEADS-PEP dataset containing 53 protein-peptide complexes, and the experimental results on solving the protein-peptide docking problem indicated that DGMOEA can effectively reduce the RMSD between the generated and the original peptide's 3D poses and achieve more competitive results.
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Affiliation(s)
- Honglei Cheng
- School of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Gai-Ge Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, China.
| | - Liyan Chen
- Institute of Big Data and Information Technology, Wenzhou University, Wenzhou, China
| | - Rui Wang
- College of Systems Engineering, National University of Defense Technology, Changsha, China; Xiangjiang Laboratory, Changsha, China
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3
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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] [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.
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Affiliation(s)
- Aysima Hacisuleyman
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Burak Erman
- Chemical and Biological Engineering, Koc University, Istanbul, Turkey
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4
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Li H, Yan Y, Zhao X, Huang SY. Inclusion of Desolvation Energy into Protein–Protein Docking through Atomic Contact Potentials. J Chem Inf Model 2022; 62:740-750. [DOI: 10.1021/acs.jcim.1c01483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Xuejun Zhao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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5
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Abstract
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliable experimental techniques pave the way for introducing computational methods in the interaction prediction. Automated methods reduced the difficulties but could not yet replace experimental studies as the field is still evolving. Interaction prediction problem being critical needs highly accurate results, but none of the existing methods could offer reliable performance that can parallel with experimental results yet. This article aims to assess the existing computational docking algorithms, their challenges, and future scope. Blind docking techniques are quite helpful when no information other than the individual structures are available. As more and more complex structures are being added to different databases, information-driven approaches can be a good alternative. Artificial intelligence, ruling over the major fields, is expected to take over this domain very shortly.
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6
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Network-based protein-protein interaction prediction method maps perturbations of cancer interactome. PLoS Genet 2021; 17:e1009869. [PMID: 34727106 PMCID: PMC8610286 DOI: 10.1371/journal.pgen.1009869] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 11/23/2021] [Accepted: 10/09/2021] [Indexed: 01/09/2023] Open
Abstract
The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.
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Perthold JW, Oostenbrink C. GroScore: Accurate Scoring of Protein–Protein Binding Poses Using Explicit-Solvent Free-Energy Calculations. J Chem Inf Model 2019; 59:5074-5085. [DOI: 10.1021/acs.jcim.9b00687] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jan Walther Perthold
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
| | - Chris Oostenbrink
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria
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8
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Yan Y, Wen Z, Zhang D, Huang SY. Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method. Nucleic Acids Res 2019; 46:e56. [PMID: 29506237 PMCID: PMC5961370 DOI: 10.1093/nar/gky113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Accepted: 02/08/2018] [Indexed: 11/15/2022] Open
Abstract
RNA–RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA–RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA–RNA interactions based on a training set of 97 diverse RNA–RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA–RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA–RNA complexes.
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Affiliation(s)
- Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
| | - Zeyu Wen
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
| | - Di Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China
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9
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Kong R, Wang F, Zhang J, Wang F, Chang S. CoDockPP: A Multistage Approach for Global and Site-Specific Protein–Protein Docking. J Chem Inf Model 2019; 59:3556-3564. [DOI: 10.1021/acs.jcim.9b00445] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Feng Wang
- School of Information Science & Engineering, Changzhou University, Changzhou 213164, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Fengfei Wang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
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10
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Jenardhanan P, Panneerselvam M, Mathur PP. Targeting Kinase Interaction Networks: A New Paradigm in PPI Based Design of Kinase Inhibitors. Curr Top Med Chem 2019; 19:467-485. [PMID: 31184298 DOI: 10.2174/1568026619666190304155711] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 01/20/2019] [Accepted: 02/06/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Kinases are key modulators in regulating diverse range of cellular activities and are an essential part of the protein-protein interactome. Understanding the interaction of kinases with different substrates and other proteins is vital to decode the cell signaling machinery as well as causative mechanism for disease onset and progression. OBJECTIVE The objective of this review is to present all studies on the structure and function of few important kinases and highlight the protein-protein interaction (PPI) mechanism of kinases and the kinase specific interactome databases and how such studies could be utilized to develop anticancer drugs. METHODS The article is a review of the detailed description of the various domains in kinases that are involved in protein-protein interactions and specific inhibitors developed targeting these PPI domains. RESULTS The review has surfaced in depth the interacting domains in key kinases and their features and the roles of PPI in the human kinome and the various signaling cascades that are involved in certain types of cancer. CONCLUSION The insight availed into the mechanism of existing peptide inhibitors and peptidomimetics against kinases will pave way for the design and generation of domain specific peptide inhibitors with better productivity and efficiency and the various software and servers available can be of great use for the identification and analysis of protein-protein interactions.
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Affiliation(s)
| | - Manivel Panneerselvam
- Department of Biotechnology, BJM School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Premendu P Mathur
- Department of Biochemistry & Molecular Biology, School of Life Sciences, Pondicherry University, Puducherry, India
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11
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Porter KA, Desta I, Kozakov D, Vajda S. What method to use for protein-protein docking? Curr Opin Struct Biol 2019; 55:1-7. [PMID: 30711743 PMCID: PMC6669123 DOI: 10.1016/j.sbi.2018.12.010] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 12/22/2018] [Indexed: 10/27/2022]
Abstract
A number of well-established servers perform 'free' docking of proteins of known structures. In contrast, template-based docking can start from sequences if structures are available for complexes that are homologous to the target. On the basis of the results of the CAPRI-CASP structure prediction experiments, template-based methods yield more accurate predictions if good templates can be found, but generally fail without such templates. However, free global docking, or focused docking around even poor quality template-based models, can still generate acceptable docked structures in these cases. In accordance with the analysis of a benchmark set, free docking of heterodimers yields acceptable or better predictions in the top 10 models for around 40% of structures. However, it is likely that a combination of template-based and free docking methods can perform better for targets that have template structures available. Another way of improving the reliability of predictions is adding experimental information as restraints, an option built into several docking servers.
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Affiliation(s)
- Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA.
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Department of Chemistry, Boston University, Boston, MA 02215, USA.
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12
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Yu W, Jo S, Lakkaraju SK, Weber DJ, MacKerell AD. Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology. Proteins 2019; 87:289-301. [PMID: 30582220 PMCID: PMC6408985 DOI: 10.1002/prot.25650] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 12/06/2018] [Accepted: 12/19/2018] [Indexed: 01/05/2023]
Abstract
Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.
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Affiliation(s)
- Wenbo Yu
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
| | | | | | - David J. Weber
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
| | - Alexander D. MacKerell
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201
- Institute for Bioscience and Biotechnology Research (IBBR), Rockville, MD 20850
- Center for Biomolecular Therapeutics (CBT), School of Medicine, University of Maryland, Baltimore, MD 21201
- SilcsBio LLC, Baltimore, MD 21202
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13
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Galeazzi R, Laudadio E, Falconi E, Massaccesi L, Ercolani L, Mobbili G, Minnelli C, Scirè A, Cianfruglia L, Armeni T. Protein-protein interactions of human glyoxalase II: findings of a reliable docking protocol. Org Biomol Chem 2019; 16:5167-5177. [PMID: 29971290 DOI: 10.1039/c8ob01194j] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Glyoxalase II (GlxII) is an antioxidant glutathione-dependent enzyme, which catalyzes the hydrolysis of S-d-lactoylglutathione to form d-lactic acid and glutathione (GSH). The last product is the most important thiol reducing agent present in all eukaryotic cells that have mitochondria and chloroplasts. It is generally known that GSH plays a crucial role not only in the cellular redox state but also in various cellular processes. One of them is protein S-glutathionylation, a process that can occur through an oxidation reaction of proteins' thiol groups by GSH. Changes in protein S-glutathionylation have been associated with a range of human diseases such as diabetes, cardiovascular and pulmonary diseases, neurodegenerative diseases and cancer. Within a major project aimed at elucidating the role of GlxII in the mechanism of S-glutathionylation, a reliable computational protocol consisting of a protein-protein docking approach followed by atomistic Molecular Dynamics (MD) simulations was developed and it was applied to the prediction of molecular associations between human GlxII (in the presence and absence of GSH) and some proteins that are known to be S-glutathionylated in vitro, such as actin, malate dehydrogenase (MDH) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The computational results show a high propensity of GlxII to interact with actin and MDH through its active site and a high stability of the GlxII-protein systems when GSH is present. Moreover, close proximities of GSH with actin and MDH cysteine residues have been found, suggesting that GlxII could be able to perform protein S-glutathionylation by using the GSH molecule present in its catalytic site.
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Affiliation(s)
- Roberta Galeazzi
- Department of Life and Environmental Sciences, Università Politecnica delle Marche, Ancona, Italy.
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14
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Kaczor AA, Bartuzi D, Stępniewski TM, Matosiuk D, Selent J. Protein-Protein Docking in Drug Design and Discovery. Methods Mol Biol 2019; 1762:285-305. [PMID: 29594778 DOI: 10.1007/978-1-4939-7756-7_15] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.
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Affiliation(s)
- Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, Lublin, Poland. .,School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
| | - Damian Bartuzi
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, Lublin, Poland
| | - Tomasz Maciej Stępniewski
- GPCR Drug Discovery Group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Spain
| | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Lab, Medical University of Lublin, Lublin, Poland
| | - Jana Selent
- GPCR Drug Discovery Group, Research Programme on Biomedical Informatics (GRIB), Universitat Pompeu Fabra (UPF)-Hospital del Mar Medical Research Institute (IMIM), Parc de Recerca Biomèdica de Barcelona (PRBB), Barcelona, Spain
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15
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Hayashi T, Matsuzaki Y, Yanagisawa K, Ohue M, Akiyama Y. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions. BMC Bioinformatics 2018; 19:62. [PMID: 29745830 PMCID: PMC5998897 DOI: 10.1186/s12859-018-2073-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Background Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. Results In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. Conclusion MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/. Electronic supplementary material The online version of this article (10.1186/s12859-018-2073-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Takanori Hayashi
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Yuri Matsuzaki
- Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Keisuke Yanagisawa
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan. .,AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8560, Japan.
| | - Yutaka Akiyama
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan. .,AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8560, Japan. .,Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
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Barradas-Bautista D, Rosell M, Pallara C, Fernández-Recio J. Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems. PROTEIN-PROTEIN INTERACTIONS IN HUMAN DISEASE, PART A 2018; 110:203-249. [DOI: 10.1016/bs.apcsb.2017.06.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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17
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Barradas-Bautista D, Fernández-Recio J. Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations. PLoS One 2017; 12:e0183643. [PMID: 28841721 PMCID: PMC5571915 DOI: 10.1371/journal.pone.0183643] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 08/08/2017] [Indexed: 01/23/2023] Open
Abstract
Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.
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Affiliation(s)
| | - Juan Fernández-Recio
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona, Spain
- * E-mail:
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18
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Moal IH, Barradas-Bautista D, Jiménez-García B, Torchala M, van der Velde A, Vreven T, Weng Z, Bates PA, Fernández-Recio J. IRaPPA: information retrieval based integration of biophysical models for protein assembly selection. Bioinformatics 2017; 33:1806-1813. [PMID: 28200016 PMCID: PMC5783285 DOI: 10.1093/bioinformatics/btx068] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 01/26/2017] [Accepted: 02/12/2017] [Indexed: 01/23/2023] Open
Abstract
MOTIVATION In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. RESULTS Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. AVAILABILITY AND IMPLEMENTATION IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/∼SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. CONTACT moal@ebi.ac.uk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Iain H Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | - Didier Barradas-Bautista
- Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | - Brian Jiménez-García
- Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
| | | | - Arjan van der Velde
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Juan Fernández-Recio
- Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
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19
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Barradas-Bautista D, Moal IH, Fernández-Recio J. A systematic analysis of scoring functions in rigid-body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining. Proteins 2017; 85:1287-1297. [DOI: 10.1002/prot.25289] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 03/08/2017] [Accepted: 03/20/2017] [Indexed: 12/24/2022]
Affiliation(s)
- Didier Barradas-Bautista
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB Research Program in Computational Biology; Barcelona 08034 Spain
| | - Iain H. Moal
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB Research Program in Computational Biology; Barcelona 08034 Spain
- European Molecular Biology Laboratory; European Bioinformatics Institute, Wellcome Trust Genome Campus; Hinxton Cambridge CB10 1SD United Kingdom
| | - Juan Fernández-Recio
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Joint BSC-CRG-IRB Research Program in Computational Biology; Barcelona 08034 Spain
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20
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Pagadala NS, Syed K, Tuszynski J. Software for molecular docking: a review. Biophys Rev 2017; 9:91-102. [PMID: 28510083 DOI: 10.1007/s12551-016-0247-1] [Citation(s) in RCA: 644] [Impact Index Per Article: 92.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2016] [Accepted: 12/27/2016] [Indexed: 11/26/2022] Open
Abstract
Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.
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Affiliation(s)
- Nataraj S Pagadala
- Department of Medical Microbiology and Immunology, Li Ka Shing Institute of Virology, 6-020 Katz Group Centre, University of Alberta, Edmonton, Alberta, T6G 2E1, Canada.
| | - Khajamohiddin Syed
- Unit for Drug Discovery Research, Department of Health Sciences, Faculty of Health and Environmental Sciences, Central University of Technology, Bloemfontein, 9300, Free State, South Africa
| | - Jack Tuszynski
- Department of Experimental Oncology, Cross Cancer Institute, Edmonton, Alberta, Canada
- Department of Physics, University of Alberta, Edmonton, Alberta, Canada
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21
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. Molecular Docking at a Glance. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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22
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Rigid-Docking Approaches to Explore Protein-Protein Interaction Space. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:33-55. [PMID: 27830312 DOI: 10.1007/10_2016_41] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.
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23
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Exploring the potential of global protein–protein docking: an overview and critical assessment of current programs for automatic ab initio docking. Drug Discov Today 2015; 20:969-77. [DOI: 10.1016/j.drudis.2015.03.007] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Revised: 02/24/2015] [Accepted: 03/13/2015] [Indexed: 12/24/2022]
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24
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Pei J, Yin N, Ma X, Lai L. Systems Biology Brings New Dimensions for Structure-Based Drug Design. J Am Chem Soc 2014; 136:11556-65. [DOI: 10.1021/ja504810z] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Jianfeng Pei
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Ning Yin
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xiaomin Ma
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Beijing
National Laboratory for Molecular Science, State Key Laboratory for
Structural Chemistry of Unstable and Stable Species, College of Chemistry
and Molecular Engineering, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Peking University, Beijing 100871, China
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25
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Huang SY. Search strategies and evaluation in protein–protein docking: principles, advances and challenges. Drug Discov Today 2014; 19:1081-96. [DOI: 10.1016/j.drudis.2014.02.005] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Revised: 01/04/2014] [Accepted: 02/24/2014] [Indexed: 01/10/2023]
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26
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Zhang C, Tang B, Wang Q, Lai L. Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening. Proteins 2014; 82:2472-82. [PMID: 24854898 DOI: 10.1002/prot.24611] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Revised: 05/06/2014] [Accepted: 05/09/2014] [Indexed: 12/18/2022]
Abstract
Target structure-based virtual screening, which employs protein-small molecule docking to identify potential ligands, has been widely used in small-molecule drug discovery. In the present study, we used a protein-protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all-to-all protein-protein docking run on a large dataset was performed. The three-dimensional rigid docking program SDOCK was used to examine protein-protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z-score, and convergency of the low-score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all-to-all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor-α (TNFα), which is a well-known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top-ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein-protein docking for the discovery of novel binding proteins for specific protein targets.
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Affiliation(s)
- Changsheng Zhang
- BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
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Guo D, Liu S, Huang Y, Xiao Y. Preorientation of protein and RNA just before contacting. J Biomol Struct Dyn 2013; 31:716-28. [DOI: 10.1080/07391102.2012.708604] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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28
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Abstract
Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.
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29
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Zhang C, Lai L. Automatch: Target-binding protein design and enzyme design by automatic pinpointing potential active sites in available protein scaffolds. Proteins 2012; 80:1078-94. [DOI: 10.1002/prot.24009] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2011] [Revised: 11/17/2011] [Accepted: 11/18/2011] [Indexed: 11/10/2022]
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