51
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Taslimi P. Evaluation of in vitro inhibitory effects of some natural compounds on tyrosinase activity and molecular docking study: Antimelanogenesis potential. J Biochem Mol Toxicol 2020; 34:e22566. [PMID: 32614502 DOI: 10.1002/jbt.22566] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 05/29/2020] [Accepted: 06/17/2020] [Indexed: 01/28/2023]
Abstract
Tyrosinase enzyme is a functional oxidase that is extensively divided in nature. It is the main enzyme in melanin synthesis and is also involved in designating the color of mammalian hair and skin. Additionally, it is accountable for the unfavorable enzymatic browning that happens in plant-derived foods, limiting the shelf-life of new-cut crops with the resultant economic harm. Recently, there has been a remarkable concern to study the inhibitory activity of the tyrosinase enzyme and some inhibitory molecules isolated from natural sources. For tyrosinase enzyme, afzelin, narcissoside, justiciresinol, thalassiolin B, carpachromene, neobavaisoflavone, and kojic acid (as standard) as natural phenols have IC50 values in the range of 2.37-7.90 µM. Theoretical methods, such as gaussian software program and molecular modeling, were used to compare the biological and chemical activity values of molecules. To compare the biochemical and chemical activity values of molecules, chemical activities with quantum chemical parameters, and biological activities against tyrosinase with the ID of 5M8L molecules were investigated.
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Affiliation(s)
- Parham Taslimi
- Department of Biotechnology, Faculty of Science, Bartin University, Bartin, Turkey
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52
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Ghasemzadeh S, Riazi GH. Inhibition of Tau amyloid fibril formation by folic acid: In-vitro and theoretical studies. Int J Biol Macromol 2020; 154:1505-1516. [DOI: 10.1016/j.ijbiomac.2019.11.032] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 10/19/2019] [Accepted: 11/05/2019] [Indexed: 10/25/2022]
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53
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Rosell M, Fernández-Recio J. Docking approaches for modeling multi-molecular assemblies. Curr Opin Struct Biol 2020; 64:59-65. [PMID: 32615514 PMCID: PMC7324114 DOI: 10.1016/j.sbi.2020.05.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/13/2020] [Accepted: 05/21/2020] [Indexed: 12/12/2022]
Abstract
Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling.
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Affiliation(s)
- Mireia Rosell
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de La Rioja - Gobierno de La Rioja, 26007 Logroño, Spain
| | - Juan Fernández-Recio
- Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain; Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de La Rioja - Gobierno de La Rioja, 26007 Logroño, Spain.
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54
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Andreani J, Quignot C, Guerois R. Structural prediction of protein interactions and docking using conservation and coevolution. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1470] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Jessica Andreani
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
| | - Chloé Quignot
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
| | - Raphael Guerois
- Université Paris‐Saclay CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) Gif‐sur‐Yvette France
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55
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Yan Y, Tao H, He J, Huang SY. The HDOCK server for integrated protein–protein docking. Nat Protoc 2020; 15:1829-1852. [DOI: 10.1038/s41596-020-0312-x] [Citation(s) in RCA: 288] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 02/03/2020] [Indexed: 12/27/2022]
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56
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Dudko HV, Urban VA, Davidovskii AI, Veresov VG. Structure-based modeling of turnover of Bcl-2 family proteins bound to voltage-dependent anion channel 2 (VDAC2): Implications for the mechanisms of proapoptotic activation of Bak and Bax in vivo. Comput Biol Chem 2020; 85:107203. [DOI: 10.1016/j.compbiolchem.2020.107203] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 12/31/2019] [Accepted: 01/13/2020] [Indexed: 12/15/2022]
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57
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Koukos P, Bonvin A. Integrative Modelling of Biomolecular Complexes. J Mol Biol 2020; 432:2861-2881. [DOI: 10.1016/j.jmb.2019.11.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 12/31/2022]
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58
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Padhorny D, Porter KA, Ignatov M, Alekseenko A, Beglov D, Kotelnikov S, Ashizawa R, Desta I, Alam N, Sun Z, Brini E, Dill K, Schueler-Furman O, Vajda S, Kozakov D. ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking. Proteins 2020; 88:1082-1090. [PMID: 32142178 DOI: 10.1002/prot.25887] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 02/27/2020] [Accepted: 03/04/2020] [Indexed: 01/01/2023]
Abstract
Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template-based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template-based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use at https://tbm.cluspro.org, is demonstrated by predicting the protein-protein targets of rounds 38 to 45 of CAPRI.
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Affiliation(s)
- Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.,Institute of Computer Aided Design of the Russian Academy of Sciences, Moscow, Russia
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.,Acpharis Inc., Holliston, Massachusetts, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.,Innopolis University, Innopolis, Russia
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York, USA.,Department of Chemistry, Stony Brook University, Stony Brook, New York, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, The Hebrew University, Jerusalem, Israel
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.,Department of Chemistry, Boston University, Boston, Massachusetts, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA
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59
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Singh A, Dauzhenka T, Kundrotas PJ, Sternberg MJE, Vakser IA. Application of docking methodologies to modeled proteins. Proteins 2020; 88:1180-1188. [PMID: 32170770 DOI: 10.1002/prot.25889] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 02/15/2020] [Accepted: 03/07/2020] [Indexed: 12/12/2022]
Abstract
Protein docking is essential for structural characterization of protein interactions. Besides providing the structure of protein complexes, modeling of proteins and their complexes is important for understanding the fundamental principles and specific aspects of protein interactions. The accuracy of protein modeling, in general, is still less than that of the experimental approaches. Thus, it is important to investigate the applicability of docking techniques to modeled proteins. We present new comprehensive benchmark sets of protein models for the development and validation of protein docking, as well as a systematic assessment of free and template-based docking techniques on these sets. As opposed to previous studies, the benchmark sets reflect the real case modeling/docking scenario where the accuracy of the models is assessed by the modeling procedure, without reference to the native structure (which would be unknown in practical applications). We also expanded the analysis to include docking of protein pairs where proteins have different structural accuracy. The results show that, in general, the template-based docking is less sensitive to the structural inaccuracies of the models than the free docking. The near-native docking poses generated by the template-based approach, typically, also have higher ranks than those produces by the free docking (although the free docking is indispensable in modeling the multiplicity of protein interactions in a crowded cellular environment). The results show that docking techniques are applicable to protein models in a broad range of modeling accuracy. The study provides clear guidelines for practical applications of docking to protein models.
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Affiliation(s)
- Amar Singh
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Taras Dauzhenka
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Petras J Kundrotas
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, South Kensington, London, UK
| | - Ilya A Vakser
- Computational Biology Program, The University of Kansas, Lawrence, Kansas, USA.,Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, USA
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60
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Cao Y, Shen Y. Energy-based graph convolutional networks for scoring protein docking models. Proteins 2020; 88:1091-1099. [PMID: 32144844 DOI: 10.1002/prot.25888] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 01/15/2020] [Accepted: 02/26/2020] [Indexed: 12/18/2022]
Abstract
Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent protein and complex structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes' features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy-based graph convolutional networks (EGCN) with multihead attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.
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Affiliation(s)
- Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas
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61
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Abstract
There is a large gap between the numbers of known protein-protein interactions and the corresponding experimentally solved structures of protein complexes. Fortunately, this gap can be in part bridged by computational structure modeling methods. Currently, template-based modeling is the most accurate means to predict both individual protein structures and protein complexes. One of the major issues in template-based modeling is to identify homologous structures that could be utilized as templates. To simplify this task, we have developed the PPI3D web server. The server is not only able to search for homologous protein complexes, but also provides means to analyze identified interactions and to model protein complexes. In recent CASP and CAPRI experiments, PPI3D proved to be a useful tool for homology modeling of multimeric proteins. In this chapter, we provide a brief description of the PPI3D web server capabilities and how to use the server for modeling of protein complexes.
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Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.
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62
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Alekseenko A, Kotelnikov S, Ignatov M, Egbert M, Kholodov Y, Vajda S, Kozakov D. ClusPro LigTBM: Automated Template-based Small Molecule Docking. J Mol Biol 2019; 432:3404-3410. [PMID: 31863748 DOI: 10.1016/j.jmb.2019.12.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/03/2019] [Accepted: 12/04/2019] [Indexed: 12/31/2022]
Abstract
The template-based approach has been essential for achieving high-quality models in the recent rounds of blind protein-protein docking competition CAPRI (Critical Assessment of Predicted Interactions). However, few such automated methods exist for protein-small molecule docking. In this paper, we present an algorithm for template-based docking of small molecules. It searches for known complexes with ligands that have partial coverage of the target ligand, performs conformational sampling and template-guided energy refinement to produce a variety of possible poses, and then scores the refined poses. The algorithm is available as the automated ClusPro LigTBM server. It allows the user to specify the target protein as a PDB file and the ligand as a SMILES string. The server then searches for templates and uses them for docking, presenting the user with top-scoring poses and their confidence scores. The method is tested on the Astex Diverse benchmark, as well as on the targets from the last round of the D3R (Drug Design Data Resource) Grand Challenge. The server is publicly available as part of the ClusPro docking server suite at https://ligtbm.cluspro.org/.
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Affiliation(s)
- Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA; Innopolis University, 420500, Innopolis, Russia
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA; Institute for Advanced Computational Sciences, Stony Brook University, 11794, Stony Brook, NY, USA
| | - Megan Egbert
- Department of Biomedical Engineering, Boston University, 02215, Boston, MA, USA
| | | | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, 02215, Boston, MA, USA; Department of Chemistry, Boston University, 02215, Boston, MA, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA; Institute for Advanced Computational Sciences, Stony Brook University, 11794, Stony Brook, NY, USA.
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63
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Christoffer C, Terashi G, Shin WH, Aderinwale T, Maddhuri Venkata Subramaniya SR, Peterson L, Verburgt J, Kihara D. Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46. Proteins 2019; 88:948-961. [PMID: 31697428 DOI: 10.1002/prot.25850] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 10/07/2019] [Accepted: 11/03/2019] [Indexed: 01/17/2023]
Abstract
We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.
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Affiliation(s)
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana.,Department of Chemistry Education, Sunchon National University, Suncheon, Jeollanam-do, Republic of Korea
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Lenna Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana.,Department of Biological Sciences, Purdue University, West Lafayette, Indiana.,Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana.,Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio
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64
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Carlesso A, Chintha C, Gorman AM, Samali A, Eriksson LA. Effect of Kinase Inhibiting RNase Attenuator (KIRA) Compounds on the Formation of Face-to-Face Dimers of Inositol-Requiring Enzyme 1: Insights from Computational Modeling. Int J Mol Sci 2019; 20:ijms20225538. [PMID: 31698846 PMCID: PMC6887741 DOI: 10.3390/ijms20225538] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 10/28/2019] [Accepted: 11/05/2019] [Indexed: 12/15/2022] Open
Abstract
Inositol-requiring enzyme 1α (IRE1α) is a transmembrane dual kinase/ribonuclease protein involved in propagation of the unfolded protein response (UPR). Inositol-requiring enzyme 1α is currently being explored as a potential drug target due to the growing evidence of its role in variety of disease conditions. Upon activation, IRE1 cleaves X-box binding protein 1 (XBP1) mRNA through its RNase domain. Small molecules targeting the kinase site are known to either increase or decrease RNase activity, but the allosteric relationship between the kinase and RNase domains of IRE1α is poorly understood. Subsets of IRE1 kinase inhibitors (known as “KIRA” compounds) bind to the ATP-binding site and allosterically impede the RNase activity. The KIRA compounds are able to regulate the RNase activity by stabilizing the monomeric form of IRE1α. In the present work, computational analysis, protein–protein and protein–ligand docking studies, and molecular dynamics simulations were applied to different IRE1 dimer systems to provide structural insights into the perturbation of IRE1 dimers by small molecules kinase inhibitors that regulate the RNase activity. By analyzing structural deviations, energetic components, and the number of hydrogen bonds in the interface region, we propose that the KIRA inhibitors act at an early stage of IRE1 activation by interfering with IRE1 face-to-face dimer formation thus disabling the activation of the RNase domain. This work sheds light on the mechanism of action of KIRA compounds and may assist in development of further compounds in, for example, cancer therapeutics. The work also provides information on the sequence of events and protein–protein interactions initiating the unfolded protein response.
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Affiliation(s)
- Antonio Carlesso
- Department of Chemistry and Molecular Biology, University of Gothenburg, 405 30 Göteborg, Sweden;
| | - Chetan Chintha
- Apoptosis Research Centre, National University of Ireland Galway, H91 TK33, Galway, Ireland; (C.C.); (A.M.G.); (A.S.)
| | - Adrienne M. Gorman
- Apoptosis Research Centre, National University of Ireland Galway, H91 TK33, Galway, Ireland; (C.C.); (A.M.G.); (A.S.)
| | - Afshin Samali
- Apoptosis Research Centre, National University of Ireland Galway, H91 TK33, Galway, Ireland; (C.C.); (A.M.G.); (A.S.)
| | - Leif A. Eriksson
- Department of Chemistry and Molecular Biology, University of Gothenburg, 405 30 Göteborg, Sweden;
- Correspondence: ; Tel.: +46-31786-9117
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65
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Popov P, Grudinin S, Kurdiuk A, Buslaev P, Redon S. Controlled-advancement rigid-body optimization of nanosystems. J Comput Chem 2019; 40:2391-2399. [PMID: 31254466 DOI: 10.1002/jcc.26016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/23/2019] [Accepted: 06/06/2019] [Indexed: 11/11/2022]
Abstract
In this study, we propose a novel optimization algorithm, with application to the refinement of molecular complexes. Particularly, we consider optimization problem as the calculation of quasi-static trajectories of rigid bodies influenced by the inverse-inertia-weighted energy gradient and introduce the concept of advancement region that guarantees displacement of a molecule strictly within a relevant region of conformational space. The advancement region helps to avoid typical energy minimization pitfalls, thus, the algorithm is suitable to work with arbitrary energy functions and arbitrary types of molecular complexes without necessary tuning of its hyper-parameters. Our method, called controlled-advancement rigid-body optimization of nanosystems (Carbon), is particularly useful for the large-scale molecular refinement, as for example, the putative binding candidates obtained with protein-protein docking pipelines. Implementation of Carbon with user-friendly interface is available in the SAMSON platform for molecular modeling at https://www.samson-connect.net. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Petr Popov
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Sergei Grudinin
- CNRS, Grenoble INP, LJK, University Grenoble Alpes, Inria, 38000, Grenoble, France
| | - Andrii Kurdiuk
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Pavel Buslaev
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Stephane Redon
- CNRS, Grenoble INP, LJK, University Grenoble Alpes, Inria, 38000, Grenoble, France
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66
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Porter KA, Padhorny D, Desta I, Ignatov M, Beglov D, Kotelnikov S, Sun Z, Alekseenko A, Anishchenko I, Cong Q, Ovchinnikov S, Baker D, Vajda S, Kozakov D. Template-based modeling by ClusPro in CASP13 and the potential for using co-evolutionary information in docking. Proteins 2019; 87:1241-1248. [PMID: 31444975 DOI: 10.1002/prot.25808] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 07/21/2019] [Accepted: 07/30/2019] [Indexed: 12/29/2022]
Abstract
As a participant in the joint CASP13-CAPRI46 assessment, the ClusPro server debuted its new template-based modeling functionality. The addition of this feature, called ClusPro TBM, was motivated by the previous CASP-CAPRI assessments and by the proven ability of template-based methods to produce higher-quality models, provided templates are available. In prior assessments, ClusPro submissions consisted of models that were produced via free docking of pre-generated homology models. This method was successful in terms of the number of acceptable predictions across targets; however, analysis of results showed that purely template-based methods produced a substantially higher number of medium-quality models for targets for which there were good templates available. The addition of template-based modeling has expanded ClusPro's ability to produce higher accuracy predictions, primarily for homomeric but also for some heteromeric targets. Here we review the newest additions to the ClusPro web server and discuss examples of CASP-CAPRI targets that continue to drive further development. We also describe ongoing work not yet implemented in the server. This includes the development of methods to improve template-based models and the use of co-evolutionary information for data-assisted free docking.
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Affiliation(s)
- Kathryn A Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York.,Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Andrey Alekseenko
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, Washington.,Institute for Protein Design, University of Washington, Seattle, Washington
| | - Qian Cong
- Department of Biochemistry, University of Washington, Seattle, Washington.,Institute for Protein Design, University of Washington, Seattle, Washington
| | - Sergey Ovchinnikov
- Center for Systems Biology, Harvard University, Cambridge, Massachusetts
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington.,Institute for Protein Design, University of Washington, Seattle, Washington.,Howard Hughes Medical Institute, University of Washington, Seattle, Washington
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts.,Department of Chemistry, Boston University, Boston, Massachusetts
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
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67
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Shashikala HBM, Chakravorty A, Alexov E. Modeling Electrostatic Force in Protein-Protein Recognition. Front Mol Biosci 2019; 6:94. [PMID: 31608289 PMCID: PMC6774301 DOI: 10.3389/fmolb.2019.00094] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 09/11/2019] [Indexed: 12/25/2022] Open
Abstract
Electrostatic interactions are important for understanding molecular interactions, since they are long-range interactions and can guide binding partners to their correct binding positions. To investigate the role of electrostatic forces in molecular recognition, we calculated electrostatic forces between binding partners separated at various distances. The investigation was done on a large set of 275 protein complexes using recently developed DelPhiForce tool and in parallel, evaluating the total electrostatic force via electrostatic association energy. To accomplish the goal, we developed a method to find an appropriate direction to move one chain of protein complex away from its bound position and then calculate the corresponding electrostatic force as a function of separation distance. It is demonstrated that at large distances between the partners, the electrostatic force (magnitude and direction) is consistent among the protocols used and the main factors contributing to it are the net charge of the partners and their interfaces. However, at short distances, where partners form specific pair-wise interactions or de-solvation penalty becomes significant, the outcome depends on the precise balance of these factors. Based on the electrostatic force profile (force as a function of distance), we group the cases into four distinctive categories, among which the most intriguing is the case termed "soft landing." In this case, the electrostatic force at large distances is favorable assisting the partners to come together, while at short distance it opposes binding, and thus slows down the approach of the partners toward their physical binding.
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68
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Nilofer C, Sukhwal A, Mohanapriya A, Sakharkar MK, Kangueane P. Small protein-protein interfaces rich in electrostatic are often linked to regulatory function. J Biomol Struct Dyn 2019; 38:3260-3279. [PMID: 31495333 DOI: 10.1080/07391102.2019.1657040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Protein-protein interaction (PPI) is critical for several biological functions in living cells through the formation of an interface. Therefore, it is of interest to characterize protein-protein interfaces using an updated non-redundant structural dataset of 2557 homo (identical subunits) and 393 hetero (different subunits) dimer protein complexes determined by X-ray crystallography. We analyzed the interfaces using van der Waals (vdW), hydrogen bonding and electrostatic energies. Results show that on average homo and hetero interfaces are similar. Hence, we further grouped the 2950 interfaces based on percentage vdW to total energies into dominant (≥60%) and sub-dominant (<60%) vdW interfaces. Majority (92%) of interfaces have dominant vdW energy with large interface size (146 ± 87 (homo) and 137 ± 76 (hetero) residues) and interface area (1622 ± 1135 Å2 (homo) and 1579 ± 1060 Å2 (hetero)). However, a proportion (8%) of interfaces have sub-dominant vdW energy with small interface size (85 ± 46 (homo) and 88 ± 36 (hetero) residues) and interface area (823 ± 538 Å2 (homo) and 881 ± 377 Å2 (hetero)). It is found that large interfaces have two-fold more interface area and interface size than small interfaces with increasing hydrogen bonding energy to interface size. However, small interfaces have three-fold more electrostatics energy than large interfaces with increasing electrostatics to interface size. Thus, 8% of complexes having small interfaces with limited interface area and sub-dominant vdW energy are rich in electrostatics. It is interesting to observe that complexes having small interfaces are often associated with regulatory function. Hence, the observed structural features with known molecular function provide insights for the better understanding of PPI.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Christina Nilofer
- Biomedical Informatics (P) Ltd., Pondicherry, India.,School of Biosciences & Technology, VIT University, Vellore, Tamil Nadu, India
| | - Anshul Sukhwal
- National Centre for Biological Sciences (NCBS), Bangalore, India
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69
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Li ZL, Buck M. Modified Potential Functions Result in Enhanced Predictions of a Protein Complex by All-Atom Molecular Dynamics Simulations, Confirming a Stepwise Association Process for Native Protein-Protein Interactions. J Chem Theory Comput 2019; 15:4318-4331. [PMID: 31241940 DOI: 10.1021/acs.jctc.9b00195] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The relative prevalence of native protein-protein interactions (PPIs) are the cornerstone for understanding the structure, dynamics and mechanisms of function of protein complexes. In this study, we develop a scheme for scaling the protein-water interaction in the CHARMM36 force field, in order to better fit the solvation free energy of amino acids side-chain analogues. We find that the molecular dynamics simulation with the scaled force field, CHARMM36s, as well as a recently released version, CHARMM36m, effectively improve on the overly sticky association of proteins, such as ubiquitin. We investigate the formation of a heterodimer protein complex between the SAM domains of the EphA2 receptor and the SHIP2 enzyme by performing a combined total of 48 μs simulations with the different potential functions. While the native SAM heterodimer is only predicted at a low rate of 6.7% with the original CHARMM36 force field, the yield is increased to 16.7% with CHARMM36s, and to 18.3% with CHARMM36m. By analyzing the 25 native SAM complexes formed in the simulations, we find that their formation involves a preorientation guided by Coulomb interactions, consistent with an electrostatic steering mechanism. In 12 cases, the complex could directly transform to the native protein interaction surfaces with only small adjustments in domain orientation. In the other 13 cases, orientational and/or translational adjustments are needed to reach the native complex. Although the tendency for non-native complexes to dissociate has nearly doubled with the modified potential functions, a dissociation followed by a reassociation to the correct complex structure is still rare. Instead, the remaining non-native complexes undergo configurational changes/surface searching, which, however, rarely leads to native structures on a time scale of 250 ns. These observations provide a rich picture of the mechanisms of protein-protein complex formation and suggest that computational predictions of native complex PPIs could be improved further.
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Affiliation(s)
- Zhen-Lu Li
- Department of Physiology and Biophysics , Case Western Reserve University, School of Medicine , 10900 Euclid Avenue , Cleveland , Ohio 44106 , United States
| | - Matthias Buck
- Department of Physiology and Biophysics , Case Western Reserve University, School of Medicine , 10900 Euclid Avenue , Cleveland , Ohio 44106 , United States.,Departments of Pharmacology and Neurosciences, and Case Comprehensive Cancer Center , Case Western Reserve University, School of Medicine , 10900 Euclid Avenue , Cleveland , Ohio 44106 , United States
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70
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Computational approaches to macromolecular interactions in the cell. Curr Opin Struct Biol 2019; 55:59-65. [PMID: 30999240 DOI: 10.1016/j.sbi.2019.03.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 03/08/2019] [Indexed: 12/15/2022]
Abstract
Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.
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