1
|
Kotelnikov S, Ashizawa R, Popov KI, Khan O, Ignatov M, Li SX, Hassan M, Coutsias EA, Poda G, Padhorny D, Tropsha A, Vajda S, Kozakov D. Accurate ligand-protein docking in CASP15 using the ClusPro LigTBM server. Proteins 2023; 91:1822-1828. [PMID: 37697630 PMCID: PMC10947245 DOI: 10.1002/prot.26587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/31/2023] [Accepted: 08/09/2023] [Indexed: 09/13/2023]
Abstract
In the ligand prediction category of CASP15, the challenge was to predict the positions and conformations of small molecules binding to proteins that were provided as amino acid sequences or as models generated by the AlphaFold2 program. For most targets, we used our template-based ligand docking program ClusPro ligTBM, also implemented as a public server available at https://ligtbm.cluspro.org/. Since many targets had multiple chains and a number of ligands, several templates, and some manual interventions were required. In a few cases, no templates were found, and we had to use direct docking using the Glide program. Nevertheless, ligTBM was shown to be a very useful tool, and by any ranking criteria, our group was ranked among the top five best-performing teams. In fact, all the best groups used template-based docking methods. Thus, it appears that the AlphaFold2-generated models, despite the high accuracy of the predicted backbone, have local differences from the x-ray structure that make the use of direct docking methods more challenging. The results of CASP15 confirm that this limitation can be frequently overcome by homology-based docking.
Collapse
Affiliation(s)
- Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Ryota Ashizawa
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Konstantin I. Popov
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Omeir Khan
- Department of Chemistry, Boston University, Boston, MA, USA
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Stan Xiaogang Li
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Mosavverul Hassan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Evangelos A. Coutsias
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Gennady Poda
- Drug Discovery Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Alexander Tropsha
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sandor Vajda
- Department of Chemistry, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
2
|
Hashemi N, Hao B, Ignatov M, Paschalidis IC, Vakili P, Vajda S, Kozakov D. Improved prediction of MHC-peptide binding using protein language models. Front Bioinform 2023; 3:1207380. [PMID: 37663788 PMCID: PMC10469926 DOI: 10.3389/fbinf.2023.1207380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Major histocompatibility complex Class I (MHC-I) molecules bind to peptides derived from intracellular antigens and present them on the surface of cells, allowing the immune system (T cells) to detect them. Elucidating the process of this presentation is essential for regulation and potential manipulation of the cellular immune system. Predicting whether a given peptide binds to an MHC molecule is an important step in the above process and has motivated the introduction of many computational approaches to address this problem. NetMHCPan, a pan-specific model for predicting binding of peptides to any MHC molecule, is one of the most widely used methods which focuses on solving this binary classification problem using shallow neural networks. The recent successful results of Deep Learning (DL) methods, especially Natural Language Processing (NLP-based) pretrained models in various applications, including protein structure determination, motivated us to explore their use in this problem. Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding. Using the standard performance metrics in this area, and the same training and test sets, we show that our models outperform NetMHCpan4.1, currently considered as the-state-of-the-art.
Collapse
Affiliation(s)
- Nasser Hashemi
- Division of Systems Engineering, Boston University, Boston, MA, United States
| | - Boran Hao
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Ioannis Ch. Paschalidis
- Division of Systems Engineering, Boston University, Boston, MA, United States
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| | - Pirooz Vakili
- Division of Systems Engineering, Boston University, Boston, MA, United States
| | - Sandor Vajda
- Division of Systems Engineering, Boston University, Boston, MA, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
- Department of Chemistry, Boston University, Boston, MA, United States
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
| |
Collapse
|
3
|
Ignatov M, Jindal A, Kotelnikov S, Beglov D, Posternak G, Tang X, Maisonneuve P, Poda G, Batey RA, Sicheri F, Whitty A, Tonge PJ, Vajda S, Kozakov D. High Accuracy Prediction of PROTAC Complex Structures. J Am Chem Soc 2023; 145:7123-7135. [PMID: 36961978 PMCID: PMC10240388 DOI: 10.1021/jacs.2c09387] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2023]
Abstract
The design of PROteolysis-TArgeting Chimeras (PROTACs) requires bringing an E3 ligase into proximity with a target protein to modulate the concentration of the latter through its ubiquitination and degradation. Here, we present a method for generating high-accuracy structural models of E3 ligase-PROTAC-target protein ternary complexes. The method is dependent on two computational innovations: adding a "silent" convolution term to an efficient protein-protein docking program to eliminate protein poses that do not have acceptable linker conformations and clustering models of multiple PROTACs that use the same E3 ligase and target the same protein. Results show that the largest consensus clusters always have high predictive accuracy and that the ensemble of models can be used to predict the dissociation rate and cooperativity of the ternary complex that relate to the degrading activity of the PROTAC. The method is demonstrated by applications to known PROTAC structures and a blind test involving PROTACs against BRAF mutant V600E. The results confirm that PROTACs function by stabilizing a favorable interaction between the E3 ligase and the target protein but do not necessarily exploit the most energetically favorable geometry for interaction between the proteins.
Collapse
Affiliation(s)
- Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
| | - Akhil Jindal
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215 USA
- Acpharis Inc., Holliston, Massachusetts 01746, USA
| | - Ganna Posternak
- Center for Molecular, Cell and Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario L4K-M9W, Canada
- Department of Chemistry, University of Toronto, Toronto, Ontario L4K-M9W, Canada
| | - Xiaojing Tang
- Center for Molecular, Cell and Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario L4K-M9W, Canada
| | - Pierre Maisonneuve
- Center for Molecular, Cell and Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario L4K-M9W, Canada
| | - Gennady Poda
- Drug Discovery Program, Ontario Institute for Cancer Research, Toronto, Ontario L4K-M9W, Canada
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario L4K-M9W, Canada
| | - Robert A. Batey
- Department of Chemistry, University of Toronto, Toronto, Ontario L4K-M9W, Canada
| | - Frank Sicheri
- Center for Molecular, Cell and Systems Biology, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario L4K-M9W, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario L4K-M9W, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario L4K-M9W, Canada
| | - Adrian Whitty
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, USA
| | - Peter J. Tonge
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215 USA
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
| |
Collapse
|
4
|
Zhang Q, Kounde CS, Mondal M, Greenfield JL, Baker JR, Kotelnikov S, Ignatov M, Tinworth CP, Zhang L, Conole D, De Vita E, Kozakov D, McCluskey A, Harling JD, Fuchter MJ, Tate EW. Light-mediated multi-target protein degradation using arylazopyrazole photoswitchable PROTACs (AP-PROTACs). Chem Commun (Camb) 2022; 58:10933-10936. [PMID: 36065962 PMCID: PMC9521323 DOI: 10.1039/d2cc03092f] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Light-activable spatiotemporal control of PROTAC-induced protein degradation was achieved with novel arylazopyrazole photoswitchable PROTACs (AP-PROTACs). The use of a promiscuous kinase inhibitor in the design enables this unique photoswitchable PROTAC to selectively degrade four protein kinases together with on/off optical control using different wavelengths of light. A new class of arylazopyrazole photoswitchable PROTACs (AP-PROTACs) enables light-triggered degradation of a specific ensemble of protein kinases.![]()
Collapse
Affiliation(s)
- Qisi Zhang
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| | - Cyrille S Kounde
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| | - Milon Mondal
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| | - Jake L Greenfield
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| | - Jennifer R Baker
- Chemistry, School of Environmental & Life Sciences, the University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Mikhail Ignatov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Christopher P Tinworth
- GlaxoSmithKline, Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - Leran Zhang
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| | - Daniel Conole
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| | - Elena De Vita
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, 11794, USA.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Adam McCluskey
- Chemistry, School of Environmental & Life Sciences, the University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia
| | - John D Harling
- GlaxoSmithKline, Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire, SG1 2NY, UK
| | - Matthew J Fuchter
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| | - Edward W Tate
- Department of Chemistry, Imperial College London, London, W12 0BZ, UK.
| |
Collapse
|
5
|
Lensink MF, Brysbaert G, Mauri T, Nadzirin N, Velankar S, Chaleil RAG, Clarence T, Bates PA, Kong R, Liu B, Yang G, Liu M, Shi H, Lu X, Chang S, Roy RS, Quadir F, Liu J, Cheng J, Antoniak A, Czaplewski C, Giełdoń A, Kogut M, Lipska AG, Liwo A, Lubecka EA, Maszota-Zieleniak M, Sieradzan AK, Ślusarz R, Wesołowski PA, Zięba K, Del Carpio Muñoz CA, Ichiishi E, Harmalkar A, Gray JJ, Bonvin AMJJ, Ambrosetti F, Vargas Honorato R, Jandova Z, Jiménez-García B, Koukos PI, Van Keulen S, Van Noort CW, Réau M, Roel-Touris J, Kotelnikov S, Padhorny D, Porter KA, Alekseenko A, Ignatov M, Desta I, Ashizawa R, Sun Z, Ghani U, Hashemi N, Vajda S, Kozakov D, Rosell M, Rodríguez-Lumbreras LA, Fernandez-Recio J, Karczynska A, Grudinin S, Yan Y, Li H, Lin P, Huang SY, Christoffer C, Terashi G, Verburgt J, Sarkar D, Aderinwale T, Wang X, Kihara D, Nakamura T, Hanazono Y, Gowthaman R, Guest JD, Yin R, Taherzadeh G, Pierce BG, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Sun Y, Zhu S, Shen Y, Park T, Woo H, Yang J, Kwon S, Won J, Seok C, Kiyota Y, Kobayashi S, Harada Y, Takeda-Shitaka M, Kundrotas PJ, Singh A, Vakser IA, Dapkūnas J, Olechnovič K, Venclovas Č, Duan R, Qiu L, Xu X, Zhang S, Zou X, Wodak SJ. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment. Proteins 2021; 89:1800-1823. [PMID: 34453465 PMCID: PMC8616814 DOI: 10.1002/prot.26222] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/24/2021] [Accepted: 08/05/2021] [Indexed: 12/19/2022]
Abstract
We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.
Collapse
Affiliation(s)
- Marc F Lensink
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Guillaume Brysbaert
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Théo Mauri
- CNRS UMR8576 UGSF, Institute for Structural and Functional Glycobiology, University of Lille, Lille, France
| | - Nurul Nadzirin
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | | | - Tereza Clarence
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Bin Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Guangbo Yang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ming Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xufeng Lu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Raj S Roy
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Farhan Quadir
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, USA
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
| | - Anna Antoniak
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Artur Giełdoń
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Mateusz Kogut
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Emilia A Lubecka
- Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland
| | | | | | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | - Patryk A Wesołowski
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
- Intercollegiate Faculty of Biotechnology, University of Gdansk and Medical University of Gdansk, Gdansk, Poland
| | - Karolina Zięba
- Faculty of Chemistry, University of Gdansk, Gdansk, Poland
| | | | - Eiichiro Ichiishi
- International University of Health and Welfare Hospital (IUHW Hospital), Nasushiobara City, Japan
| | - Ameya Harmalkar
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeffrey J Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alexandre M J J Bonvin
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo Vargas Honorato
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Zuzana Jandova
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Panagiotis I Koukos
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Siri Van Keulen
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W Van Noort
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Manon Réau
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - 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, Russia
| | - 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
| | - 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
| | - 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
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - 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
| | - Zhuyezi Sun
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Nasser Hashemi
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - 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
| | - Mireia Rosell
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Luis A Rodríguez-Lumbreras
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Juan Fernandez-Recio
- Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC - Universidad de la Rioja - Gobierno de La Rioja, Logrono, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | | | - Sergei Grudinin
- Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana, USA
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, USA
| | - Tsukasa Nakamura
- Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan
| | - Yuya Hanazono
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Tokai, Ibaraki, Japan
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Johnathan D Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Ghazaleh Taherzadeh
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, Maryland, USA
| | | | - Zhen Cao
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Luigi Cavallo
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Romina Oliva
- University of Naples "Parthenope", Napoli, Italy
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, Texas, USA
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jonghun Won
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Yasuomi Kiyota
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Yoshiki Harada
- School of Pharmacy, Kitasato University, Minato-ku, Tokyo, Japan
| | | | - Petras J Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Amar Singh
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, USA
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Shuang Zhang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
| | - Xiaoqin Zou
- Institute for Data Science and Informatics, University of Missouri, Columbia, Missouri, USA
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri, USA
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri, USA
- Department of Biochemistry, University of Missouri, Columbia, Missouri, USA
| | | |
Collapse
|
6
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
7
|
Abstract
The process of creating a model of the structure formed by a pair of interacting molecules is commonly referred to as docking. Protein docking is one of the most studied topics in computational and structural biology with applications to drug design and beyond. In this chapter, we describe ClusPro, a web server for protein-protein and protein-peptide docking. As an input, the server requires two Protein Data Bank (PDB) files (protein-protein mode) or a PDB file for the protein and a sequence for the ligand (protein-peptide mode). Its output consists of ten models of the resulting structure formed by the two objects upon interaction. The server typically produces results in less than 4 h. The server also provides tools (via "Advanced Options" list) for a user to fine-tune the results using any additional knowledge about the interaction process, e.g., small-angle X-ray scattering (SAXS) profile or distance restraints.
Collapse
Affiliation(s)
- Andrey Alekseenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Institute of Computer Aided Design of the Russian Academy of Sciences, Moscow, Russia
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Institute of Computer Aided Design of the Russian Academy of Sciences, Moscow, Russia.,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA
| | - George Jones
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Maria Sabitova
- Department of Mathematics, Queens College and CUNY Graduate Center, Flushing, NY, USA
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA. .,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. .,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA.
| |
Collapse
|
8
|
Kotelnikov S, Alekseenko A, Liu C, Ignatov M, Padhorny D, Brini E, Lukin M, Coutsias E, Dill KA, Kozakov D. Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:179-189. [PMID: 31879831 DOI: 10.1007/s10822-019-00257-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/19/2019] [Indexed: 12/25/2022]
Abstract
We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.
Collapse
Affiliation(s)
- Sergei Kotelnikov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Innopolis University, Innopolis, Russia
| | - Andrey Alekseenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Mark Lukin
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Evangelos Coutsias
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA. .,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. .,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA.
| |
Collapse
|
9
|
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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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/.
Collapse
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.
| |
Collapse
|
10
|
Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG, Gerguri T, Bates PA, Laine E, Carbone A, Grudinin S, Kong R, Liu RR, Xu XM, Shi H, Chang S, Eisenstein M, Karczynska A, Czaplewski C, Lubecka E, Lipska A, Krupa P, Mozolewska M, Golon Ł, Samsonov S, Liwo A, Crivelli S, Pagès G, Karasikov M, Kadukova M, Yan Y, Huang SY, Rosell M, Rodríguez-Lumbreras LA, Romero-Durana M, Díaz-Bueno L, Fernandez-Recio J, Christoffer C, Terashi G, Shin WH, Aderinwale T, Subraman SRMV, Kihara D, Kozakov D, Vajda S, Porter K, Padhorny D, Desta I, Beglov D, Ignatov M, Kotelnikov S, Moal IH, Ritchie DW, de Beauchêne IC, Maigret B, Devignes MD, Echartea MER, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Cao Y, Shen Y, Baek M, Park T, Woo H, Seok C, Braitbard M, Bitton L, Scheidman-Duhovny D, Dapkūnas J, Olechnovič K, Venclovas Č, Kundrotas PJ, Belkin S, Chakravarty D, Badal VD, Vakser IA, Vreven T, Vangaveti S, Borrman T, Weng Z, Guest JD, Gowthaman R, Pierce BG, Xu X, Duan R, Qiu L, Hou J, Merideth BR, Ma Z, Cheng J, Zou X, Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue L, Jiménez-García B, van Noort CW, Honorato RV, Bonvin AMJJ, Wodak SJ. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment. Proteins 2019; 87:1200-1221. [PMID: 31612567 PMCID: PMC7274794 DOI: 10.1002/prot.25838] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 12/28/2022]
Abstract
We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.
Collapse
Affiliation(s)
- Marc F. Lensink
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Guillaume Brysbaert
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Nurul Nadzirin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Tereza Gerguri
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Elodie Laine
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
| | - Alessandra Carbone
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
- Institut Universitaire de France (IUF), Paris, France
| | - Sergei Grudinin
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ran-Ran Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xi-Ming Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Miriam Eisenstein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Emilia Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland
| | | | - Paweł Krupa
- Polish Academy of Sciences, Institute of Physics, Warsaw, Poland
| | | | - Łukasz Golon
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | | | - Guillaume Pagès
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | | | - Maria Kadukova
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mireia Rosell
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | - Luis A. Rodríguez-Lumbreras
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | | | | | - Juan Fernandez-Recio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
- Instituto de Biología Molecular de Barcelona (IBMB-CSIC), Barcelona, Spain
| | | | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Kathryn Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sergey Kotelnikov
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Iain H. Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | | | | | | | | | - Didier Barradas-Bautista
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Zhen Cao
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Luigi Cavallo
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Romina Oliva
- Department of Sciences and Technologies, University of Naples “Parthenope”, Napoli, Italy
| | - 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
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Merav Braitbard
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lirane Bitton
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Scheidman-Duhovny
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Petras J. Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Saveliy Belkin
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Devlina Chakravarty
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Varsha D. Badal
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Ilya A. Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Thom Vreven
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Sweta Vangaveti
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Tyler Borrman
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Zhiping Weng
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, Missouri
| | - Benjamin Ryan Merideth
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
- Department of Biochemistry, University of Missouri, Columbia, Missouri
| | - Panagiotis I. Koukos
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Cunliang Geng
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mikael E. Trellet
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Adrien S. J. Melquiond
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Li Xue
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W. van Noort
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo V. Honorato
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | | |
Collapse
|
11
|
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: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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.
Collapse
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
| |
Collapse
|
12
|
Ignatov M, Kazennov A, Kozakov D. ClusPro FMFT-SAXS: Ultra-fast Filtering Using Small-Angle X-ray Scattering Data in Protein Docking. J Mol Biol 2018; 430:2249-2255. [DOI: 10.1016/j.jmb.2018.03.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Revised: 03/08/2018] [Accepted: 03/12/2018] [Indexed: 02/01/2023]
|
13
|
Ignatov M. Interstimulus Interference Effect with Stroop-Type Stimuli. Perception 1996. [DOI: 10.1068/v96p0302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The interstimulus interference in reacting to Stroop-type stimuli was investigated. Two aspects of the interstimulus organisation were analysed: the serial structure of the items in the test sheets and the spatial structure of the items on different test sheet types. More difficult serial structures were expected in cases where the correct colour-naming response to an incongruous combination was the suppressed word-naming response of either the previous or the next incongruous stimulus. Variation in the spatial organisation of the items was aimed at causing different opportunities for perceiving several adjacent items at once. As a third factor the study included not a characteristic of the test material, but a related cognitive style variable—the field dependence/independence, measured by a version of the Gottschaldt embedded figures. Every test condition (printed words in incongruous colours) was matched with a control condition (patches of colour) in a double-mirror design. A factorial design of 2 × 3 × 3 was applied and the data were processed with the aid of a three-way ANOVA. The results confirmed the importance of the interstimulus organisation of multiple Stroop-type stimuli. It is inferred that the extent to which perceptual and, in particular, selective-attention processes affect Stroop colour naming performance might be only a fraction of the whole interstimulus and intrastimulus interference effect.
Collapse
|
14
|
Ignatov M, Ignatova T, Stankov E. [Psychological stress in the dental profession]. Stomatologiia (Sofiia) 1983; 65:44-7. [PMID: 6592831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|