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Mou M, Pan Z, Zhou Z, Zheng L, Zhang H, Shi S, Li F, Sun X, Zhu F. A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites. RESEARCH (WASHINGTON, D.C.) 2023; 6:0240. [PMID: 37771850 PMCID: PMC10528219 DOI: 10.34133/research.0240] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 09/08/2023] [Indexed: 09/30/2023]
Abstract
The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.
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Affiliation(s)
- Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Zhimeng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Lingyan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang UniversitySchool of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Hallen MA, Martin JW, Ojewole A, Jou JD, Lowegard AU, Frenkel MS, Gainza P, Nisonoff HM, Mukund A, Wang S, Holt GT, Zhou D, Dowd E, Donald BR. OSPREY 3.0: Open-source protein redesign for you, with powerful new features. J Comput Chem 2018; 39:2494-2507. [PMID: 30368845 PMCID: PMC6391056 DOI: 10.1002/jcc.25522] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 06/14/2018] [Indexed: 12/14/2022]
Abstract
We present osprey 3.0, a new and greatly improved release of the osprey protein design software. Osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein-protein binding. Osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Mark A. Hallen
- Department of Computer Science, Duke University, Durham, NC
27708
- Toyota Technological Institute at Chicago, Chicago, IL
60637
| | | | - Adegoke Ojewole
- Program in Computational Biology and Bioinformatics, Duke
University Medical Center, Durham, NC 27710
| | - Jonathan D. Jou
- Department of Computer Science, Duke University, Durham, NC
27708
| | - Anna U. Lowegard
- Program in Computational Biology and Bioinformatics, Duke
University Medical Center, Durham, NC 27710
| | - Marcel S. Frenkel
- Department of Biochemistry, Duke University Medical Center,
Durham, NC 27710
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC
27708
| | | | - Aditya Mukund
- Department of Computer Science, Duke University, Durham, NC
27708
| | - Siyu Wang
- Program in Computational Biology and Bioinformatics, Duke
University Medical Center, Durham, NC 27710
| | - Graham T. Holt
- Program in Computational Biology and Bioinformatics, Duke
University Medical Center, Durham, NC 27710
| | - David Zhou
- Department of Computer Science, Duke University, Durham, NC
27708
| | - Elizabeth Dowd
- Department of Computer Science, Duke University, Durham, NC
27708
| | - Bruce R. Donald
- Department of Computer Science, Duke University, Durham, NC
27708
- Department of Chemistry, Duke University, Durham, NC
27708
- Department of Biochemistry, Duke University Medical Center,
Durham, NC 27710
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