Wei Zheng1, Xiaogen Zhou1, Qiqige Wuyun2, Robin Pearce1, Yang Li1,3, Yang Zhang1,4. 1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109. 2. Computer Science and Engineering Department, Michigan State University, East Lansing, MI 48824, USA. 3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. 4. Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
Abstract
MOTIVATION: Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence. RESULTS: We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with coevolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2549 proteins and generated correct single- and multi-domain classifications with a Matthew's correlation coefficient of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method. For proteins with discontinuous domains, the domain boundary detection and normalized domain overlapping scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/FUpred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence. RESULTS: We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with coevolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2549 proteins and generated correct single- and multi-domain classifications with a Matthew's correlation coefficient of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method. For proteins with discontinuous domains, the domain boundary detection and normalized domain overlapping scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins. AVAILABILITY AND IMPLEMENTATION: https://zhanglab.ccmb.med.umich.edu/FUpred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Jalil Villalobos-Alva; Luis Ochoa-Toledo; Mario Javier Villalobos-Alva; Atocha Aliseda; Fernando Pérez-Escamirosa; Nelly F Altamirano-Bustamante; Francine Ochoa-Fernández; Ricardo Zamora-Solís; Sebastián Villalobos-Alva; Cristina Revilla-Monsalve; Nicolás Kemper-Valverde; Myriam M Altamirano-Bustamante Journal: Front Bioeng Biotechnol Date: 2022-07-07
Authors: Wei Zheng; Qiqige Wuyun; Xiaogen Zhou; Yang Li; Peter L Freddolino; Yang Zhang Journal: Nucleic Acids Res Date: 2022-04-14 Impact factor: 19.160