Functional site discovery from incomplete training data: a case study with nucleic acid binding proteins
- 1Shanghai Jiao Tong University, China
- 2University of Illinois at Chicago, United States
Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g. identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein's function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmark this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind RNA and DNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both RNA and DNA, and on one protein-DNA benchmark, it achieves near perfect classification.
Keywords: machine learning, Functional discovery, Protein interaction, bioinformatics, Protein function
Received: 21 Dec 2018;
Accepted: 11 Jul 2019.
Edited by:Tao Huang, Shanghai Institutes for Biological Sciences (CAS), China
Copyright: © 2019 Lu, Wang, Langlois, Langlois and Genchev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Hui Lu, Shanghai Jiao Tong University, Shanghai, China, firstname.lastname@example.org