Graphical models for identifying pore‐forming proteins

Abstract

Pore‐forming toxins (PFTs) are proteins that form lesions in biological membranes. Better understanding of the structure and function of these proteins will be beneficial in a number of biotechnological applications, including the development of new pest control methods in agriculture. When searching for new pore formers, existing sequence homology‐based methods fail to discover truly novel proteins with low sequence identity to known proteins. Search methodologies based on protein structures would help us move beyond this limitation. As the number of known structures for PFTs is very limited, it's quite challenging to identify new proteins having similar structures using computational approaches like deep learning. In this article, we therefore propose a sample‐efficient graphical model, where a protein structure graph is first constructed according to consensus secondary structures. A semi‐Markov conditional random fields model is then developed to perform protein sequence segmentation. We demonstrate that our method is able to distinguish structurally similar proteins even in the absence of sequence similarity (pairwise sequence identity < 0.4)—a feat not achievable by traditional approaches like HMMs. To extract proteins of interest from a genome‐wide protein database for further study, we also develop an efficient framework for UniRef50 with 43 million proteins.