Protein homology detection with sparse models

Abstract

Establishing structural or functional relationship between sequences, for instance to infer the structural class of an unannotated protein, is a key task in biological analysis. Protein sequences undergo complex transformations such as mutation, insertion and deletion during the evolutionary process and typically share low sequence similarity on the superfamily level, making the task for remote homology detection based on primary sequence only very challenging. Based on previous studies stating that knowledge based on only a subset of critical positions and the preferred symbols on such positions are sufficient for remote homology detection, we present a series of works, each enforcing different notion of sparsity, to recover such critical positions. We first start with a generative model and present the sparse profile hidden Markov models. Such generative approach recovers some critical patterns and motivates the need for discriminative learning. In our second study, we present a discriminative approach to recover such critical positions and the preferred symbols. In our third study, we address the issue of very few positive training examples, accompanied by a large number of negative training examples, which is typical in many remote homology detection task. Such issue motivates the need for semi-supervised learning. However, though containing abundant useful and critical information, large uncurated sequence databases also contain a lot of noise, which may compromise the quality of the classifiers. As a result, we present a systematic and biologically motivated framework for semi-supervised learning with large uncurated sequence databases. Combined with a very fast string kernel, our method not only realizes rapid and accurate remote homology detection and show state-of-the-art performance, but also recovers some critical patterns conserved in superfamilies.