Automated Protein Subfamily Identification and Classification

Abstract

Function prediction by homology is widely used to provide preliminary functional annotations for genes for which experimental evidence of function is unavailable or limited. This approach has been shown to be prone to systematic error, including percolation of annotation errors through sequence databases. Phylogenomic analysis avoids these errors in function prediction but has been difficult to automate for high-throughput application. To address this limitation, we present a computationally efficient pipeline for phylogenomic classification of proteins. This pipeline uses the SCI-PHY (Subfamily Classification in Phylogenomics) algorithm for automatic subfamily identification, followed by subfamily hidden Markov model (HMM) construction. A simple and computationally efficient scoring scheme using family and subfamily HMMs enables classification of novel sequences to protein families and subfamilies. Sequences representing entirely novel subfamilies are differentiated from those that can be classified to subfamilies in the input training set using logistic regression. Subfamily HMM parameters are estimated using an information-sharing protocol, enabling subfamilies containing even a single sequence to benefit from conservation patterns defining the family as a whole or in related subfamilies. SCI-PHY subfamilies correspond closely to functional subtypes defined by experts and to conserved clades found by phylogenetic analysis. Extensive comparisons of subfamily and family HMM performances show that subfamily HMMs dramatically improve the separation between homologous and non-homologous proteins in sequence database searches. Subfamily HMMs also provide extremely high specificity of classification and can be used to predict entirely novel subtypes. The SCI-PHY Web server at http://phylogenomics.berkeley.edu/SCI-PHY/ allows users to upload a multiple sequence alignment for subfamily identification and subfamily HMM construction. Biologists wishing to provide their own subfamily definitions can do so. Source code is available on the Web page. The Berkeley Phylogenomics Group PhyloFacts resource contains pre-calculated subfamily predictions and subfamily HMMs for more than 40,000 protein families and domains at http://phylogenomics.berkeley.edu/phylofacts/.

Exploring the structure and function paradigm

“…Others apply sensitive profile-profile based comparison strategies with residue conservation scores finely tuned to recognising functional homologues [64][65][66]. One of the challenges in developing methods for large scale application is the lack of any large datasets to optimise thresholds for safe functional inheritance.…”

Fast and automated functional classification with MED‐SuMo: An application on purine‐binding proteins

“…However, without known related sequences or structures their detection is difficult. 89 Innovative novel approaches have been proposed, that is, the use of hydrophobicity distribution on protein structures using the fuzzy oil drop model, 59 the destabilization of limited protein regions, 90 phylogenomic classification of protein sequences, 91 or the classification of known protein catalytic sites. 92 Prediction of protein functional sites is an important step in identifying small-molecule interactions for drug discovery 93 and to optimize the drugs targeting these sites.…”

The Basics of Protein Sequence Analysis

“…from a set of sequences with different domain compositions) using the SCI-PHY algorithm based on HMMs. 99 Finally, unlike other methods that calculate their similarity matrices based on alignments, CLUSS 100 (http://prospectus.usherbrooke.ca/CLUSS/) performs clustering based on a matching amino acid subsequences, which makes it applicable both to alignable and unalignable sequences, e.g. products of circular permutation etc.…”

“…As an example, the SCI-PHY server (http://phylogenomics.berkeley.edu/SCI-PHY/) allows users to upload a MSA for subfamily identification and subfamily HMM construction. 99 Further, analysis of the phylogenetic tree in connection with the known (or assumed) tree of hosts (organisms) can be used to deduce major evolutionary events in the protein family, e.g. gene duplications, gene losses, which provide the basis for discrimination between orthologs and paralogs and may guide functional predictions (review: 156 ).…”