Recognition of related proteins by iterative template refinement (ITR)

Abstract

Predicting the structural fold of a protein is an important and challenging problem. Available computer programs for determining whether a protein sequence is compatible with a known 3-dimensional structure fall into 2 categories: (1) structure-based methods, in which structural features such as local conformation and solvent accessibility are encoded in a template, and (2) sequence-based methods, in which aligned sequences of a set of related proteins are encoded in a template. In both cases, the programs use a static template based on a predetermined set of proteins. Here, we describe a computer-based method, called iterative template refinement (ITR), that uses templates combining structure-based and sequence-based information and employs an iterative search procedure to detect related proteins and sequentially add them to the templates. Starting from a single protein of known structure, ITR performs sequential cycles of database search to construct an expanding tree of templates with the aim of identifying subtle relationships among proteins. Evaluating the performance of ITR on 6 proteins, we found that the method automatically identified a variety of subtle structural similarities to other proteins. For example, the method identified structural similarity between arabinose-binding protein and phosphofructokinase, a relationship that has not been widely recognized.Keywords: dynamic programming; inverted protein structure prediction; local environment template; multiple sequence template; profile; protein structureThe number of distinct protein structural folds is thought to be relatively small, perhaps less than 1,000 (Chothia, 1992). If so, many of the 6 0 , O O O proteins in existing sequence databases must adopt conformations similar to an already known structure. However, it remains a challenging problem to place proteins known only by their amino acid sequences into the correct structural family.Various computer programs have been developed for using a fixed template for detecting structurally related proteins. Broadly speaking, they fall into 2 categories:1. Structure-bused methods. In this approach, a template encoding information about a specific structure is used to search the protein database to find other sequences compatible with the structure. The first such approach involved classifying each position in a structure according to its solvent accessibility, local conformation, and polarity (Bowie et al., 1991). Subsequent generalizations have included other types of environments defined according to the nature of contacts made by each residue

Embedding strategies for effective use of information from multiple sequence alignments

“…The strategy is automated readily, and might be combined with iterative searching methods (Tatusov et al, 1994;Yi & Lander, 1994) to provide an even more powerful automated system. Embedding strategies are general, and therefore can be extended to other situations in which motif-based alignment information is available for a group of sequences.…”

Intermediate sequences increase the detection of homology between sequences

“…In attempts to overcome this limitation, matching methods have been developed that use the features present in multiple aligned sequences of protein families. Examples of such work are sequence templates (Taylor, 1986;Bashford et al, 1987;Tatusov et al, 1994;Yi & Lander, 1994), pro®les (Gribskov et al, 1987;Luthy et al, 1994;Thompson et al, 1994) and hidden Markov models (Krogh et al, 1994;Baldi et al, 1994;Eddy, 1995;Eddy et al, 1995). The problem with these procedures is that (i) multiple divergent sequences are required for signi®cant improvements over what is found from single sequence searches, (ii) the accurate alignment of related sequences with low residue identities involves some expertise, and (iii) the scoring schemes for models based on multiple sequence alignments do not at present give thresholds that de®ne high coverage and low error.…”

Phylogenetic profiles reveal evolutionary relationships within the “twilight zone” of sequence similarity

“…More recently, advanced sequence comparison methods have been developed on the basis of shared features of sets of related sequences such as protein families. Examples of such approaches are templates (12,13), profiles (14)(15)(16), hidden Markov models [HMMs; (17,18)], and PSI-BLAST [position-specific iterated BLAST (19)]. In addition, threading algorithms are also intended to improve detection of homologous pairs from the sequence space in the twilight zone.…”