Protein fold recognition by total alignment probability

eShadow: a tool for comparing closely related sequences

Primate sequence comparisons are difficult to interpret due to the high degree of sequence similarity shared between such closely related species. Recently, a novel method, phylogenetic shadowing, has been pioneered for predicting functional elements in the human genome through the analysis of multiple primate sequence alignments. We have expanded this theoretical approach to create a computational tool, eShadow, for the identification of elements under selective pressure in multiple sequence alignments of closely related genomes, such as in comparisons of human-to-primate or mouse-to-rat DNA. This tool integrates two different statistical methods and allows for the dynamic visualization of the resulting conservation profile. eShadow also includes a versatile optimization module capable of training the underlying Hidden Markov Model to differentially predict functional sequences. This module grants the tool high flexibility in the analysis of multiple sequence alignments and in comparing sequences with different divergence rates. Here, we describe the eShadow comparative tool and its potential uses for analyzing both multiple nucleotide and protein alignments to predict putative functional elements.

Triage protein fold prediction

We have constructed, in a completely automated fashion, a new structure template library for threading that represents 358 distinct SCOP folds where each model is mathematically represented as a Hidden Markov model (HMM). Because the large number of models in the library can potentially dilute the prediction measure, a new triage method for fold prediction is employed. In the first step of the triage method, the most probable structural class is predicted using a set of manually constructed, high-level, generalized structural HMMs that represent seven general protein structural classes: all-alpha, all-beta, alpha/beta, alpha+beta, irregular small metal-binding, transmembrane beta-barrel, and transmembrane alpha-helical. In the second step, only those fold models belonging to the determined structural class are selected for the final fold prediction. This triage method gave more predictions as well as more correct predictions compared with a simple prediction method that lacks the initial classification step. Two different schemes of assigning Bayesian model priors are presented and discussed.

Performance evaluation of a new algorithm for the detection of remote homologs with sequence comparison

A detailed analysis of the performance of hybrid, a new sequence alignment algorithm developed by Yu and coworkers that combines Smith Waterman local dynamic programming with a local version of the maximum-likelihood approach, was made to access the applicability of this algorithm to the detection of distant homologs by sequence comparison. We analyzed the statistics of hybrid with a set of nonhomologous protein sequences from the SCOP database and found that the statistics of the scores from hybrid algorithm follows an Extreme Value Distribution with lambda approximately 1, as previously shown by Yu et al. for the case of artificially generated sequences. Local dynamic programming was compared to the hybrid algorithm by using two different test data sets of distant homologs from the PFAM and COGs protein sequence databases. The studies were made with several score functions in current use including OPTIMA, a new score function originally developed to detect remote homologs with the Smith Waterman algorithm. We found OPTIMA to be the best score function for both both dynamic programming and the hybrid algorithms. The ability of dynamic programming to discriminate between homologs and nonhomologs in the two sets of distantly related sequences is slightly better than that of hybrid algorithm. The advantage of producing accurate score statistics with only a few simulations may overcome the small differences in performance and make this new algorithm suitable for detection of homologs in conjunction with a wide range of score functions and gap penalties.

Motif-based fold assignment

Conventional fold recognition techniques rely mainly on the analysis of the entire sequence of a protein. We present an MBA method to improve performance of any conventional sequence-based fold assignment. The method uses sequence motifs, such as those defined in the Prosite database, and the SwissProt annotation of the fold library. When combined with a simple SDP method, the coverage of MBA is comparable to the results obtained with PSI-BLAST. However, the set of the MBA predictions is significantly different from that of PSI-BLAST, leading to a 40% increase of the coverage for the combined MBA/PSI-BLAST method. The MBA approach can be easily adopted to include the results of sequence-independent function prediction methods and alternative motif and annotation databases. The method is available through the web server localized at http://www.doe-mbi.ucla.edu/mba.

3D-1D threading methods for protein fold recognition

The threading approach to protein fold recognition attempts to evaluate how well a query sequence fits into an already-solved fold. 3D-1D threaders rely on matching 1-dimensional strings of 3-dimensional information predicted from the query sequence with corresponding features of the target structure. In many cases this is combined with a sequence comparison. The combination of sequence and structure information has been shown to improve the accuracy of fold recognition, relative to the exclusive use of sequence or structure. In this paper, we review progress made since the introduction of threading methods a decade ago, highlighting recent advances. We focus on two emerging methods that are unconventional 3D-1D threaders: proximity correlation matrices and parallel cascade identification.