Abstract
BackgroundSensitive remote homology detection and accurate alignments especially in the midnight zone of sequence similarity are needed for better function annotation and structural modeling of proteins. An algorithm, AlignHUSH for HMM-HMM alignment has been developed which is capable of recognizing distantly related domain families The method uses structural information, in the form of predicted secondary structure probabilities, and hydrophobicity of amino acids to align HMMs of two sets of aligned sequences. The effect of using adjoining column(s) information has also been investigated and is found to increase the sensitivity of HMM-HMM alignments and remote homology detection.ResultsWe have assessed the performance of AlignHUSH using known evolutionary relationships available in SCOP. AlignHUSH performs better than the best HMM-HMM alignment methods and is observed to be even more sensitive at higher error rates. Accuracy of the alignments obtained using AlignHUSH has been assessed using the structure-based alignments available in BaliBASE. The alignment length and the alignment quality are found to be appropriate for homology modeling and function annotation. The alignment accuracy is found to be comparable to existing methods for profile-profile alignments.ConclusionsA new method to align HMMs has been developed and is shown to have better sensitivity at error rates of 10% and above when compared to other available programs. The proposed method could effectively aid obtaining clues to functions of proteins of yet unknown function.A web-server incorporating the AlignHUSH method is available at http://crick.mbu.iisc.ernet.in/~alignhush/
Highlights
Sensitive remote homology detection and accurate alignments especially in the midnight zone of sequence similarity are needed for better function annotation and structural modeling of proteins
The commonly used profiles are the PSI-BLAST generated Position Specific Scoring Matrices (PSSMs) derived from query dependent alignments [5,7], Multiple Sequence Alignment (MSA) based position specific gap penalty profiles [8] and Hidden Markov Models (HMMs) [9,10]
The structure is still seen to be conserved and use of structural information can lead to an improvement in the remote homology detection [18]
Summary
Sensitive remote homology detection and accurate alignments especially in the midnight zone of sequence similarity are needed for better function annotation and structural modeling of proteins. The commonly used profiles are the PSI-BLAST generated PSSMs derived from query dependent alignments [5,7], Multiple Sequence Alignment (MSA) based position specific gap penalty profiles [8] and HMMs [9,10]. HMMs have been shown to be more sensitive than other profile based sequence-profile search methods [11]. This is usually attributed to the ability of HMMs to parameterize position specific gaps. The use of dirichlet mixture priors in estimation of amino acid probabilities in a MSA column could be a reason for the success of HMMs [12]
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