HMM'S INTERPOLATION OF PROTIENS FOR PROFILE ANALYSIS

Bioinformatics, 2005

A simple approach for sensitive detection of distant relationships among protein families and for sequence-structure alignment via comparison of hidden Markov models based on their quasi-consensus sequences is presented. Using a previously published benchmark dataset, the approach is demonstrated to give better homology detection and yields alignments with improved accuracy in comparison to an existing state-of-the-art dynamic-programming profile-profile comparison method. This method also runs significantly faster and is therefore suitable for a server covering the rapidly increasing structure database. A server based on this method is available at

BMC bioinformatics, 2014

Protein sequence profile-profile alignment is an important approach to recognizing remote homologs and generating accurate pairwise alignments. It plays an important role in protein sequence database search, protein structure prediction, protein function prediction, and phylogenetic analysis. In this work, we integrate predicted solvent accessibility, torsion angles and evolutionary residue coupling information with the pairwise Hidden Markov Model (HMM) based profile alignment method to improve profile-profile alignments. The evaluation results demonstrate that adding predicted relative solvent accessibility and torsion angle information improves the accuracy of profile-profile alignments. The evolutionary residue coupling information is helpful in some cases, but its contribution to the improvement is not consistent. Incorporating the new structural information such as predicted solvent accessibility and torsion angles into the profile-profile alignment is a useful way to improve ...

1996

Background: For genome sequencing projects to achieve their full impact on biology and medicine, each protein sequence must be identified with its threedimensional structure. Fold assignment methods (also called profile and threading methods) attempt to assign sequences to known protein folds by computing the compatibility of sequence to fold.

Methods in Molecular Biology, 2011

Homology modeling is based on the observation that related protein sequences adopt similar three-dimensional structures. Hence, a homology model of a protein can be derived using related protein structure(s) as modeling template(s). A key step in this approach is the establishment of correspondence between residues of the protein to be modeled and those of modeling template(s). This step, often referred to as sequencestructure alignment, is one of the major determinants of the accuracy of a homology model. This chapter gives an overview of methods for deriving sequence-structure alignments and discusses recent methodological developments leading to improved performance. However, no method is perfect. How to fi nd alignment regions that may have errors and how to make improvements? This is another focus of this chapter. Finally, the chapter provides a practical guidance of how to get the most of the available tools in maximizing the accuracy of sequence-structure alignments. At present, homology or comparative modeling is the most accurate and therefore the most widely used protein structure prediction approach. Homology modeling is based on the empirical observation that evolutionary-related proteins (to be more preciseevolutionary-related protein domains) tend to have similar three-dimensional (3D) structures. Moreover, protein structural features often remain preserved long after the sequence signal is lost to mutations, insertions, and deletions. Therefore, 3D structure is considered to be the most robustly conserved feature of homologous proteins, certainly more conserved than the sequence or molecular function. Although there are some convincing exceptions to this rule ( 1 ) , it still holds for the absolute majority of cases.

Journal of Molecular Biology, 2001

Of the sequence comparison methods, pro®le-based methods perform with greater selectively than those that use pairwise comparisons. Of the pro®le methods, hidden Markov models (HMMs) are apparently the best. The ®rst part of this paper describes calculations that (i) improve the performance of HMMs and (ii) determine a good procedure for creating HMMs for sequences of proteins of known structure. For a family of related proteins, more homologues are detected using multiple models built from diverse single seed sequences than from one model built from a good alignment of those sequences. A new procedure is described for detecting and correcting those errors that arise at the model-building stage of the procedure. These two improvements greatly increase selectivity and coverage.

Journal of Molecular Biology, 1982

Proceedings of the 2006 SIAM International Conference on Data Mining, 2006

Profile Hidden Markov Models (PHMMs) are recognized as powerful computational vehicles for homology search of protein sequences. Extant PHMM training approaches either use completely unaligned or aligned sequences. The PHMMs resulting from these two training approaches present contrasting tradeoffs w.r.t. alignment information and the accuracy of the search outcome. This paper describes a PHMM based technique for modeling protein families from partially aligned sequences. By exploiting the observation that partially aligned sequences give rise to independent subsequences, PHMMs corresponding to these subsequences are composed to build PHMMs for the entire sequences. An interesting aspect of the technique is that it gives rise to a family of PHMMs which are parameterized w.r.t. the alignment information. We present experimental comparison of the performance of our technique against several state of the art homology detection methods.

The computational complexity of evaluating homologies between a gene sequence and profile Hidden Markov Models (HMMs) is relatively high. Unfortunately, researchers must re-evaluate matches every time they discover an error in a sequence or encounter a mutation of the sequence. Since these occurrences are frequent, it is desirable to have a low complexity procedure for updating the matching result when a small perturbation in a given input gene sequence is observed. In this paper, we describe such a procedure based on a sensitivity analysis of the Viterbi algorithm used to evaluate the similarity of an unknown gene sequence and a profile HMMs. By extending single arc tolerance bounds to the evaluation of the relative change in all nodes' distances from a root node, our algorithm skips all unperturbed parts of a sequence. As a result, our proposed algorithm can update the matching decision in only 20% of the time required by the current approach that computes a new match with the perturbed sequence and base HMM model.

Bioinformatics, 2004

Motivation: Alignments of two multiple-sequence alignments, or statistical models of such alignments (profiles), have important applications in computational biology. The increased amount of information in a profile versus a single sequence can lead to more accurate alignments and more sensitive homolog detection in database searches. Several profile-profile alignment methods have been proposed and have been shown to improve sensitivity and alignment quality compared with sequence-sequence methods (such as BLAST) and profilesequence methods (e.g. PSI-BLAST). Here we present a new approach to profile-profile alignment we call Comparison of Alignments by Constructing Hidden Markov Models (HMMs) (COACH). COACH aligns two multiple sequence alignments by constructing a profile HMM from one alignment and aligning the other to that HMM. Results: We compare the alignment accuracy of COACH with two recently published methods: Yona and Levitt's prof_sim and Sadreyev and Grishin's COMPASS. On two sets of reference alignments selected from the FSSP database, we find that COACH is able, on average, to produce alignments giving the best coverage or the fewest errors, depending on the chosen parameter settings. Availability: COACH is freely available from www.drive5.com/ lobster Contact: bob@drive5.com Bioinformatics 20

Pattern Recognition, 2006

Currently, Profile Hidden Markov Models (Profile HMMs) are the methodology of choice for probabilistic protein family modeling. Unfortunately, despite substantial progress the general problem of remote homology analysis is still far from being solved. In this article we propose new approaches for robust protein family modeling by consequently exploiting general pattern recognition techniques. A new feature based representation of amino acid sequences serves as the basis for semi-continuous protein family HMMs. Due to this paradigm shift in processing biological sequences the complexity of family models can be reduced substantially resulting in less parameters which need to be trained. This is especially favorable when only little training data is available as in most current tasks of molecular biology research. In various experiments we prove the superior performance of advanced stochastic protein family modeling for remote homology analysis which is especially relevant for e.g. drug discovery applications.