Protein Sequence Families Research Articles

Markovian Structures in Biological Sequence Alignments

Abstract The alignment of multiple homologous biopolymer sequences is crucial in research on protein modeling and engineering, molecular evolution, and prediction in terms of both gene function and gene product structure. In this article we provide a coherent view of the two recent models used for multiple sequence alignment—the hidden Markov model (HMM) and the block-based motif model—to develop a set of new algorithms that have both the sensitivity of the block-based model and the flexibility of the HMM. In particular, we decompose the standard HMM into two components: the insertion component, which is captured by the so-called “propagation model,” and the deletion component, which is described by a deletion vector. Such a decomposition serves as a basis for rational compromise between biological specificity and model flexibility. Furthermore, we introduce a Bayesian model selection criterion that—in combination with the propagation model, genetic algorithm, and other computational aspects—forms the core of PROBE, a multiple alignment and database search methodology. The application of our method to a GTPase family of protein sequences yields an alignment that is confirmed by comparison with known tertiary structures.

Markovian Structures in Biological Sequence Alignments

The alignment of multiple homologous biopolymer sequences is crucial in research on protein modeling and engineering, molecular evolution, and prediction in terms of both gene function and gene product structure. In this article we provide a coherent view of the two recent models used for multiple sequence alignment—the hidden Markov model (HMM) and the block-based motif model—to develop a set of new algorithms that have both the sensitivity of the block-based model and the flexibility of the HMM. In particular, we decompose the standard HMM into two components: the insertion component, which is captured by the so-called “propagation model,” and the deletion component, which is described by a deletion vector. Such a decomposition serves as a basis for rational compromise between biological specificity and model flexibility. Furthermore, we introduce a Bayesian model selection criterion that—in combination with the propagation model, genetic algorithm, and other computational aspects—forms the core of PROBE, a multiple alignment and database search methodology. The application of our method to a GTPase family of protein sequences yields an alignment that is confirmed by comparison with known tertiary structures.

Dictionary of recurrent domains in protein structures.

The rapid growth in the number of experimentally determined three-dimensional protein structures has sharpened the need for comprehensive and up-to-date surveys of known structures. Classic work on protein structure classification has made it clear that a structural survey is best carried out at the level of domains, i.e., substructures that recur in evolution as functional units in different protein contexts. We present a method for automated domain identification from protein structure atomic coordinates based on quantitative measures of compactness and, as the new element, recurrence. Compactness criteria are used to recursively divide a protein into a series of successively smaller and smaller substructures. Recurrence criteria are used to select an optimal size level of these substructures, so that many of the chosen substructures are common to different proteins at a high level of statistical significance. The joint application of these criteria automatically yields consistent domain definitions between remote homologs, a result difficult to achieve using compactness criteria alone. The method is applied to a representative set of 1,137 sequence-unique protein families covering 6,500 known structures. Clustering of the resulting set of domains (substructures) yields 594 distinct fold classes (types of substructures). The Dali Domain Dictionary (http://www.embl-ebi.ac.uk/dali/) not only provides a global structural classification, but also a comprehensive description of families of protein sequences grouped around representative proteins of known structure. The classification will be continuously updated and can serve as a basis for improving our understanding of protein evolution and function and for evolving optimal strategies to complete the map of all natural protein structures.

Hidden Markov Model Analysis of Motifs in Steroid Dehydrogenases and Their Homologs

The increasing size of protein sequence databases is straining methods of sequence analysis, even as the increased information offers opportunities for sophisticated analyses of protein structure, function, and evolution. Here we describe a method that uses artificial intelligence-based algorithms to build models of families of protein sequences. These models can be used to search protein sequence databases for remote homologs. The MEME (Multiple Expectation-maximization for Motif Elicitation) software package identifies motif patterns in a protein family, and these motifs are combined into a hidden Markvov model (HMM) for use as a database searching tool. Meta-MEME is sensitive and accurate, as well as automated and unbiased, making it suitable for the analysis of large datasets. We demonstrate Meta-MEME on a family of dehydrogenases that includes mammalian 11β-hydroxysteroid and 17β-hydroxysteroid dehydrogenase and their homologs in the short chain alcohol dehydrogenase family. We chose this dataset because it is large and phylogenetically diverse, providing a good test of the sensitivity and selectivity of Meta-MEME on a protein family of biological interest. Indeed, Meta-MEME identifies at least 350 members of this family in Genpept96 and clearly separates these sequences from non-homologous proteins. We also show how the MEME motif output can be used for phylogenetic analysis.

Score distributions for simultaneous matching to multiple motifs.

Several computer algorithms now exist for discovering multiple motifs (expressed as weight matrices) that characterize a family of protein sequences known to be homologous. This paper describes a method for performing similarity searches of protein sequence databases using such a group of motifs. By simultaneously using all the motifs that characterize a protein family, the sensitivity and specificity of the database search are increased. We define the p-value for a target sequence to be the probability of a random sequence of the same length scoring as well or better in comparison to all the motifs that characterize the family. (The p-value of a database search can be determined from this value and the size of the database.) We show that estimating the distribution of single motif scores by a Gaussian extreme value distribution is insufficiently accurate to provide a useful estimate of the p-value, but that this deficiency can be corrected by reestimating the parameters of the underlying Gaussian distribution from observed scores for comparison of a given motif and sequence database. These parameters are used to calculate a "reduced variate" which has a Gumbel limiting distribution. Multiple motif scores are combined to give a single p-value by using the sum of the reduced variates for the motif scores as the test statistic. We give a computationally efficient approximation to the distribution of the sum of independent Gumbel random variables and verify experimentally that it closely approximates the distribution of the test statistic. Experiments on pseudorandom sequences show that the approximated p-values are conservative, so the significance of high scores in database searches will not be overstated. Experiments with real protein sequences and motifs identified by the MEME algorithm show that determining an overall p-value based on the combination of multiple motifs gives significantly better database search results than using p-values of single motifs.

HomologyPlot: Searching for homology to a family of proteins using a database of unique conserved patterns

A new database of conserved amino acid residues is derived from the multiple sequence alignment of over 84 families of protein sequences that have been reported in the literature. This database contains sequences of conserved hydrophobic core patterns which are probably important for structure and function, since they are conserved for most sequences in that family. This database differs from other single-motif or signature databases reported previously, since it contains multiple patterns for each family. The new database is used to align a new sequence with the conserved regions of a family. This is analogous to reports in the literature where multiple sequence alignments are used to improve a sequence alignment. A program called HomologyPlot (suitable for IBM or compatible computers) uses this database to find homology of a new sequence to a family of protein sequences. There are several advantages to using multiple patterns. First, the program correctly identifies a new sequence as a member of a known family. Second, the search of the entire database is rapid and requires less than one minute. This is similar to performing a multiple sequence alignment of a new sequence to all of the known protein family sequences. Third, the alignment of a new sequence to family members is reliable and can reproduce the alignment of conserved regions already described in the literature. The speed and efficiency of this method is enhanced, since there is no need to score for insertions or deletions as is done in the more commonly used sequence alignment methods. In this method only the patterns are aligned. HomologyPlot also provides general information on each family, as well as a listing of patterns in a family.

Fragment Ranking in Modelling of Protein Structure: Conformationally Constrained Environmental Amino Acid Substitution Tables

Conformationally constrained environment-dependent amino acid residue substitution tables have been constructed from a database comprising 33 homologous families of protein sequences aligned on the basis of their three-dimensional structures. Residues are allotted to one of 216 (or 54) classes of combinations of structural features. These include nine main-chain conformation classes, three classes of side-chain accessibility and eight (or two) classes of side-chain involvement in three types of hydrogen bond. Seven different main-chain conformational classes outside of regions of regular structure were identified in an analysis of the distributions of φ-ψ torsion angles in 84 high-resolution crystallographic structures. Residue substitutions at equivalent positions in the structural alignments are included where the main-chain conformational class is conserved. Frequency data in the form of 216 (or 54) environment specific (20 × 20 residue type) matrices are then converted to probabilities. Two smoothing regimes incorporating entropy-driven weights were applied to the set of 54 tables. Predicted residue substitutions have been generated for individual residue positions in β-hairpins and the hypervariable regions of the immunoglobulins. These have been compared with the observed sequence variation at the same positions using rank correlation methods. Measurements of χ 2 distances demonstrate the considerable improvement in predictive power at key residue positions identified from interactive graphics studies when compared to the Dayhoff MDM250 mutation matrix. An illustrative example is given of an application of the method in the ranking of loop fragments in model building studies of structurally variable regions in two subtilisins. A combined template scoring procedure is found to be 26-fold more discriminatory than the Dayhoff matrix. The success rate is approximately 85%.

The significant conservative and variable regions of the homologous protein sequences.

A method of identification of significant conservative and variable regions in homologous protein sequences is presented. A set of aligned homologous sequences is divided into two groups consisting of m and n most related sequences. Each pair of sequences from different group is compared using unitary similarity matrix. The superposition of pairwise comparisons scanned by a window of 10 amino acid residues gives intergroup local variability profile (VP). Area S of the figure between the VP and its mean value line is compared with averaged area S(r) of 1000 VPs of artificial homologous protein families. The difference (S-S(r)) given in standard deviation units sigma r is believed to be the amino acid substitution overall irregularity along the homologous protein sequences OI = (S-S(r))/sigma r. If OI greater than 2, the real VP extrema containing the surplus of area S-(S(r) + 2 sigma r) are cut off. The cut off stretches are likely to be significant conservative and variable regions. The significant conservative and variable regions of six homologous sequence families (phospholipases A2, cytochromes b, alpha-subunits of Na, K-ATPase, L- and M-subunits of photosynthetic bacteria photoreaction centre and human rhodopsins) were identified. It was shown that for artificial homologous protein sequences derived by k-fold lengthening of natural proteins the OI value rises as square root of k. To compare the degree of substitution irregularity in homologous protein sequence families of different length L the value of standard substitution overall irregularity for L = 250 is proposed.