MRFalign: Protein Homology Detection through Alignment of Markov Random Fields

Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/.Comment: Accepted by both RECOMB 2014 and PLOS Computational Biolog

Using Structure to Explore the Sequence Alignment Space of Remote Homologs

Protein structure modeling by homology requires an accurate sequence alignment between the query protein and its structural template. However, sequence alignment methods based on dynamic programming (DP) are typically unable to generate accurate alignments for remote sequence homologs, thus limiting the applicability of modeling methods. A central problem is that the alignment that is “optimal” in terms of the DP score does not necessarily correspond to the alignment that produces the most accurate structural model. That is, the correct alignment based on structural superposition will generally have a lower score than the optimal alignment obtained from sequence. Variations of the DP algorithm have been developed that generate alternative alignments that are “suboptimal” in terms of the DP score, but these still encounter difficulties in detecting the correct structural alignment. We present here a new alternative sequence alignment method that relies heavily on the structure of the template. By initially aligning the query sequence to individual fragments in secondary structure elements and combining high-scoring fragments that pass basic tests for “modelability”, we can generate accurate alignments within a small ensemble. Our results suggest that the set of sequences that can currently be modeled by homology can be greatly extended

Structural RNA Homology Search and Alignment Using Covariance Models

Functional RNA elements do not encode proteins, but rather function directly as RNAs. Many different types of RNAs play important roles in a wide range of cellular processes, including protein synthesis, gene regulation, protein transport, splicing, and more. Because important sequence and structural features tend to be evolutionarily conserved, one way to learn about functional RNAs is through comparative sequence analysis - by collecting and aligning examples of homologous RNAs and comparing them. Covariance models: CMs) are powerful computational tools for homology search and alignment that score both the conserved sequence and secondary structure of an RNA family. However, due to the high computational complexity of their search and alignment algorithms, searches against large databases and alignment of large RNAs like small subunit ribosomal RNA: SSU rRNA) are prohibitively slow. Large-scale alignment of SSU rRNA is of particular utility for environmental survey studies of microbial diversity which often use the rRNA as a phylogenetic marker of microorganisms. In this work, we improve CM methods by making them faster and more sensitive to remote homology. To accelerate searches, we introduce a query-dependent banding: QDB) technique that makes scoring sequences more efficient by restricting the possible lengths of structural elements based on their probability given the model. We combine QDB with a complementary filtering method that quickly prunes away database subsequences deemed unlikely to receive high CM scores based on sequence conservation alone. To increase search sensitivity, we apply two model parameterization strategies from protein homology search tools to CMs. As judged by our benchmark, these combined approaches yield about a 250-fold speedup and significant increase in search sensitivity compared with previous implementations. To accelerate alignment, we apply a method that uses a fast sequence-based alignment of a target sequence to determine constraints for the more expensive CM sequence- and structure-based alignment. This technique reduces the time required to align one SSU rRNA sequence from about 15 minutes to 1 second with a negligible effect on alignment accuracy. Collectively, these improvements make CMs more powerful and practical tools for RNA homology search and alignment

Back-translation for discovering distant protein homologies

Frameshift mutations in protein-coding DNA sequences produce a drastic change in the resulting protein sequence, which prevents classic protein alignment methods from revealing the proteins' common origin. Moreover, when a large number of substitutions are additionally involved in the divergence, the homology detection becomes difficult even at the DNA level. To cope with this situation, we propose a novel method to infer distant homology relations of two proteins, that accounts for frameshift and point mutations that may have affected the coding sequences. We design a dynamic programming alignment algorithm over memory-efficient graph representations of the complete set of putative DNA sequences of each protein, with the goal of determining the two putative DNA sequences which have the best scoring alignment under a powerful scoring system designed to reflect the most probable evolutionary process. This allows us to uncover evolutionary information that is not captured by traditional alignment methods, which is confirmed by biologically significant examples.Comment: The 9th International Workshop in Algorithms in Bioinformatics (WABI), Philadelphia : \'Etats-Unis d'Am\'erique (2009

An Alternative Model of Amino Acid Replacement

The observed correlations between pairs of homologous protein sequences are typically explained in terms of a Markovian dynamic of amino acid substitution. This model assumes that every location on the protein sequence has the same background distribution of amino acids, an assumption that is incompatible with the observed heterogeneity of protein amino acid profiles and with the success of profile multiple sequence alignment. We propose an alternative model of amino acid replacement during protein evolution based upon the assumption that the variation of the amino acid background distribution from one residue to the next is sufficient to explain the observed sequence correlations of homologs. The resulting dynamical model of independent replacements drawn from heterogeneous backgrounds is simple and consistent, and provides a unified homology match score for sequence-sequence, sequence-profile and profile-profile alignment.Comment: Minor improvements. Added figure and reference

MAVID: Constrained ancestral alignment of multiple sequences

We describe a new global multiple alignment program capable of aligning a large number of genomic regions. Our progressive alignment approach incorporates the following ideas: maximum-likelihood inference of ancestral sequences, automatic guide-tree construction, protein based anchoring of ab-initio gene predictions, and constraints derived from a global homology map of the sequences. We have implemented these ideas in the MAVID program, which is able to accurately align multiple genomic regions up to megabases long. MAVID is able to effectively align divergent sequences, as well as incomplete unfinished sequences. We demonstrate the capabilities of the program on the benchmark CFTR region which consists of 1.8Mb of human sequence and 20 orthologous regions in marsupials, birds, fish, and mammals. Finally, we describe two large MAVID alignments: an alignment of all the available HIV genomes and a multiple alignment of the entire human, mouse and rat genomes

In Silico Characterization of Pectate Lyase Protein Sequences from Different Source Organisms

A total of 121 protein sequences of pectate lyases were subjected to homology search, multiple sequence alignment, phylogenetic tree construction, and motif analysis. The phylogenetic tree constructed revealed different clusters based on different source organisms representing bacterial, fungal, plant, and nematode pectate lyases. The multiple accessions of bacterial, fungal, nematode, and plant pectate lyase protein sequences were placed closely revealing a sequence level similarity. The multiple sequence alignment of these pectate lyase protein sequences from different source organisms showed conserved regions at different stretches with maximum homology from amino acid residues 439–467, 715–816, and 829–910 which could be used for designing degenerate primers or probes specific for pectate lyases. The motif analysis revealed a conserved Pec_Lyase_C domain uniformly observed in all pectate lyases irrespective of variable sources suggesting its possible role in structural and enzymatic functions