Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction.

BackgroundOne of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences will presumably produce a more powerful profile, but distantly related sequences can be difficult to align accurately using only sequence information. Therefore, it would be expected that the use of protein structure alignments to improve the selection and alignment of diverse sequence homologs might yield improved profiles. However, the actual utility of such an approach has remained unclear.ResultsWe explored several iterative protocols for the generation of profile hidden Markov models. These protocols were tailored to allow the inclusion of protein structure alignments in the process, and were used for large-scale creation and benchmarking of structure alignment-enhanced models. We found that models using structure alignments did not provide an overall improvement over sequence-only models for superfamily-level structure predictions. However, the results also revealed that the structure alignment-enhanced models were complimentary to the sequence-only models, particularly at the edge of the "twilight zone". When the two sets of models were combined, they provided improved results over sequence-only models alone. In addition, we found that the beneficial effects of the structure alignment-enhanced models could not be realized if the structure-based alignments were replaced with sequence-based alignments. Our experiments with different iterative protocols for sequence-only models also suggested that simple protocol modifications were unable to yield equivalent improvements to those provided by the structure alignment-enhanced models. Finally, we found that models using structure alignments provided fold-level structure assignments that were superior to those produced by sequence-only models.ConclusionWhen attempting to predict the structure of remote homologs, we advocate a combined approach in which both traditional models and models incorporating structure alignments are used

Understanding diversity of human innate immunity receptors: analysis of surface features of leucine-rich repeat domains in NLRs and TLRs.

BackgroundThe human innate immune system uses a system of extracellular Toll-like receptors (TLRs) and intracellular Nod-like receptors (NLRs) to match the appropriate level of immune response to the level of threat from the current environment. Almost all NLRs and TLRs have a domain consisting of multiple leucine-rich repeats (LRRs), which is believed to be involved in ligand binding. LRRs, found also in thousands of other proteins, form a well-defined "horseshoe"-shaped structural scaffold that can be used for a variety of functions, from binding specific ligands to performing a general structural role. The specific functional roles of LRR domains in NLRs and TLRs are thus defined by their detailed surface features. While experimental crystal structures of four human TLRs have been solved, no structure data are available for NLRs.ResultsWe report a quantitative, comparative analysis of the surface features of LRR domains in human NLRs and TLRs, using predicted three-dimensional structures for NLRs. Specifically, we calculated amino acid hydrophobicity, charge, and glycosylation distributions within LRR domain surfaces and assessed their similarity by clustering. Despite differences in structural and genomic organization, comparison of LRR surface features in NLRs and TLRs allowed us to hypothesize about their possible functional similarities. We find agreement between predicted surface similarities and similar functional roles in NLRs and TLRs with known agonists, and suggest possible binding partners for uncharacterized NLRs.ConclusionDespite its low resolution, our approach permits comparison of molecular surface features in the absence of crystal structure data. Our results illustrate diversity of surface features of innate immunity receptors and provide hints for function of NLRs whose specific role in innate immunity is yet unknown

Ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses.

BackgroundFungi play critical roles in many ecosystems, cause serious diseases in plants and animals, and pose significant threats to human health and structural integrity problems in built environments. While most fungal diversity remains unknown, the development of PCR primers for the internal transcribed spacer (ITS) combined with next-generation sequencing has substantially improved our ability to profile fungal microbial diversity. Although the high sequence variability in the ITS region facilitates more accurate species identification, it also makes multiple sequence alignment and phylogenetic analysis unreliable across evolutionarily distant fungi because the sequences are hard to align accurately. To address this issue, we created ghost-tree, a bioinformatics tool that integrates sequence data from two genetic markers into a single phylogenetic tree that can be used for diversity analyses. Our approach starts with a "foundation" phylogeny based on one genetic marker whose sequences can be aligned across organisms spanning divergent taxonomic groups (e.g., fungal families). Then, "extension" phylogenies are built for more closely related organisms (e.g., fungal species or strains) using a second more rapidly evolving genetic marker. These smaller phylogenies are then grafted onto the foundation tree by mapping taxonomic names such that each corresponding foundation-tree tip would branch into its new "extension tree" child.ResultsWe applied ghost-tree to graft fungal extension phylogenies derived from ITS sequences onto a foundation phylogeny derived from fungal 18S sequences. Our analysis of simulated and real fungal ITS data sets found that phylogenetic distances between fungal communities computed using ghost-tree phylogenies explained significantly more variance than non-phylogenetic distances. The phylogenetic metrics also improved our ability to distinguish small differences (effect sizes) between microbial communities, though results were similar to non-phylogenetic methods for larger effect sizes.ConclusionsThe Silva/UNITE-based ghost tree presented here can be easily integrated into existing fungal analysis pipelines to enhance the resolution of fungal community differences and improve understanding of these communities in built environments. The ghost-tree software package can also be used to develop phylogenetic trees for other marker gene sets that afford different taxonomic resolution, or for bridging genome trees with amplicon trees.Availabilityghost-tree is pip-installable. All source code, documentation, and test code are available under the BSD license at https://github.com/JTFouquier/ghost-tree

Phylogenetic relationships of Indian caecilians (Amphibia: Gymnophiona) inferred from mitochondrial rRNA gene sequences

India has a diverse caecilian fauna, including representatives of three of the six currently recognized families, the Caeciliidae, Ichthyophiidae, the endemic Uraeotyphlidae, but previous molecular phylogenetic studies of caecilians have not included sequences for any Indian caecilians. Partial 12S and 16S mitochondrial gene sequences were obtained for a single representative of each of the caecilian families found in India and aligned against previously reported sequences for 13 caecilian species. The resulting alignment (16 taxa, 1200 sites, of which 288 cannot be aligned unambiguously) was analyzed using parsimony, maximum-likelihood, and distance methods. As judged by bootstrap proportions, decay indices, and leaf stabilities, well-supported relationships of the Indian caecilians are recovered from the alignment. The data (1) corroborate the hypothesis, based on morphology, that the Uraeotyphlidae and Ichthyophiidae are sister taxa, (2) recover a monophyletic Ichthyophiidae, including Indian and South East Asian representatives, and (3) place the Indian caeciliid Gegeneophis ramaswamii as the sister group of the caeciliid caecilians of the Seychelles. Rough estimates of divergence times suggest an origin of the Uraeotyphlidae and Ichthyophiidae while India was isolated from Laurasia and Africa and are most consistent with an Indian origin of these families and subsequent dispersal of ichthyophiids into South East Asia

PSI-BLAST-ISS: an intermediate sequence search tool for estimation of the position-specific alignment reliability

BACKGROUND: Protein sequence alignments have become indispensable for virtually any evolutionary, structural or functional study involving proteins. Modern sequence search and comparison methods combined with rapidly increasing sequence data often can reliably match even distantly related proteins that share little sequence similarity. However, even highly significant matches generally may have incorrectly aligned regions. Therefore when exact residue correspondence is used to transfer biological information from one aligned sequence to another, it is critical to know which alignment regions are reliable and which may contain alignment errors. RESULTS: PSI-BLAST-ISS is a standalone Unix-based tool designed to delineate reliable regions of sequence alignments as well as to suggest potential variants in unreliable regions. The region-specific reliability is assessed by producing multiple sequence alignments in different sequence contexts followed by the analysis of the consistency of alignment variants. The PSI-BLAST-ISS output enables the user to simultaneously analyze alignment reliability between query and multiple homologous sequences. In addition, PSI-BLAST-ISS can be used to detect distantly related homologous proteins. The software is freely available at: . CONCLUSION: PSI-BLAST-ISS is an effective reliability assessment tool that can be useful in applications such as comparative modelling or analysis of individual sequence regions. It favorably compares with the existing similar software both in the performance and functional features

Evolutionary Covariant Positions within Calmodulin EF-hand Sequences Promote Ligand Binding

Intracellular calcium signaling is an essential regulatory mechanism through calcium-mediated signal transduction pathways involved in many cell processes, such as exocytosis, motility, apoptosis, excitability, transcription, and muscle contraction. The calcium-binding, ubiquitous, and highly conserved protein calmodulin (CaM) is an important regulator of hundreds of target proteins involved in cellular calcium signaling. CaM comprises of two pairs of EF-hand calcium-binding domains and these structural regions of the protein are highly conserved. Studying the molecular mechanisms underlying the binding of calcium to the EF-hands of CaM is critical in understanding the calcium-mediated cellular processes and how improper binding of calcium can lead to various human pathologies. Previous site-specific binding measurements indicate that each of the four EF-hands of CaM have distinct affinities for calcium. In this study, we have utilized covariance patterns and site-specific mutagenesis to analyze calcium affinity in the two EF-hands of the N-lobe of CaM in order to determine the specific amino acids that are evolutionarily conserved to coordinate calcium. The specific amino acids in CaM that we studied are theorized to coevolve, which means that in their protein coding genes, when a mutation occurs, a compensatory mutation is likely to follow to conserve structure and function of CaM. Since CaM is a highly conserved protein with a known structure, covariance analyses will help in understanding which amino acid contacts are most important for the coordination of calcium in the EF-hands of CaM and to determine which amino acids are under evolutionary constraint. Covariance algorithms, multiple sequence analyses and accompanied protein structure analyses were used to identify the two high scoring amino acid pairs in the N-lobe EF-hands: positions 22 and 24 in EF-hand site 1 and positions 58 and 60 in EF-hand site 2. The amino acids in these locations were mutated and accompanied calcium binding was measured to better understand the effects of the mutations on calcium binding. We have found that both the D24N mutation in site 1 and the D58N mutation in site 2 disrupt binding likely due to the removal of a necessary aspartate in the binding site. However, the combined D58N and N60D mutations restore binding in site 2 by providing the necessary aspartate in the covariant location. The N60D mutation by itself has little impact on calcium binding in site 2. Therefore, it is evident that evolution conserves at least one aspartate in the covariant positions of the binding site and the presence of two aspartates in the covariant positions of the binding site has little affect on calcium binding. We are currently studying the covariant positions in site 1 and future work includes structurally analyzing the covariant positions in the C-lobe of CaM and studying covariance patterns of other calcium-binding proteins with EF-hand binding domains.Biochemistr

High quality protein sequence alignment by combining structural profile prediction and profile alignment using SABERTOOTH

<p>Abstract</p> <p>Background</p> <p>Protein alignments are an essential tool for many bioinformatics analyses. While sequence alignments are accurate for proteins of high sequence similarity, they become unreliable as they approach the so-called 'twilight zone' where sequence similarity gets indistinguishable from random. For such distant pairs, structure alignment is of much better quality. Nevertheless, sequence alignment is the only choice in the majority of cases where structural data is not available. This situation demands development of methods that extend the applicability of accurate sequence alignment to distantly related proteins.</p> <p>Results</p> <p>We develop a sequence alignment method that combines the prediction of a structural profile based on the protein's sequence with the alignment of that profile using our recently published alignment tool SABERTOOTH. In particular, we predict the contact vector of protein structures using an artificial neural network based on position-specific scoring matrices generated by PSI-BLAST and align these predicted contact vectors. The resulting sequence alignments are assessed using two different tests: First, we assess the alignment quality by measuring the derived structural similarity for cases in which structures are available. In a second test, we quantify the ability of the significance score of the alignments to recognize structural and evolutionary relationships. As a benchmark we use a representative set of the SCOP (structural classification of proteins) database, with similarities ranging from closely related proteins at SCOP family level, to very distantly related proteins at SCOP fold level. Comparing these results with some prominent sequence alignment tools, we find that SABERTOOTH produces sequence alignments of better quality than those of Clustal W, T-Coffee, MUSCLE, and PSI-BLAST. HHpred, one of the most sophisticated and computationally expensive tools available, outperforms our alignment algorithm at family and superfamily levels, while the use of SABERTOOTH is advantageous for alignments at fold level. Our alignment scheme will profit from future improvements of structural profiles prediction.</p> <p>Conclusions</p> <p>We present the automatic sequence alignment tool SABERTOOTH that computes pairwise sequence alignments of very high quality. SABERTOOTH is especially advantageous when applied to alignments of remotely related proteins. The source code is available at <url>http://www.fkp.tu-darmstadt.de/sabertooth_project/</url>, free for academic users upon request.</p