MUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography

Co-evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co-evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co-evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co-evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co-adaptation') from other forces that, in a less specific way, can also create general patterns of co-evolution.

CLEMAPS is a tool for multiple alignment of protein structures. It distinguishes itself from other existing algorithms for multiple structure alignment by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of three angles formed by C(alpha) pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. The input 3D structures are first converted to sequences of conformational letters. Each string of a fixed length is then taken as the center seed to search other sequences for neighbors of the seed, which are strings similar to the seed. A seed and its neighbors form a center-star, which corresponds to a fragment set of local structural similarity shared by many proteins. The detection of center-stars using CLESUM is extremely efficient. Local similarity is a necessary, but insufficient, condition for structural alignment. Once center-stars are found, the spatial consistency between any two stars are examined to find consistent star duads using atomic coordinates. Consistent duads are later joined to create a core for multiple alignment, which is further polished to produce the final alignment. The utility of CLEMAPS is tested on various protein structure ensembles.

While a number of approaches have been geared toward multiple sequence alignments, to date there have been very few approaches to multiple structure alignment and detection of a recurring substructural motif. Among these, none performs both multiple structure comparison and motif detection simultaneously. Further, none considers all structures at the same time, rather than initiating from pairwise molecular comparisons. We present such a multiple structural alignment algorithm. Given an ensemble of protein structures, the algorithm automatically finds the largest common substructure (core) of C(alpha) atoms that appears in all the molecules in the ensemble. The detection of the core and the structural alignment are done simultaneously. Additional structural alignments also are obtained and are ranked by the sizes of the substructural motifs, which are present in the entire ensemble. The method is based on the geometric hashing paradigm. As in our previous structural comparison algorithms, it compares the structures in an amino acid sequence order-independent way, and hence the resulting alignment is unaffected by insertions, deletions and protein chain directionality. As such, it can be applied to protein surfaces, protein-protein interfaces and protein cores to find the optimally, and suboptimally spatially recurring substructural motifs. There is no predefinition of the motif. We describe the algorithm, demonstrating its efficiency. In particular, we present a range of results for several protein ensembles, with different folds and belonging to the same, or to different, families. Since the algorithm treats molecules as collections of points in three-dimensional space, it can also be applied to other molecules, such as RNA, or drugs.

BackgroundThe World Health Organization (WHO) report stated that Acinetobacter baumannii had been classified as one of the most important pathogenic bacteria causing nosocomial infection in hospital patients due to multi-drug resistance (MDR). It is vital to find out new bacterial drug targets and annotated their structure and function for the exploration of new anti-bacterial agents. The present study utilized a systematic route to prioritize the potential drug targets that belong to Mur family of Acinetobacter baumannii and identify their homologous proteins using a computational approach such as sequence similarity search, multiple sequence alignment, phylogenetic analysis, protein sequence, and protein structure analysis. ResultsFrom the results of protein sequence analysis of eight Mur family proteins, they divided into three main enzymatic classes namely transferases (MurG, MurA and MraY), ligases (MurC, MurD, MurE, and MurF), and oxidoreductase (MurB). Based on the results of intra-comparative protein sequence analysis and enzymatic classification, we have chosen MurB, MurE, and MurG as the prioritized drug targets from A. baumannii and subjected them for further detailed studies of inter-species comparison. This inter-species comparison help us to explore the sequential and structural properties of homologous proteins in other species and hence, opens a gateway for new target identification and using common inhibitor for different bacterial species caused by various diseases. The pairwise sequence alignment results between A. baumannii’s MurB with A. calcoaceticus’s MurB, A. baumannii’s MurE with A. seifertii’s MurE, and A. baumannii’s MurG with A. pittii’s MurG showed that every group of the proteins are highly similar with each other and they showed sequence identity of 95.7% and sequence similarity of 97.2%. ConclusionTogether with the results of secondary and three-dimensional structure predictions explained that three selected proteins (MurB, MurE, and MurG) from A. baumannii and their related proteins (AcMurB, AsMurE, and ApMurG) belong to mixed αβ class and they are very similar.

An apparent paradox in computational RNA structure prediction is that many methods, in advance, require a multiple alignment of a set of related sequences, when searching for a common structure between them. However, such a multiple alignment is hard to obtain even for few sequences with low sequence similarity without simultaneously folding and aligning them. Furthermore, it is of interest to conduct a multiple alignment of RNA sequence candidates found from searching as few as two genomic sequences. Here, based on the PMcomp program, we present a global multiple alignment program, foldalignM, which performs especially well on few sequences with low sequence similarity, and is comparable in performance with state of the art programs in general. In addition, it can cluster sequences based on sequence and structure similarity and output a multiple alignment for each cluster. Furthermore, preliminary results with local datasets indicate that the program is useful for post processing foldalign pairwise scans. The program foldalignM is implemented in JAVA and is, along with some accompanying PERL scripts, available at http://foldalign.ku.dk/

Remote protein homology detection is a problem of detecting evolutionary relationship between proteins at low sequence similarity level. Among several problems in remote protein homology detection include the questions of determining which combination of multiple alignment and classification techniques is the best as well as the misalignment of protein sequences during the alignment process. Therefore, this paper deals with remote protein homology detection via assessing the impact of using structural information on protein multiple alignments over sequence information. This paper further presents the best combinations of multiple alignment and classification programs to be chosen. This paper also improves the quality of the multiple alignments via integration of a refinement algorithm. The framework of this paperbegan with datasets preparation on datasets from SCOP version 1.73, followed by multiple alignments of the protein sequences using CLUSTALW, MAFFT, ProbCons and T-Coffee for sequence-based multiple alignments and 3DCoffee, MAMMOTH-mult, MUSTANG and PROMALS3D for structural-based multiple alignments. Next, a refinement algorithm was applied on the protein sequences to reduce misalignments. Lastly, the aligned protein sequences were classified using the pHMMs generative classifier such as HMMER and SAM and also SVMs discriminative classifier such as SVM-Fold and SVM-Struct. The performances of assessed programs were evaluated using ROC, Precision and Recall tests. The result from this paper shows that the combination of refined SVM-Struct and PROMALS3D performs the best against other programs, which suggests that this combination is the best for RPHD. This paper also shows that the use of the refinement algorithm increases the performance of the multiple alignments programs by at least 4%.KeywordsClassificationMultiple alignmentRemote protein homologySupport Vector Machines

Multiple Sequence Alignment (MSA) is a fundamental problem in computational biology that is used to understand the evolutionary history of protein, DNA, or RNA sequences. An optimal alignment for two sequences can efficiently be found using dynamic programming, but computing optimal alignments for more sequences continues to be a hard problem. A common method to solve MSA problems is A* search with admissible heuristics, computed from subsets of the input sequences. In this paper, we consider MSA from the perspective of cost partitioning and relate the existing heuristics for MSA to uniform cost partitioning and post-hoc optimization, two well-known techniques from the automated planning literature. We show that the MSA heuristics are bounded by uniform cost partitioning and that post-hoc optimization yields strictly dominating heuristics. For a common benchmark set of protein sequences and a set of DNA sequences, we show that the theoretical dominance relations between the heuristics carry over to practical instances.

Phylogenomic analysis addresses the limitations of function prediction based on annotation transfer, and has been shown to enable the highest accuracy in prediction of protein molecular function. The Berkeley Phylogenomics Group provides a series of web servers for phylogenomic analysis: classification of sequences to pre-computed families and subfamilies using the PhyloFacts Phylogenomic Encyclopedia, FlowerPower clustering of proteins sharing the same domain architecture, MUSCLE multiple sequence alignment, SATCHMO simultaneous alignment and tree construction and SCI-PHY subfamily identification. The PhyloBuilder web server provides an integrated phylogenomic pipeline starting with a user-supplied protein sequence, proceeding to homolog identification, multiple alignment, phylogenetic tree construction, subfamily identification and structure prediction. The Berkeley Phylogenomics Group resources are available at http://phylogenomics.berkeley.edu.

A multiple sequence alignment of 44 serine/threonine-specific protein phosphatases has been performed. This reveals the position of a common conserved catalytic core, the location of invariant residues, insertions and deletions. The multiple alignment has been used to guide and improve a consensus secondary-structure prediction for the common catalytic core. The location of insertions and deletions has aided in defining the positions of surface loops and turns. The prediction suggests that the core protein phosphatase structure comprises two domains: the first has a single, beta sheet flanked by alpha helices, while the second is predominantly alpha helical. Knowledge of the core secondary structures provides a guide for the design of site-directed-mutagenesis experiments that will not disrupt the native phosphatase fold. A sequence similarity between eukaryotic serine/threonine protein phosphatases and the Escherichia coli diadenosine tetraphosphatase has been identified. This extends over the N-terminal 100 residues of bacteriophage phosphatases and E. coli diadenosine tetraphosphatase. Residues which are invariant amongst these classes are likely to be important in catalysis and protein folding. These include Arg92, Asn138, Asp59, Asp88, Gly58, Gly62, Gly87, Gly93, Gly137, His61, His139 and Val90 and fall into three clusters with the consensus sequences GD(IVTL)HG, GD(LYF)V(DA)RG and GNH, where brackets surround alternative amino acids. The first two consensus sequences are predicted to fall in the beta-alpha and beta-beta loops of a beta-alpha-beta-beta secondary-structure motif. This places the predicted phosphate-binding site at the N-terminus of the alpha helix, where phosphate binding may be stabilised by the alpha-helix dipole.

Fourier and power law analysis of structural complexity in cornea and lens

Characterizing the variability within an ensemble of protein structures is a common requirement in structural biology and bioinformatics. With the increasing number of protein structures becoming available, there is a need for new tools capable of automating the structural comparison of large ensemble of structures. We present Protein Structural Statistics (PSS), a command-line program written in Perl for Unix-like environments, dedicated to the calculation of structural statistics for a set of proteins. PSS can perform multiple sequence alignments, structure superpositions, calculate Cartesian and dihedral coordinate statistics, and execute cluster analyses. An HTML report that contains a convenient summary of results with figures, tables, and hyperlinks can also be produced. PSS is a new tool providing an automated way to compare multiple structures. It integrates various types of structural analyses through an user-friendly and flexible interface, facilitating the access to powerful but more specialized programs. PSS is easy to modify and extend and is distributed under a free and open source license. The relevance of PSS is illustrated by examples of application to pertinent biological problems.

There has been a rapid increase in the amount of mutational data due to, amongst other things, an increase in single nucleotide polymorphism (SNP) data and the use of site-directed mutagenesis as a tool to help dissect out functional properties of proteins. Many manually curated databases have been developed to index point mutations but they are not sustainable with the ever-increasing volume of scientific literature. There have been considerable efforts in the automatic extraction of mutation specific information from raw text involving use of various text-mining approaches. However, one of the key problems is to link these mutations with its associated protein and to present this data in such a way that researchers can immediately contextualize it within a structurally related family of proteins. To aid this process, we have developed an application called MutationMapper. Point mutations are extracted from abstracts and are validated against protein sequences in Uniprot as far as possible. Our methodology differs in a fundamental way from the usual text-mining approach. Rather than start with abstracts, we start with protein sequences, which facilitates greatly the process of validating a potential point mutation identified in an abstract. The results are displayed as mutations mapped on to the protein sequence or a multiple sequence alignment. The latter enables one to readily pick up mutations performed at equivalent positions in related proteins. We demonstrate the use of MutationMapper against several examples including a single sequence and multiple sequence alignments. The application is available as a web-service at http://mutationmapper.bioch.ox.ac.uk.

An approach for COFFEE objective function to global DNA multiple sequence alignment

NCBI's Conserved Domain Database (CDD, http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) is a protein classification and annotation resource. It comprises a collection of annotated multiple sequence alignment (MSA) models representing ancient conserved protein domains, basic units of protein function and protein evolution. These MSAs are available as position specific score matrices (PSSMs) for the rapid identification of conserved domains in protein sequences via Reverse Position Specific -BLAST (RPS-BLAST), a variation of the commonly used PSI-BLAST method. CDD includes manually curated domain models that are organized into family hierarchies, which use protein 3D structure information explicitly to refine domain models and provide insights into the relationship between sequence conservation and molecular function. The domain models can be visualized and studied interactively using NCBI's CDTree/Cn3D software applications. Conserved functional annotations such as ligand-binding sites and/or catalytic active sites are highlighted on protein structures sharing significant homology to the query, thereby pointing out or inferring the underlying mechanism(s) of the protein's function. Here, we present the functionally annotated, evolutionary hierarchical classification of the seven-transmembrane G-protein coupled receptors (7TM GPCRs), which represent the largest family of membrane-bound proteins across the three kingdoms of life. GPCRs are involved in a wide range of physiological roles including tumor growth and metastasis. With the increasing availability of high-resolution crystal structures of 7TM receptors, we recently undertook a systematic comparative analysis of the highly divergent GPCRs. Orphan GPCR subfamilies, which only contain uncharacterized protein sequences, have putative functions and the location of 7TM helices annotated by inference from the molecular and cellular functions of known related proteins with available 3D structure, and/or based on the available literature. We demonstrate the utility of CDTree/Cn3D in studying the molecular evolution of protein domain families, and in accelerating the pace of GPCR drug discovery by enabling researchers to obtain valuable clues regarding as-yet-unidentified molecular functions and mechanisms. The CDTree/Cn3D software is freely available for download via the CDD website. Acknowledgement: This research was supported by the Intramural Research Program of the National Library of Medicine, NIH. Citation Format: James S. Song, Noreen R. Gonzales, Roxanne A. Yamashita, Aron Marchler-Bauer, Stephen H. Bryant. Evolutionary, structural, and functional insights into the seven-transmembrane GPCR superfamily through NCBI's Conserved Domain Database. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1090. doi:10.1158/1538-7445.AM2015-1090

BackgroundHazelnut is reported as a causative agent of allergic reactions. However it is also an edible nut with health benefits. The allergenic characteristics of hazelnut-samples after autoclaving (AC) and high-pressure (HHP) processing have been studied and are also presented here. Previous studies demonstrated that AC treatments were responsible for structural transformation of protein structure motifs. Thus, structural analyses of allergen proteins from hazelnut were carried out to observe what is occurring in relation to the specific-IgE recognition of the related allergenic proteins. The aims of this work are to evaluate the effect of AC and HHP processing on hazelnut in vitro allergenicity using human-sera and to analyse the complexity of hazelnut allergen-protein structures.MethodsHazelnut-samples were subjected to AC and HHP processing. The specific IgE- reactivity was studied in 15 allergic clinic-patients via western blotting analyses. A series of homology-based-bioinformatics 3D-models (Cora 1, Cora 8, Cora 9 and Cora 11) were generated for the antigens included in the study to analyse the co mplexity of their protein structure. This study is supported by the Declaration of Helsinki and subsequent ethical guidelines.ResultsA severe reduction in vitro in allergenicity to hazelnut after AC processing was observed in the allergic clinic-patients studied. The specific-IgE binding of some of the described immunoreactive hazelnut protein-bands: Cora 1 ~18KDa, Cora 8 ~9KDa, Cora 9 ~35-40KDa and Cora 11 ~47-48 KDa decreases. Furthermore a relevant glycosylation was assigned and visualized via structural analysis of proteins (3D-modelling) for the first time in the protein-allergen Cora 11 showing a new role which could open a new door for allergenicity-unravellings.ConclusionHazelnut allergenicity-studies in vivo via Prick-Prick and other means using AC processing are crucial to verify the data we observed via in vitro analyses. Glycosylation studies provided us with clues to elucidate, in the near future, mechanisms of the structures that contribute to hazelnut allergenicity, which thus, in turn, help alleviate food allergens.