Scores for sequence searches and alignments

Contemporary selection on MHC genes in a free-living ruminant population

Genes within the major histocompatibility complex (MHC) are the most variable identified in vertebrates. Pathogen-mediated selection is believed to be the main force maintaining MHC diversity. However, relatively few studies have demonstrated contemporary selection on MHC genes. Here, we examine associations between MHC variation and several fitness measurements including total fitness and five fitness components, in 3400 wild Soay sheep (Ovis aries) monitored between 1989 and 2012. In terms of total fitness, measured as lifetime breeding success of all individuals born, we found haplotypes named C and D were associated with decreased and increased male total fitness respectively. In terms of fitness components, juvenile survival was associated with haplotype divergence while individual haplotypes (C, D and F) were associated with adult fitness components. Consistent with the increased male total fitness, the rarest haplotype D has increased in frequency throughout the study period more than expected under neutral expectations. Our results demonstrate contemporary natural selection is acting on MHC class II genes in Soay sheep and the mode of selection on specific fitness components can be different mode from selection on total fitness.

Associations between MHC class II variation and phenotypic traits in a free-living sheep population

Pathogen-mediated selection (PMS) is thought to maintain the high level of allelic diversity observed in the major histocompatibility complex (MHC) class II genes. A comprehensive way to demonstrate contemporary selection is to examine associations between MHC variation and individual fitness. As individual fitness is hard to measure, many studies examine associations between MHC variation and phenotypic traits, including direct or indirect measures of adaptive immunity thought to contribute to fitness. Here, we tested associations between MHC class II variation and five phenotypic traits measured in free-living sheep captured in August: weight, strongyle faecal egg count, and plasma IgA, IgE and IgG immunoglobulin titres against the gastrointestinal nematode parasite Teladorsagia circumcincta. We found no association between MHC class II variation and weight or strongyle faecal egg count. We did, however, find associations between MHC class II variation and immunoglobulin levels which varied with isotype, age and sex. Our results suggest associations between MHC and phenotypic traits are more likely to be found for traits more closely associated with pathogen defence than integrative traits such as bodyweight and highlight the association between MHC variation and antibodies in wild populations.

Molecularly imprinted polymers by epitope imprinting: a journey from molecular interactions to the available bioinformatics resources to scout for epitope templates

The molecular imprinting of proteins is the process of forming biomimetics with entailed protein-recognition by means of a template-assisted synthesis. Protein-imprinted polymers (pMIPs) have been successfully employed in separations, assays, sensors, and imaging. From a technical point of view, imprinting a protein is both costly, for protein expression and purification, and challenging, for the preservation of the protein's structural properties. In fact, the imprinting process needs to guarantee the preservation of the same protein three-dimensional conformation that later would be recognized. So far, the captivating idea to imprint just a portion of the protein, i.e., an epitope, instead of the whole, proved successful, offering reduced costs, compatibility with many synthetic conditions (solvents, pH, temperatures), and fine-tuning of the peptide sequence so to target specific physiological and functional conditions of the protein, such as post-translational modifications. Here, protein-protein interactions and the biochemical features of the epitopes are inspected, deriving lessons to prepare more effective pMIPs. Epitopes are categorized in linear or structured, immunogenic or not, located at the protein's surface or buried in its core and the imprinting strategies are discussed. Moreover, attention is given to freely available online bioinformatics resources that might offer key tools to gain further rationale amid the selection process of suitable epitopes templates.

Patterns of MHC-dependent sexual selection in a free-living population of sheep

The MHC is one of the most polymorphic gene clusters in vertebrates and play an essential role in adaptive immunity. Apart from pathogen-mediated selection, sexual selection can also contribute to the maintenance of MHC diversity. MHC-dependent sexual selection could occur via several mechanisms but at present there is no consensus as to which of these mechanisms are involved and their importance. Previous studies have often suffered from limited genetic and behavioural data and small sample size, and were rarely able to examine all the mechanisms together, determine whether signatures of MHC-based non-random mating are independent of genomic effects or differentiate whether MHC-dependent sexual selection takes place at the pre- or post-copulatory stage. In this study, we use Monte Carlo simulation to investigate evidence for non-random MHC-dependent mating patterns by all three mechanisms in a free-living population of Soay sheep. Using 1710 sheep diplotyped at the MHC class IIa region and genome-wide SNPs, together with field observations of consorts, we found sexual selection against a particular haplotype in males at the pre-copulatory stage and sexual selection against female MHC heterozygosity during the rut. We also found MHC-dependent disassortative mating at the post-copulatory stage, along with strong evidence of inbreeding avoidance at both stages. However, results from generalized linear mixed models suggest that the pattern of MHC-dependent disassortative mating could be a by-product of inbreeding avoidance. Our results therefore suggest that while multiple apparent mechanisms of non-random mating with respect to the MHC may occur, some of them have alternative explanations.

The Search for Peptide Epitopes for Molecular Imprinting Through Bioinformatics

Epitope imprinting is an effective strategy to prepare molecularly imprinted polymers (MIPs) for protein recognition. Indeed, the idea to use as a template just a fragment of the protein of interest, called the epitope, instead of the whole protein, presents some key advantages for the imprinting process, in particular: cutting the costs for MIP production and avoiding protein unfolding during the imprinting process, so to ultimately improve the quality of the stamped binding sites. How to select an epitope for the imprinting is the strategic question. Here, the bioinformatics tools to search for suitable epitopes for the imprinting process and rational tools to select the most suitable epitope are briefly introduced along with protocols for their practical use.

Divergent Allele Advantage at Human MHC Genes: Signatures of Past and Ongoing Selection

The highly polymorphic genes of the major histocompatibility complex (MHC) play a key role in adaptive immunity. Divergent allele advantage, a mechanism of balancing selection, is proposed to contribute to their exceptional polymorphism. It assumes that MHC genotypes with more divergent alleles allow for broader antigen-presentation to immune effector cells, by that increasing immunocompetence. However, the direct correlation between pairwise sequence divergence and the corresponding repertoire of bound peptides has not been studied systematically across different MHC genes. Here, we investigated this relationship for five key classical human MHC genes (human leukocyte antigen; HLA-A, -B, -C, -DRB1, and -DQB1), using allele-specific computational binding prediction to 118,097 peptides derived from a broad range of human pathogens. For all five human MHC genes, the genetic distance between two alleles of a heterozygous genotype was positively correlated with the total number of peptides bound by these two alleles. In accordance with the major antigen-presentation pathway of MHC class I molecules, HLA-B and HLA-C alleles showed particularly strong correlations for peptides derived from intracellular pathogens. Intriguingly, this bias coincides with distinct protein compositions between intra- and extracellular pathogens, possibly suggesting adaptation of MHC I molecules to present specifically intracellular peptides. Eventually, we observed significant positive correlations between an allele’s average divergence and its population frequency. Overall, our results support the divergent allele advantage as a meaningful quantitative mechanism through which pathogen-mediated selection leads to the evolution of MHC diversity.

Molecularly Imprinted Polymers for Cell Recognition

Since their conception 50 years ago, molecularly imprinted polymers (MIPs) have seen extensive development both in terms of synthetic routes and applications. Cells are perhaps the most challenging target for molecular imprinting. Although early work was based almost entirely around microprinting methods, recent developments have shifted towards epitope imprinting to generate MIP nanoparticles (NPs). Simultaneously, the development of techniques such as solid phase MIP synthesis has solved many historic issues of MIP production. This review briefly describes various approaches used in cell imprinting with a focus on applications of the created materials in imaging, drug delivery, diagnostics, and tissue engineering.

SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment

Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are commonly used because they provide a standard, predefined way of modeling substitutions. However, researchers often do not know if a given matrix family or any individual matrix within a family is the most suitable. Furthermore, predefined matrix families may inaccurately reflect a particular hypothesis that a researcher wishes to model or otherwise result in unsatisfactory alignments. In these cases, the ability to compare the effects of one or more custom matrices may be needed. This laborious process is often performed manually because the ability to simultaneously load multiple matrices and then compare their effects on alignments is not readily available in current software tools. This paper presents SubVis, an interactive R package for loading and applying multiple substitution matrices to pairwise alignments. Users can simultaneously explore alignments resulting from multiple predefined and custom substitution matrices. SubVis utilizes several of the alignment functions found in R, a common language among protein scientists. Functions are tied together with the Shiny platform which allows the modification of input parameters. Information regarding alignment quality and individual amino acid substitutions is displayed with the JavaScript language which provides interactive visualizations for revealing both high-level and low-level alignment information.

Computational complexity of algorithms for sequence comparison, short-read assembly and genome alignment

A multitude of algorithms for sequence comparison, short-read assembly and whole-genome alignment have been developed in the general context of molecular biology, to support technology development for high-throughput sequencing, numerous applications in genome biology and fundamental research on comparative genomics. The computational complexity of these algorithms has been previously reported in original research papers, yet this often neglected property has not been reviewed previously in a systematic manner and for a wider audience. We provide a review of space and time complexity of key sequence analysis algorithms and highlight their properties in a comprehensive manner, in order to identify potential opportunities for further research in algorithm or data structure optimization. The complexity aspect is poised to become pivotal as we will be facing challenges related to the continuous increase of genomic data on unprecedented scales and complexity in the foreseeable future, when robust biological simulation at the cell level and above becomes a reality.

Inferring Function from Homology

Recent technological advances in sequencing and high-throughput DNA cloning have resulted in the generation of vast quantities of biological sequence data. Ideally the functions of individual genes and proteins predicted by these methods should be assessed experimentally within the context of a defined hypothesis. However, if no hypothesis is known a priori, or the number of sequences to be assessed is large, bioinformatics techniques may be useful in predicting function.This chapter proposes a pipeline of freely available Web-based tools to analyze protein-coding DNA and peptide sequences of unknown function. Accumulated information obtained during each step of the pipeline is used to build a testable hypothesis of function.The following methods are described in detail: 1. Annotation of gene function through Protein domain detection (SMART and Pfam). 2. Sequence similarity methods for homolog detection (BLAST and DELTA-BLAST). 3. Comparing sequences to whole genome data.

Graph pyramids for protein function prediction

Background

Uncovering the hidden organizational characteristics and regularities among biological sequences is the key issue for detailed understanding of an underlying biological phenomenon. Thus pattern recognition from nucleic acid sequences is an important affair for protein function prediction. As proteins from the same family exhibit similar characteristics, homology based approaches predict protein functions via protein classification. But conventional classification approaches mostly rely on the global features by considering only strong protein similarity matches. This leads to significant loss of prediction accuracy.

Methods

Here we construct the Protein-Protein Similarity (PPS) network, which captures the subtle properties of protein families. The proposed method considers the local as well as the global features, by examining the interactions among 'weakly interacting proteins' in the PPS network and by using hierarchical graph analysis via the graph pyramid. Different underlying properties of the protein families are uncovered by operating the proposed graph based features at various pyramid levels.

Results

Experimental results on benchmark data sets show that the proposed hierarchical voting algorithm using graph pyramid helps to improve computational efficiency as well the protein classification accuracy. Quantitatively, among 14,086 test sequences, on an average the proposed method misclassified only 21.1 sequences whereas baseline BLAST score based global feature matching method misclassified 362.9 sequences. With each correctly classified test sequence, the fast incremental learning ability of the proposed method further enhances the training model. Thus it has achieved more than 96% protein classification accuracy using only 20% per class training data.

Non-native, N-terminal Hsp70 molecular motor recognition elements in transit peptides support plastid protein translocation

Previously, we identified the N-terminal domain of transit peptides (TPs) as a major determinant for the translocation step in plastid protein import. Analysis of Arabidopsis TP dataset revealed that this domain has two overlapping characteristics, highly uncharged and Hsp70-interacting. To investigate these two properties, we replaced the N-terminal domains of the TP of the small subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase and its reverse peptide with a series of unrelated peptides whose affinities to the chloroplast stromal Hsp70 have been determined. Bioinformatic analysis indicated that eight out of nine peptides in this series are not similar to the TP N terminus. Using in vivo and in vitro protein import assays, the majority of the precursors containing Hsp70-binding elements were targeted to plastids, whereas none of the chimeric precursors lacking an N-terminal Hsp70-binding element were targeted to the plastids. Moreover, a pulse-chase assay showed that two chimeric precursors with the most uncharged peptides failed to translocate into the stroma. The ability of multiple unrelated Hsp70-binding elements to support protein import verified that the majority of TPs utilize an N-terminal Hsp70-binding domain during translocation and expand the mechanistic view of the import process. This work also indicates that synthetic biology may be utilized to create de novo TPs that exceed the targeting activity of naturally occurring sequences.

LAHEDES: the LAGLIDADG homing endonuclease database and engineering server

LAGLIDADG homing endonucleases (LHEs) are DNA cleaving enzymes, also termed ‘meganucleases’ that are employed as gene-targeting reagents. This use of LHEs requires that their DNA specificity be altered to match sequences in genomic targets. The choice of the most appropriate LHE to target a particular gene is facilitated by the growing number of such enzymes with well-characterized activities and structures. ‘LAHEDES’ (The LAGLIDADG Homing Endonuclease Database and Engineering Server) provides both an online archive of LHEs with validated DNA cleavage specificities and DNA-binding interactions, as well as a tool for the identification of DNA sequences that might be targeted by various LHEs. Searches can be performed using four separate scoring algorithms and user-defined choices of LHE scaffolds. The webserver subsequently provides information regarding clusters of amino acids that should be interrogated during engineering and selection experiments. The webserver is fully open access and can be found at http://homingendonuclease.net.

3PFDB--a database of best representative PSSM profiles (BRPs) of protein families generated using a novel data mining approach

BACKGROUND

Protein families could be related to each other at broad levels that group them as superfamilies. These relationships are harder to detect at the sequence level due to high evolutionary divergence. Sequence searches are strongly directed and influenced by the best representatives of families that are viewed as starting points. PSSMs are useful approximations and mathematical representations of protein alignments, with wide array of applications in bioinformatics approaches like remote homology detection, protein family analysis, detection of new members and evolutionary modelling. Computational intensive searches have been performed using the neural network based sensitive sequence search method called FASSM to identify the Best Representative PSSMs for families reported in Pfam database version 22.

RESULTS

We designed a novel data mining approach for the assessment of individual sequences from a protein family to identify a single Best Representative PSSM profile (BRP) per protein family. Using the approach, a database of protein family-specific best representative PSSM profiles called 3PFDB has been developed. PSSM profiles in 3PFDB are curated using performance of individual sequence as a reference in a rigorous scoring and coverage analysis approach using FASSM. We have assessed the suitability of 10, 85,588 sequences derived from seed or full alignments reported in Pfam database (Version 22). Coverage analysis using FASSM method is used as the filtering step to identify the best representative sequence, starting from full length or domain sequences to generate the final profile for a given family. 3PFDB is a collection of best representative PSSM profiles of 8,524 protein families from Pfam database.

CONCLUSION

Availability of an approach to identify BRPs and a curated database of best representative PSI-BLAST derived PSSMs for 91.4% of current Pfam family will be a useful resource for the community to perform detailed and specific analysis using family-specific, best-representative PSSM profiles. 3PFDB can be accessed using the URL: http://caps.ncbs.res.in/3pfdb.

Chapter 8. Methods for in silico prediction of microbial polyketide and nonribosomal peptide biosynthetic pathways from DNA sequence data

Fore-knowledge of the secondary metabolic potential of cultivated and previously uncultivated microorganisms can potentially facilitate the process of natural product discovery. By combining sequence-based knowledge with biochemical precedent, translated gene sequence data can be used to rapidly derive structural elements encoded by secondary metabolic gene clusters from microorganisms. These structural elements provide an estimate of the secondary metabolic potential of a given organism and a starting point for identification of potential lead compounds in isolation/structure elucidation campaigns. The accuracy of these predictions for a given translated gene sequence depends on the biochemistry of the metabolite class, similarity to known metabolite gene clusters, and depth of knowledge concerning its biosynthetic machinery. This chapter introduces methods for prediction of structural elements for two well-studied classes: modular polyketides and nonribosomally encoded peptides. A bioinformatics tool is presented for rapid preliminary analysis of these modular systems, and prototypical methods for converting these analyses into substructural elements are described.

Aligning sequences by minimum description length

This paper presents a new information theoretic framework for aligning sequences in bioinformatics. A transmitter compresses a set of sequences by constructing a regular expression that describes the regions of similarity in the sequences. To retrieve the original set of sequences, a receiver generates all strings that match the expression. An alignment algorithm uses minimum description length to encode and explore alternative expressions; the expression with the shortest encoding provides the best overall alignment. When two substrings contain letters that are similar according to a substitution matrix, a code length function based on conditional probabilities defined by the matrix will encode the substrings with fewer bits. In one experiment, alignments produced with this new method were found to be comparable to alignments from CLUSTALW. A second experiment measured the accuracy of the new method on pairwise alignments of sequences from the BAliBASE alignment benchmark.

Inferring function from homology

Modern molecular biology approaches often result in the accumulation of abundant biological sequence data. Ideally, the function of individual proteins predicted using such data would be determined experimentally. However, if a gene of interest has no predictable function or if the amount of data is too large to experimentally assess individual genes, bioinformatics techniques may provide additional information to allow the inference of function. This chapter proposes a pipeline of freely available Web-based tools to analyze protein-coding DNA sequences of unknown function. Accumulated information obtained during each step of the pipeline is used to build a testable hypothesis of function. The basis and use of sequence similarity methods of homologue detection are described, with emphasis on BLAST and PSI-BLAST. Annotation of gene function through protein domain detection using SMART and Pfam, and the potential for comparison to whole genome data are discussed.

Expression, purification and preliminary crystallographic studies on the catalytic region of the nonreceptor tyrosine kinase Fes

The proto-oncogene tyrosine protein kinase c-fps/fes encodes a structurally unique protein (Fes) of the nonreceptor protein-tyrosine kinase (PTK) family. Its expression has been demonstrated in myeloid haematopoietic cells, vascular endothelial cells and in neurons. In human-derived and murine-derived cell lines, the activated form of this kinase can induce cellular transformation; moreover, it has been shown that Fes is involved in the regulation of cell-cell and cell-matrix interactions mediated by adherens junctions and focal adhesions. The N-terminus of Fes contains the FCH (Fps/Fes/Fer/CIP4 homology) domain, which is unique to the Fes/Fer kinase family. It is followed by three coiled-coil domains and an SH2 (Src-homology 2) domain. The catalytic region (Fes-CR) is located at the C-terminus of the protein. The successful expression, purification and crystallization of the catalytic part of Fes (Fes-CR) are described.

eBLOCKs: enumerating conserved protein blocks to achieve maximal sensitivity and specificity

Classifying proteins into families and superfamilies allows identification of functionally important conserved domains. The motifs and scoring matrices derived from such conserved regions provide computational tools that recognize similar patterns in novel sequences, and thus enable the prediction of protein function for genomes. The eBLOCKs database enumerates a cascade of protein blocks with varied conservation levels for each functional domain. A biologically important region is most stringently conserved among a smaller family of highly similar proteins. The same region is often found in a larger group of more remotely related proteins with a reduced stringency. Through enumeration, highly specific signatures can be generated from blocks with more columns and fewer family members, while highly sensitive signatures can be derived from blocks with fewer columns and more members as in a superfamily. By applying PSI-BLAST and a modified K-means clustering algorithm, eBLOCKs automatically groups protein sequences according to different levels of similarity. Multiple sequence alignments are made and trimmed into a series of ungapped blocks. Motifs and position-specific scoring matrices were derived from eBLOCKs and made available for sequence search and annotation. The eBLOCKs database provides a tool for high-throughput genome annotation with maximal specificity and sensitivity. The eBLOCKs database is freely available on the World Wide Web at http://motif.stanford.edu/eblocks/ to all users for online usage. Academic and not-for-profit institutions wishing copies of the program may contact Douglas L. Brutlag (brutlag@stanford.edu). Commercial firms wishing copies of the program for internal installation may contact Jacqueline Tay at the Stanford Office of Technology Licensing (jacqueline.tay@stanford.edu; http://otl.stanford.edu/).

PANDORA: keyword-based analysis of protein sets by integration of annotation sources

Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.

3MATRIX and 3MOTIF: a protein structure visualization system for conserved sequence motifs

Computational methods such as sequence alignment and motif construction are useful in grouping related proteins into families, as well as helping to annotate new proteins of unknown function. These methods identify conserved amino acids in protein sequences, but cannot determine the specific functional or structural roles of conserved amino acids without additional study. In this work, we present 3MATRIX (http://3matrix.stanford.edu) and 3MOTIF (http://3motif.stanford.edu), a web-based sequence motif visualization system that displays sequence motif information in its appropriate three-dimensional (3D) context. This system is flexible in that users can enter sequences, keywords, structures or sequence motifs to generate visualizations. In 3MOTIF, users can search using discrete sequence motifs such as PROSITE patterns, eMOTIFs, or any other regular expression-like motif. Similarly, 3MATRIX accepts an eMATRIX position-specific scoring matrix, or will convert a multiple sequence alignment block into an eMATRIX for visualization. Each query motif is used to search the protein structure database for matches, in which the motif is then visually highlighted in three dimensions. Important properties of motifs such as sequence conservation and solvent accessible surface area are also displayed in the visualizations, using carefully chosen color shading schemes.

The functional importance of disease-associated mutation

BACKGROUND

For many years, scientists believed that point mutations in genes are the genetic switches for somatic and inherited diseases such as cystic fibrosis, phenylketonuria and cancer. Some of these mutations likely alter a protein's function in a manner that is deleterious, and they should occur in functionally important regions of the protein products of genes. Here we show that disease-associated mutations occur in regions of genes that are conserved, and can identify likely disease-causing mutations.

RESULTS

To show this, we have determined conservation patterns for 6185 non-synonymous and heritable disease-associated mutations in 231 genes. We define a parameter, the conservation ratio, as the ratio of average negative entropy of analyzable positions with reported mutations to that of every analyzable position in the gene sequence. We found that 84.0% of the 231 genes have conservation ratios less than one. 139 genes had eleven or more analyzable mutations and 88.0% of those had conservation ratios less than one.

CONCLUSIONS

These results indicate that phylogenetic information is a powerful tool for the study of disease-associated mutations. Our alignments and analysis has been made available as part of the database at http://cancer.stanford.edu/mut-paper/. Within this dataset, each position is annotated with the analysis, so the most likely disease-causing mutations can be identified.

An integrated approach to the analysis and modeling of protein sequences and structures. II. On the relationship between sequence and structural similarity for proteins that are not obviously related in sequence

Here, we discuss the relationship between protein sequence and protein structural similarity. It is established that a protein structural distance (PSD) of 2.0 is a threshold above which two proteins are unlikely to have a detectable pairwise sequence relationship. A precise correlation is established between the level of sequence similarity, defined by a normalized Smith-Waterman score, and the probability that two proteins will have a similar structure (defined by pairwise PSD<2). This correlation can be used in evaluating the likelihood for success in a comparative modeling procedure. We establish the existence of a correlation between sequence and structural similarity for pairs of proteins that are related in structure but whose sequence relationship is not detectable using standard pairwise sequence alignments. Although it is well known that there is a close relationship between sequence and structural similarity for pairwise sequence identities greater than about 30 %, there has been little discussion as to the possible existence of such a relationship for pairs of proteins in or below the twilight zone of sequence similarity (<25 % pairwise sequence identity). Possible implications of our results for the evolution of protein structure are discussed.

Computer applications in biomolecular sciences. Part 2: bioinformatics and genome projects

This article defines and describes some of the basics of bioinformatics and projects aimed at sequencing entire genomes. Emphasis is placed on some of the ways in which the primary structures of nucleic acids and proteins may be investigated and analysed to gain meaningful biological information using computers and appropriate software. The importance of the world wide net and access to it is given prominence, particularly in bioinformatics research and teaching.

Towards more meaningful hierarchical classification of amino acid scoring matrices

Hierarchical classifications of the 20 amino acids according to residue relationships within scoring matrices have not hitherto been tested for reliability. In fact, testing here of the residue groupings obtained thus from 18 published matrices shows that they vary considerably in reliability. This behaviour gives a new insight then into the matrices with respect to the relationships between the amino acid scores contained therein. For example, other than the trivial grouping of the 20 amino acids, no reliable residue groupings are present in all 18 matrix amino acid hierarchical classifications. Hierarchical classification of the 18 scoring matrices themselves is investigated in terms of matrix representation and choice of similarity and dissimilarity measures for matrix comparison. There is no absolute standard against which to compare a matrix clustering, of course, but it is possible to assess the usefulness of a measure for the purpose in terms of the reliability of the calculated tree. Matrix representation is shown to be important. Finally, a novel two-step approach for hierarchical classification of the 18 amino acid scoring matrices is described.

Minimal-risk scoring matrices for sequence analysis

We introduce a minimal-risk method for estimating the frequencies of amino acids at conserved positions in a protein family. Our method, called minimal-risk estimation, finds the optimal weighting between a set of observed amino acid counts and a set of pseudofrequencies, which represent prior information about the frequencies. We compute the optimal weighting by minimizing the expected distance between the estimated frequencies and the true population frequencies, measured by either a squared-error or a relative-entropy metric. Our method accounts for the source of the pseudofrequencies, which arise either from the background distribution of amino acids or from applying a substitution matrix to the observed data. Our frequency estimates therefore depend on the size and composition of the observed data as well as the source of the pseudofrequencies. We convert our frequency estimates into minimal-risk scoring matrices for sequence analysis. A large-scale cross-validation study, involving 48 variants of seven methods, shows that the best performing method is minimal-risk estimation using the squared-error metric. Our method is implemented in the package EMATRIX, which is available on the Internet at http://motif.stanford.edu/ematrix.

Three-dimensional profiles: a new tool to identify protein surface similarities

We report a procedure for the description and comparison of protein surfaces, which is based on a three-dimensional (3D) transposition of the profile method for sensitive protein homology sequence searches. Although the principle of the method can be applied to detect similarities to a single protein surface, the possibility of extending this approach to protein families displaying common structural and/or functional properties, makes it a more powerful tool. In analogy to profiles derived from the multiple alignment of protein sequences, we derive a 3D surface profile from a protein structure or from a multiple structure alignment of several proteins. The 3D profile is used to screen the protein structure database, searching for similar protein surfaces. The application of the procedure to SH2 and SH3 binding pockets and to the nucleotide binding pocket associated with the p-loop structural motif is described. The SH2 and SH3 3D profiles can identify all the SH2 and SH3 binding regions present in the test dataset; the p-loop 3D profile is able to recognize all the p-loop-containing proteins present in the test dataset. Analysis of the p-loop 3D profile allowed the identification of a positive charge whose position is conserved in space but not in sequence. The best ranking non-p-loop-containing protein is an ADP-forming succinyl coenzyme A synthetase, whose nucleotide-binding region has not yet been identified.

Unification of protein families

Computational biology exploits the evolutionary connectivity between proteins and protein families to predict structural and functional properties of uncharacterized gene products. In the past year, conceptual and statistical refinements have substantially improved algorithms for the detection of remote homologues. In conjunction with the rapid growth of biological databases, the global organization of proteins into sequence families, functional families and structural families has become both pertinent and feasible.

Genomics and computational molecular biology

There has been a dramatic increase in the number of completely sequenced bacterial genomes during the past two years as a result of the efforts both of public genome agencies and the pharmaceutical industry. The availability of completely sequenced genomes permits more systematic analyses of genes, evolution and genome function than was otherwise possible. Using computational methods - which are used to identify genes and their functions including statistics, sequence similarity, motifs, profiles, protein folds and probabilistic models - it is possible to develop characteristic genome signatures, assign functions to genes, identify pathogenic genes, identify metabolic pathways, develop diagnostic probes and discover potential drug-binding sites. All of these directions are critical to understanding bacterial growth, pathogenicity and host-pathogen interactions.

Non-canonical inteins

Previous analyses have shown that inteins (protein splicing elements) employ two structural organizations: the 'canonical' Nintein-Dod-inteinC found in dozens of inteins and a 'non-canonical' Nintein-inteinC described in two inteins, where Nintein at the N-terminus and inteinC at the C-terminus are conserved domains involved in self-splicing and Dod is the Dod DNA endonuclease (DNase). In this study, four non-canonical inteins, each with unique structural features, have been identified using alignment-based Hidden Markov Models. A Nintein-inteinC intein, carrying an unprecedented replacement of the N-terminal catalytic Cys(Ser) by Ala, is described in a putative ATPase encoded by Methanococcus jannaschii . Three replicative proteins of Synechocystis spp. contain inteins with the organizations: (i) Nintein minus X minus inteinC over Dod, where X is an uncharacterized domain and Dod DNase is located in an alternative open reading frame (ORF) being embedded between two novel CG and YK domains; (ii) Nintein-HN-inteinC, where HN stands for phage-like DNase from the EX1H-HX3H family; (iii) Nintein>|<inteinC, where >|< indicates that the intein domains are associated with a disrupted host protein encoded by two spatially separated ORFs. The expression of some of these newly identified inteins may affect the intein hosts. The variety of structural forms of inteins could have evolved through invasion of self-splicing proteases by different mobile DNases or the departure of mobile DNases from canonical inteins.

Do aligned sequences share the same fold?

Sequence comparison remains a powerful tool to assess the structural relatedness of two proteins. To develop a sensitive sequence-based procedure for fold recognition, we performed an exhaustive global alignment (with zero end gap penalties) between sequences of protein domains with known three-dimensional folds. The subset of 1.3 million alignments between sequences of structurally unrelated domains was used to derive a set of analytical functions that represent the probability of structural significance for any sequence alignment at a given sequence identity, sequence similarity and alignment score. Analysis of overlap between structurally significant and insignificant alignments shows that sequence identity and sequence similarity measures are poor indicators of structural relatedness in the "twilight zone", while the alignment score allows much better discrimination between alignments of structurally related and unrelated sequences for a wide variety of alignment settings. A fold recognition benchmark was used to compare eight different substitution matrices with eight sets of gap penalties. The best performing matrices were Gonnet and Blosum50 with normalized gap penalties of 2.4/0.15 and 2.0/0.15, respectively, while the positive matrices were the worst performers. The derived functions and parameters can be used for fold recognition via a multilink chain of probability weighted pairwise sequence alignments.