Recognition of related proteins by iterative template refinement (ITR).

Predicting the structural fold of a protein is an important and challenging problem. Available computer programs for determining whether a protein sequence is compatible with a known 3-dimensional structure fall into 2 categories: (1) structure-based methods, in which structural features such as local conformation and solvent accessibility are encoded in a template, and (2) sequence-based methods, in which aligned sequences of a set of related proteins are encoded in a template. In both cases, the programs use a static template based on a predetermined set of proteins. Here, we describe a computer-based method, called iterative template refinement (ITR), that uses templates combining structure-based and sequence-based information and employs an iterative search procedure to detect related proteins and sequentially add them to the templates. Starting from a single protein of known structure, ITR performs sequential cycles of database search to construct an expanding tree of templates with the aim of identifying subtle relationships among proteins. Evaluating the performance of ITR on 6 proteins, we found that the method automatically identified a variety of subtle structural similarities to other proteins. For example, the method identified structural similarity between arabinose-binding protein and phosphofructokinase, a relationship that has not been widely recognized.     

The FEATURE framework for protein function annotation: modeling new functions,improving performance,and extending to novel applications

Structural genomics efforts contribute new protein structures that often lack significant sequence and fold similarity to known proteins. Traditional sequence and structure-based methods may not be sufficient to annotate the molecular functions of these structures. Techniques that combine structural and functional modeling can be valuable for functional annotation. FEATURE is a flexible framework for modeling and recognition of functional sites in macromolecular structures. Here, we present an overview of the main components of the FEATURE framework, and describe the recent developments in its use. These include automating training sets selection to increase functional coverage, coupling FEATURE to structural diversity generating methods such as molecular dynamics simulations and loop modeling methods to improve performance, and using FEATURE in large-scale modeling and structure determination efforts.     

Accurate prediction of protein structural class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods.     

Comparing protein sequence-based and predicted secondary structure-based methods for identification of remote homologs.

We have compared a novel sequence-structure matching technique, FORESST, for detecting remote homologs to three existing sequence based methods, including local amino acid sequence similarity by BLASTP, hidden Markov models (HMMs) of sequences of protein families using SAM, HMMs based on sequence motifs identified using meta-MEME. FORESST compares predicted secondary structures to a library of structural families of proteins, using HMMs. Altogether 45 proteins from nine structural families in the database CATH were used in a cross-validated test of the fold assignment accuracy of each method. Local sequence similarity of a query sequence to a protein family is measured by the highest segment pair (HSP) score. Each of the HMM-based approaches (FORESST, MEME, amino acid sequence-based HMM) yielded log-odds score for the query sequence. In order to make a fair comparison among these methods, the scores for each method were converted to Z-scores in a uniform way by comparing the raw scores of a query protein with the corresponding scores for a set of unrelated proteins. Z-Scores were analyzed as a function of the maximum pairwise sequence identity (MPSID) of the query sequence to sequences used in training the model. For MPSID above 20%, the Z-scores increase linearly with MPSID for the sequence-based methods but remain roughly constant for FORESST. Below 15%, average Z-scores are close to zero for the sequence-based methods, whereas the FORESST method yielded average Z-scores of 1.8 and 1.1, using observed and predicted secondary structures, respectively. This demonstrates the advantage of the sequence-structure method for detecting remote homologs.     

Comparison of sequence-based and structure-based phylogenetic trees of homologous proteins: Inferences on protein evolution

Several studies based on the known three-dimensional (3-D) structures of proteins show that two homologous proteins with insignificant sequence similarity could adopt a common fold and may perform same or similar biochemical functions. Hence, it is appropriate to use similarities in 3-D structure of proteins rather than the amino acid sequence similarities in modelling evolution of distantly related proteins. Here we present an assessment of using 3-D structures in modelling evolution of homologous proteins. Using a dataset of 108 protein domain families of known structures with at least 10 members per family we present a comparison of extent of structural and sequence dissimilarities among pairs of proteins which are inputs into the construction of phylogenetic trees. We find that correlation between the structure-based dissimilarity measures and the sequence-based dissimilarity measures is usually good if the sequence similarity among the homologues is about 30% or more. For protein families with low sequence similarity among the members, the correlation coefficient between the sequence-based and the structure-based dissimilarities are poor. In these cases the structure-based dendrogram clusters proteins with most similar biochemical functional properties better than the sequence-similarity based dendrogram. In multi-domain protein families and disulphide-rich protein families the correlation coefficient for the match of sequence-based and structure-based dissimilarity (SDM) measures can be poor though the sequence identity could be higher than 30%. Hence it is suggested that protein evolution is best modelled using 3-D structures if the sequence similarities (SSM) of the homologues are very low.     

Structure- and sequence-based function prediction for non-homologous proteins

The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.     

Assessing strategies for improved superfamily recognition

There are more than 200 completed genomes and over 1 million nonredundant sequences in public repositories. Although the structural data are more sparse (approximately 13,000 nonredundant structures solved to date), several powerful sequence-based methodologies now allow these structures to be mapped onto related regions in a significant proportion of genome sequences. We review a number of publicly available strategies for providing structural annotations for genome sequences, and we describe the protocol adopted to provide CATH structural annotations for completed genomes. In particular, we assess the performance of several sequence-based protocols employing Hidden Markov model (HMM) technologies for superfamily recognition, including a new approach (SAMOSA [sequence augmented models of structure alignments]) that exploits multiple structural alignments from the CATH domain structure database when building the models. Using a data set of remote homologs detected by structure comparison and manually validated in CATH, a single-seed HMM library was able to recognize 76% of the data set. Including the SAMOSA models in the HMM library showed little gain in homolog recognition, although a slight improvement in alignment quality was observed for very remote homologs. However, using an expanded 1D-HMM library, CATH-ISL increased the coverage to 86%. The single-seed HMM library has been used to annotate the protein sequences of 120 genomes from all three major kingdoms, allowing up to 70% of the genes or partial genes to be assigned to CATH superfamilies. It has also been used to recruit sequences from Swiss-Prot and TrEMBL into CATH domain superfamilies, expanding the CATH database eightfold.     

Towards fully automated structure-based function prediction in structural genomics: a case study

As the global Structural Genomics projects have picked up pace, the number of structures annotated in the Protein Data Bank as hypothetical protein or unknown function has grown significantly. A major challenge now involves the development of computational methods to assign functions to these proteins accurately and automatically. As part of the Midwest Center for Structural Genomics (MCSG) we have developed a fully automated functional analysis server, ProFunc, which performs a battery of analyses on a submitted structure. The analyses combine a number of sequence-based and structure-based methods to identify functional clues. After the first stage of the Protein Structure Initiative (PSI), we review the success of the pipeline and the importance of structure-based function prediction. As a dataset, we have chosen all structures solved by the MCSG during the 5 years of the first PSI. Our analysis suggests that two of the structure-based methods are particularly successful and provide examples of local similarity that is difficult to identify using current sequence-based methods. No one method is successful in all cases, so, through the use of a number of complementary sequence and structural approaches, the ProFunc server increases the chances that at least one method will find a significant hit that can help elucidate function. Manual assessment of the results is a time-consuming process and subject to individual interpretation and human error. We present a method based on the Gene Ontology (GO) schema using GO-slims that can allow the automated assessment of hits with a success rate approaching that of expert manual assessment.     

Accurate sequence-based prediction of catalytic residues

MOTIVATION: Prediction of catalytic residues provides useful information for the research on function of enzymes. Most of the existing prediction methods are based on structural information, which limits their use. We propose a sequence-based catalytic residue predictor that provides predictions with quality comparable to modern structure-based methods and that exceeds quality of state-of-the-art sequence-based methods. RESULTS: Our method (CRpred) uses sequence-based features and the sequence-derived PSI-BLAST profile. We used feature selection to reduce the dimensionality of the input (and explain the input) to support vector machine (SVM) classifier that provides predictions. Tests on eight datasets and side-by-side comparison with six modern structure- and sequence-based predictors show that CRpred provides predictions with quality comparable to current structure-based methods and better than sequence-based methods. The proposed method obtains 15-19% precision and 48-58% TP (true positive) rate, depending on the dataset used. CRpred also provides confidence values that allow selecting a subset of predictions with higher precision. The improved quality is due to newly designed features and careful parameterization of the SVM. The features incorporate amino acids characterized by the highest and the lowest propensities to constitute catalytic residues, Gly that provides flexibility for catalytic sites and sequence motifs characteristic to certain catalytic reactions. Our features indicate that catalytic residues are on average more conserved when compared with the general population of residues and that highly conserved amino acids characterized by high catalytic propensity are likely to form catalytic sites. We also show that local (with respect to the sequence) hydrophobicity contributes towards the prediction.     

MUMMALS: multiple sequence alignment improved by using hidden Markov models with local structural information

We have developed MUMMALS, a program to construct multiple protein sequence alignment using probabilistic consistency. MUMMALS improves alignment quality by using pairwise alignment hidden Markov models (HMMs) with multiple match states that describe local structural information without exploiting explicit structure predictions. Parameters for such models have been estimated from a large library of structure-based alignments. We show that (i) on remote homologs, MUMMALS achieves statistically best accuracy among several leading aligners, such as ProbCons, MAFFT and MUSCLE, albeit the average improvement is small, in the order of several percent; (ii) a large collection (>10000) of automatically computed pairwise structure alignments of divergent protein domains is superior to smaller but carefully curated datasets for estimation of alignment parameters and performance tests; (iii) reference-independent evaluation of alignment quality using sequence alignment-dependent structure superpositions correlates well with reference-dependent evaluation that compares sequence-based alignments to structure-based reference alignments.     

Convergent evolution in structural elements of proteins investigated using cross profile analysis

Background  

Evolutionary relations of similar segments shared by different protein folds remain controversial, even though many examples of such segments have been found. To date, several methods such as those based on the results of structure comparisons, sequence-based classifications, and sequence-based profile-profile comparisons have been applied to identify such protein segments that possess local similarities in both sequence and structure across protein folds. However, to capture more precise sequence-structure relations, no method reported to date combines structure-based profiles, and sequence-based profiles based on evolutionary information. The former are generally regarded as representing the amino acid preferences at each position of a specific conformation of protein segment. They might reflect the nature of ancient short peptide ancestors, using the results of structural classifications of protein segments.     

On the role of structural information in remote homology detection and sequence alignment: new methods using hybrid sequence profiles

Structural alignments often reveal relationships between proteins that cannot be detected using sequence alignment alone. However, profile search methods based entirely on structural alignments alone have not been found to be effective in finding remote homologs. Here, we explore the role of structural information in remote homolog detection and sequence alignment. To this end, we develop a series of hybrid multidimensional alignment profiles that combine sequence, secondary and tertiary structure information into hybrid profiles. Sequence-based profiles are profiles whose position-specific scoring matrix is derived from sequence alignment alone; structure-based profiles are those derived from multiple structure alignments. We compare pure sequence-based profiles to pure structure-based profiles, as well as to hybrid profiles that use combined sequence-and-structure-based profiles, where sequence-based profiles are used in loop/motif regions and structural information is used in core structural regions. All of the hybrid methods offer significant improvement over simple profile-to-profile alignment. We demonstrate that both sequence-based and structure-based profiles contribute to remote homology detection and alignment accuracy, and that each contains some unique information. We discuss the implications of these results for further improvements in amino acid sequence and structural analysis.     

CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation

Background  

The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods.     

DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations

MOTIVATION: The binding of endogenous antigenic peptides to MHC class I molecules is an important step during the immunologic response of a host against a pathogen. Thus, various sequence- and structure-based prediction methods have been proposed for this purpose. The sequence-based methods are computationally efficient, but are hampered by the need of sufficient experimental data and do not provide a structural interpretation of their results. The structural methods are data-independent, but are quite time-consuming and thus not suited for screening of whole genomes. Here, we present a new method, which performs sequence-based prediction by incorporating information obtained from molecular modeling. This allows us to perform large databases screening and to provide structural information of the results. RESULTS: We developed a SVM-trained, quantitative matrix-based method for the prediction of MHC class I binding peptides, in which the features of the scoring matrix are energy terms retrieved from molecular dynamics simulations. At the same time we used the equilibrated structures obtained from the same simulations in a simple and efficient docking procedure. Our method consists of two steps: First, we predict potential binders from sequence data alone and second, we construct protein-peptide complexes for the predicted binders. So far, we tested our approach on the HLA-A0201 allele. We constructed two prediction models, using local, position-dependent (DynaPred(POS)) and global, position-independent (DynaPred) features. The former model outperformed the two sequence-based methods used in our evaluation; the latter shows a much higher generalizability towards other alleles than the position-dependent models. The constructed peptide structures can be refined within seconds to structures with an average backbone RMSD of 1.53 A from the corresponding experimental structures.     

A ligand-centric analysis of the diversity and evolution of protein-ligand relationships in E.coli

As enzymes evolve and diverge from common ancestor sequences, they often keep their overall reaction chemistry but specialize in the binding of different cognate ligands. This study borrows methods for the computational assessment of 2D similarity of small molecules from the field of chemoinformatics, to examine the extent of structure conservation of cognate ligands binding to similar proteins. Proteins from 87 structural superfamilies from Escherichia coli form the core dataset, which is extended using homologues with functional assignments from any organism. We find that correlation of the substrate similarity with protein similarity (measured by either sequence-based or structure-based scores) can only be clearly established for very similar proteins. At low sequence identities, the superfamily to which a protein belongs can give helpful clues to its function, and more importantly, the confidence attached to such clues is superfamily-dependent. Our data indicate that only a few superfamilies show great substrate diversity, and that most exhibit conservation of at least part of the structural scaffold of the substrate.     

Structural basis for target protein recognition by the protein disulfide reductase thioredoxin

Thioredoxin is ubiquitous and regulates various target proteins through disulfide bond reduction. We report the structure of thioredoxin (HvTrxh2 from barley) in a reaction intermediate complex with a protein substrate, barley alpha-amylase/subtilisin inhibitor (BASI). The crystal structure of this mixed disulfide shows a conserved hydrophobic motif in thioredoxin interacting with a sequence of residues from BASI through van der Waals contacts and backbone-backbone hydrogen bonds. The observed structural complementarity suggests that the recognition of features around protein disulfides plays a major role in the specificity and protein disulfide reductase activity of thioredoxin. This novel insight into the function of thioredoxin constitutes a basis for comprehensive understanding of its biological role. Moreover, comparison with structurally related proteins shows that thioredoxin shares a mechanism with glutaredoxin and glutathione transferase for correctly positioning substrate cysteine residues at the catalytic groups but possesses a unique structural element that allows recognition of protein disulfides.     

Structure-Based Phylogenetic Analysis of the Lipocalin Superfamily

Lipocalins constitute a superfamily of extracellular proteins that are found in all three kingdoms of life. Although very divergent in their sequences and functions, they show remarkable similarity in 3-D structures. Lipocalins bind and transport small hydrophobic molecules. Earlier sequence-based phylogenetic studies of lipocalins highlighted that they have a long evolutionary history. However the molecular and structural basis of their functional diversity is not completely understood. The main objective of the present study is to understand functional diversity of the lipocalins using a structure-based phylogenetic approach. The present study with 39 protein domains from the lipocalin superfamily suggests that the clusters of lipocalins obtained by structure-based phylogeny correspond well with the functional diversity. The detailed analysis on each of the clusters and sub-clusters reveals that the 39 lipocalin domains cluster based on their mode of ligand binding though the clustering was performed on the basis of gross domain structure. The outliers in the phylogenetic tree are often from single member families. Also structure-based phylogenetic approach has provided pointers to assign putative function for the domains of unknown function in lipocalin family. The approach employed in the present study can be used in the future for the functional identification of new lipocalin proteins and may be extended to other protein families where members show poor sequence similarity but high structural similarity.