Analogy-based protein structure prediction: III. Optimizing the combination of the substitution matrix and pseudopotentials used to align protein sequences with spatial structures

A general and fast method for maximizing the “recognition ability” of a linear combination of an arbitrary number of various methods used to recognize protein structures and produce sequence-to-structure alignments for the structurally analogous proteins is described. It is shown that, at a low level of sequence similarity, the optimal combination of methods displays a significantly higher recognition ability than each method alone; the leading role in this combination is played by (1) pseudopotentials of long-range interactions, (2) matrices of secondary structure similarity, and (3) amino acid substitution matrices. In the case of a high sequence similarity, substitution matrices play the leading and practically the sole role in the optimal combination, although the addition of pseudopotentials of long-range interactions and matrices of secondary structure similarity somewhat increases the recognition ability of the combined method.     

Reduction of protein sequence complexity by residue grouping

It is well known that there are some similarities among various naturally occurring amino acids. Thus, the complexity in protein systems could be reduced by sorting these amino acids with similarities into groups and then protein sequences can be simplified by reduced alphabets. This paper discusses how to group similar amino acids and whether there is a minimal amino acid alphabet by which proteins can be folded. Various reduced alphabets are obtained by reserving the maximal information for the simplified protein sequence compared with the parent sequence using global sequence alignment. With these reduced alphabets and simplified similarity matrices, we achieve recognition of the protein fold based on the similarity score of the sequence alignment. The coverage in dataset SCOP40 for various levels of reduction on the amino acid types is obtained, which is the number of homologous pairs detected by program BLAST to the number marked by SCOP40. For the reduced alphabets containing 10 types of amino acids, the ability to detect distantly related folds remains almost at the same level as that by the alphabet of 20 types of amino acids, which implies that 10 types of amino acids may be the degree of freedom for characterizing the complexity in proteins.     

Improved pairwise alignments of proteins in the Twilight Zone using local structure predictions

MOTIVATION: In recent years, advances have been made in the ability of computational methods to discriminate between homologous and non-homologous proteins in the 'twilight zone' of sequence similarity, where the percent sequence identity is a poor indicator of homology. To make these predictions more valuable to the protein modeler, they must be accompanied by accurate alignments. Pairwise sequence alignments are inferences of orthologous relationships between sequence positions. Evolutionary distance is traditionally modeled using global amino acid substitution matrices. But real differences in the likelihood of substitutions may exist for different structural contexts within proteins, since structural context contributes to the selective pressure. RESULTS: HMMSUM (HMMSTR-based substitution matrices) is a new model for structural context-based amino acid substitution probabilities consisting of a set of 281 matrices, each for a different sequence-structure context. HMMSUM does not require the structure of the protein to be known. Instead, predictions of local structure are made using HMMSTR, a hidden Markov model for local structure. Alignments using the HMMSUM matrices compare favorably to alignments carried out using the BLOSUM matrices or structure-based substitution matrices SDM and HSDM when validated against remote homolog alignments from BAliBASE. HMMSUM has been implemented using local Dynamic Programming and with the Bayesian Adaptive alignment method.     

Improving the alignment quality of consistency based aligners with an evaluation function using synonymous protein words

Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently.In this paper, we present a flexible similarity measure for residue pairs to improve the quality of protein sequence alignment. Our approach, called SymAlign, relies on the identification of conserved words found across a sizeable fraction of the considered dataset, and supported by evolutionary analysis. These words are then used to define a position specific substitution matrix that better reflects the biological significance of local similarity. The experiment results show that the SymAlign scoring scheme can be incorporated within T-Coffee to improve sequence alignment accuracy. We also demonstrate that SymAlign is less sensitive to the presence of structurally non-similar proteins. In the analysis of the relationship between sequence identity and structure similarity, SymAlign can better differentiate structurally similar proteins from non- similar proteins. We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted n-grams. In our analysis of the alignments thus produced, sequence conservation becomes a better indicator of structural similarity. SymAlign also provides alignment visualization that can display sub-optimal alignments on dot-matrices. The visualization makes it easy to identify well-supported alternative alignments that may not have been identified by dynamic programming. SymAlign is available at http://bio-cluster.iis.sinica.edu.tw/SymAlign/.     

Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids

Sequence alignment is a common method for finding protein structurally conserved/similar regions. However, sequence alignment is often not accurate if sequence identities between to-be-aligned sequences are less than 30%. This is because that for these sequences, different residues may play similar structural roles and they are incorrectly aligned during the sequence alignment using substitution matrix consisting of 20 types of residues. Based on the similarity of physicochemical features, residues can be clustered into a few groups. Using such simplified alphabets, the complexity of protein sequences is reduced and at the same time the key information encoded in the sequences remains. As a result, the accuracy of sequence alignment might be improved if the residues are properly clustered. Here, by using a database of aligned protein structures (DAPS), a new clustering method based on the substitution scores is proposed for the grouping of residues, and substitution matrices of residues at different levels of simplification are constructed. The validity of the reduced alphabets is confirmed by relative entropy analysis. The reduced alphabets are applied to recognition of protein structurally conserved/similar regions by sequence alignment. The results indicate that the accuracy or efficiency of sequence alignment can be improved with the optimal reduced alphabet with N around 9.     

HIV-specific probabilistic models of protein evolution

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1-the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses.     

Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids

Sequence alignment is a common method for finding protein structurally conserved/similar regions. However, sequence alignment is often not accurate if sequence identities between to-be-aligned sequences are less than 30%. This is because that for these sequences, different residues may play similar structural roles and they are incorrectly aligned during the sequence alignment using substitution matrix consisting of 20 types of residues. Based on the similarity of physicochemical features, residues can be clustered into a few groups. Using such simplified alphabets, the complexity of protein sequences is reduced and at the same time the key information encoded in the sequences remains. As a result, the accuracy of sequence alignment might be improved if the residues are properly clustered. Here, by using a database of aligned protein structures (DAPS), a new clustering method based on the substitution scores is proposed for the grouping of residues, and substitution matrices of residues at different levels of simplification are constructed. The validity of the reduced alphabets is confirmed by relative entropy analysis. The reduced alphabets are applied to recognition of protein structurally conserved/similar regions by sequence alignment. The results indicate that the accuracy or efficiency of sequence alignment can be improved with the optimal reduced alphabet with N around 9. Supported by the National Natural Science Foundation of China (Grant Nos. 90403120, 10474041 and 10021001) and the Nonlinear Project (973) of the NSM     

Use of residue pairs in protein sequence-sequence and sequence-structure alignments

Two new sets of scoring matrices are introduced: H2 for the protein sequence comparison and T2 for the protein sequence-structure correlation. Each element of H2 or T2 measures the frequency with which a pair of amino acid types in one protein, k-residues apart in the sequence, is aligned with another pair of residues, of given amino acid types (for H2) or in given structural states (for T2), in other structurally homologous proteins. There are four types, corresponding to the k-values of 1 to 4, for both H2 and T2. These matrices were set up using a large number of structurally homologous protein pairs, with little sequence homology between the pair, that were recently generated using the structure comparison program SHEBA. The two scoring matrices were incorporated into the main body of the sequence alignment program SSEARCH in the FASTA package and tested in a fold recognition setting in which a set of 107 test sequences were aligned to each of a panel of 3,539 domains that represent all known protein structures. Six procedures were tested; the straight Smith-Waterman (SW) and FASTA procedures, which used the Blosum62 single residue type substitution matrix; BLAST and PSI-BLAST procedures, which also used the Blosum62 matrix; PASH, which used Blosum62 and H2 matrices; and PASSC, which used Blosum62, H2, and T2 matrices. All procedures gave similar results when the probe and target sequences had greater than 30% sequence identity. However, when the sequence identity was below 30%, a similar structure could be found for more sequences using PASSC than using any other procedure. PASH and PSI-BLAST gave the next best results.     

Estimation of amino acid residue substitution rates at local spatial regions and application in protein function inference: a Bayesian Monte Carlo approach

The amino acid sequences of proteins provide rich information for inferring distant phylogenetic relationships and for predicting protein functions. Estimating the rate matrix of residue substitutions from amino acid sequences is also important because the rate matrix can be used to develop scoring matrices for sequence alignment. Here we use a continuous time Markov process to model the substitution rates of residues and develop a Bayesian Markov chain Monte Carlo method for rate estimation. We validate our method using simulated artificial protein sequences. Because different local regions such as binding surfaces and the protein interior core experience different selection pressures due to functional or stability constraints, we use our method to estimate the substitution rates of local regions. Our results show that the substitution rates are very different for residues in the buried core and residues on the solvent-exposed surfaces. In addition, the rest of the proteins on the binding surfaces also have very different substitution rates from residues. Based on these findings, we further develop a method for protein function prediction by surface matching using scoring matrices derived from estimated substitution rates for residues located on the binding surfaces. We show with examples that our method is effective in identifying functionally related proteins that have overall low sequence identity, a task known to be very challenging.     

Fold-specific substitution matrices for protein classification

MOTIVATION: Methods that focus on secondary structures, such as Position Specific Scoring Matrices and Hidden Markov Models, have proved useful for assigning proteins to families. However, for assigning proteins to an attribute class within a family these methods may introduce more free parameters than are needed. There are fewer members and there is less variability among sequences within a family. We describe a method for organizing proteins in a family that exhibits up to an order of magnitude reduction in the number of parameters. The basis is the log odds ratio commonly used to measure similarity. We adapt this to characterize the sequence dissimilarities that give rise to attribute differentiation. This leads to the definition of Class Attribute Substitution Matrices (CLASSUM), a dual of the BLOSUM. RESULTS: The method was applied to classify sequences hierarchically in the lambda and kappa subgroups of the immunoglobulin superfamily. Positions conferring class were identified based on the degree of amino acid variability at a position. The CLASSUM computed for these positions classified better than 90% of test data correctly compared with 35-50% for BLOSUM-62. The expected value for a random matrix is 14%. The results suggest that family-specific data-derived substitution matrices can improve the resolution of automated methods that use generic substitution matrices for searching for and classifying proteins.     

MANIFOLD: protein fold recognition based on secondary structure, sequence similarity and enzyme classification

We present a protein fold recognition method, MANIFOLD, which uses the similarity between target and template proteins in predicted secondary structure, sequence and enzyme code to predict the fold of the target protein. We developed a non-linear ranking scheme in order to combine the scores of the three different similarity measures used. For a difficult test set of proteins with very little sequence similarity, the program predicts the fold class correctly in 34% of cases. This is an over twofold increase in accuracy compared with sequence-based methods such as PSI-BLAST or GenTHREADER, which score 13-14% correct first hits for the same test set. The functional similarity term increases the prediction accuracy by up to 3% compared with using the combination of secondary structure similarity and PSI-BLAST alone. We argue that using functional and secondary structure information can increase the fold recognition beyond sequence similarity.     

Processing enzyme specificity is a consequence of pro-hormone precursor protein conformation.

Peptide-hormones are synthesized as higher molecular weight, precursor proteins which must initially undergo limited endoproteolysis to yield the bioactive peptide(s). The ability of two different endoproteinases, gonadotropin-associated peptide (GAP)-releasing enzyme and atrial granule serine proteinase (which are likely to be the physiologically relevant processing enzymes of bovine hypothalamic pro-gonadotropin-releasing hormone/gonadotropin-associated peptide and bovine pro-atrial natriuretic factor precursor proteins, respectively), to act at their own recognition sequences within their relevant pro-hormone proteins has now been contrasted with their ability to act at the recognition sequence for the alternate enzyme or to act at their own recognition sequence when it is placed within the protein framework of the alternate precursor protein. The results show that each enzyme acts with specificity at its own recognition sequence even when it is placed within the framework of the alternate pro-hormone. However, the enzymes fail to act (or act in a non-specific manner) at the alternate recognition sequence even if it is placed within the peptide framework of its own pro-hormone protein. Thus, despite the fact that both recognition sequences are similar in sequence and residue composition and that both contain a doublet of basic amino acids, it appears that sequence and the local conformation assumed by the processing site within the pro-hormone protein are essential for each endoproteinase to act with fidelity. As part of our continuing work, we now also report several newly determined physicochemical properties of hypothalamic GAP-releasing enzyme, the processing enzyme of pro-gonadotropin-releasing hormone/GAP protein.     

Amino acid similarity matrices based on force fields

MOTIVATION: We propose a general method for deriving amino acid substitution matrices from low resolution force fields. Unlike current popular methods, the approach does not rely on evolutionary arguments or alignment of sequences or structures. Instead, residues are computationally mutated and their contribution to the total energy/score is collected. The average of these values over each position within a set of proteins results in a substitution matrix. RESULTS: Example substitution matrices have been calculated from force fields based on different philosophies and their performance compared with conventional substitution matrices. Although this can produce useful substitution matrices, the methodology highlights the virtues, deficiencies and biases of the source force fields. It also allows a rather direct comparison of sequence alignment methods with the score functions underlying protein sequence to structure threading. AVAILABILITY: Example substitution matrices are available from http://www.rsc.anu.edu.au/~zsuzsa/suppl/matrices.html. SUPPLEMENTARY INFORMATION: The list of proteins used for data collection and the optimized parameters for the alignment are given as supplementary material at http://www.rsc.anu.edu.au/~zsuzsa/suppl/matrices.html.     

Inferring protein interactions from phylogenetic distance matrices

Finding the interacting pairs of proteins between two different protein families whose members are known to interact is an important problem in molecular biology. We developed and tested an algorithm that finds optimal matches between two families of proteins by comparing their distance matrices. A distance matrix provides a measure of the sequence similarity of proteins within a family. Since the protein sets of interest may have dozens of proteins each, the use of an efficient approximate solution is necessary. Therefore the approach we have developed consists of a Metropolis Monte Carlo optimization algorithm which explores the search space of possible matches between two distance matrices. We demonstrate that by using this algorithm we are able to accurately match chemokines and chemokine-receptors as well as the tgfbeta family of ligands and their receptors.     

Angle-distance image matching techniques for protein structure comparison

Proteins that contain similar structural elements often have analogous functions regardless of the degree of sequence similarity or structure connectivity in space. In general, protein structure comparison (PSC) provides a straightforward methodology for biologists to determine critical aspects of structure and function. Here, we developed a novel PSC technique based on angle-distance image (A-D image) transformation and matching, which is independent of sequence similarity and connectivity of secondary structure elements (SSEs). An A-D image is constructed by utilizing protein secondary structure information. According to various types of SSEs, the mutual SSE pairs of the query protein are classified into three different types of sub-images. Subsequently, corresponding sub-images between query and target protein structures are compared using modified cross-correlation approaches to identify the similarity of various patterns. Structural relationships among proteins are displayed by hierarchical clustering trees, which facilitate the establishment of the evolutionary relationships between structure and function of various proteins.Four standard testing datasets and one newly created dataset were used to evaluate the proposed method. The results demonstrate that proteins from these five datasets can be categorized in conformity with their spatial distribution of SSEs. Moreover, for proteins with low sequence identity that share high structure similarity, the proposed algorithms are an efficient and effective method for structural comparison.     

Self-organizing hierarchic networks for pattern recognition in protein sequence.

We present a method based on hierarchical self-organizing maps (SOMs) for recognizing patterns in protein sequences. The method is fully automatic, does not require prealigned sequences, is insensitive to redundancy in the training set, and works surprisingly well even with small learning sets. Because it uses unsupervised neural networks, it is able to extract patterns that are not present in all of the unaligned sequences of the learning set. The identification of these patterns in sequence databases is sensitive and efficient. The procedure comprises three main training stages. In the first stage, one SOM is trained to extract common features from the set of unaligned learning sequences. A feature is a number of ungapped sequence segments (usually 4-16 residues long) that are similar to segments in most of the sequences of the learning set according to an initial similarity matrix. In the second training stage, the recognition of each individual feature is refined by selecting an optimal weighting matrix out of a variety of existing amino acid similarity matrices. In a third stage of the SOM procedure, the position of the features in the individual sequences is learned. This allows for variants with feature repeats and feature shuffling. The procedure has been successfully applied to a number of notoriously difficult cases with distinct recognition problems: helix-turn-helix motifs in DNA-binding proteins, the CUB domain of developmentally regulated proteins, and the superfamily of ribokinases. A comparison with the established database search procedure PROFILE (and with several others) led to the conclusion that the new automatic method performs satisfactorily.     

Prediction of glycolipid-binding domains from the amino acid sequence of lipid raft-associated proteins: application to HpaA, a protein involved in the adhesion of Helicobacter pylori to gastrointestinal cells

Protein-glycolipid interactions mediate the attachment of various pathogens to the host cell surface as well as the association of numerous cellular proteins with lipid rafts. Thus, it is of primary importance to identify the protein domains involved in glycolipid recognition. Using structure similarity searches, we could identify a common glycolipid-binding domain in the three-dimensional structure of several proteins known to interact with lipid rafts. Yet the three-dimensional structure of most raft-targeted proteins is still unknown. In the present study, we have identified a glycolipid-binding domain in the amino acid sequence of a bacterial adhesin (Helicobacter pylori adhesin A, HpaA). The prediction was based on the major properties of the glycolipid-binding domains previously characterized by structural searches. A short (15-mer) synthetic peptide corresponding to this putative glycolipid-binding domain was synthesized, and we studied its interaction with glycolipid monolayers at the air-water interface. The synthetic HpaA peptide recognized LacCer but not Gb3. This glycolipid specificity was in line with that of the whole bacterium. Molecular modeling studies gave some insights into this high selectivity of interaction. It also suggested that Phe147 in HpaA played a key role in LacCer recognition, through sugar-aromatic CH-pi stacking interactions with the hydrophobic side of the galactose ring of LacCer. Correspondingly, the replacement of Phe147 with Ala strongly affected LacCer recognition, whereas substitution with Trp did not. Our method could be used to identify glycolipid-binding domains in microbial and cellular proteins interacting with lipid shells, rafts, and other specialized membrane microdomains.     

Prediction of protein disorder on amino acid substitutions

Intrinsically disordered regions of proteins are known to have many functional roles in cell signaling and regulatory pathways. The altered expression of these proteins due to mutations is associated with various diseases. Currently, most of the available methods focus on predicting the disordered proteins or the disordered regions in a protein. On the other hand, methods developed for predicting protein disorder on mutation showed a poor performance with a maximum accuracy of 70%. Hence, in this work, we have developed a novel method to classify the disorder-related amino acid substitutions using amino acid properties, substitution matrices, and the effect of neighboring residues that showed an accuracy of 90.0% with a sensitivity and specificity of 94.9 and 80.6%, respectively, in 10-fold cross-validation. The method was evaluated with a test set of 20% data using 10 iterations, which showed an average accuracy of 88.9%. Furthermore, we systematically analyzed the features responsible for the better performance of our method and observed that neighboring residues play an important role in defining the disorder of a given residue in a protein sequence. We have developed a prediction server to identify disorder-related mutations, and it is available at http://www.iitm.ac.in/bioinfo/DIM_Pred/.     

Crystal structure of hypothetical protein TTHB192 from Thermus thermophilus HB8 reveals a new protein family with an RNA recognition motif-like domain

We have determined the crystal structure of hypothetical protein TTHB192 from Thermus thermophilus HB8 at 1.9 A resolution. This protein is a member of the Escherichia coli ygcH sequence family, which contains approximately 15 sequence homologs of bacterial origin. These homologs have a high isoelectric point. The crystal structure reveals that TTHB192 consists of two independently folded domains, and that each domain exhibits a ferredoxin-like fold with a four-stranded antiparallel beta-sheet packed on one side by alpha-helices. These two tandem domains face each other to generate a beta-sheet platform. TTHB192 displays overall structural similarity to Sex-lethal protein and poly(A)-binding protein fragments. These proteins have RNA binding activity which is supported by a beta-sheet platform formed by two tandem repeats of an RNA recognition motif domain with signature sequence motifs on the beta-sheet surface. Although TTHB192 does not have the same signature sequence motif as the RNA recognition motif domain, the presence of an evolutionarily conserved basic patch on the beta-sheet platform could be functionally relevant for nucleic acid-binding. This report shows that TTHB192 and its sequence homologs adopt an RNA recognition motif-like domain and provides the first testable functional hypothesis for this protein family.