Information on the secondary structure improves the quality of protein sequence alignment

The most popular algorithms employed in the pairwise alignment of protein primary structures (Smith-Watermann (SW) algorithm, FASTA, BLAST, etc.) only analyze the amino acid sequence. The SW algorithm is the most accurate, yielding alignments that agree best with superimpositions of the corresponding spatial structures of proteins. However, even the SW algorithm fails to reproduce the spatial structure alignment when the sequence identity is lower than 30%. The objective of this work was to develop a new and more accurate algorithm taking the secondary structure of proteins into account. The alignments generated by this algorithm and having the maximal weight with the secondary structure considered proved to be more accurate than SW alignments. With sequences having less than 30% identity, the accuracy (i.e., the portion of reproduced positions of a reference alignment obtained by superimposing the protein spatial structures) of the new algorithm is 58 vs. 35% of the SW algorithm. The accuracy of the new algorithm is much the same with secondary structures established experimentally or predicted theoretically. Hence, the algorithm is applicable to proteins with unknown spatial structures. The program is available at ftp://194.149.64.196/STRUSWER/.     

Comparison of sequence and structure alignments for protein domains

Profile search methods based on protein domain alignments have proven to be useful tools in comparative sequence analysis. Domain alignments used by currently available search methods have been computed by sequence comparison. With the growth of the protein structure database, however, alignments of many domain pairs have also been computed by structure comparison. Here, we examine the extent to which information from these two sources agrees. We measure agreement with respect to identification of homologous regions in each protein, that is, with respect to the location of domain boundaries. We also measure agreement with respect to identification of homologous residue sites by comparing alignments and assessing the accuracy of the molecular models they predict. We find that domain alignments in publicly available collections based on sequence and structure comparison are largely consistent. However, the homologous regions identified by sequence comparison are often shorter than those identified by 3D structure comparison. In addition, when overall sequence similarity is low alignments from sequence comparison produce less accurate molecular models, suggesting that they less accurately identify homologous sites. These observations suggest that structure comparison results might be used to improve the overall accuracy of domain alignment collections and the performance of profile search methods based on them.     

A comparison of position-specific score matrices based on sequence and structure alignments

Sequence comparison methods based on position-specific score matrices (PSSMs) have proven a useful tool for recognition of the divergent members of a protein family and for annotation of functional sites. Here we investigate one of the factors that affects overall performance of PSSMs in a PSI-BLAST search, the algorithm used to construct the seed alignment upon which the PSSM is based. We compare PSSMs based on alignments constructed by global sequence similarity (ClustalW and ClustalW-pairwise), local sequence similarity (BLAST), and local structure similarity (VAST). To assess performance with respect to identification of conserved functional or structural sites, we examine the accuracy of the three-dimensional molecular models predicted by PSSM-sequence alignments. Using the known structures of those sequences as the standard of truth, we find that model accuracy varies with the algorithm used for seed alignment construction in the pattern local-structure (VAST) > local-sequence (BLAST) > global-sequence (ClustalW). Using structural similarity of query and database proteins as the standard of truth, we find that PSSM recognition sensitivity depends primarily on the diversity of the sequences included in the alignment, with an optimum around 30-50% average pairwise identity. We discuss these observations, and suggest a strategy for constructing seed alignments that optimize PSSM-sequence alignment accuracy and recognition sensitivity.     

18S rRNA alignments derived from different secondary structure models can produce alternative phylogenies

Molecular sequence data are often aligned on the basis of secondary and/or tertiary structure models. However, these models are regularly updated and sometimes differ depending on the way in which they were constructed. We examined whether the choice of a particular 18S rRNA secondary structure model as alignment basis influences phylogeny inference. We therefore compared 18S rRNA phylogenies derived from alignments based on different models. We used: 1. Maximum parsimony; 2. The neighbour-joining method; 3. The maximum-likelihood approach; and 4. Evolutionary parsimony. This demonstrated that the secondary structure model on which an alignment is based may influence: 1. The tree topologies found by these four methods; 2. The numbers of most parsimonious trees found; and 3. The statistical values calculated by the evolutionary parsimony method.     

Gaps in structurally similar proteins: towards improvement of multiple sequence alignment

An algorithm was developed to locally optimize gaps from the FSSP database. Over 2 million gaps were identified from all versus all FSSP structure comparisons, and datasets of non-identical gaps and flanking regions comprising between 90,000 and 135,000 sequence fragments were extracted for statistical analysis. Relative to background frequencies, gaps were enriched in residue types with small side chains and high turn propensity (D, G, N, P, S), and were depleted in residue types with hydrophobic side chains (C, F, I, L, V, W, Y). In contrast, regions flanking a gap exhibited opposite trends in amino acid frequencies, i.e., enrichment in hydrophobic residues and a high degree of secondary structure. Log-odds scores of residue type as a function of position in or around a gap were derived from the statistics. Three simple experiments demonstrated that these scores contained significant predictive information. First, regions where gaps were observed in single sequences taken from HOMSTRAD structure-based multiple sequence alignments generally scored higher than regions where gaps were not observed. Second, given the correct pairwise-aligned cores, the actual positions of gaps could be reproduced from sequence more accurately using the structurally-derived statistics than by using random pairwise alignments. Finally, revision of the Clustal-W residue-specific gap opening parameters with this new information improved the agreement of Clustal-W alignments with the structure-based alignments. At least three applications for these results are envisioned: improvement of gap penalties in pairwise (or multiple) sequence alignment, prediction of regions of single sequences likely (or unlikely) to contain indels, and more accurate placement of gaps in automated pairwise structure alignment.     

SFESA: a web server for pairwise alignment refinement by secondary structure shifts

Background

Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate.

Results

We developed the SFESA web server to refine pairwise protein sequence alignments. Compared to the previous version of SFESA, which required a set of 3D coordinates for a protein, the new server will search a sequence database for the closest homolog with an available 3D structure to be used as a template. For each alignment block defined by secondary structure elements in the template, SFESA evaluates alignment variants generated by local shifts and selects the best-scoring alignment variant. A scoring function that combines the sequence score of profile-profile comparison and the structure score of template-derived contact energy is used for evaluation of alignments. PROMALS pairwise alignments refined by SFESA are more accurate than those produced by current advanced alignment methods such as HHpred and CNFpred. In addition, SFESA also improves alignments generated by other software.

Conclusions

SFESA is a web-based tool for alignment refinement, designed for researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. To our knowledge, the SFESA web server is the only tool that refines alignments by evaluating local shifts of secondary structure elements. The SFESA web server is available at http://prodata.swmed.edu/sfesa.     

Assessing secondary structure assignment of protein structures by using pairwise sequence-alignment benchmarks

How to make an objective assignment of secondary structures based on a protein structure is an unsolved problem. Defining the boundaries between helix, sheet, and coil structures is arbitrary, and commonly accepted standard assignments do not exist. Here, we propose a criterion that assesses secondary structure assignment based on the similarity of the secondary structures assigned to pairwise sequence-alignment benchmarks, where these benchmarks are determined by prior structural alignments of the protein pairs. This criterion is used to rank six secondary structure assignment methods: STRIDE, DSSP, SECSTR, KAKSI, P-SEA, and SEGNO with three established sequence-alignment benchmarks (PREFAB, SABmark, and SALIGN). STRIDE and KAKSI achieve comparable success rates in assigning the same secondary structure elements to structurally aligned residues in the three benchmarks. Their success rates are between 1-4% higher than those of the other four methods. The consensus of STRIDE, KAKSI, SECSTR, and P-SEA, called SKSP, improves assignments over the best single method in each benchmark by an additional 1%. These results support the usefulness of the sequence-alignment benchmarks as a means to evaluate secondary structure assignment. The SKSP server and the benchmarks can be accessed at http://sparks.informatics.iupui.edu     

Identification of related proteins with weak sequence identity using secondary structure information

Molecular modeling of proteins is confronted with the problem of finding homologous proteins, especially when few identities remain after the process of molecular evolution. Using even the most recent methods based on sequence identity detection, structural relationships are still difficult to establish with high reliability. As protein structures are more conserved than sequences, we investigated the possibility of using protein secondary structure comparison (observed or predicted structures) to discriminate between related and unrelated proteins sequences in the range of 10%-30% sequence identity. Pairwise comparison of secondary structures have been measured using the structural overlap (Sov) parameter. In this article, we show that if the secondary structures likeness is >50%, most of the pairs are structurally related. Taking into account the secondary structures of proteins that have been detected by BLAST, FASTA, or SSEARCH in the noisy region (with high E: value), we show that distantly related protein sequences (even with <20% identity) can be still identified. This strategy can be used to identify three-dimensional templates in homology modeling by finding unexpected related proteins and to select proteins for experimental investigation in a structural genomic approach, as well as for genome annotation.     

Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence

We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non-redundant domains collected recently by Cuff and Barton (Proteins 1999;34:508-519; Proteins 2000;40:502-511). We have introduced a variable size window that allowed us to include sequences as short as 20-30 residues. A significant improvement over the previous versions of GOR algorithm was obtained by combining the PSI-BLAST multiple sequence alignments with the GOR method. The new algorithm will form the basis for the future GOR V release on an online prediction server. The average accuracy of the prediction of secondary structure with multiple sequence alignment and full jack-knife procedure was 73.5%. The accuracy of the prediction increases to 74.2% by limiting the prediction to 375 (of 513) sequences having at least 50 PSI-BLAST alignments. The average accuracy of the prediction of the new improved program without using multiple sequence alignments was 67.5%. This is approximately a 3% improvement over the preceding GOR IV algorithm (Garnier J, Gibrat JF, Robson B. Methods Enzymol 1996;266:540-553; Kloczkowski A, Ting K-L, Jernigan RL, Garnier J. Polymer 2002;43:441-449). We have discussed alternatives to the segment overlap (Sov) coefficient proposed by Zemla et al. (Proteins 1999;34:220-223).     

Comprehensively designed consensus of standalone secondary structure predictors improves Q3 by over 3%

Protein fold is defined by a spatial arrangement of three types of secondary structures (SSs) including helices, sheets, and coils/loops. Current methods that predict SS from sequences rely on complex machine learning-derived models andprovide the three-state accuracy (Q₃) at about 82%. Further improvements in predictive quality could be obtained with a consensus-based approach, which so far received limited attention. We perform first-of-its-kind comprehensive design of a SS consensus predictor (SScon), in which we consider 12 modern standalone SS predictors and utilize Support Vector Machine (SVM) to combine their predictions. Using a large benchmark data-set with 10 random training-test splits, we show that a simple, voting-based consensus of carefully selected base methods improves Q₃ by 1.9% when compared to the best single predictor. Use of SVM provides additional 1.4% improvement with the overall Q₃ at 85.6% and segment overlap (SOV₃) at 83.7%, when compared to 82.3 and 80.9%, respectively, obtained by the best individual methods. We also show strong improvements when the consensus is based on ab-initio methods, with Q₃ = 82.3% and SOV₃ = 80.7% that match the results from the best template-based approaches. Our consensus reduces the number of significant errors where helix is confused with a strand, provides particularly good results for short helices and strands, and gives the most accurate estimates of the content of individual SSs in the chain. Case studies are used to visualize the improvements offered by the consensus at the residue level. A web-server and a standalone implementation of SScon are available at http://biomine.ece.ualberta.ca/SSCon/.     

Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure

The present study is an attempt to develop a neural network-based method for predicting the real value of solvent accessibility from the sequence using evolutionary information in the form of multiple sequence alignment. In this method, two feed-forward networks with a single hidden layer have been trained with standard back-propagation as a learning algorithm. The Pearson's correlation coefficient increases from 0.53 to 0.63, and mean absolute error decreases from 18.2 to 16% when multiple-sequence alignment obtained from PSI-BLAST is used as input instead of a single sequence. The performance of the method further improves from a correlation coefficient of 0.63 to 0.67 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields a mean absolute error value of 15.2% between the experimental and predicted values, when tested on two different nonhomologous and nonredundant datasets of varying sizes. The method consists of two steps: (1) in the first step, a sequence-to-structure network is trained with the multiple alignment profiles in the form of PSI-BLAST-generated position-specific scoring matrices, and (2) in the second step, the output obtained from the first network and PSIPRED-predicted secondary structure information is used as an input to the second structure-to-structure network. Based on the present study, a server SARpred (http://www.imtech.res.in/raghava/sarpred/) has been developed that predicts the real value of solvent accessibility of residues for a given protein sequence. We have also evaluated the performance of SARpred on 47 proteins used in CASP6 and achieved a correlation coefficient of 0.68 and a MAE of 15.9% between predicted and observed values.     

Prediction of protein secondary structure from amino acid sequence

The conformational parametersP _k for each amino acid species (j=1–20) of sequential peptides in proteins are presented as the product ofP _i,k , wherei is the number of the sequential residues in thekth conformational state (k=-helix,-sheet,-turn, or unordered structure). Since the average parameter for ann-residue segment is related to the average probability of finding the segment in the kth state, it becomes a geometric mean of (P _k )_av=(P _i,k ) ^1/n with amino acid residuei increasing from 1 ton. We then used ln(P_k)_av to convert a multiplicative process to a summation, i.e., ln(P _k ) _av =(1/n)P _i,k (i=1 ton) for ease of operation. However, this is unlike the popular Chou-Fasman algorithm, which has the flaw of using the arithmetic mean for relative probabilities. The Chou-Fasman algorithm happens to be close to our calculations in many cases mainly because the difference between theirP _k and our InP _k is nearly constant for about one-half of the 20 amino acids. When stronger conformation formers and breakers exist, the difference become larger and the prediction at the N- and C-terminal-helix or-sheet could differ. If the average conformational parameters of the overlapping segments of any two states are too close for a unique solution, our calculations could lead to a different prediction.     

Native secondary structure topology has near minimum contact energy among all possible geometrically constrained topologies

Secondary structure topology in this article refers to the order and the direction of the secondary structures, such as helices and strands, with respect to the protein sequence. Even when the locations of the secondary structure Cα atoms are known, there are still (N!2^N)(M!2^M) different possible topologies for a protein with N helices and M strands. This work explored the question if the native topology is likely to be identified among a large set of all possible geometrically constrained topologies through an evaluation of the residue contact energy formed by the secondary structures, instead of the entire chain. We developed a contact pair specific and distance specific multiwell function based on the statistical characterization of the side chain distances of 413 proteins in the Protein Data Bank. The multiwell function has specific parameters to each of the 210 pairs of residue contacts. We illustrated a general mathematical method to extend a single well function to a multiwell function to represent the statistical data. We have performed a mutation analysis using 50 proteins to generate all the possible geometrically constrained topologies of the secondary structures. The result shows that the native topology is within the top 25% of the list ranked by the effective contact energies of the secondary structures for all the 50 proteins, and is within the top 5% for 34 proteins. As an application, the method was used to derive the structure of the skeletons from a low resolution density map that can be obtained through electron cryomicroscopy. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Combining evolutionary information and neural networks to predict protein secondary structure

Using evolutionary information contained in multiple sequence alignments as input to neural networks, secondary structure can be predicted at significantly increased accuracy. Here, we extend our previous three-level system of neural networks by using additional input information derived from multiple alignments. Using a position-specific conservation weight as part of the input increases performance. Using the number of insertions and deletions reduces the tendency for overprediction and increases overall accuracy. Addition of the global amino acid content yields a further improvement, mainly in predicting structural class. The final network system has a sustained overall accuracy of 71.6% in a multiple cross-validation test on 126 unique protein chains. A test on a new set of 124 recently solved protein structures that have no significant sequence similarity to the learning set confirms the high level of accuracy. The average cross-validated accuracy for all 250 sequence-unique chains is above 72%. Using various data sets, the method is compared to alternative prediction methods, some of which also use multiple alignments: the performance advantage of the network system is at least 6 percentage points in three-state accuracy. In addition, the network estimates secondary structure content from multiple sequence alignments about as well as circular dichroism spectroscopy on a single protein and classifies 75% of the 250 proteins correctly into one of four protein structural classes. Of particular practical importance is the definition of a position-specific reliability index. For 40% of all residues the method has a sustained three-state accuracy of 88%, as high as the overall average for homology modelling. A further strength of the method is greatly increased accuracy in predicting the placement of secondary structure segments. © 1994 Wiley-Liss, Inc.     

Effect of using suboptimal alignments in template-based protein structure prediction

Computational protein structure prediction remains a challenging task in protein bioinformatics. In the recent years, the importance of template-based structure prediction is increasing because of the growing number of protein structures solved by the structural genomics projects. To capitalize the significant efforts and investments paid on the structural genomics projects, it is urgent to establish effective ways to use the solved structures as templates by developing methods for exploiting remotely related proteins that cannot be simply identified by homology. In this work, we examine the effect of using suboptimal alignments in template-based protein structure prediction. We showed that suboptimal alignments are often more accurate than the optimal one, and such accurate suboptimal alignments can occur even at a very low rank of the alignment score. Suboptimal alignments contain a significant number of correct amino acid residue contacts. Moreover, suboptimal alignments can improve template-based models when used as input to Modeller. Finally, we use suboptimal alignments for handling a contact potential in a probabilistic way in a threading program, SUPRB. The probabilistic contacts strategy outperforms the partly thawed approach, which only uses the optimal alignment in defining residue contacts, and also the re-ranking strategy, which uses the contact potential in re-ranking alignments. The comparison with existing methods in the template-recognition test shows that SUPRB is very competitive and outperforms existing methods.     

Structural refinement of protein segments containing secondary structure elements: Local sampling, knowledge-based potentials, and clustering

In this article, we present an iterative, modular optimization (IMO) protocol for the local structure refinement of protein segments containing secondary structure elements (SSEs). The protocol is based on three modules: a torsion-space local sampling algorithm, a knowledge-based potential, and a conformational clustering algorithm. Alternative methods are tested for each module in the protocol. For each segment, random initial conformations were constructed by perturbing the native dihedral angles of loops (and SSEs) of the segment to be refined while keeping the protein body fixed. Two refinement procedures based on molecular mechanics force fields - using either energy minimization or molecular dynamics - were also tested but were found to be less successful than the IMO protocol. We found that DFIRE is a particularly effective knowledge-based potential and that clustering algorithms that are biased by the DFIRE energies improve the overall results. Results were further improved by adding an energy minimization step to the conformations generated with the IMO procedure, suggesting that hybrid strategies that combine both knowledge-based and physical effective energy functions may prove to be particularly effective in future applications.     

Refinement of the structure of protein–RNA complexes by residual dipolar coupling analysis

The main limitation in NMR-determined structures of nucleic acids and their complexes with proteins derives from the elongated, non-globular nature of physiologically important DNA and RNA molecules. Since it is generally not possible to obtain long-range distance constraints between distinct regions of the structure, long-range properties such as bending or kinking at sites of protein recognition cannot be determined accurately nor precisely. Here we show that use of residual dipolar couplings in the refinement of the structure of a protein–RNA complex improves the definition of the long-range properties of the RNA. These features are often an important aspect of molecular recognition and biological function; therefore, their improved definition is of significant value in RNA structural biology.     

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the "usefulness" and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.     

Improved RNA secondary structure prediction by maximizing expected pair accuracy

Free energy minimization has been the most popular method for RNA secondary structure prediction for decades. It is based on a set of empirical free energy change parameters derived from experiments using a nearest-neighbor model. In this study, a program, MaxExpect, that predicts RNA secondary structure by maximizing the expected base-pair accuracy, is reported. This approach was first pioneered in the program CONTRAfold, using pair probabilities predicted with a statistical learning method. Here, a partition function calculation that utilizes the free energy change nearest-neighbor parameters is used to predict base-pair probabilities as well as probabilities of nucleotides being single-stranded. MaxExpect predicts both the optimal structure (having highest expected pair accuracy) and suboptimal structures to serve as alternative hypotheses for the structure. Tested on a large database of different types of RNA, the maximum expected accuracy structures are, on average, of higher accuracy than minimum free energy structures. Accuracy is measured by sensitivity, the percentage of known base pairs correctly predicted, and positive predictive value (PPV), the percentage of predicted pairs that are in the known structure. By favoring double-strandedness or single-strandedness, a higher sensitivity or PPV of prediction can be favored, respectively. Using MaxExpect, the average PPV of optimal structure is improved from 66% to 68% at the same sensitivity level (73%) compared with free energy minimization.