Hidden Markov models that use predicted secondary structures for fold recognition.

There are many proteins that share the same fold but have no clear sequence similarity. To predict the structure of these proteins, so called "protein fold recognition methods" have been developed. During the last few years, improvements of protein fold recognition methods have been achieved through the use of predicted secondary structures (Rice and Eisenberg, J Mol Biol 1997;267:1026-1038), as well as by using multiple sequence alignments in the form of hidden Markov models (HMM) (Karplus et al., Proteins Suppl 1997;1:134-139). To test the performance of different fold recognition methods, we have developed a rigorous benchmark where representatives for all proteins of known structure are matched against each other. Using this benchmark, we have compared the performance of automatically-created hidden Markov models with standard-sequence-search methods. Further, we combine the use of predicted secondary structures and multiple sequence alignments into a combined method that performs better than methods that do not use this combination of information. Using only single sequences, the correct fold of a protein was detected for 10% of the test cases in our benchmark. Including multiple sequence information increased this number to 16%, and when predicted secondary structure information was included as well, the fold was correctly identified in 20% of the cases. Moreover, if the correct secondary structure was used, 27% of the proteins could be correctly matched to a fold. For comparison, blast2, fasta, and ssearch identifies the fold correctly in 13-17% of the cases. Thus, standard pairwise sequence search methods perform almost as well as hidden Markov models in our benchmark. This is probably because the automatically-created multiple sequence alignments used in this study do not contain enough diversity and because the current generation of hidden Markov models do not perform very well when built from a few sequences.     

Development and validation of a consistency based multiple structure alignment algorithm

SUMMARY: We introduce an algorithm that uses the information gained from simultaneous consideration of an entire group of related proteins to create multiple structure alignments (MSTAs). Consistency-based alignment (CBA) first harnesses the information contained within regions that are consistently aligned among a set of pairwise superpositions in order to realign pairs of proteins through both global and local refinement methods. It then constructs a multiple alignment that is maximally consistent with the improved pairwise alignments. We validate CBA's alignments by assessing their accuracy in regions where at least two of the aligned structures contain the same conserved sequence motif. RESULTS: CBA correctly aligns well over 90% of motif residues in superpositions of proteins belonging to the same family or superfamily, and it outperforms a number of previously reported MSTA algorithms.     

A segment alignment approach to protein comparison

MOTIVATION: Local structure segments (LSSs) are small structural units shared by unrelated proteins. They are extensively used in protein structure comparison, and predicted LSSs (PLSSs) are used very successfully in ab initio folding simulations. However, predicted or real LSSs are rarely exploited by protein sequence comparison programs that are based on position-by-position alignments. RESULTS: We developed a SEgment Alignment algorithm (SEA) to compare proteins described as a collection of predicted local structure segments (PLSSs), which is equivalent to an unweighted graph (network). Any specific structure, real or predicted corresponds to a specific path in this network. SEA then uses a network matching approach to find two most similar paths in networks representing two proteins. SEA explores the uncertainty and diversity of predicted local structure information to search for a globally optimal solution. It simultaneously solves two related problems: the alignment of two proteins and the local structure prediction for each of them. On a benchmark of protein pairs with low sequence similarity, we show that application of the SEA algorithm improves alignment quality as compared to FFAS profile-profile alignment, and in some cases SEA alignments can match the structural alignments, a feat previously impossible for any sequence based alignment methods.     

Protein structure prediction by threading methods: Evaluation of current techniques

This paper evaluates the results of a protein structure prediction contest. The predictions were made using threading procedures, which employ techniques for aligning sequences with 3D structures to select the correct fold of a given sequence from a set of alternatives. Nine different teams submitted 86 predictions, on a total of 21 target proteins with little or no sequence homology to proteins of known structure. The 3D structures of these proteins were newly determined by experimental methods, but not yet published or otherwise available to the predictors. The predictions, made from the amino acid sequence alone, thus represent a genuine test of the current performance of threading methods. Only a subset of all the predictions is evaluated here. It corresponds to the 44 predictions submitted for the 11 target proteins seen to adopt known folds. The predictions for the remaining 10 proteins were not analyzed, although weak similarities with known folds may also exist in these proteins. We find that threading methods are capable of identifying the correct fold in many cases, but not reliably enough as yet. Every team predicts correctly a different set of targets, with virtually all targets predicted correctly by at least one team. Also, common folds such as TIM barrels are recognized more readily than folds with only a few known examples. However, quite surprisingly, the quality of the sequence-structure alignments, corresponding to correctly recognized folds, is generally very poor, as judged by comparison with the corresponding 3D structure alignments. Thus, threading can presently not be relied upon to derive a detailed 3D model from the amino acid sequence. This raises a very intriguing question: how is fold recognition achieved? Our analysis suggests that it may be achieved because threading procedures maximize hydrophobic interactions in the protein core, and are reasonably good at recognizing local secondary structure. © 1995 Wiley-Liss, Inc.     

A study on protein sequence alignment quality

One of the most central methods in bioinformatics is the alignment of two protein or DNA sequences. However, so far large-scale benchmarks examining the quality of these alignments are scarce. On the other hand, recently several large-scale studies of the capacity of different methods to identify related sequences has led to new insights about the performance of fold recognition methods. To increase our understanding about fold recognition methods, we present a large-scale benchmark of alignment quality. We compare alignments from several different alignment methods, including sequence alignments, hidden Markov models, PSI-BLAST, CLUSTALW, and threading methods. For most methods, the alignment quality increases significantly at about 20% sequence identity. The difference in alignment quality between different methods is quite small, and the main difference can be seen at the exact positioning of the sharp rise in alignment quality, that is, around 15-20% sequence identity. The alignments are improved by using structural information. In general, the best alignments are obtained by methods that use predicted secondary structure information and sequence profiles obtained from PSI-BLAST. One interesting observation is that for different pairs many different methods create the best alignments. This finding implies that if a method that could select the best alignment method for each pair existed, a significant improvement of the alignment quality could be gained.     

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.     

Structure-dependent sequence alignment for remotely related proteins

MOTIVATION: The quality of a model structure derived from a comparative modeling procedure is dictated by the accuracy of the predicted sequence-template alignment. As the sequence-template pairs are increasingly remote in sequence relationship, the prediction of the sequence-template alignments becomes increasingly problematic with sequence alignment methods. Structural information of the template, used in connection with the sequence relationship of the sequence-template pair, could significantly improve the accuracy of the sequence-template alignment. In this paper, we describe a sequence-template alignment method that integrates sequence and structural information to enhance the accuracy of sequence-template alignments for distantly related protein pairs. RESULTS: The structure-dependent sequence alignment (SDSA) procedure was optimized for coverage and accuracy on a training set of 412 protein pairs; the structures for each of the training pairs are similar (RMSD< approximately 4A) but the sequence relationship is undetectable (average pair-wise sequence identity = 8%). The optimized SDSA procedure was then applied to extend PSI-BLAST local alignments by calculating the global alignments under the constraint of the residue pairs in the local alignments. This composite alignment procedure was assessed with a testing set of 1421 protein pairs, of which the pair-wise structures are similar (RMSD< approximately 4A) but the sequences are marginally related at best in each pair (average pair-wise sequence identity = 13%). The assessment showed that the composite alignment procedure predicted more aligned residues pairs with an average of 27% increase in correctly aligned residues over the standard PSI-BLAST alignments for the protein pairs in the testing set.     

基于堆积协变信息与最小自由能预测含伪结的RNA二级结构

RNA伪结预测是RNA研究的一个难点问题。文中提出一种基于堆积协变信息与最小自由能的RNA伪结预测方法。该方法使用已知结构的RNA比对序列(ClustalW比对和结构比对)测试此方法, 侧重考虑相邻碱基对之间相互作用形成的堆积协变信息, 并结合最小自由能方法对碱基配对综合评分, 通过逐步迭代求得含伪结的RNA二级结构。结果表明, 此方法能正确预测伪结, 其平均敏感性和特异性优于参考算法, 并且结构比对的预测性能比ClustalW比对的预测性能更加稳定。文中同时讨论了不同协变信息权重因子对预测性能的影响, 发现权重因子比值在l1: l2=5:1时, 预测性能达到最优。     

Sequence-similar, structure-dissimilar protein pairs in the PDB

It is often assumed that in the Protein Data Bank (PDB), two proteins with similar sequences will also have similar structures. Accordingly, it has proved useful to develop subsets of the PDB from which "redundant" structures have been removed, based on a sequence-based criterion for similarity. Similarly, when predicting protein structure using homology modeling, if a template structure for modeling a target sequence is selected by sequence alone, this implicitly assumes that all sequence-similar templates are equivalent. Here, we show that this assumption is often not correct and that standard approaches to create subsets of the PDB can lead to the loss of structurally and functionally important information. We have carried out sequence-based structural superpositions and geometry-based structural alignments of a large number of protein pairs to determine the extent to which sequence similarity ensures structural similarity. We find many examples where two proteins that are similar in sequence have structures that differ significantly from one another. The source of the structural differences usually has a functional basis. The number of such proteins pairs that are identified and the magnitude of the dissimilarity depend on the approach that is used to calculate the differences; in particular sequence-based structure superpositioning will identify a larger number of structurally dissimilar pairs than geometry-based structural alignments. When two sequences can be aligned in a statistically meaningful way, sequence-based structural superpositioning provides a meaningful measure of structural differences. This approach and geometry-based structure alignments reveal somewhat different information and one or the other might be preferable in a given application. Our results suggest that in some cases, notably homology modeling, the common use of nonredundant datasets, culled from the PDB based on sequence, may mask important structural and functional information. We have established a data base of sequence-similar, structurally dissimilar protein pairs that will help address this problem (http://luna.bioc.columbia.edu/rachel/seqsimstrdiff.htm).     

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.     

PROMALS3D: a tool for multiple protein sequence and structure alignments

Although multiple sequence alignments (MSAs) are essential for a wide range of applications from structure modeling to prediction of functional sites, construction of accurate MSAs for distantly related proteins remains a largely unsolved problem. The rapidly increasing database of spatial structures is a valuable source to improve alignment quality. We explore the use of 3D structural information to guide sequence alignments constructed by our MSA program PROMALS. The resulting tool, PROMALS3D, automatically identifies homologs with known 3D structures for the input sequences, derives structural constraints through structure-based alignments and combines them with sequence constraints to construct consistency-based multiple sequence alignments. The output is a consensus alignment that brings together sequence and structural information about input proteins and their homologs. PROMALS3D can also align sequences of multiple input structures, with the output representing a multiple structure-based alignment refined in combination with sequence constraints. The advantage of PROMALS3D is that it gives researchers an easy way to produce high-quality alignments consistent with both sequences and structures of proteins. PROMALS3D outperforms a number of existing methods for constructing multiple sequence or structural alignments using both reference-dependent and reference-independent evaluation methods.     

Predicting protein interaction interfaces from protein sequences: case studies of subtilisin and phycocyanin

Identification of protein interaction interfaces is very important for understanding the molecular mechanisms underlying biological phenomena. Here, we present a novel method for predicting protein interaction interfaces from sequences by using PAM matrix (PIFPAM). Sequence alignments for interacting proteins were constructed and parsed into segments using sliding windows. By calculating distance matrix for each segment, the correlation coefficients between segments were estimated. The interaction interfaces were predicted by extracting highly correlated segment pairs from the correlation map. The predictions achieved an accuracy 0.41-0.71 for eight intraprotein interaction examples, and 0.07-0.60 for four interprotein interaction examples. Compared with three previously published methods, PIFPAM predicted more contacting site pairs for 11 out of the 12 example proteins, and predicted at least 34% more contacting site pairs for eight proteins of them. The factors affecting the predictions were also analyzed. Since PIFPAM uses only the alignments of the two interacting proteins as input, it is especially useful when no three-dimensional protein structure data are available.     

Prediction of consensus RNA secondary structures including pseudoknots

Most functional RNA molecules have characteristic structures that are highly conserved in evolution. Many of them contain pseudoknots. Here, we present a method for computing the consensus structures including pseudoknots based on alignments of a few sequences. The algorithm combines thermodynamic and covariation information to assign scores to all possible base pairs, the base pairs are chosen with the help of the maximum weighted matching algorithm. We applied our algorithm to a number of different types of RNA known to contain pseudoknots. All pseudoknots were predicted correctly and more than 85 percent of the base pairs were identified.     

Predicting reliable regions in protein alignments from sequence profiles

For applications such as comparative modelling one major issue is the reliability of sequence alignments. Reliable regions in alignments can be predicted using sub-optimal alignments of the same pair of sequences. Here we show that reliable regions in alignments can also be predicted from multiple sequence profile information alone.Alignments were created for a set of remotely related pairs of proteins using five different test methods. Structural alignments were used to assess the quality of the alignments and the aligned positions were scored using information from the observed frequencies of amino acid residues in sequence profiles pre-generated for each template structure. High-scoring regions of these profile-derived alignment scores were a good predictor of reliably aligned regions.These profile-derived alignment scores are easy to obtain and are applicable to any alignment method. They can be used to detect those regions of alignments that are reliably aligned and to help predict the quality of an alignment. For those residues within secondary structure elements, the regions predicted as reliably aligned agreed with the structural alignments for between 92% and 97.4% of the residues. In loop regions just under 92% of the residues predicted to be reliable agreed with the structural alignments. The percentage of residues predicted as reliable ranged from 32.1% for helix residues to 52.8% for strand residues.This information could also be used to help predict conserved binding sites from sequence alignments. Residues in the template that were identified as binding sites, that aligned to an identical amino acid residue and where the sequence alignment agreed with the structural alignment were in highly conserved, high scoring regions over 80% of the time. This suggests that many binding sites that are present in both target and template sequences are in sequence-conserved regions and that there is the possibility of translating reliability to binding site prediction.     

Comprehensive evaluation of protein structure alignment methods: scoring by geometric measures

We report the largest and most comprehensive comparison of protein structural alignment methods. Specifically, we evaluate six publicly available structure alignment programs: SSAP, STRUCTAL, DALI, LSQMAN, CE and SSM by aligning all 8,581,970 protein structure pairs in a test set of 2930 protein domains specially selected from CATH v.2.4 to ensure sequence diversity. We consider an alignment good if it matches many residues, and the two substructures are geometrically similar. Even with this definition, evaluating structural alignment methods is not straightforward. At first, we compared the rates of true and false positives using receiver operating characteristic (ROC) curves with the CATH classification taken as a gold standard. This proved unsatisfactory in that the quality of the alignments is not taken into account: sometimes a method that finds less good alignments scores better than a method that finds better alignments. We correct this intrinsic limitation by using four different geometric match measures (SI, MI, SAS, and GSAS) to evaluate the quality of each structural alignment. With this improved analysis we show that there is a wide variation in the performance of different methods; the main reason for this is that it can be difficult to find a good structural alignment between two proteins even when such an alignment exists. We find that STRUCTAL and SSM perform best, followed by LSQMAN and CE. Our focus on the intrinsic quality of each alignment allows us to propose a new method, called "Best-of-All" that combines the best results of all methods. Many commonly used methods miss 10-50% of the good Best-of-All alignments. By putting existing structural alignments into proper perspective, our study allows better comparison of protein structures. By highlighting limitations of existing methods, it will spur the further development of better structural alignment methods. This will have significant biological implications now that structural comparison has come to play a central role in the analysis of experimental work on protein structure, protein function and protein evolution.     

Overcoming sequence misalignments with weighted structural superposition

An appropriate structural superposition identifies similarities and differences between homologous proteins that are not evident from sequence alignments alone. We have coupled our Gaussian‐weighted RMSD (wRMSD) tool with a sequence aligner and seed extension (SE) algorithm to create a robust technique for overlaying structures and aligning sequences of homologous proteins (HwRMSD). HwRMSD overcomes errors in the initial sequence alignment that would normally propagate into a standard RMSD overlay. SE can generate a corrected sequence alignment from the improved structural superposition obtained by wRMSD. HwRMSD's robust performance and its superiority over standard RMSD are demonstrated over a range of homologous proteins. Its better overlay results in corrected sequence alignments with good agreement to HOMSTRAD. Finally, HwRMSD is compared to established structural alignment methods: FATCAT, secondary‐structure matching, combinatorial extension, and Dalilite. Most methods are comparable at placing residue pairs within 2 Å, but HwRMSD places many more residue pairs within 1 Å, providing a clear advantage. Such high accuracy is essential in drug design, where small distances can have a large impact on computational predictions. This level of accuracy is also needed to correct sequence alignments in an automated fashion, especially for omics‐scale analysis. HwRMSD can align homologs with low‐sequence identity and large conformational differences, cases where both sequence‐based and structural‐based methods may fail. The HwRMSD pipeline overcomes the dependency of structural overlays on initial sequence pairing and removes the need to determine the best sequence‐alignment method, substitution matrix, and gap parameters for each unique pair of homologs. Proteins 2012. © 2012 Wiley Periodicals, Inc.     

Homology-based modeling of 3D structures of protein-protein complexes using alignments of modified sequence profiles

Customary practice in predicting 3D structures of protein-protein complexes is employment of various docking methods when the structures of separate monomers are known a priori. The alternative approach, i.e. the template-based prediction with pure sequence information as a starting point, is still considered as being inferior mostly due to presumption that the pool of available structures of protein-protein complexes, which can serve as putative templates, is not sufficiently large. Recently, however, several labs have developed databases containing thousands of 3D structures of protein-protein complexes, which enable statistically reliable testing of homology-based algorithms. In this paper we report the results on homology-based modeling of 3D structures of protein complexes using alignments of modified sequence profiles. The method, called HOMology-BAsed COmplex Prediction (HOMBACOP), has two distinctive features: (I) extra weight on aligning interfacial residues in the dynamical programming algorithm, and (II) increased gap penalties for the interfacial segments. The method was tested against our recently developed ProtCom database and against the Boston University protein-protein BENCHMARK. In both cases, models generated were compared to the models built on basis of customarily protein structure initiative (PSI)-BLAST sequence alignments. It was found that existence of homologous (by the means of PSI-BLAST) templates (44% of cases) enables both methods to produce models of good quality, with the profiles method outperforming the PSI-BLAST models (with respect to the percentage of correctly predicted residues on the complex interface and fraction of native interfacial contacts). The models were evaluated according to the CAPRI assessment criteria and about two thirds of the models were found to fall into acceptable and medium-quality categories. The same comparison of a larger set of 463 protein complexes showed again that profiles generate better models. We further demonstrate, using our ProtCom database, the suitability of the profile alignment algorithm in detecting remote homologues between query and template sequences, where the PSI-BLAST method fails.     

LTHREADER: prediction of extracellular ligand-receptor interactions in cytokines using localized threading

Identification of extracellular ligand-receptor interactions is important for drug design and the treatment of diseases. Difficulties in detecting these interactions using high-throughput experimental techniques motivate the development of computational prediction methods. We propose a novel threading algorithm, LTHREADER, which generates accurate local sequence-structure interface alignments and integrates various statistical scores and experimental binding data to predict interactions within ligand-receptor families. LTHREADER uses a profile of secondary structure and solvent accessibility predictions with residue contact maps to guide and constrain alignments. Using a decision tree classifier and low-throughput experimental data for training, it combines information inferred from statistical interaction potentials, energy functions, correlated mutations, and conserved residue pairs to predict interactions. We apply our method to cytokines, which play a central role in the development of many diseases including cancer and inflammatory and autoimmune disorders. We tested our approach on two representative families from different structural classes (all-alpha and all-beta proteins) of cytokines. In comparison with the state-of-the-art threader RAPTOR, LTHREADER generates on average 20% more accurate alignments of interacting residues. Furthermore, in cross-validation tests, LTHREADER correctly predicts experimentally confirmed interactions for a common binding mode within the 4-helical long-chain cytokine family with 75% sensitivity and 86% specificity with 40% gain in sensitivity compared to RAPTOR. For the TNF-like family our method achieves 70% sensitivity with 55% specificity with 70% gain in sensitivity. LTHREADER combines information from multiple complex templates when such data are available. When only one solved structure is available, a localized PSI-BLAST approach also outperforms standard threading methods with 25%-50% improvements in sensitivity.     

Structure-based evaluation of sequence comparison and fold recognition alignment accuracy

The biological role, biochemical function, and structure of uncharacterized protein sequences is often inferred from their similarity to known proteins. A constant goal is to increase the reliability, sensitivity, and accuracy of alignment techniques to enable the detection of increasingly distant relationships. Development, tuning, and testing of these methods benefit from appropriate benchmarks for the assessment of alignment accuracy.Here, we describe a benchmark protocol to estimate sequence-to-sequence and sequence-to-structure alignment accuracy. The protocol consists of structurally related pairs of proteins and procedures to evaluate alignment accuracy over the whole set. The set of protein pairs covers all the currently known fold types. The benchmark is challenging in the sense that it consists of proteins lacking clear sequence similarity.Correct target alignments are derived from the three-dimensional structures of these pairs by rigid body superposition. An evaluation engine computes the accuracy of alignments obtained from a particular algorithm in terms of alignment shifts with respect to the structure derived alignments. Using this benchmark we estimate that the best results can be obtained from a combination of amino acid residue substitution matrices and knowledge-based potentials.