Protein structure modeling by homology requires an accurate sequence alignment between the query protein and its structural template. However, sequence alignment methods based on dynamic programming (DP) are typically unable to generate accurate alignments for remote sequence homologs, thus limiting the applicability of modeling methods. A central problem is that the alignment that is “optimal” in terms of the DP score does not necessarily correspond to the alignment that produces the most accurate structural model. That is, the correct alignment based on structural superposition will generally have a lower score than the optimal alignment obtained from sequence. Variations of the DP algorithm have been developed that generate alternative alignments that are “suboptimal” in terms of the DP score, but these still encounter difficulties in detecting the correct structural alignment. We present here a new alternative sequence alignment method that relies heavily on the structure of the template. By initially aligning the query sequence to individual fragments in secondary structure elements and combining high-scoring fragments that pass basic tests for “modelability”, we can generate accurate alignments within a small ensemble. Our results suggest that the set of sequences that can currently be modeled by homology can be greatly extended. It has been suggested that, for nearly every protein sequence, there is already a protein with a similar structure in current protein structure databases. However, with poor or undetectable sequence relationships, it is expected that accurate alignments and models cannot be generated. Here we show that this is not the case, and that whenever structural relationship exists, there are usually local sequence relationships that can be used to generate an accurate alignment, no matter what the global sequence identity. However, this requires an alternative to the traditional dynamic programming algorithm and the consideration of a small ensemble of alignments. We present an algorithm, S4, and demonstrate that it is capable of generating accurate alignments in nearly all cases where a structural relationship exists between two proteins. Our results thus constitute an important advance in the full exploitation of the information in structural databases. That is, the expectation of an accurate alignment suggests that a meaningful model can be generated for nearly every sequence for which a suitable template exists. Most protein sequences do not have an experimentally determined structure and at least 40% do not even have a sequence homolog with a known structure Numerous variations of both the residue-residue similarity score and gap penalty have been developed to address these issues. Individual residue-based scoring functions have been replaced with more complex profile-profile Even with these more sophisticated approaches, there are still many issues that will confound the generation of an accurate alignment. Moreover, it is generally necessary to consider an ensemble of alternative alignments in order to produce an accurate model at low sequence identity ranges. Such ensembles are frequently called “suboptimal” since by necessity they have lower scores than the optimal alignment produced by DP. A variety of suboptimal sequence alignment schemes have been reported. Waterman ie. highest-scoring) under a different set of conditions. One problem faced by all suboptimal methods is how to adequately sample the gigantic space of possibilities. Jaroszewski et alSaqi and Sternberg Sampling Shifts in Secondary Structures), that takes an approach that is fundamentally different from the standard dynamic programming algorithm. In validation tests that we describe below, we show that S4 is highly effective at producing an accurate alignment within a set of 100 top-ranked alternatives and can almost always produce such an alignment within a set of 1000 alternative alignments. The utility of the S4 approach is most evident when the query/template sequence identities are low, but S4 also improves accuracy when the homology is clear. Our results are shown to constitute a significant improvement over DP-based alternative alignment methods, which we show is due to unique features of the algorithm, in particular to the effective use of the 3-dimensional structure of the template. The ability to generate a small set of alignments likely to contain the correct one suggests that S4 offers the possibility of significantly improving the accuracy of homology models, extending the number of sequences that can currently be modeled based on existing structures in the PDB.We describe here a new method to generate suboptimal alignments, S4 . As described in Of course, there is an inherent difficulty in comparing the performance of a method which generates an ensemble to a method which generates a single alignment. In fact, the optimal alignment is the most accurate in many cases  and is more often than not in the top 5% in a set of 1,000 alignments ranked by IAD. The average rank is ∼200 however, so there is generally room for improvement, and our main point here is not that the optimal alignment shouldn't be used, but that an ensemble is necessary to generate an alignment that makes an accurate model, especially for highly remote/query template pairs. In practice, the optimal alignment would always be part of such an ensemble.To determine the extent to which the improvement in alignment quality of S4 relative to the optimal alignment is due simply to the increased number of alignments generated, we also compared S4 to two versions of the conventional DP-based Waterman algorithm for generating alternative alignments th percentile cutoff within each segment, indicating that S4 produced a model for 10% of the pairs that was as accurate as possible, i.e., as good as the model produced by the correct, structure-based alignment.The results shown in Since they use the same scoring function, the improved performance of S4 compared to HMAP seen in In A specific example illustrates S4's approach to sampling alignment space. Despite being classified as different folds, these two proteins have high overall structural similarity and thus an alignment exists that would generate an accurate model. The structural alignment for this pair describes the proper correspondence of all eight of the SSEs that are common between the template and query, as depicted in the first two rows of the alignment shown in Overall, out of an ensemble of 1,000 alignments, the best alignment from S4 has an IAD of 0.56 and an FDS2 of 97% compared to the correct alignment and the TM-score of the corresponding model is 0.50 . In contrast, the best alignment generated by the constrained Waterman approach  had an IAD of 15.4. That the improvement in accuracy of S4 is due to differences in sampling can clearly be seen by calculating average IADs of the alignments in each ensemble, but here with respect to the DP-based optimal alignment instead of the correct alignment. The constrained Waterman approach is “trapped” near this incorrect alignment (average IAD of 0.3 and standard deviation of 0.8). Even though S4 samples the DP-based optimal alignment, it also searches far from this alignment .Though we have shown that S4 generates accurate alignments to almost every template appropriate for a given query sequence, we have not discussed how to identify these templates or how to select the correct alignment from the S4 ensemble. However, the results shown in By building models from templates selected by other methods, but based on alignments generated by S4, these templates can be exploited assuming an accurate model evaluation procedure can be applied. There is a wide array of such tools that range from measures of the suitability for residues to be in a given environment , S4 is able to find an alignment that is more than twice as accurate and S4's performance decreases only slightly among the pairs that are not detected at all by PSI-BLAST, which comprise over half the benchmark set. The results shown in Since S4 produces accurate alignments in nearly every case where there is a structural similarity that leads to an accurate model, this suggests that, using a model-building and evaluation procedure, templates with scores that are outside the range of what is usually considered significant for a particular method could also be identified. Using the widely used tool PSI-BLAST as an example, about half of the templates in our data set were identified as significant . As shown in potential alignments that need to be considered  for many cases in our benchmark. An ideal modeling strategy then would be one that generates an ensemble with S4 and simply adds the optimal alignment to that ensemble. This would ensure the best of both worlds at no increase in computational cost. Moreover, the S4 algorithm is independent of the underlying residue-residue scoring function employed. In the work presented here, the HMAP profile-profile method was used, but the sampling algorithm used in S4 could be applied using any other residue-residue scoring function. Therefore, if better scoring functions are available or if future improvements to scoring functions are able to raise the level of accuracy of the DP-based methods, S4's performance using the same scoring function should improve as well.To ensure that a meaningful structural relationship existed within each query-template pair, several conditions had to be met: the protein structural distance (PSD) The S4 algorithm is described in detail as six distinct steps below see . OverallFor every pair of primary fragments we perform a recursive search for “secondary” fragments to fill in the region defined by the fragment endpoints, if the fragments in the pair belong to non-consecutive SSE's. For example, in A “fragment alignment” is a list of primary and secondary fragments in order from the N- to C-terminal. Two examples of fragment alignments are shown in To enumerate all fragment alignments that are possible within our set of primary and secondary fragments, S4 connects the N-terminal pseudo-fragment d(t denotes the distance (in Å) in the template structure between the Cα atoms of the corresponding, aligned template residues. The factor of 3.3 was determined by studying a database of several hundred high-resolution structures. It was found that the maximum distance traversed by a loop was slightly over 3 Å per residue, which is, of course, roughly the length of an individual amino acid. We used 3.3 to allow our algorithm to keep some pairings of fragments whose loops would normally be ‘over-stretched’. The purpose of this test was to remove only blatantly incorrect fragment pairings, since loops that were just slightly over-stretched may be fixed when the full alignment is found in Step 5 of the algorithm.Fragment alignments are rejected if there are not enough residues in the query sequence to bridge the gap between any two consecutive fragments. Specifically, we require that th-percentile for each measure and one of these three values had to surpass the 90th-percentile. The measures are listed below.Three other measures were used to eliminate fragment alignments that are unlikely to produce good models: preserved core contacts, query energy and template energy. For an alignment to be kept, all three of these measures must have values above the 66A pair of residues in the template structure is considered to be a “core contact” if both residues in the pair are buried (60% or more of surface area inaccessible), have Cβ atoms that are within 6 Å and are both hydrophobic . An alignment that pairs hydrophobic amino acids in the query with template residues in a core contact generates a preserved core contact.An implementation of the DFIRE statistical potential Similar to evaluating the statistical energy of the pseudomodel, we calculate the energy of the aligned template residues, which we term the “template energy”. In effect, this is the statistical energy of a subset of the template structure. The motivation behind this is to recognize and remove alignments that pair query residues with an unlikely combination of template SSEs. This often occurs when the template is a multi-domain protein and the query is a single domain. In these cases, the highest scoring fragments may be spread out across multiple domains of the template in a structure that does not resemble a folded protein. Calculating this value allows S4 to eliminate many such alignments.Lastly, to decrease the redundancy of the final results, some fragment alignments are removed due to their similarity to a higher-scoring alignment. Fragment alignments are considered redundant if they align to the same template SSEs, have all corresponding fragments within a shift of 4, and an inter-alignment distance (IAD) of less than 1.At this stage in the process, no full alignments in the normal sense have been created, only fragment alignments, which are just lists of fragments. A round of alignment sampling using the full sequences of the query and template is used to generate a final alignment from each fragment alignment. In this final step, alignments are restricted to a specific region of the dynamic program matrix. The boundaries of the region extend 3 residues above and below the fragments in each fragment alignment. The loop regions are constrained only by the boundaries of the surrounding fragments Click here for additional data file.


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. 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.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/.We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted  Experimentally determining a protein's structure is labor-intensive and time-consuming. Homology search, on the other hand, is a very effective way to predict the properties of an uncharacterized protein Most protein sequence alignment tools rely on a scoring function to measure the similarity between residues and give penalties for insertion/deletions. The quality of the resulting alignments is greatly influenced by the scoring function. The most common scoring functions are called substitution matrices. They include the PAM matrices The last issue has been an intense focus of research over the last few years, and many methods have been produced that make an attempt to model amino acid mutations in a position specific manner. All these methods have their reliance on a pre-assembled multiple sequence alignment (MSA) used as a profile. They include Hidden Markov Models (HMMs) NP-hard problem, hence a large number of methods have been developed to address this important biological problem Optimizing an MSA has been shown to be an n-gram fragment of amino acids that reflects the sequence variation in the evolution. Synonymous n-grams can be effectively used to improve protein secondary structure predictions The excessive greediness of the progressive alignment can be tackled by improving the quality of the first pairwise alignments. This goal was the original motivation for the development of a novel class of progressive aligners known as consistency based progressive aligners. The original algorithm was developed for the T-Coffee and later re-implemented in several aligners including ProbCons In this paper, we extend the application of synonyms to the problem of sequence alignment and present a method, called SymAlign, to demonstrate how protein synonyms can be used to improve the quality of protein sequence alignment. We applied this method to two well-known benchmark datasets and estimated the alignment quality using either reference alignments or structure based evaluation methods (RMSD and iRMSD). Our results show that SymAlign can generate alignments with better RMSD/iRMSD. We also demonstrate that SymAlign is less sensitive to structurally non-similar proteins when they are aligned together with structurally similar proteins. Finally, in the analysis of the relationship between sequence identity and structure similarity, we demonstrate that SymAlign can better differentiate structurally similar proteins from non-similar proteins.A synonym is a word that has identical or similar meaning as another word in the same language. For example, lovely, pretty, attractive, gorgeous, and so on all have similar meaning as beautiful but with different spelling in English. They are literally interchangeable with each other without changing the semantics of a sentence. Polysemy is the opposite phenomenon, that is to say the possibility for a single word to have different alternative meanings, depending on the context. For example, the word “play” could be used as a verb, in the sense “being involved in a game” or as a noun referring to a piece of writing performed by actors. The exact meaning of a polysemy depends on the context in which the word occurs. Technically, it is possible to align words in two sentences according to the meanings of known words and to infer the meaning of an unknown word from the word it is aligned with. We used this idea to propose the definition and identification of synonyms in protein sequences.It is well known that a protein structure is encoded by its amino acid sequence. Therefore, a protein sequence can be treated as a text written in a language whose alphabet comprises 20 letters. The protein's structure is analogous to the semantic meaning of the text. However, the translation from sequence to structure remains a mystery. It is well known that homologous proteins share similar structures We consider homologous proteins as sentences with similar meanings. They contain similar information and describe similar actions. Taking this analogy further, we may consider that the mutations between homologous sequences are like synonyms in two sentences. Thus we can create a mapping relationship between those sequence variations. In terms of natural language, a group of homologous sequences can be treated as a series of texts with identical or similar meanings. These texts can therefore be used to infer synonymous relationship from sequence alignments. The main limitation here is that we do not have much knowledge on how protein languages are composed and the precise rules or grammar indicating the boundaries of protein words.n-gram model. The simplicity of the underlying modeling is very popular in formal linguistic analysis and bioinformatics sequence comparison. For instance, the BLAST's algorithm uses an n-gram model to generate a collection of similar words A and B with high sequence identity , we use a sliding window to extract all word pairs to define synonyms. For each word w in protein A, if there is another word w' in protein B aligned with w and there is no gap between w and w', we say w and w' are synonymous. The rationale is that since proteins A and B are similar in sequences, the two words would probably express similar structures. By extending the collection of proteins similar to protein A, we can define a larger number of synonyms for each word in A.Structure conformation is a highly complicated process where both long and short range interactions play an important role. Long-range interactions are very hard to model, but the short range ones can be effectively estimated using the An important difference between protein synonyms and similar words in BLAST is that protein synonyms are defined from biologically significant sequence alignments (context-sensitive) while similar words are more or less context-free. Protein words share some properties with natural language words, including polysemy: the same protein word may appear in different proteins with different structures. Kabsch and Sander demonstrated that the same five residues can be a part of an alpha-helix in one protein and a part of a beta-strand in another protein, and they suggested that pentapeptide structure is strongly dependent on sequence context S and T to be aligned, we define synonyms for each word in S and T by using a sliding window to screen the sequence alignments between one of the target sequences and its similar sequences. We perform a PSI-BLAST search to generate a number of sequence alignments between S and S' (similar sequences of S). Each synonym sw extracted from S' is associated with a word in S. All synonyms for the words in protein S and T are collected this way. Whenever a word sw is found that are both synonymous with a word sw in S and a word tw in T, then sw and tw are considered to be synonymous by transitivity. In practice, given a target protein pair S and T to be aligned, S = 1s2s3…sms and T = 1t2t3…tnt, where is, jt are the i-th residue of S and j-th residue of T, respectively. A protein word swi, = isis+1i+l…s−1 represents the subsequence of length l, which begins at is and ends at i+ls−1. Let sswi, denote a synonym of the word swi,, and F be the frequency of sswi,, which is the number of appearances among the similar proteins of S. Likewise, let tswj, represent a synonym of the word twj,, and F be the frequency of tswj,. If sswi, = tswj,, we say swi, and twj, share a synonym and we define a similarity score between swi, and twj, by sim_s = +F)/2, which means that all of the residue pairs , , …,  are given a similarity score of sim_s. The final similarity score between a residue pair  is determined by the summation of sim_s for all common synonyms that cover residues is and jt.First, we introduce some notations used in this subsection. Consider we are given two sequences N groups equally, i.e., each group has equal number of residue pairs. The first group of residue pairs is assigned an alignment score of N, and the second group N−1, and so forth. N is set to be 500 in this study; however, if the number of alignable residue pairs is less than 500, then N is set to be the number of alignable residue pairs.To avoid some residue pairs having much higher scores than others and resulting in a broken alignment, we normalize similarity scores by a simple normalization scheme. All the residue pairs are ranked by their similarity scores and we divide them into T-Coffee can generate an alignment based on customized alignment scores between residue pairs. We transform the similarity scores of all residue pairs into a library file. A library file is a list of pairs of residues that are considered alignable with a designated score. When dealing with MSAs, we generate a library file for each pair of protein sequences. T-Coffee builds a guide tree based on the input library files and reports a multiple sequence alignment. BASICB-DHIPIn order to compare SymAlign with profile-profile alignment methods, we selected two scoring functions to calculate the alignment score for each residue pair and used T-Coffee to produce sequence alignments. The two scoring functions, S and T, BASIC is defined as follows:D is the alignment score for the residue pair , A and B represent the log-odds scoring matrices of the profiles for protein S and T, respectively, and M is the substitution matrix (M is BLOSUM62 in this study). BASIC compares two proteins from their profiles with the scores from the background comparison matrix.BASIC is a profile–profile alignment method designed for fold recognition, which was successfully applied at CASP3 Ã and B ~ represent the profiles of probabilities for the 20 amino acids at each position in S and T, respectively, while other symbols are same as those in BASIC. We generate all log-odds score and probability data for each protein sequence by PSI-BLAST.B-DHIP is also a profile-profile alignment comparison method for fold recognition, which uses a standard dot-product between vectors of log-odds scores and probabilities of the 20 amino acids. It is defined as follows:To estimate the accuracy of a sequence alignment, researchers often use a reference-dependent measure, the quality score (Q-score). It is defined as the ratio of the number of correctly aligned residue pairs compared with the reference alignment. The measure that depends on a reference alignment has been criticized for the limited capacity to capture the structural correctness of an alignment globally http://boscoh.com/protein/rmsd-root-mean-square-deviation, which is used to calculate the RMSD between PDB structures based on Kabsch's algorithm N), α-carbon (CA), carbon (C) and oxygen (O) atoms on its backbone. Because RMSD is used to measure the difference between two protein structures, we only evaluated the performance of pairwise sequence alignment. When measuring the performance of multiple sequence alignments, we calculated the average RMSD between every pair of structures within the MSA. The iRMSD was estimated using the implementation distributed with the T-Coffee package.In this study, we evaluated the performance using Q-score, iRMSD and RMSD. We downloaded and modified the program from the We assessed the performances on two benchmark databases: BAliBASE 3.0 PREFAB 4.0 contains 1,682 pairwise reference alignments. We filtered out some proteins that do not have PDB structures or whose sequences are inconsistent with the associated PDB entries. We also filtered out protein pairs having more than 20% identity. The resulting subset includes 1,553 sequences and 954 pairwise alignments. The average sequence identity is about 10.01%. Like RV11, we conducted the analysis on the entire sequences in PREFAB and ignored the annotation of core regions.We compared the performance of SymAlign with eight state-of-the-art methods: ClustalW (version 2.1) The comparison of pairwise sequence alignments on the BAliBASE and PREFAB are summarized in SymAlign achieves the best performance on the two datasets in terms of all the three measures, and Probalign achieves the second best ranking in iRMSD and RMSD measures. Since SymAlign computes the similarity scores of residue pairs and uses T-Coffee to generate alignments, the results of the two methods can be directly compared. SymAlign achieves an improvement of 6.2% in Q-score and 2 Å in RMSD over T-Coffee on BAliBASE's RV11, and 1.11% in Q-score and 1.73 Å in RMSD on PREFAB.Furthermore, observed from We further compared these methods on multiple sequence alignments of the BAliBASE's RV11, and the results are shown in iP and iP′, let SeqIdy denote the sequence identity of iP and iP′ calculated by T-Coffee, and TMscore denote the TM-score of iP and iP′. Note that TM-score is a score between  and TMscore. We then tested on the new test set U' given by {1P, P2, …, kP, P1′, P2′, …, kP′} For example, we add proteins BB11035.1dvh, BB11002.1ihv_A, BB11033.1erv, and BB11026.1ubi into BB11001 as outliers. The new test set BB11001' includes four original protein members and the four outliers.Note that the protein members in each MSA of RV11 are specially selected. Although their mutual sequence identities are below 20%, they are structurally similar. This defines a very challenging situation albeit not entirely realistic. In practice, it is common to align a set of sequences containing non-homologous ones. Unfortunately, the progressive alignment procedure can be strongly affected by non-homologous sequences. In order to estimate the alignment quality when facing such problem, we simulated this situation by adding “outliers” into each test set of RV11 as described below. Given two sequences We intended to disturb the guide tree by adding outliers. The outliers are involved in the process of MSA, but not involved in the evaluation of alignment quality. They serve as noises to test the robustness of an alignment tool. To compare the performance of different tools on the new dataset RV11 with outliers (denoted as RV11'), we calculated the RMSD on the original protein pairs according to the resulting MSA. The comparison results on RV11' are shown in the second column of The performance of a method on benchmark datasets may not always be representative of its performance on real datasets. Indeed, a real dataset sometimes includes protein sequences that may not be structurally similar. However, there is little discussion in the literature about the alignment quality for protein sequences with no structural similarities. In reality, given sequence alignments with <20% identity, one has difficulty in distinguishing between structural similarity and non-similarity no matter how accurate an aligner can achieve on these sequences. Thus in this section, we evaluated each method by how its alignment results reflect structural similarity or non-similarity.To this end, we generated all possible sequence pairs in BAliBASE's RV11 and PREFAB, i.e., 31,707 and 1,205,128 pairs, respectively. A recent study on TM-align SymAlign consistently achieved the highest precisions among all sequence aligners at the three different identity thresholds, especially at 15% identity threshold; it achieved 81.13% precision on RV11, outperforming the second best aligner  by a big gap of 52.36%. It shows that SymAlign has a much lower tendency than other aligners to over-estimate similarity between structurally non-similar sequences. On the other hand, SymAlign's recalls at the three identity thresholds are very close to those of TM-Align, showing SymAlign's alignments are very close to structure based alignments by TM-align to reflect true structural similarity. As a result, the identity estimated on SymAlign alignments is more informative for structure similarity.1bb9 and 1ov3_A selected from BAliBASE's RV11, where 1bb9 corresponds to the vertical axis while 1ov3_A corresponds to the horizontal axis. Dots are shaded according to a grayscale reflecting the number of shared synonyms associated with each residue pair. The darker the dot, the larger the number of shared synonyms. The reference alignment is illustrated by red dots. As one can see, the left side of the matrix shows an alternative alignment with a pattern very similar to the reference alignment.Most sequence alignment tools only report for each pair of proteins a single alignment with the highest score and provide visualization in the text of sequences to show the alignment result. However, the alignment is not necessarily the best one. Dot matrices are convenient ways to represent alternative alignments. We show here  how the We aligned the two sequences according to the two diagonal patterns shown in the figure and measured their RMSDs. The reference alignment produces an RMSD of 9.12 Å, while the other one produces an RMSD of 13.65 Å. This observation is totally consistent with an inspection of the structures showing that 1ov3_A is probably a tetramer and 1bb9 a dimer of some common homologous ancestral repeat.In this paper, we present a new method, SymAlign, to align protein sequences, using the concept of protein synonyms, instead of a substitution matrix or a position-specific scoring matrix used by traditional alignment tools to calculate the alignment score between residue pairs. We demonstrate that the shared synonyms can improve the similarity estimate between equivalent residues. SymAlign is evaluated on the most difficult test sets of BAliBASE and PREFAB, and experiments show that SymAlign can align sequences more accurately than alternative methods.An interesting novelty in the benchmark described here is our assessment of the impact of unrelated sequences. We define a fairly realistic situation to compare various methods. By altering each test set through the addition of unrelated sequences, we demonstrate that SymAlign is very robust to outliers. This should be an essential feature for any sequence aligner because the inclusion of outliers within a group of homologues frequently occurs in sequence analysis.SymAlign can align sequences to better indicate their preserved 3D structures than standard sequence aligners. On the benchmark datasets, we show that whenever SymAlign delivers an alignment with more than 15% identity, the considered sequences are more likely to be in the same fold.Furthermore, SymAlign displays not only the optimal but also the sub-optimal alignments on dot-matrices. A fine grayscale makes it easy to identify alternative alignments that may not have been identified by dynamic programming. It is especially useful when aligning distantly related sequences. Altogether SymAlign should prove an interesting development for T-Coffee. The increased accuracy provided by SymAlign will be especially important and useful in all situations where accurately aligning distantly relates homologues is a limiting step.


Multiple Sequence Alignment (MSA) is a basic tool for bioinformatics research and analysis. It has been used essentially in almost all bioinformatics tasks such as protein structure modeling, gene and protein function prediction, DNA motif recognition, and phylogenetic analysis. Therefore, improving the accuracy of multiple sequence alignment is important for advancing many bioinformatics fields.We designed and developed a new method, MSACompro, to synergistically incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into the currently most accurate posterior probability-based MSA methods to improve the accuracy of multiple sequence alignments. The method is different from the multiple sequence alignment methods (e.g. 3D-Coffee) that use the tertiary structure information of some sequences since the structural information of our method is fully predicted from sequences. To the best of our knowledge, applying predicted relative solvent accessibility and contact map to multiple sequence alignment is novel. The rigorous benchmarking of our method to the standard benchmarks  clearly demonstrated that incorporating predicted protein structural information improves the multiple sequence alignment accuracy over the leading multiple protein sequence alignment tools without using this information, such as MSAProbs, ProbCons, Probalign, T-coffee, MAFFT and MUSCLE. And the performance of the method is comparable to the state-of-the-art method PROMALS of using structural features and additional homologous sequences by slightly lower scores.http://sysbio.rnet.missouri.edu/multicom_toolbox/.MSACompro is an efficient and reliable multiple protein sequence alignment tool that can effectively incorporate predicted protein structural information into multiple sequence alignment. The software is available at  Aligning multiple evolutionarily related protein sequences is a fundamental technique for studying protein function, structure, and evolution. Multiple sequence alignment methods are often an essential component for solving challenging bioinformatics problems such as protein function prediction, protein homology identification, protein structure prediction, protein interaction study, mutagenesis analysis, and phylogenetic tree construction. During the last thirty years or so, a number of methods and tools have been developed for multiple sequence alignment, which have made fundamental contributions to the development of the bioinformatics field.State of the art multiple sequence alignment methods adapt some popular techniques to improve alignment accuracy, such as iterative alignment , progresIn order to use the predicted structural information to advance the state of the art of multiple sequence alignment, we first compared the existing multiple sequence alignment tools ,4,5-37 oFollowing the general scheme in MSAProbs , MSACompX = , Y = , where x1, x2,......, xn1 and y1, y2,......,yn2 are lists of the residues in X and Y, respectively. n1 is the length of sequence X, and n2 is the length of sequence Y. Suppose xi is the i-th amino acid in sequence X, and yj is the j-th amino acid in sequence Y. We let aln denote a global alignment between X and Y, ALN the set of of all the possible global alignments of X and Y, and aln* ∈ ALN the true pairwise alignment of X and Y. The posterior probability that the i-th residue in X (xi) is aligned to the j-th residue (yj) in Y in aln* is defined as:For two protein sequences X and Y in a sequence group S to be aligned, we denote P denotes the probability that aln is the true alignment aln*: Thus, the posterior probability n1 × n2 matrix PXY is a collection of all the values p (p(xi ~ yj) for short) for 1 ≤ xi ≤ n1, 1 ≤ yj ≤ n2. The calculation process of the pairwise posterior probability matrix is described as follows.F) of alignments based on dynamic programming. F denotes the probability of all partial global alignments of X and Y ending at position . FM  is the probability of all partial global alignments with xi aligned to yj, Fy, is the probability of all partial global alignments with yj aligned to a gap, and FX is the probability of all partial global alignments with xi aligned to a gap. Accordingly, the partition function can be calculated recursively as follows:As in MSAProbs, two different methods (a pair hidden Markov model and a partition function) are used to compute the pairwise posterior probability matrices  is the amino acid similarity score between xi and yj. One element of the substitution matrix s, SS(ss(xi), ss(yj)) is the similarity score between the secondary structure (ss(xi)) of residue xi in protein X and that of residue yj in protein Y according to the secondary structure similarity matrix SS, SA(sa(xi), sa(yj)) is the similarity score between the relative solvent accessibility (sa(xi)) of residue xi in protein X and that of residue yj in protein Y according to the solvent accessibility similarity matrix SA. W1, W2, W3 are weights used to control the influence of the amino acid substitution score, secondary structure similarity score, and solvent accessibility similarity score. The secondary structure and solvent accessibility can be automatically predicted by SSpro/ACCpro [http://sysbio.rnet.missouri.edu/multicom_toolbox/) using a multi-threading technique implemented in MSACompro, or alternatively be provided by a user. The values of the three weights are set to 0.4, 0.5, and 0.1 by default, and can be adjusted by users. The ensembles of bidirectional recurrent neural network architectures in ACCpro are used to discriminate between two different states of relative solvent accessibility, higher or lower than the accessibility cutoff - 25% of the total surface area of a residue [β is a parameter measuring the deviation between suboptimal and optimal alignments, gap(gap ≤ 0) is the gap open penalty, and ext(ext ≤ 0) is the gap extension penalty.In the formula above, o/ACCpro   as follows:, where two identical solvent accessibilities receive a score of 1 and different ones receive a 0. It is worth noting that we used the simple identity scoring matrix for secondary structure and solvent accessibility here. Employing more advance scoring matrices defined in  may lead1, n2) till position  with xi aligned to yj., where As in MSAProbs, the second kind of pairwise probability matrix lgorithm ,5,45. Th PROMALS  that letPXY is calculated as the root mean square of the corresponding values in The final posterior probability matrix p1(xi ~ yi) and p2(xi ~ yi) denote a posterior probability element in two kinds of posterior probability matrices  of the global alignment is computed according to an optimal sub-alignment score matrix AS. The optimal sub-alignment score AS denotes the score of the optimal sub-alignment ending at residues i and j in X and Y. The AS matrix is recursively calculated as:The posterior probability matrix AS  is the optimal score of the full global alignment between X and Y, which is denoted as Optscore.CMscore, for the optimal pairwise alignment of X and Y, assuming that the spatially neighboring residues of two aligned residues should have a higher tendency to be aligned together. CMscore is calculated from the contact map correlation score matrix CMapXY based on the residue-residue contact map matrices CMapX and CMapY of X and Y.In addition to the optimal alignment score, we introduce a contact map score, Assuming the optimal global alignment of X and Y is represented as,we can generate a new alignment after removing the pairs containing gaps:, which can be denoted asn is the length of the new alignment without gaps, where CMapX and CMapY, shown below:From this alignment, we can construct two contact map matrices, http://sysbio.rnet.missouri.edu/multicom_toolbox/). The contact map correlation score matrix CMapXY is calculated as the multiplication of CMapX and CMapY:by NNcon   is the number of leafs in cluster X.After the guide tree is constructed, sequences are weighted according to the schemes inferred in .To reduce the bias of sampling similar sequences, we use a weighted scheme to transform the former posterior probability aswX and wY are, respectively, the weight of sequences X and Y, wZ is the weight of a sequence Z other than X or Y in the given group of sequences, and wN is the sum of sequence weights in dataset S.We first use the guide tree to generate a multiple sequence alignment by progressively aligning two clusters of the most similar sequences together. As in MSAProbs , we alsoWe tested MSACompro in comparison to three benchmarks: BAliBASE, SABmark and OXBENCH, and evaluated the alignment results in terms of sum-of-pairs (SP) score and true column (TC) score. The SP score is the number of correctly aligned pairs of residue in the test alignment divided by the total number of aligned pairs of residues in core blocks of the reference alignment . The TC 1 for amino acid similarity and weight w2 for secondary structure information on BAliBASE 3.0 data sets. MSACompro worked wholly the best if the weight w1 for amino acid similarity and the weight w2 for secondary structure information were 0.4 and 0.5, respectively. Since the sum of w1, w2 and wc is 1, we can deduce that wc is 0.1 if w1 and w2 are 0.4 and 0.5. Then we focused on the effect of residue-residue contact map information under two different scenarios: using secondary structure and relevant solvent accessibility information by keeping the w1, w2, and w3 at their optimum values , or excluding that information by setting both w2 and w3 as 0. Evaluation results on BAliBASE 3.0 database were found to improve the most when wc is 0.9 by integrating both secondary structure and relevant solvent accessibility information. Additionally, to avoid over-fitting, we tested MSACompro against SABmark 1.65 and OXBENCH data sets using this set of parameters independently, and found that a significant improvement was also gained in comparison to other leading protein multiple sequence alignment tools. More details can be found in the next section, "A comprehensive study on the effect of predicted structural information on the alignment accuracy". Consequently, the weights w1, w2, w3 and wc are respectively set at 0.4, 0.5, 0.1 and 0.9 in MSACompro by default. All other tools were also evaluated under default parameters.To understand how various parameters of MSACompro affect alignment accuracy, some experiments were carried out to evaluate these variants based on two algorithm changes: (1) combining amino acids, secondary structure, and relative solvent accessibility information into the partition function calculation using respective weights for each of them; (2) computing the pairwise distance from both the pairwise posterior probability matrices and the pairwise contact map similarity matrices by introducing the weight wc for contact map information. To optimize the parameters, we used BAliBASE 3.0 data sets as training sets, and SABmark 1.65 and OXBENCH data sets as testing sets. Firstly, we focused on the effect of secondary structure and solvent accessibility information by testing different values of weight wFirstly, we evaluated these methods on BAliBASE  - the moSecondly, we evaluated MSACompro and other tools without the help of structural information on the SABmark database , which iThirdly, we also assessed all the tools without using the structural information on the OXBENCH database . OXBENCHFinally, we also compared the SP scores and TC scores of MSACompro and other tools which adopt the structural information on the six subsets of BAliBASE database, SABmark database and OXBENCH database. Tables In order to check if alignment score differences between MSACompro and the other alignment methods are statistically significant, we carried out the Wilcoxon matched-pair signed-rank test  on both In addition to alignment accuracy, alignment speed is also a factor to consider in time-critical applications. Because it is difficult to rigorously compare the speed of different methods due to the difference in implementation and inputs, we only report the roughly estimated running time of the different methods on BAliBASE based our empirical observations. The fastest methods are ClustalW, MAFFT, MUSCLE, and POA, which used less than one hour. The medium-speed methods that used a few hours to less than one day include FSA, Opal, Probalign, MSAProbs, ProbCons, T-coffee, MUMMALS, and DIALIGN-TX. The more time demanding methods are MSACompro, PROMALS, and PROMALS3D because they need to generate extra information for alignment. We ran both PROMALS and MSACompro on the BAliBASE 3.0 database on an 4 eight-core (i.e. 32 CPU cores) Linux server to calculate their running time. It took about 4 days and 6 hours for PROMALS to run on the whole BAliBASE 3.0 data sets, and about 9 hours and 13 minutes for MSACompro to run on the same data sets. MSACompro was faster because it used a multiple-threading implementation to call SSpro/ACCpro to predict secondary structure and solvent accessibility in parallel. Out of about 9 hours and 13 minutes, about four hours and 17 minutes were used by MSACompro to align sequences if secondary structure and solvent accessibility information was provided. However, if only one CPU core is used, it took around 6 days and 14 hours for SSpro and ACCpro called by MSACompro to predict secondary structure and solvent accessibility information alone, which is time-consuming. Therefore, MSACompro will be slower than PROMALS if it runs a single CPU core, but faster on multiple (> = 3) CPU cores. As for PROMALS3D, it used about 9 days to extract tertiary structure information and make alignments.To understand the impact of predicted secondary structure, relative solvent accessibility, and contact map on the accuracy of multiple sequence alignment, we tested their effects on alignments individually or in combination by adjusting the values of their weights used in the partition function (i.e. for secondary structure and solvent accessibility) or in the distance calculation (i.e. for contact map).1 (weight for amino acid sequence information) and w2 (weight for secondary structure information), the sum of which was kept as 1, and setting the values of w3 (weight for relative solvent accessibility) and wc (weight for contact map) to 0. The results for different w2 values on the SABmark data sets are shown in Table 2 = 0). The highest accuracy is achieved when w2 is set to .5, at which point the score is 8 points greater than the baseline. w2 = 1 means that only secondary structure is used to calculate the posterior alignment probability in the partition function (i.e. equation set (2)), but amino acid sequence similarity is still used to calculate the other posterior alignment probability by the pair Hidden Markov Models. Figures We studied the effect of secondary structure information by adjusting the values of w1 and w3 and setting the values of w2 and wc to 0. The SP and TC scores with respect to different weight values are shown in Tables 3 was set to 0.5 or 0.6.Similarly, we studied the effect of relative solvent accessibility on the SABmark by adjusting the values of wc and setting w2 and w3 to 0. We used NNcon to successfully predict the contact maps for subset RV11, RV30, 42 out of 44 alignments in RV12, 38 out of 40 in RV20, 33 out of 46 in RV40, and 14 out of 16 in RV50. We tested the MSACompro method against this data with contact predictions. Tables c values on the subsets of the BAliBASE dataset. The results show that using contact information improved the alignment accuracy on some, but not all, subsets.We investigated the effect of contact map information on the BAliBASE 3.0 data set by adjusting wWe adjusted the values of w1 (weight for amino acid), w2 (weight for secondary structure) and w3 (weight for relative solvent accessibility) simultaneously to investigate the effect of using secondary structure and relative solvent accessibility together. SP and TC scores on different parameter combinations are shown in Tables c for contact information, while keeping the w1, w2, and w3 at their optimum values . Tables c values from no contact information (wc = 0) to maximum contact information (wc = 1). The results show that the improvement caused by contact information seems not to be substantial and significant.In order to study whether or not contact information can be used effectively with secondary structure and solvent accessibility, we adjusted the weight wIn this work, we designed a new method to incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into multiple protein sequence alignment. Our experiments on three standard benchmarks showed that the method improved multiple sequence alignment accuracy over most existing methods without using secondary structure and solvent accessibility information. However, the performance of the method is comparable to PROMALS and PROMALS3D by slightly lower scores on some subsets and behind it by a large margin on SABMARK probably because these two methods used homologous sequences or tertiary structure information in addition to secondary structure information. Since multiple sequence alignment is often a crucial step for bioinformatics analysis, this new method may help improve the solutions to many bioinformatics problems such as protein sequence analysis, protein structure prediction, protein function prediction, protein interaction analysis, protein mutagenesis and protein engineering.JC and XD designed the algorithm. XD implemented the algorithm and carried out the experiments. XD and JC analyzed the data. XD and JC wrote the manuscript. XD and JC approved it.


By identifying geometric similarities, structure alignment algorithms provide critical insights into protein functional similarities. Existing structure alignment tools adopt a two-stage approach to structure alignment by decoupling and iterating between the assignment evaluation and structure superposition problems. We introduce a novel approach, SAS-Pro, which addresses the assignment evaluation and structure superposition simultaneously by formulating the alignment problem as a single bilevel optimization problem. The new formulation does not require the sequentiality constraints, thus generalizing the scope of the alignment methodology to include non-sequential protein alignments. We employ derivative-free optimization methodologies for searching for the global optimum of the highly nonlinear and non-differentiable RMSD function encountered in the proposed model. Alignments obtained with SAS-Pro have better RMSD values and larger lengths than those obtained from other alignment tools. For non-sequential alignment problems, SAS-Pro leads to alignments with high degree of similarity with known reference alignments. The source code of SAS-Pro is available for download at  Protein alignment is a problem that has gained tremendous attention in bioinformatics and proteomics due to its applicability in protein clustering, identifying homology relationships, and inferring structure-activity relationships about new and existing proteins. Proteins may be compared with each other through sequence alignment, where the similarities between the proteins are identified through similarities within their amino acid residue sequences. Research on protein sequence alignment has led to the development of numerous dynamic programming algorithms The aim of protein structure alignment is to determine structural similarities between a given pair of proteins so that further functional relationships between them may be identified. Thus, protein structure alignment tools are useful in systematic classification of proteins based on their functional and homology relationships. They may be further employed in predicting functional properties of newly discovered or newly synthesized proteins based on structural similarity with existing proteins. Protein structure alignment tools may also be used in the pharmaceutical industry to determine alternative options for existing drugs, or development of personalized medication. Further applications are also possible in the bio-catalysis and other protein-based product industries, where structure alignment tools could help in development of new protein-based products.Over the past three decades, a variety of algorithms have been developed for finding protein structural alignments, which has turned out to be a very difficult computational problem. Kolodny et al. Early protein structure comparisons were based on computing the root mean square deviation (RMSD) amongst two protein structures of known residue correspondence. In order to make such comparisons on a large-scale, McLachlan The two-step approach to structural alignment has clear computational advantages and results in very fast implementations. However, by decoupling the inter-dependence between the assignment and superposition problems, alignment tools based on this approach may produce suboptimal alignments. In this work, we present a novel approach, Simultaneous Alignment and Superposition of PROteins (SAS-Pro), that combines the evaluation of the assignment and the rotation-translation problems into a single bilevel optimization formulation. We further propose a combination of optimization algorithms, which we demonstrate leads to a practical computational approach for the solution of the proposed formulation.By eliminating the residue-sequentiality constraints, the SAS-Pro approach is additionally capable of providing both sequential and non-sequential structure alignments. Most structure alignment tools developed in the past are designed to provide only sequential alignments between protein structures. However, there exist a multitude of similar protein pairs that exhibit non-sequential structure similarities. Thus, development of alignment tools to identify non-sequential similarities is important. This problem is only recently being addressed through the development of alignment tools such as STSA The remainder of this paper is structured as follows. After stating the protein structure alignment problem, the SAS-Pro optimization model is presented and a numerical solution algorithm is proposed. The implementation is subsequently discussed along with computational results, followed by conclusions.Consider proteins A and B to be structurally aligned. Let The problem of minimizing the RMSD may be represented as the following mixed-integer nonlinear optimization program:A two-stage solution approach employed by existing alignment tools decouples the effects of In typical approaches, values for the assignment variables The SAS-Pro model reformulates the MINLP model into a single bilevel optimization problem. For any given The master problem of the SAS-Pro model optimizes over the solution of the subproblem Evaluation of the function Kolodny and Linial As mentioned above, an optimal solution of the MINLP is feasible to our reformulation. In order for an optimal solution to be identified, suitable algorithms must be used to solve the master problem to global optimality. Indeed, there exist derivative-free optimization (DFO) algorithms that can achieve this goal based on dense sampling of the domain The landscape of the RMSD function with varying values of the rotation angles We performed extensive computational analysis with 28 different DFO solvers, based on a variety of techniques that included direct search, pattern search, surrogate management frameworks, domain partitioning methods, local search, global search, deterministic and stochastic algorithms Our interface to SNOBFIT is based on the ‘mydfo’ interface developed by Rios automatically, i.e., without requiring the user to specify it. This is achieved here as follows.The solution to the SAS-Pro model is dependent on the parameter Proteins with high level of similarity have a large length of alignment, usually corresponding to 85% or more of size of the smaller protein. Hence, the number of biologically relevant residues matches is expected to be between to 85% to 100% of the size of the smaller protein. To identify the best value for Lower levels of similarities between proteins may arise while attempting to obtain a match of a smaller substructure from one protein with other proteins. In order to use SAS-Pro in such a context, it is advisable to isolate the relevant substructure in question before comparing with larger proteins. This will increase the chances of obtaining a suitable alignment within the limits of choice of the parameter The solution to the SAS-Pro model involves determining the optimal values of both the assignment variables In the course of our computational experimentations, we observed that, for proteins with similar sizes, a good approximation of the translation vectors is very often obtained if the centroids of the two protein structures are required to coincide. Thus, while comparing proteins of similar sizes, the number of degrees of freedom for optimization may be reduced to only the three rotation angles. As demonstrated in For structural comparison of proteins with different sizes, the SAS-Pro algorithm offers an option to utilize all six degrees of freedom. In this case, in order to maintain solution quality of the DFO solvers, we found it necessary to increase the number of function evaluations to 1000 for each value of The solution to the SAS-Pro model is usually a non-sequential structure alignment between the two proteins. However, a sequential alignment is easy to extract from the non-sequential alignment obtained from the SAS-Pro algorithm in a post-processing step. A dynamic programming algorithm was designed to identify the largest sequential alignment amongst the aligned residues provided by SAS-Pro. This algorithm sequentially evaluates the largest length of sequential alignment terminating at residue For sequential protein alignments, where the sequence of the amino acid residues is preserved in the alignment, many suitable similarity measures, such as the Structure Alignment Score SAS The similarity between proteins is then determined using the proposed SASThe best non-sequential structure alignment obtained from the SAS-Pro algorithm may include multiple local small-length matches as opposed to a single large global alignment. This disorder of the alignment can be measured by the value of the fragment length. A disordered alignment is expected to have a small fragment length, while a biologically relevant ordered alignment is expected to have a large fragment length, thus providing lower SASWe performed computational experiments based on three data sets:the Sokol data set the Skolnick data set the RIPC data set http://eudoxus.cheme.cmu.edu/saspro/SAS-Pro.html.An all-to-all pairwise alignment for all the proteins in the Sokol and Skolnick data sets was obtained, resulting in 850 pairwise alignment problems with 222 similar protein pairs and 628 dissimilar protein pairs. The Sokol data set includes 20 similar protein pairs that align sequentially. The Skolnick data set consists of proteins from four fold families: a) Flavodoxin-like fold CheY-related, b) Plastocyanin, c) TIM beta/alpha-barrel, and d) Ferratin. Protein pairs within the same fold family are termed as similar pairs and exhibit sequential similarity. The RIPC data set consists of 23 protein alignment problems for which a biologically relevant reference alignment is available. These 23 alignment problems are complex and exhibit non-sequential structure similarities. The complexity of these alignments arises from repetitions, insertions/deletions, permutations, and conformational changes between the protein pairs that are not easily handled by alignment algorithms. All data sets are provided at In all tests, the typical computing time requirements for SAS-Pro were around 1 CPU minute per protein pair on an Intel Quad Core 2.83 GHz processor with 6 GB RAM, while providing sequential and non-sequential alignments with exceptional classification ability.The Sokol and Skolnick data sets were analyzed to evaluate the performance of SAS-Pro in obtaining sequential alignment problems. To obtain sequential alignments from the non-sequential alignments provided by SAS-Pro, the procedure described in the subsection entitled “Extracting sequential alignments” was used. Alignments were compared using the RMSD values as well as the geometric similarity measures SI and SAS.A comparison of the RMSD, SI, and SAS values obtained by SAS-Pro for similar and dissimilar proteins is presented in The alignments obtained from SAS-Pro were also compared with those obtained from the CE The Sokol and Skolnick data sets together include 222 similar protein pairs and 628 dissimilar protein pairs. A classification of these 850 problems into similar and dissimilar pairs was sought based on the SAS scores of the alignments obtained. The CE, SSM, and SAS-Pro alignment tools provided exact classification of these protein pairs. The STSA algorithm, however, produced very short alignments for 5 of the similar pairs, leading to an imperfect classification.We performed a computational study to determine the quality of SAS-Pro's non-sequential structure alignments utilizing the RIPC data set and the non-sequential alignment problems presented by Salem and Zaki We next present results from a computational study with the 23 protein pairs in the RIPC data set. The 3D coordinates of the C-alpha atoms for the SAS-Pro alignments for the 23 pairs are provided in vice versa. Most alignment codes match only the N-terminus half of 1nls with the C-terminus half of 1bqp. Additionally, most alignment methods align only five out of the six reference alignment points, while SAS-Pro aligns the entire protein accurately. Finally, protein pairs 1gsa-2hgs  for the development of a more comprehensive structure alignment tool.File S1.(ZIP)Click here for additional data file.File S2.(ZIP)Click here for additional data file.


HCV affects >170 million people worldwide and is a leading cause of liver diseases such as hepatocellular carcinoma. Each year, Pakistan reports hundreds of cases and now it has become a serious health issue. HCV has two transmembrane glycoproteins (E1 and E2) that are involved in virus entry through viral attachment, but because of their hypervariable nature they have become difficult targets for vaccine development.A total of 150 protein sequences of E1 and E2 belonging to genotypes 3a and 1a were retrieved from the NCBI protein database and were subjected to conservation and variation analysis using the multiple sequence alignment feature of the CLC workbench. A consensus sequence of each genotype of E1 and E2 was obtained and these consensus sequences were further analyzed to construct a global consensus sequence, which was used to design potentially conserved peptides.From the sequence conservation analysis, highly conserved residues were identified and were used to design peptides. Only two peptides were found to be conserved in the E1 protein of genotypes 3a and 1a and a total of nine conserved peptides were designed for the HCV E2 protein of those genotypes. These designed peptides could serve as useful targets in developing new inhibitory compounds.This study was designed to perform conservation and variability analysis of HCV glycoproteins and to find potentially conserved peptides among genotypes 3a and 1a  that could serve as useful targets in the development of novel inhibitory compounds, thus reducing the threat of HCV infection in Pakistan. Chronic infection with hepatitis C virus (HCV) affects >170 million individuals, approximately 3% of the world population, and is responsible for approximately 350,000 deaths every year  and owinPakistan has become a significant reservoir of HCV and each year hundreds of people become infected. Prevalence analysis has revealed that the most prevalent HCV genotype in Pakistan is 3a, except in Balochistan where the most frequent subtype is 1a ,5. EnvelTo design effective peptides, a consensus-based approach was used. A global consensus sequence was constructed using the multiple alignment feature of the CLC Workbench for HCV E1 Figure  proteinsSequences of HCV E1 and E2 of genotypes 3a and 1a were retrieved from the NCBI protein database and were subjected to conservation analysis using the multiple sequence alignment feature of the CLC workbench. From the highly conserved residues of E1, short peptides of 8–25 amino acids were designed  were selected as they are reported to be the most prevalent in Pakistan. Analyzing these sequences could help in development of inhibitory compounds with the potential not only to reduce viral titers but also contribute to reducing the threat of HCV in the Pakistani population. Moreover, phylogenetic analysis was conducted to find the evolutionary relationship between the HCV E1 and E2 genotype 3a of Pakistani origin and E1 and E2 sequences of genotypes 3a and 1a. In the case of E1 protein, genotype 1a appeared at the root of the tree and it can be inferred that genotype 3a has evolved from it. In the case of E2 protein, genotype 3a appeared at the root of the tree and it can be inferred that genotype 1a has evolved from it. The HCV envelope proteins (E1/E2) in Pakistan are highly evolutionarily related to 1a and 3a from other countries.Genotype 3a is reported as the most prevalent genotype in Pakistan except in Balochistan where the most prevalent genotype is 1a. This study was designed to identify potentially conserved peptides in the two hypervariable proteins, E1 and E2, of HCV. From the conservation analysis, highly conserved and variable regions were revealed and from the highly conserved regions peptides were designed that could serve as useful targets for developing novel inhibitory compounds against both 3a and 1a genotypes, potentially protecting the Pakistani population against the threat of HCV infection.A total of 150 HCV E1 and E2 sequences belonging to genotypes 3a and 1a were retrieved from the NCBI protein database. Consensus sequences of E1 and E2 proteins for each genotype were constructed using the multiple sequence analysis feature of the CLC workbench. These consensus sequences were further used to construct global consensus sequences for E1 and E2.The consensus sequences of E1 belonging to genotypes 3a and 1a were aligned to identify the global consensus sequence of E1 in Pakistan and other countries. A global consensus sequence for E2 protein was found using the same strategy. These global consensus sequences could help in designing effective conserved peptides against genotypes 3a and 1a. Short peptide sequences were derived from the highly conserved regions of HCV E1 and E2. As the HCV glycoproteins are highly variable and are required for viral entry, it is important to identify conserved peptides that could serve as the best targets for potential inhibitors and vaccines. In designing peptides for successful antibody production, many factors were considered. These included amino acid composition and peptide length, which can influence correct assembly, purification, and subsequent solubilization. (1) The peptide length criterion was set at 8–25. Typically, a 10–15 residue length is used for raising antisera. (2) The hydrophobic amino acid content  was not allowed to exceed 50% in the designed peptide. Conserved peptides were designed from the global consensus sequence while considering all these factors.HCV E1 and E2 sequences from all over the world were analyzed to establish their evolutionary relationships using the CLC workbench. The phylogenetic analysis feature of the workbench was used to construct phylogenetic trees for E1 and E2 proteins using the neighbor-joining method with a bootstrap value of 100.In future, we aim to analyze these peptides further to indicate methods for blocking HCV entry.The authors declare that they have no competing interests.UAA designed the study, SI performed the sequence analysis, NI helped in manuscript writing. All authors read and approved the final manuscript.


Structure comparison tools can be used to align related protein structures to identify structurally conserved and variable regions and to infer functional and evolutionary relationships. While the conserved regions often superimpose well, the variable regions appear non superimposable. Differences in homologous protein structures are thought to be due to evolutionary plasticity to accommodate diverged sequences during evolution. One of the kinds of differences between 3-D structures of homologous proteins is rigid body displacement. A glaring example is not well superimposed equivalent regions of homologous proteins corresponding to α-helical conformation with different spatial orientations. In a rigid body superimposition, these regions would appear variable although they may contain local similarity. Also, due to high spatial deviation in the variable region, one-to-one correspondence at the residue level cannot be determined accurately. Another kind of difference is conformational variability and the most common example is topologically equivalent loops of two homologues but with different conformations. In the current study, we present a refined view of the “structurally variable” regions which may contain local similarity obscured in global alignment of homologous protein structures. As structural alphabet is able to describe local structures of proteins precisely through Protein Blocks approach, conformational similarity has been identified in a substantial number of ‘variable’ regions in a large data set of protein structural alignments; optimal residue-residue equivalences could be achieved on the basis of Protein Blocks which led to improved local alignments. Also, through an example, we have demonstrated how the additional information on local backbone structures through protein blocks can aid in comparative modeling of a loop region. In addition, understanding on sequence-structure relationships can be enhanced through our approach. This has been illustrated through examples where the equivalent regions in homologous protein structures share sequence similarity to varied extent but do not preserve local structure. Comparison of protein structures is an indispensable step in understanding structure-function relationships. In most cases, first reasonable impressions on function of a protein can be generated if the protein shares high structural similarity to a protein of known function i.e., they allow a complete representation of protein fold space. Hence, for a newly determined protein structure, mining the structural databases enables the identification of protein structures/sub-structures similar to the given structure The 3-D superimposition of protein structures obtained by using structure comparison tools is very useful in quantifying structural dissimilarity and in analyzing structural divergence. Structural comparison influences classification of proteins into protein families, superfamilies etc e.g., DALI i.e., atoms, residues or secondary structures), and compute a similarity measure. They differ at the level of (i) representation of protein structure , (ii) measure of similarity and (iii) the algorithm used for comparison,  regions in the alignments of three-dimensional structures of homologous protein domains in PALI database The current study pertains to those backbone regions of homologous proteins that are not well superimposed in the rigid body superposition. These structural differences between homologues can be categorized into rigid body displacements and conformational variations. However due to rigid body displacements, an optimal superposition may not be obtained using a rigid body superposition method. Through Protein Blocks a to p, which approximate the local protein structure with an average root mean square deviation of 0.42 Å. Protein Blocks have been used in comparison of protein structures 2+ binding sites in proteins For this work, Protein Blocks (PBs) Superimposed proteins from PALI database have regions of correspondence that exhibit high structural deviation, namely “Structurally Variable Regions” (SVRs). These regions may appear “structurally variable” (not well superimposed) in a global context but may exhibit local conformational similarity. For example an α-helical region in a protein might correspond to an α-helical region in the homologue; however if the helical regions in the two proteins are in slightly different orientations they may not appear superimposed if the two structures are superimposed as a whole. Using PB Substitution Matrix (SM) coupled with CLUSTALW t test, see i.e., an increase in number by 14% . Thus, through our approach we have been able to identify local structural similarity in a substantial number of SVRs which was not known from classical approach.Analysis of the distribution of SCA and SAP allowed us to define a cutoff score of −0.42 to distinguish SVRs as conformationally similar and dissimilar see . The cuti.e., number of PB aligned with another PB and not a gap), and/or a change in the nature of PBs aligned at various positions in the alignment. 76% of SVRs showed no change in the raw number of correspondences after re-alignment. On an average, for each segment, 0.5 more PB is aligned with a PB in aSVRs as compared to bSVRs  is increased for each PB. Among the non-identical substitutions, the highest increase has been observed for the alignments of PB f (C cap β strand), PB k and l (loop to N cap α helix). A lower increase was observed in the alignments of PB a and c (N cap β strand), PB d (β strand), PB m (α helix), PBs n to p (C cap α helix) and PB h (loop). The alignments of PB m with each PB types except itself shows a drop, the highest decrease being in the alignments with PBs d, f, k, l and n. A lower decrease was observed for the alignment of PB a and c with PBs corresponding to loops, PB d with PBs corresponding to loops and capping regions of α helix, PB f (C cap β strand) with PBs corresponding to N cap β strand and loops, PB k with PBs corresponding to N and C caps of β strand and PBs l with PBs c, e, k and p. In general, this decrease concerns unrelated or dissimilar PBs and the increase is mainly observed in highly similar or identical PBs. The increase in the number of equivalences for PBs corresponding to N cap and C cap regions of helices and strands as seen in An improvement of scores is observed after re-alignment. This increase is due to a higher number of PB-PB equivalences  Local structural similarity could be identified in terms of PB sequence similarity. We have also analyzed it by comparing SDM of bSVR and aSVR alignments. Profit software With a global rigid body protein structure superposition, regions corresponding high deviations usually correspond to regions (i) that are spatially displaced although being structurally similar or (ii) with genuine difference in local conformation. Protein Blocks approach can distinguish these two scenarios. Indeed, a rigid body displacement of a local region after superposition would result in a high PB score and low SDM for the aligned regions. However, when the regions are conformationally distinct, the PB score would be low and SDM would be high.i.e., aSVRs and bSVRs.k, l, m, n, o, p and a are aligned in aSVR thus identifying local structural similarity previously unknown. Similarly, a β-strand oriented differently in the homologue could be aligned with a lower SDM using PB approach as shown in the f, k, l and series of m in the two sequences in register. As mentioned in the previous section, insertions and deletions in the middle of a helix or a strand are tolerated to a lesser extent as compared to the rest of the structure. Through our approach, gaps in the middle of a helix or a strand have been reduced/eliminated. a, c and d correspond to a small strand with a transition to coil-like region denoted by PBs k and l in the variable segment of CD4 glycoprotein  with PBs associated to strands and capping regions  as illustrated in The results on the assessment of approach in terms of SDM and PB scores have been tabulated see . On an a32.7% of the alignments showed no difference in PB scores and SDM. The plausible reasons are the already existing optimal equivalences in SVRs which could not be improved further using PB approach and/or the regions that are aligned are conformationally different. Equivalences were preserved in majority of aSVRs. One quarter of these alignments correspond to conformationally different segments  than bSVRs. 25.5% of these SVRs correspond to conformationally dissimilar regions based on the cutoff previously determined; hence such regions cannot be superimposed well. In general, changes in PB-PB equivalences were observed due to re-distribution of gaps which improved the scores; however this increase is not reflected in SDM values. The short stretches of local similarity in segments of overall different conformations led to an increase in the PB scores but with a slight increase in SDM values due to poor similarity in the remaining segment presenting complex cases of superposition. This has been explained though an example illustrated in k and l at the C terminal end). The rearrangement of PB equivalences in the remaining region decreases the score.In contrast to the above scenario, 5.6% of the aSVRs have a lower PB scores but improved SDM values. 42.02% of these segments aligned are conformationally distinct. In the remaining cases, a redistribution of gaps led to different equivalences. Here, the mean difference in SDM is −16.77 for conformationally similar segments (SAP >−0.42). Small regions of similarity are preserved while the rest of alignment undergoes a change in equivalences. In certain cases, this results in an improvement of overall superposition but a decrease in PB scores. An example is presented . The regFor 14 cases (0.023%) of alignments, no differences in SDM values were observed but a difference in scores for aligned PBs was found. For 12 out of 14 cases the PB score improved and for 2 cases PB score did not improve. 21.43% of these segments exhibit conformational dissimilarity. The mean difference in scores for the remaining segments corresponds to 0.52. The change in scores indicates change in PB-PB equivalences. In two-thirds of the cases, number of equivalences before and after realignment remains same without a change in overall atomic superposition. In the remaining one-thirds, the number of equivalent PBs  has changed without changing the SDM values.i.e., 3.2% of SVRs, no difference in PB scores were observed, but a change in SDM was seen. It is a consequence of new equivalences without a change in the nature of PBs aligned, which improved the SDM in 1.7% of these SVRs but did not improve in the rest 1.5% of SVRs. Finally, 7.7% of the aSVRs showed a decrease both in PB scores as well as higher deviation at Cα positions. It is mainly due to repeats of PBs which leads to the possibility of alternate alignments analogous to alignment of low complexity regions in amino acid sequences. The local similarity is observed at the ends of the alignment. PBs at the end come close while eliminating the gap which results in poor SDM and poor score . rmsd (1.98 Å) when superposed on the crystal structure as compared to the model generated using the original approach (rmsd: 3.50 Å).Having known that loop modeling is non-trivial and is most accurate when the equivalent regions are obtained from homologues, we have explored the use of information on local conformation through representation of templates as Protein Blocks in obtaining clues on comparative modeling. This has been exemplified through modeling exercise of a segment of Alpha-l-arabinofuranosidase protein  belonging to SCOP family c.37.1.8. The regions, encircled in Sequences of homologous proteins may evolve and diverge beyond recognition by simple homology searches. Usually, the extent of difference exhibited by sequences is higher compared to structures. In this section we show how the current analysis of consideration of PB-based alignment of SVRs can be taken to the next level of understanding of sequence-structure relationships. Here we present two examples where the local structures are very different in the pairs of homologous protein structures. In the example of elongation factors shown in In the current work, we have presented a refined view of the regions of homologous protein structures that exhibit apparent high deviation on global structural superposition. When the deviation is high, the equivalences assigned through atomic superimposition are inaccurate. Through representation of protein structures as PB sequence, conformational similarity could be identified for 159,780 (74%) variable segments, based on PB scores, an increase by 21%, compared to a classical structural alignment approach in the database of structurally aligned homologous protein structures. The improvement was also reflected in the lower SDM in 3D superposition based on new equivalences after re-alignment of SVRs. The equivalences could be refined for the capping regions of helices and strands and loops. Regions of high similarity could be located in homologous pairs of protein structures even when the aligned regions were of different lengths. Also segments which were spatially displaced could be identified and aligned efficiently. All these cases have been explained through appropriate examples. For the cases where the approach does not perform as well, the best  alignment can be chosen based on the global context in the protein structures following the principles governing protein structure; for example, regions flanking the variable segment could be considered. The best alignment could be the one with continuous helix or strand uninterrupted by gaps in the alignment. The approach can be used in identifying equivalent regions in homologous structures that do not share structural similarity and in the understanding of sequence-structure relationship. It can aid in providing clues to model loops for which homologue of similar length is unavailable. The approach can be extended to understand the effect of amino acid substitutions on the local structural alterations in the homologous protein structures. As the approach is quite general, it can be used in conjunction with any structural alignment algorithm.With an improvement in structural alignments which are central in understanding of protein structure-function and evolutionary relationships, the applications of the approach are manifold. The approach can be extended to refine regions of high deviation obtained using simultaneous superposition of multiple protein structures. The method can be improved by using gap penalties specific to PB types with respect to major secondary structures. In the near future, we propose to develop a web server based on our refinement approach. This comprehensive data set on homologous structures would serve as a valuable resource to study the extent and nature of alterations/structural rearrangements in backbone conformation of homologous structures as a consequence of substitutions (conservative as well as non conservative) and indels during the course of evolution.The protein data set was obtained from PALI a to p  now evaluated at 0.42 Å http://bioinformatics.univ-reunion.fr/PBE/) Protein Blocks (PBs) correspond to a set of 16 local prototypes, labeled from to p see , of 5 reet al., submitted). In this work, we have used the refined PB SM coupled with classical CLUSTALW approach To re-align SVRs in quest of improvement of alignments, we have adapted our previous approach To evaluate the quality of new alignments of SVRs over the previous alignments, scores were computed for both alignments. Two scores were calculated for each alignment, based on inclusion or exclusion of gaps in the alignment. Calculation of these scores would reflect the differences in two alignments of a pair of segments in terms of the substitution of PB at an alignment position as well as the lengths of the alignment. The aligned PB positions were scored based on the values from PB SM. Summation of these values was normalized by the number of PB pairs to compute the Scores for Aligned Pairs (SAP) for an alignment. To calculate scores for complete alignment (SCA), including gaps, every alignment position with a gap was given a score of -3. The scores were normalized by the length of the alignment.rmsd values. The SVRs corresponding to N and C termini were removed from the analysis. 67,234 SVRs were considered for this analysis. Rmsds for the remaining SVRs were converted into structural distance metric (SDM) as proposed by Blundell and coworkers i.e., in RMS calculation instead of dividing rmsd by 3.0 we are dividing rmsd by the highest rmsd (24.97) from all the alignments for SVRs to get the values of RMS in the range of 0 to 1.To assess the improvement of alignments using our approach, SDM of SVR before and after re-alignment were compared. PROFIT software Figure S1The distribution of scores for bSVRs (A and C) and aSVRs (B and D). (A) and (B) show the distribution of scores for bSVRs and aSVRs respectively, calculated by considering only the aligned PBs (SAP scores). (C) and (D) show the distribution of scores after including gaps in scoring (SCA) for bSVRs and aSVRs, respectively.(TIF)Click here for additional data file.Figure S2The variation of scores for aligned PBs (SAP) and scores for complete alignment (SCA) for bSVRs (A) and aSVRs (B).(TIF)Click here for additional data file.Figure S3Difference of PBs aligned before and after re-alignment. Positive values correspond to an improvement.(TIF)Click here for additional data file.Figure S4PB correspondences. A. Histogram of percentage of PB correspondences common in bSVRs and aSVRs. The plot depicts that about 30% of SVRs in the dataset share >95% of PB correspondences. B. Histogram of the percentage conservation of PB correspondences in bSVRs and aSVRs out of the common PB correspondences. The plot indicates that about 40% of SVRs exhibit very low and over 40% exhibit very high conservation of PB correspondences.(TIF)Click here for additional data file.Figure S5Distribution of gaps. A. The plot shows the difference in the percentage of gaps observed after re-alignment as compared to the percentage of gaps before re-alignment for a variable segment. B. The plot shows the distribution of difference in gap openings in the aSVRs as compared to the bSVRs.(TIF)Click here for additional data file.Figure S6Illustrative examples of superposition of SVRs before and after alignment using PBs. A: Reduced PB score and improved SDM B: Reduced PB scores and increased SDM.(TIF)Click here for additional data file.Text S1Comparison of the distribution of SAP and SCA scores in bSVRs and aSVRs.(DOC)Click here for additional data file.Text S2Correlation of SAP and SCA scores in bSVRs and aSVRs.(DOC)Click here for additional data file.