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

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

Assessing the reliability of sequence similarities detected through hydrophobic cluster analysis

Hydrophobic cluster analysis (HCA) has long been used as a tool to detect distant homologies between protein sequences, and to classify them into different folds. However, it relies on expert human intervention, and is sensitive to subjective interpretations of pattern similarities. In this study, we describe a novel algorithm to assess the similarity of hydrophobic amino acid distributions between two sequences. Our algorithm correctly identifies as misattributions several HCA-based proposals of structural similarity between unrelated proteins present in the literature. We have also used this method to identify the proper fold of a large variety of sequences, and to automatically select the most appropriate structure for homology modeling of several proteins with low sequence identity to any other member of the protein data bank. Automatic modeling of the target proteins based on these templates yielded structures with TM-scores (vs. experimental structures) above 0.60, even without further refinement. Besides enabling a reliable identification of the correct fold of an unknown sequence and the choice of suitable templates, our algorithm also shows that whereas most structural classes of proteins are very homogeneous in hydrophobic cluster composition, a tenth of the described families are compatible with a large variety of hydrophobic patterns. We have built a browsable database of every major representative hydrophobic cluster pattern present in each structural class of proteins, freely available at http://www2.ufp.pt/ pedros/HCA_db/index.htm.     

Multiple protein sequence alignment from tertiary structure comparison: assignment of global and residue confidence levels.

An algorithm is presented for the accurate and rapid generation of multiple protein sequence alignments from tertiary structure comparisons. A preliminary multiple sequence alignment is performed using sequence information, which then determines an initial superposition of the structures. A structure comparison algorithm is applied to all pairs of proteins in the superimposed set and a similarity tree calculated. Multiple sequence alignments are then generated by following the tree from the branches to the root. At each branchpoint of the tree, a structure-based sequence alignment and coordinate transformations are output, with the multiple alignment of all structures output at the root. The algorithm encoded in STAMP (STructural Alignment of Multiple Proteins) is shown to give alignments in good agreement with published structural accounts within the dehydrogenase fold domains, globins, and serine proteinases. In order to reduce the need for visual verification, two similarity indices are introduced to determine the quality of each generated structural alignment. Sc quantifies the global structural similarity between pairs or groups of proteins, whereas Pij' provides a normalized measure of the confidence in the alignment of each residue. STAMP alignments have the quality of each alignment characterized by Sc and Pij' values and thus provide a reproducible resource for studies of residue conservation within structural motifs.     

A rapid protein structure alignment algorithm based on a text modeling technique

Structural alignment of proteins is widely used in various fields of structural biology. In order to further improve the quality of alignment, we describe an algorithm for structural alignment based on text modelling techniques. The technique firstly superimposes secondary structure elements of two proteins and then, models the 3D-structure of the protein in a sequence of alphabets. These sequences are utilized by a step-by-step sequence alignment procedure to align two protein structures. A benchmark test was organized on a set of 200 non-homologous proteins to evaluate the program and compare it to state of the art programs, e.g. CE, SAL, TM-align and 3D-BLAST. On average, the results of all-against-all structure comparison by the program have a competitive accuracy with CE and TM-align where the algorithm has a high running speed like 3D-BLAST.     

Selection of a representative set of structures from Brookhaven Protein Data Bank.

Reliable structural and statistical analyses of three dimensional protein structures should be based on unbiased data. The Protein Data Bank is highly redundant, containing several entries for identical or very similar sequences. A technique was developed for clustering the known structures based on their sequences and contents of alpha- and beta-structures. First, sequences were aligned pairwise. A representative sample of sequences was then obtained by grouping similar sequences together, and selecting a typical representative from each group. The similarity significance threshold needed in the clustering method was found by analyzing similarities of random sequences. Because three dimensional structures for proteins of same structural class are generally more conserved than their sequences, the proteins were clustered also according to their contents of secondary structural elements. The results of these clusterings indicate conservation of alpha- and beta-structures even when sequence similarity is relatively low. An unbiased sample of 103 high resolution structures, representing a wide variety of proteins, was chosen based on the suggestions made by the clustering algorithm. The proteins were divided into structural classes according to their contents and ratios of secondary structural elements. Previous classifications have suffered from subjective view of secondary structures, whereas here the classification was based on backbone geometry. The concise view lead to reclassification of some structures. The representative set of structures facilitates unbiased analyses of relationships between protein sequence, function, and structure as well as of structural characteristics.     

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.     

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

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

Detection of common three-dimensional substructures in proteins.

We present a fully automatic algorithm for three-dimensional alignment of protein structures and for the detection of common substructures and structural repeats. Given two proteins, the algorithm first identifies all pairs of structurally similar fragments and subsequently clusters into larger units pairs of fragments that are compatible in three dimensions. The detection of similar substructures is independent of insertion/deletion penalties and can be chosen to be independent of the topology of loop connections and to allow for reversal of chain direction. Using distance geometry filters and other approximations, the algorithm, implemented in the WHAT IF program, is so fast that structural comparison of a single protein with the entire database of known protein structures can be performed routinely on a workstation. The method reproduces known non-trivial superpositions such as plastocyanin on azurin. In addition, we report surprising structural similarity between ubiquitin and a (2Fe-2S) ferredoxin.     

Database of homology-derived protein structures and the structural meaning of sequence alignment

The database of known protein three-dimensional structures can be significantly increased by the use of sequence homology, based on the following observations. (1) The database of known sequences, currently at more than 12,000 proteins, is two orders of magnitude larger than the database of known structures. (2) The currently most powerful method of predicting protein structures is model building by homology. (3) Structural homology can be inferred from the level of sequence similarity. (4) The threshold of sequence similarity sufficient for structural homology depends strongly on the length of the alignment. Here, we first quantify the relation between sequence similarity, structure similarity, and alignment length by an exhaustive survey of alignments between proteins of known structure and report a homology threshold curve as a function of alignment length. We then produce a database of homology-derived secondary structure of proteins (HSSP) by aligning to each protein of known structure all sequences deemed homologous on the basis of the threshold curve. For each known protein structure, the derived database contains the aligned sequences, secondary structure, sequence variability, and sequence profile. Tertiary structures of the aligned sequences are implied, but not modeled explicitly. The database effectively increases the number of known protein structures by a factor of five to more than 1800. The results may be useful in assessing the structural significance of matches in sequence database searches, in deriving preferences and patterns for structure prediction, in elucidating the structural role of conserved residues, and in modeling three-dimensional detail by homology.     

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

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

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

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

Structural Alphabets for Protein Structure Classification: A Comparison Study

Finding structural similarities between proteins often helps reveal shared functionality, which otherwise might not be detected by native sequence information alone. Such similarity is usually detected and quantified by protein structure alignment. Determining the optimal alignment between two protein structures, however, remains a hard problem. An alternative approach is to approximate each three-dimensional protein structure using a sequence of motifs derived from a structural alphabet. Using this approach, structure comparison is performed by comparing the corresponding motif sequences or structural sequences. In this article, we measure the performance of such alphabets in the context of the protein structure classification problem. We consider both local and global structural sequences. Each letter of a local structural sequence corresponds to the best matching fragment to the corresponding local segment of the protein structure. The global structural sequence is designed to generate the best possible complete chain that matches the full protein structure. We use an alphabet of 20 letters, corresponding to a library of 20 motifs or protein fragments having four residues. We show that the global structural sequences approximate well the native structures of proteins, with an average coordinate root mean square of 0.69 Å over 2225 test proteins. The approximation is best for all α-proteins, while relatively poorer for all β-proteins. We then test the performance of four different sequence representations of proteins (their native sequence, the sequence of their secondary-structure elements, and the local and global structural sequences based on our fragment library) with different classifiers in their ability to classify proteins that belong to five distinct folds of CATH. Without surprise, the primary sequence alone performs poorly as a structure classifier. We show that addition of either secondary-structure information or local information from the structural sequence considerably improves the classification accuracy. The two fragment-based sequences perform better than the secondary-structure sequence but not well enough at this stage to be a viable alternative to more computationally intensive methods based on protein structure alignment.     

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

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

Sequence and structure conservation in a protein core

In order to study structural aspects of sequence conservation in families of homologous proteins, we have analyzed structurally aligned sequences of 585 proteins grouped into 128 homologous families. The conservation of a residue in a family is defined as the average residue similarity in a given position of aligned sequences. The residue similarities were expressed in the form of log-odd substitution tables that take into account the environments of amino acids in three-dimensional structures. The protein core is defined as those residues that have less then 7% solvent accessibility. The density of a protein core is described in terms of atom packing, which is investigated as a criterion for residue substitution and conservation. Although there is no significant correlation between sequence conservation and average atom packing around nonpolar residues such as leucine, valine and isoleucine, a significant correlation is observed for polar residues in the protein core. This may be explained by the hydrogen bonds in which polar residues are involved; the better their protection from water access the more stable should be the structure in that position. Proteins 33:358–366, 1998. © 1998 Wiley-Liss, Inc.     

Accurate domain identification with structure‐anchored hidden Markov models,saHMMs

The ever increasing speed of DNA sequencing widens the discrepancy between the number of known gene products, and the knowledge of their function and structure. Proper annotation of protein sequences is therefore crucial if the missing information is to be deduced from sequence‐based similarity comparisons. These comparisons become exceedingly difficult as the pairwise identities drop to very low values. To improve the accuracy of domain identification, we exploit the fact that the three‐dimensional structures of domains are much more conserved than their sequences. Based on structure‐anchored multiple sequence alignments of low identity homologues we constructed 850 structure‐anchored hidden Markov models (saHMMs), each representing one domain family. Since the saHMMs are highly family specific, they can be used to assign a domain to its correct family and clearly distinguish it from domains belonging to other families, even within the same superfamily. This task is not trivial and becomes particularly difficult if the unknown domain is distantly related to the rest of the domain sequences within the family. In a search with full length protein sequences, harbouring at least one domain as defined by the structural classification of proteins database (SCOP), version 1.71, versus the saHMM database based on SCOP version 1.69, we achieve an accuracy of 99.0%. All of the few hits outside the family fall within the correct superfamily. Compared to Pfam_ls HMMs, the saHMMs obtain about 11% higher coverage. A comparison with BLAST and PSI‐BLAST demonstrates that the saHMMs have consistently fewer errors per query at a given coverage. Within our recommended E‐value range, the same is true for a comparison with SUPERFAMILY. Furthermore, we are able to annotate 232 proteins with 530 nonoverlapping domains belonging to 102 different domain families among human proteins labelled “unknown” in the NCBI protein database. Our results demonstrate that the saHMM database represents a versatile and reliable tool for identification of domains in protein sequences. With the aid of saHMMs, homology on the family level can be assigned, even for distantly related sequences. Due to the construction of the saHMMs, the hits they provide are always associated with high quality crystal structures. The saHMM database can be accessed via the FISH server at http://babel.ucmp.umu.se/fish/ . Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Angle-distance image matching techniques for protein structure comparison

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

Protein structure mining using a structural alphabet

We present a comprehensive evaluation of a new structure mining method called PB-ALIGN. It is based on the encoding of protein structure as 1D sequence of a combination of 16 short structural motifs or protein blocks (PBs). PBs are short motifs capable of representing most of the local structural features of a protein backbone. Using derived PB substitution matrix and simple dynamic programming algorithm, PB sequences are aligned the same way amino acid sequences to yield structure alignment. PBs are short motifs capable of representing most of the local structural features of a protein backbone. Alignment of these local features as sequence of symbols enables fast detection of structural similarities between two proteins. Ability of the method to characterize and align regions beyond regular secondary structures, for example, N and C caps of helix and loops connecting regular structures, puts it a step ahead of existing methods, which strongly rely on secondary structure elements. PB-ALIGN achieved efficiency of 85% in extracting true fold from a large database of 7259 SCOP domains and was successful in 82% cases to identify true super-family members. On comparison to 13 existing structure comparison/mining methods, PB-ALIGN emerged as the best on general ability test dataset and was at par with methods like YAKUSA and CE on nontrivial test dataset. Furthermore, the proposed method performed well when compared to flexible structure alignment method like FATCAT and outperforms in processing speed (less than 45 s per database scan). This work also establishes a reliable cut-off value for the demarcation of similar folds. It finally shows that global alignment scores of unrelated structures using PBs follow an extreme value distribution. PB-ALIGN is freely available on web server called Protein Block Expert (PBE) at http://bioinformatics.univ-reunion.fr/PBE/.     

Relationship between multiple sequence alignments and quality of protein comparative models

Comparative modeling is the method of choice, whenever applicable, for protein structure prediction, not only because of its higher accuracy compared to alternative methods, but also because it is possible to estimate a priori the quality of the models that it can produce, thereby allowing the usefulness of a model for a given application to be assessed beforehand. By and large, the quality of a comparative model depends on two factors: the extent of structural divergence between the target and the template and the quality of the sequence alignment between the two protein sequences. The latter is usually derived from a multiple sequence alignment (MSA) of as many proteins of the family as possible, and its accuracy depends on the number and similarity distribution of the sequences of the protein family. Here we describe a method to evaluate the expected difficulty, and by extension accuracy, of a comparative model on the basis of the MSA used to build it. The parameter that we derive is used to compare the results obtained in the last two editions of the Critical Assessment of Methods for Structure Prediction (CASP) experiment as a function of the difficulty of the modeling exercise. Our analysis demonstrates that the improvement in the scope and quality of comparative models between the two experiments is largely due to the increased number of available protein sequences and to the consequent increased chance that a large and appropriately spaced set of protein sequences homologous to the proteins of interest is available.     

A database of protein structure families with common folding motifs.

The availability of fast and robust algorithms for protein structure comparison provides an opportunity to produce a database of three-dimensional comparisons, called families of structurally similar proteins (FSSP). The database currently contains an extended structural family for each of 154 representative (below 30% sequence identity) protein chains. Each data set contains: the search structure; all its relatives with 70-30% sequence identity, aligned structurally; and all other proteins from the representative set that contain substructures significantly similar to the search structure. Very close relatives (above 70% sequence identity) rarely have significant structural differences and are excluded. The alignments of remote relatives are the result of pairwise all-against-all structural comparisons in the set of 154 representative protein chains. The comparisons were carried out with each of three novel automatic algorithms that cover different aspects of protein structure similarity. The user of the database has the choice between strict rigid-body comparisons and comparisons that take into account interdomain motion or geometrical distortions; and, between comparisons that require strictly sequential ordering of segments and comparisons, which allow altered topology of loop connections or chain reversals. The data sets report the structurally equivalent residues in the form of a multiple alignment and as a list of matching fragments to facilitate inspection by three-dimensional graphics. If substructures are ignored, the result is a database of structure alignments of full-length proteins, including those in the twilight zone of sequence similarity.(ABSTRACT TRUNCATED AT 250 WORDS)