Protein Structure Alignment Research Articles

Protein structure alignment beyond spatial proximity

Protein structure alignment is a fundamental problem in computational structure biology. Many programs have been developed for automatic protein structure alignment, but most of them align two protein structures purely based upon geometric similarity without considering evolutionary and functional relationship. As such, these programs may generate structure alignments which are not very biologically meaningful from the evolutionary perspective. This paper presents a novel method DeepAlign for automatic pairwise protein structure alignment. DeepAlign aligns two protein structures using not only spatial proximity of equivalent residues (after rigid-body superposition), but also evolutionary relationship and hydrogen-bonding similarity. Experimental results show that DeepAlign can generate structure alignments much more consistent with manually-curated alignments than other automatic tools especially when proteins under consideration are remote homologs. These results imply that in addition to geometric similarity, evolutionary information and hydrogen-bonding similarity are essential to aligning two protein structures.

DoSA: Database of Structural Alignments

Protein structure alignment is a crucial step in protein structure–function analysis. Despite the advances in protein structure alignment algorithms, some of the local conformationally similar regions are mislabeled as structurally variable regions (SVRs). These regions are not well superimposed because of differences in their spatial orientations. The Database of Structural Alignments (DoSA) addresses this gap in identification of local structural similarities obscured in global protein structural alignments by realigning SVRs using an algorithm based on protein blocks. A set of protein blocks is a structural alphabet that abstracts protein structures into 16 unique local structural motifs. DoSA provides unique information about 159 780 conformationally similar and 56 140 conformationally dissimilar SVRs in 74 705 pairwise structural alignments of homologous proteins. The information provided on conformationally similar and dissimilar SVRs can be helpful to model loop regions. It is also conceivable that conformationally similar SVRs with conserved residues could potentially contribute toward functional integrity of homologues, and hence identifying such SVRs could be helpful in understanding the structural basis of protein function.Database URL: http://bo-protscience.fr/dosa/

CORE: Common Region Extension Based Multiple Protein Structure Alignment for Producing Multiple Solution

Over the past several decades, biologists have conducted numerous studies examining both general and specific functions of proteins. Generally, if similarities in either the structure or sequence of amino acids exist for two proteins, then a common biological function is expected. Protein function is determined primarily based on the structure rather than the sequence of amino acids. The algorithm for protein structure alignment is an essential tool for the research. The quality of the algorithm depends on the quality of the similarity measure that is used, and the similarity measure is an objective function used to determine the best alignment. However, none of existing similarity measures became golden standard because of their individual strength and weakness. They require excessive filtering to find a single alignment. In this paper, we introduce a new strategy that finds not a single alignment, but multiple alignments with different lengths. This method has obvious benefits of high quality alignment. However, this novel method leads to a new problem that the running time for this method is considerably longer than that for methods that find only a single alignment. To address this problem, we propose algorithms that can locate a common region (CORE) of multiple alignment candidates, and can then extend the CORE into multiple alignments. Because the CORE can be defined from a final alignment, we introduce CORE* that is similar to CORE and propose an algorithm to identify the CORE*. By adopting CORE* and dynamic programming, our proposed method produces multiple alignments of various lengths with higher accuracy than previous methods. In the experiments, the alignments identified by our algorithm are longer than those obtained by TM-align by 17 % and 15.48 %, on average, when the comparison is conducted at the level of super-family and fold, respectively.

Automatic comparison and classification of protein structures

The classification and alignment of multiple three-dimensional protein structures is a powerful way to detect similarities that cannot be discovered from the sequences alone and can help to infer phylogeny. However, the alignment process remains problematic for divergent structures. We have devised a fully automatic pipeline, HSF, drawing its inspiration from well-known structural alignment methods, which given a list of structures not only aligns all pairs but also classifies them fully. We demonstrate proof of principle for the new method by aligning the currently available set of highly diverged virus coat protein structures containing double β-barrels, as well as validating the method with established test sets for multiple structural alignments. The results for the virus proteins are inline with previous observations based on biochemical, genetic and structural studies but go further, since by providing coherent alignments between sets of molecules with marked structural distortion, they facilitate the marshaling of arguments for or against homology. The classification results can therefore be readily interpreted in terms of phylogeny.

MulPBA: an efficient multiple protein structure alignment method based on a structural alphabet

The increasing number of available protein structures requires efficient tools for multiple structure comparison. Indeed, multiple structural alignments are essential for the analysis of function, evolution and architecture of protein structures. For this purpose, we proposed a new web server called multiple Protein Block Alignment (mulPBA). This server implements a method based on a structural alphabet to describe the backbone conformation of a protein chain in terms of dihedral angles. This ‘sequence-like’ representation enables the use of powerful sequence alignment methods for primary structure comparison, followed by an iterative refinement of the structural superposition. This approach yields alignments superior to most of the rigid-body alignment methods and highly comparable with the flexible structure comparison approaches. We implement this method in a web server designed to do multiple structure superimpositions from a set of structures given by the user. Outputs are given as both sequence alignment and superposed 3D structures visualized directly by static images generated by PyMol or through a Jmol applet allowing dynamic interaction. Multiple global quality measures are given. Relatedness between structures is indicated by a distance dendogram. Superimposed structures in PDB format can be also downloaded, and the results are quickly obtained. mulPBA server can be accessed at www.dsimb.inserm.fr/dsimb_tools/mulpba/.

RNA global alignment in the joint sequence–structure space using elastic shape analysis

The functions of RNAs, like proteins, are determined by their structures, which, in turn, are determined by their sequences. Comparison/alignment of RNA molecules provides an effective means to predict their functions and understand their evolutionary relationships. For RNA sequence alignment, most methods developed for protein and DNA sequence alignment can be directly applied. RNA 3-dimensional structure alignment, on the other hand, tends to be more difficult than protein structure alignment due to the lack of regular secondary structures as observed in proteins. Most of the existing RNA 3D structure alignment methods use only the backbone geometry and ignore the sequence information. Using both the sequence and backbone geometry information in RNA alignment may not only produce more accurate classification, but also deepen our understanding of the sequence–structure–function relationship of RNA molecules. In this study, we developed a new RNA alignment method based on elastic shape analysis (ESA). ESA treats RNA structures as three dimensional curves with sequence information encoded on additional dimensions so that the alignment can be performed in the joint sequence–structure space. The similarity between two RNA molecules is quantified by a formal distance, geodesic distance. Based on ESA, a rigorous mathematical framework can be built for RNA structure comparison. Means and covariances of full structures can be defined and computed, and probability distributions on spaces of such structures can be constructed for a group of RNAs. Our method was further applied to predict functions of RNA molecules and showed superior performance compared with previous methods when tested on benchmark datasets. The programs are available at http://stat.fsu.edu/ ∼jinfeng/ESA.html.

Protein structure alignment beyond spatial proximity

Protein structure alignment is a fundamental problem in computational structure biology. Many programs have been developed for automatic protein structure alignment, but most of them align two protein structures purely based upon geometric similarity without considering evolutionary and functional relationship. As such, these programs may generate structure alignments which are not very biologically meaningful from the evolutionary perspective. This paper presents a novel method DeepAlign for automatic pairwise protein structure alignment. DeepAlign aligns two protein structures using not only spatial proximity of equivalent residues (after rigid-body superposition), but also evolutionary relationship and hydrogen-bonding similarity. Experimental results show that DeepAlign can generate structure alignments much more consistent with manually-curated alignments than other automatic tools especially when proteins under consideration are remote homologs. These results imply that in addition to geometric similarity, evolutionary information and hydrogen-bonding similarity are essential to aligning two protein structures.

MICAN : a protein structure alignment algorithm that can handle Multiple-chains, Inverse alignments, C α only models, Alternative alignments, and Non-sequential alignments

BackgroundProtein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed.ResultsWe have developed a novel protein structure alignment algorithm, MICAN (a structure alignment algorithm that can handle Multiple-chain complexes, Inverse direction of secondary structures, Cα only models, Alternative alignments, and Non-sequential alignments). The algorithm was designed so as to identify the best structural alignment between protein pairs by disregarding the connectivity between secondary structure elements (SSE). One of the key feature of the algorithm is utilizing the multiple vector representation for each SSE, which enables us to correctly treat bent or twisted nature of long SSE. We compared MICAN with other 9 publicly available structure alignment programs, using both reference-dependent and reference-independent evaluation methods on a variety of benchmark test sets which include both sequential and non-sequential alignments. We show that MICAN outperforms the other existing methods for reproducing reference alignments of non-sequential test sets. Further, although MICAN does not specialize in sequential structure alignment, it showed the top level performance on the sequential test sets. We also show that MICAN program is the fastest non-sequential structure alignment program among all the programs we examined here.ConclusionsMICAN is the fastest and the most accurate program among non-sequential alignment programs we examined here. These results suggest that MICAN is a highly effective tool for automatically detecting non-trivial structural relationships of proteins, such as circular permutations and segment-swapping, many of which have been identified manually by human experts so far. The source code of MICAN is freely download-able at http://www.tbp.cse.nagoya-u.ac.jp/MICAN.

The difficulty of protein structure alignment under the RMSD

BackgroundProtein structure alignment is often modeled as the largest common point set (LCP) problem based on the Root Mean Square Deviation (RMSD), a measure commonly used to evaluate structural similarity. In the problem, each residue is represented by the coordinate of the Cαatom, and a structure is modeled as a sequence of 3D points. Out of two such sequences, one is to find two equal-sized subsequences of the maximum length, and a bijection between the points of the subsequences which gives an RMSD within a given threshold. The problem is considered to be difficult in terms of time complexity, but the reasons for its difficulty is not well-understood. Improving this time complexity is considered important in protein structure prediction and structural comparison, where the task of comparing very numerous structures is commonly encountered.ResultsTo study why the LCP problem is difficult, we define a natural variant of the problem, called the minimum aligned distance (MAD). In the MAD problem, the length of the subsequences to obtain is specified in the input; and instead of fulfilling a threshold, the RMSD between the points of the two subsequences is to be minimized. Our results show that the difficulty of the two problems does not lie solely in the combinatorial complexity of finding the optimal subsequences, or in the task of superimposing the structures. By placing a limit on the distance between consecutive points, and assuming that the points are specified as integral values, we show that both problems are equally difficult, in the sense that they are reducible to each other. In this case, both problems can be exactly solved in polynomial time, although the time complexity remains high.ConclusionsWe showed insights and techniques which we hope will lead to practical algorithms for the LCP problem for protein structures. The study identified two important factors in the problem’s complexity: (1) The lack of a limit in the distance between the consecutive points of a structure; (2) The arbitrariness of the precision allowed in the input values. Both issues are of little practical concern for the purpose of protein structure alignment. When these factors are removed, the LCP problem is as hard as that of minimizing the RMSD (MAD problem), and can be solved exactly in polynomial time.

Implementation of a Parallel Protein Structure Alignment Service on Cloud

Protein structure alignment has become an important strategy by which to identify evolutionary relationships between protein sequences. Several alignment tools are currently available for online comparison of protein structures. In this paper, we propose a parallel protein structure alignment service based on the Hadoop distribution framework. This service includes a protein structure alignment algorithm, a refinement algorithm, and a MapReduce programming model. The refinement algorithm refines the result of alignment. To process vast numbers of protein structures in parallel, the alignment and refinement algorithms are implemented using MapReduce. We analyzed and compared the structure alignments produced by different methods using a dataset randomly selected from the PDB database. The experimental results verify that the proposed algorithm refines the resulting alignments more accurately than existing algorithms. Meanwhile, the computational performance of the proposed service is proportional to the number of processors used in our cloud platform.

Meta-analysis of Protein Structural Alignment

The three-dimensional structure of a protein molecule provides significant insight into its biological function. Structural alignment of proteins is an important and widely performed task in the analysis of protein structures, whereby functionally and evolutionarily important segments are identified. However, structural alignment is a computationally difficult problem and a large number of heuristics introduced to solve it do not agree on their results. Consequently, there is no widely accepted solution to the structure alignment problem. In this study, we present a meta-analysis approach to generate a re- optimized, best-of-all result using the alignments generated from several popular methods. Evaluations of the methods on a large set of benchmark pairwise alignments indicate that TMalign (Template Modeling Alignment) provides superior alignments (except for RMSD, root mean square deviation), compared to other methods we have surveyed. Smolign (Spatial Motifs Based Multiple Protein Structure Alignment) provides smaller cores than other methods with best RMSD values. The re-optimization of the alignments using TM-align’s optimization method does not alter the relative performance of the methods. Additionally, visualization approaches to delineate the relationships of the alignment methods have been performed and their results provided

Improved Algorithms for Matching r-Separated Sets with Applications to Protein Structure Alignment

The Largest Common Point-set (LCP) and the Pattern Matching (PM) problems have received much attention in the fields of pattern matching, computer vision and computational biology. Perhaps, the most important application of these problems is the protein structural alignment, which seeks to find a superposition of a pair of input proteins that maximizes a given protein structure similarity metric. Although it has been shown that LCP and PM are both tractable problems, the running times of existing algorithms are high-degree polynomials. Here, we present novel methods for finding approximate and exact threshold-LCP and threshold-PM for r-separated sets, in general, and protein 3D structures, in particular. Improved running times of our methods are achieved by building upon several different, previously published techniques.

Intelligent Informatics in Biomedicine

Intelligent Informatics in Biomedicine

Comparing Normal Modes of Protein Structures using Webnm@ 2.0

Normal modes analysis (NMA) has been shown to be an effective computational method to study the movements of proteins, especially at the domain level. WEBnm@ (http://apps.cbu.uib.no/webnma/home) is a web-tool which provides access to calculations of these modes on C-alpha atoms of protein structures and various analyses with output as images, plots or raw data points. We have improved the efficiency for input processing and have added new functionality that interprets the normal modes calculated. In the Single Analysis section, we have included an interactive visualisation of the lowest six mode vectors, the calculation of the correlation matrix based on all the modes vectors and the overlap analysis with another conformation of the same structure. The newest section, Comparative Analysis, calculates and compares the normal modes of a set of aligned protein structures, which is currently not available in any other tool. It includes comparative analyses such as fluctuations profiles, deformation energies and comparison of modes calculated on the input structures using the root mean square inner product (RMSIP). For this part, more than one structure can be submitted along with a FASTA file of their alignment. In addition to the updates, we have also provided a SOAP web-service for a more programmable interface for both of these sections.

On the difference in quality between current heuristic and optimal solutions to the protein structure alignment problem.

The importance of pairwise protein structural comparison in biomedical research is fueling the search for algorithms capable of finding more accurate structural match of two input proteins in a timely manner. In recent years, we have witnessed rapid advances in the development of methods for approximate and optimal solutions to the protein structure matching problem. Albeit slow, these methods can be extremely useful in assessing the accuracy of more efficient, heuristic algorithms. We utilize a recently developed approximation algorithm for protein structure matching to demonstrate that a deep search of the protein superposition space leads to increased alignment accuracy with respect to many well-established measures of alignment quality. The results of our study suggest that a large and important part of the protein superposition space remains unexplored by current techniques for protein structure alignment.

Fast protein structure alignment using Gaussian overlap scoring of backbone peptide fragment similarity

Aligning and comparing protein structures is important for understanding their evolutionary and functional relationships. With the rapid growth of protein structure databases in recent years, the need to align, superpose and compare protein structures rapidly and accurately has never been greater. Many structural alignment algorithms have been described in the past 20 years. However, achieving an algorithm that is both accurate and fast remains a considerable challenge. We have developed a novel protein structure alignment algorithm called 'Kpax', which exploits the highly predictable covalent geometry of C(α) atoms to define multiple local coordinate frames in which backbone peptide fragments may be oriented and compared using sensitive Gaussian overlap scoring functions. A global alignment and hence a structural superposition may then be found rapidly using dynamic programming with secondary structure-specific gap penalties. When superposing pairs of structures, Kpax tends to give tighter secondary structure overlays than several popular structure alignment algorithms. When searching the CATH database, Kpax is faster and more accurate than the very efficient Yakusa algorithm, and it gives almost the same high level of fold recognition as TM-Align while being more than 100 times faster.

Towards the development of standardized methods for comparison, ranking and evaluation of structure alignments

Pairwise alignment of protein structures is a fundamental task in structural bioinformatics. There are numerous computer programs in the public domain that produce alignments for a given pair of protein structures, but the results obtained by the various programs generally differ substantially. Hence, in the application of such programs the question arises which of the alignment programs are the most trustworthy in the sense of overall performance, and which programs provide the best result for a given pair of proteins. The major problem in comparing, evaluating and judging alignment results is that there is no clear notion of the optimality of an alignment. As a consequence, the numeric criteria and scores reported by the individual structure alignment programs are largely incomparable. Here we report on the development and application of a new approach for the evaluation of structure alignment results. The method uses the translation vector and rotation matrix to generate the superposition of two structures but discards the alignment reported by the individual programs. The optimal alignment is then generated in standardized form based on a suitably implemented dynamic programming algorithm where the length of the alignment is the single most informative parameter. We demonstrate that some of the most popular programs in protein structure research differ considerably in their overall performance. In particular, each of the programs investigated here produced in at least in one case the best and the worst alignment compared with all others. Hence, at the current state of development of structure comparison techniques, it is advisable to use several programs in parallel and to choose the optimal alignment in the way reported here. The computer software that implement the method described here is freely available at http://melolab.org/stovca.

Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment.

BackgroundThe quality of multiple protein structure alignments are usually computed and assessed based on geometric functions of the coordinates of the backbone atoms from the protein chains. These purely geometric methods do not utilize directly protein sequence similarity, and in fact, determining the proper way to incorporate sequence similarity measures into the construction and assessment of protein multiple structure alignments has proved surprisingly difficult.ResultsWe present Formatt, a multiple structure alignment based on the Matt purely geometric multiple structure alignment program, that also takes into account sequence similarity when constructing alignments. We show that Formatt outperforms Matt and other popular structure alignment programs on the popular HOMSTRAD benchmark. For the SABMark twilight zone benchmark set that captures more remote homology, Formatt and Matt outperform other programs; depending on choice of embedded sequence aligner, Formatt produces either better sequence and structural alignments with a smaller core size than Matt, or similarly sized alignments with better sequence similarity, for a small cost in average RMSD.ConclusionsConsidering sequence information as well as purely geometric information seems to improve quality of multiple structure alignments, though defining what constitutes the best alignment when sequence and structural measures would suggest different alignments remains a difficult open question.

Adenine Binding Mode Is a Key Factor in Triggering the Early Release of NADH in Coenzyme A-dependent Methylmalonate Semialdehyde Dehydrogenase

Structural dynamics associated with cofactor binding have been shown to play key roles in the catalytic mechanism of hydrolytic NAD(P)-dependent aldehyde dehydrogenases (ALDH). By contrast, no information is available for their CoA-dependent counterparts. We present here the first crystal structure of a CoA-dependent ALDH. The structure of the methylmalonate semialdehyde dehydrogenase (MSDH) from Bacillus subtilis in binary complex with NAD(+) shows that, in contrast to what is observed for hydrolytic ALDHs, the nicotinamide ring is well defined in the electron density due to direct and H(2)O-mediated hydrogen bonds with the carboxamide. The structure also reveals that a conformational isomerization of the NMNH is possible in MSDH, as shown for hydrolytic ALDHs. Finally, the adenine ring is substantially more solvent-exposed, a result that could be explained by the presence of a Val residue at position 229 in helix α(F) that reduces the depth of the binding pocket and the absence of Gly-225 at the N-terminal end of helix α(F). Substitution of glycine for Val-229 and/or insertion of a glycine residue at position 225 resulted in a significant decrease of the rate constant associated with the dissociation of NADH from the NADH/thioacylenzyme complex, thus demonstrating that the weaker stabilization of the adenine ring is a key factor in triggering the early NADH release in the MSDH-catalyzed reaction. This study provides for the first time structural insights into the mechanism whereby the cofactor binding mode is responsible at least in part for the different kinetic behaviors of the hydrolytic and CoA-dependent ALDHs.

A Stochastic Evolutionary Model for Protein Structure Alignment and Phylogeny

We present a stochastic process model for the joint evolution of protein primary and tertiary structure, suitable for use in alignment and estimation of phylogeny. Indels arise from a classic Links model, and mutations follow a standard substitution matrix, whereas backbone atoms diffuse in three-dimensional space according to an Ornstein-Uhlenbeck process. The model allows for simultaneous estimation of evolutionary distances, indel rates, structural drift rates, and alignments, while fully accounting for uncertainty. The inclusion of structural information enables phylogenetic inference on time scales not previously attainable with sequence evolution models. The model also provides a tool for testing evolutionary hypotheses and improving our understanding of protein structural evolution.