Pairwise Structure Alignment Research Articles

RCSB protein Data Bank: exploring protein 3D similarities via comprehensive structural alignments.

Tools for pairwise alignments between 3D structures of proteins are of fundamental importance for structural biology and bioinformatics, enabling visual exploration of evolutionary and functional relationships. However, the absence of a user-friendly, browser-based tool for creating alignments and visualizing them at both 1D sequence and 3D structural levels makes this process unnecessarily cumbersome. We introduce a novel pairwise structure alignment tool (rcsb.org/alignment) that seamlessly integrates into the RCSB Protein Data Bank (RCSB PDB) research-focused RCSB.org web portal. Our tool and its underlying application programming interface (alignment.rcsb.org) empowers users to align several protein chains with a reference structure by providing access to established alignment algorithms (FATCAT, CE, TM-align, or Smith-Waterman 3D). The user-friendly interface simplifies parameter setup and input selection. Within seconds, our tool enables visualization of results in both sequence (1D) and structural (3D) perspectives through the RCSB PDB RCSB.org Sequence Annotations viewer and Mol* 3D viewer, respectively. Users can effortlessly compare structures deposited in the PDB archive alongside more than a million incorporated Computed Structure Models coming from the ModelArchive and AlphaFold DB. Moreover, this tool can be used to align custom structure data by providing a link/URL or uploading atomic coordinate files directly. Importantly, alignment results can be bookmarked and shared with collaborators. By bridging the gap between 1D sequence and 3D structures of proteins, our tool facilitates deeper understanding of complex evolutionary relationships among proteins through comprehensive sequence and structural analyses. The alignment tool is part of the RCSB PDB research-focused RCSB.org web portal and available at rcsb.org/alignment. Programmatic access is available via alignment.rcsb.org. Frontend code has been published at github.com/rcsb/rcsb-pecos-app. Visualization is powered by the open-source Mol* viewer (github.com/molstar/molstar and github.com/molstar/rcsb-molstar) plus the Sequence Annotations in 3D Viewer (github.com/rcsb/rcsb-saguaro-3d).

TM-search: An Efficient and Effective Tool for Protein Structure Database Search.

The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify ∼90% of all high TM-score hits, which is 2-10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.

Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm.

The hERG potassium channel serves as an annexed target for drug discovery because the associated off-target inhibitory activity may cause serious cardiotoxicity. Quantitative structure-activity relationship (QSAR) models were developed to predict inhibitory activities against the hERG potassium channel, utilizing the three-dimensional (3D) distribution of quantum mechanical electrostatic potential (ESP) as the molecular descriptor. To prepare the optimal atomic coordinates of dataset molecules, pairwise 3D structural alignments were carried out in order for the quantum mechanical cross correlation between the template and other molecules to be maximized. This alignment method stands out from the common atom-by-atom matching technique, as it can handle structurally diverse molecules as effectively as chemical derivatives that share an identical scaffold. The alignment problem prevalent in 3D-QSAR methods was ameliorated substantially by dividing the dataset molecules into seven subsets, each of which contained molecules with similar molecular weights. Using an artificial neural network algorithm to find the functional relationship between the quantum mechanical ESP descriptors and the experimental hERG inhibitory activities, highly predictive 3D-QSAR models were derived for all seven molecular subsets to the extent that the squared correlation coefficients exceeded 0.79. Given their simplicity in model development and strong predictability, the 3D-QSAR models developed in this study are expected to function as an effective virtual screening tool for assessing the potential cardiotoxicity of drug candidate molecules.

Saccharomyces cerevisiae NRE1 and IRC24 Encode Paralogous Benzil Oxidoreductases.

Irc24p is a benzil oxidoreductase encoded on chromosome IX of Saccharomyces cerevisiae . We identified a putative paralog, Nre1p, encoded 284 bp downstream. Both proteins are small, cytoplasmic, and are 52% identical (70% similar). PANTHER and PFAM analysis of the amino acid sequences and rigid pairwise structure alignment predicted a conserved active site and Rossmann folds in both, implicating NADH or NADPH as likely cofactors. We purified hexahistidine-tagged Irc24p and Nre1p. Both proteins catalyze the reduction of the diketone benzil with similar kinetics and a preference for NADPH. This is the first demonstration of in vitro function for Nre1p.

US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes.

Structure comparison and alignment are of fundamental importance in structural biology studies. We developed the first universal platform, US-align, to uniformly align monomer and complex structures of different macromolecules-proteins, RNAs and DNAs. The pipeline is built on a uniform TM-score objective function coupled with a heuristic alignment searching algorithm. Large-scale benchmarks demonstrated consistent advantages of US-align over state-of-the-art methods in pairwise and multiple structure alignments of different molecules. Detailed analyses showed that the main advantage of US-align lies in the extensive optimization of the unified objective function powered by efficient heuristic search iterations, which substantially improve the accuracy and speed of the structural alignment process. Meanwhile, the universal protocol fusing different molecular and structural types helps facilitate the heterogeneous oligomer structure comparison and template-based protein-protein and protein-RNA/DNA docking.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

Analysis of Protein Intermolecular Interactions with MAFFT-DASH.

The Database of Aligned Structural Homologs (DASH) is a tool for efficiently navigating the Protein Data Bank (PDB) by means of pre-computed pairwise structural alignments. We recently showed that, by integrating DASH structural alignments with the multiple sequence alignment (MSA) software MAFFT, we were able to significantly improve MSA accuracy without dramatically increasing manual or computational complexity. In the latest DASH update, such queries are not limited to PDB entries but can also be launched from user-provided protein coordinates. Here, we describe a further extension of DASH that retrieves intermolecular interactions of all structurally similar domains in the PDB to a query domain of interest. We illustrate these new features using a model of the NYN domain of the ribonuclease N4BP1 as an example. We show that the protein-nucleotide interactions returned are distributed on the surface of the NYN domain in an asymmetric manner, roughly centered on the known nuclease active site.

PmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP

BackgroundStructure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons. To more efficiently deal with informative multiple structure alignment tasks, we propose pmTM-align, a parallel protein structure alignment approach based on mTM-align/TM-align. pmTM-align contains two stages to handle pairwise structure alignments with Spark and the phylogenetic tree-based multiple structure alignment task on a single computer with OpenMP.ResultsExperiments with the SABmark dataset showed that parallelization along with data structure optimization provided considerable speedup for mTM-align. The Spark-based structure alignments achieved near ideal scalability with large datasets, and the OpenMP-based construction of the phylogenetic tree accelerated the incremental alignment of multiple structures and metrics computation by a factor of about 2–5.ConclusionspmTM-align enables scalable pairwise and multiple structure alignment computing and offers more timely responses for medium to large-sized input data than existing alignment tools such as mTM-align.

GADP-align: A genetic algorithm and dynamic programming-based method for structural alignment of proteins.

Introduction: Similarity analysis of protein structure is considered as a fundamental step to give insight into the relationships between proteins. The primary step in structural alignment is looking for the optimal correspondence between residues of two structures to optimize the scoring function. An exhaustive search for finding such a correspondence between two structures is intractable. Methods: In this paper, a hybrid method is proposed, namely GADP-align, for pairwise protein structure alignment. The proposed method looks for an optimal alignment using a hybrid method based on a genetic algorithm and an iterative dynamic programming technique. To this end, the method first creates an initial map of correspondence between secondary structure elements (SSEs) of two proteins. Then, a genetic algorithm combined with an iterative dynamic programming algorithm is employed to optimize the alignment. Results: The GADP-align algorithm was employed to align 10 ‘difficult to align’ protein pairs in order to evaluate its performance. The experimental study shows that the proposed hybrid method produces highly accurate alignments in comparison with the methods using exactly the dynamic programming technique. Furthermore, the proposed method prevents the local optimal traps caused by the unsuitable initial guess of the corresponding residues. Conclusion: The findings of this paper demonstrate that employing the genetic algorithm along with the dynamic programming technique yields highly accurate alignments between a protein pair by exploring the global alignment and avoiding trapping in local alignments.

PTM-MatAlign: A Fast GPU-based Algorithm for Pairwise Protein Structure Alignment

PTM-MatAlign: A Fast GPU-based Algorithm for Pairwise Protein Structure Alignment

Protein stability governed by its structural plasticity is inferred by physicochemical factors and salt bridges

Several organisms, specifically microorganisms survive in a wide range of harsh environments including extreme temperature, pH, and salt concentration. We analyzed systematically a large number of protein sequences with their structures to understand their stability and to discriminate extremophilic proteins from their non-extremophilic orthologs. Our results highlighted that the strategy for the packing of the protein core was influenced by the environmental stresses through substitutive structural events through better ionic interaction. Statistical analysis showed that a significant difference in number and composition of amino acid exist among them. The negative correlation of pairwise sequence alignments and structural alignments indicated that most of the extremophile and non-extremophile proteins didn’t contain any association for maintaining their functional stability. A significant numbers of salt bridges were noticed on the surface of the extremostable proteins. The Ramachandran plot data represented more occurrences of amino acids being present in helix and sheet regions of extremostable proteins. We also found that a significant number of small nonpolar amino acids and moderate number of charged amino acids like Arginine and Aspartic acid represented more nonplanar Omega angles in their peptide bond. Thus, extreme conditions may predispose amino acid composition including geometric variability for molecular adaptation of extremostable proteins against atmospheric variations and associated changes under natural selection pressure. The variation of amino acid composition and structural diversifications in proteins play a major role in evolutionary adaptation to mitigate climate change.

Discovering a novel resistance gene for carbapenem resistance in Parabacteroides timonensis using Whole Genome Sequencing

Up to 95% of the human microbiome consists of anaerobic bacteria. However, they are not as well studied as the aerobic bacteria. Among all anaerobes, the Bacteroides group is the best known and most studied. There has been an increase in carbapenem resistance in Bacteroidales strains without a known cause. We isolated a Parabacteroides timonensis strain, related to Bacteroides, resistant for amoxicillin, clindamycin and meropenem. The strain was sequenced using the Miseq (Illumina, San Diego, CA), followed by a de novo assembly using CLC workbench to create a draft genome. Resistance genes were detected with ResFinder (https://cge.cbs.dtu.dk/services/ResFinder/). Genome annotation was performed by RAST (http://rast.nmpdr.org/). Protein modeling was performed, on genes of interest, using Phyre2 (http://www.sbg.bio.ic.ac.uk/phyre2/html/page.cgi?id=index) and 3DLigandSite (http://www.sbg.bio.ic.ac.uk/3dligandsite/). FatCat (http://fatcat.burnham.org/) was used to do a pairwise structure alignment to compare protein structures. The draft genome had a size of 6,356,323 bp, present on 95 contigs. Using Resfinder 1 resistance gene was found, tetQ. A possible novel resistance gene for a metallo β-lactamase related to cfiA was found using RAST. The protein model showed similarities with the model for cfiA and includes several Zn2+ ions, used by metallo β-lactamase to hydrolyze carbapenems. Our results indicate the presence of a not yet described metallo β-lactamase. There are also indications that a conjugative transposon is present, enabling horizontal gene transfer. No erm genes, encoding for clindamycin resistance, were detected in the draft genome.

Antlion optimization algorithm for pairwise structural alignment with bi-objective functions

This research work concentrates on the pairwise protein structure alignment of numerous protein structures. This is considered to be a problematic job which is an NP-hard problem. This work proposed a bi-objective-based antlion optimization algorithm (BO-ALO) which is aimed to improve the geometrical and evolutionary relationship among the protein structures. The bi-objective functions are the RMSD and TM scores which aim to minimize the RMSD score and maximize the TM score. The BO-ALO method has been compared with various widespread existing methods such as SPalignNS, UniAlign, DALI, MICAN, GANGSTA, DeepAlign, TM-align, CE, ant colony optimization and artificial bee colony optimization methods. The experiments were taken from the benchmark datasets, namely SCOPe and CATH. Also, the predicted alignments were compared with gold standard benchmark databases such as CDD, MALIDUP, MALISAM and HOMSTRAD. The outcomes of the proposed method have better bi-objective function scores and performance measures than other well-known existing approaches. This work also evaluates the proposed method with its biological significance of predicting the common gene ontology functions among the aligned protein structures. Finally, the statistical significance of the BO-ALO is computed by employing the Wilcoxon matched-signed rank test. Also, the statistical significant results of the proposed methods give better results when compared to other existing approaches.

MTM-align: an algorithm for fast and accurate multiple protein structure alignment.

As protein structure is more conserved than sequence during evolution, multiple structure alignment can be more informative than multiple sequence alignment, especially for distantly related proteins. With the rapid increase of the number of protein structures in the Protein Data Bank, it becomes urgent to develop efficient algorithms for multiple structure alignment. A new multiple structure alignment algorithm (mTM-align) was proposed, which is an extension of the highly efficient pairwise structure alignment program TM-align. The algorithm was benchmarked on four widely used datasets, HOMSTRAD, SABmark_sup, SABmark_twi and SISY-multiple, showing that mTM-align consistently outperforms other algorithms. In addition, the comparison with the manually curated alignments in the HOMSTRAD database shows that the automated alignments built by mTM-align are in general more accurate. Therefore, mTM-align may be used as a reliable complement to construct multiple structure alignments for real-world applications. http://yanglab.nankai.edu.cn/mTM-align. zhng@umich.edu or yangjy@nankai.edu.cn. Supplementary data are available at Bioinformatics online.

CompPSA: A Component-Based Pairwise RNA Secondary Structure Alignment Algorithm

CompPSA: A Component-Based Pairwise RNA Secondary Structure Alignment Algorithm

Pairwise RNA secondary structure alignment with conserved stem pattern.

The regulatory functions performed by non-coding RNAs are related to their 3D structures, which are, in turn, determined by their secondary structures. Pairwise secondary structure alignment gives insight into the functional similarity between a pair of RNA sequences. Numerous exact or heuristic approaches have been proposed for computational alignment. However, the alignment becomes intractable when arbitrary pseudoknots are allowed. Also, since non-coding RNAs are, in general, more conserved in structures than sequences, it is more effective to perform alignment based on the common structural motifs discovered. We devised a method to approximate the true conserved stem pattern for a secondary structure pair, and constructed the alignment from it. Experimental results suggest that our method identified similar RNA secondary structures better than the existing tools, especially for large structures. It also successfully indicated the conservation of some pseudoknot features with biological significance. More importantly, even for large structures with arbitrary pseudoknots, the alignment can usually be obtained efficiently. Our algorithm has been implemented in a tool called PSMAlign. The source code of PSMAlign is freely available at http://homepage.cs.latrobe.edu.au/ypchen/psmalign/.

MultiSETTER: web server for multiple RNA structure comparison.

BackgroundUnderstanding the architecture and function of RNA molecules requires methods for comparing and analyzing their tertiary and quaternary structures. While structural superposition of short RNAs is achievable in a reasonable time, large structures represent much bigger challenge. Therefore, we have developed a fast and accurate algorithm for RNA pairwise structure superposition called SETTER and implemented it in the SETTER web server. However, though biological relationships can be inferred by a pairwise structure alignment, key features preserved by evolution can be identified only from a multiple structure alignment. Thus, we extended the SETTER algorithm to the alignment of multiple RNA structures and developed the MultiSETTER algorithm.ResultsIn this paper, we present the updated version of the SETTER web server that implements a user friendly interface to the MultiSETTER algorithm. The server accepts RNA structures either as the list of PDB IDs or as user-defined PDB files. After the superposition is computed, structures are visualized in 3D and several reports and statistics are generated.ConclusionTo the best of our knowledge, the MultiSETTER web server is the first publicly available tool for a multiple RNA structure alignment. The MultiSETTER server offers the visual inspection of an alignment in 3D space which may reveal structural and functional relationships not captured by other multiple alignment methods based either on a sequence or on secondary structure motifs.

CombAlign: a code for generating a one-to-many sequence alignment from a set of pairwise structure-based sequence alignments.

BackgroundIn order to better define regions of similarity among related protein structures, it is useful to identify the residue-residue correspondences among proteins. Few codes exist for constructing a one-to-many multiple sequence alignment derived from a set of structure or sequence alignments, and a need was evident for creating such a tool for combining pairwise structure alignments that would allow for insertion of gaps in the reference structure.ResultsThis report describes a new Python code, CombAlign, which takes as input a set of pairwise sequence alignments (which may be structure based) and generates a one-to-many, gapped, multiple structure- or sequence-based sequence alignment (MSSA). The use and utility of CombAlign was demonstrated by generating gapped MSSAs using sets of pairwise structure-based sequence alignments between structure models of the matrix protein (VP40) and pre-small/secreted glycoprotein (sGP) of Reston Ebolavirus and the corresponding proteins of several other filoviruses. The gapped MSSAs revealed structure-based residue-residue correspondences, which enabled identification of structurally similar versus differing regions in the Reston proteins compared to each of the other corresponding proteins.ConclusionsCombAlign is a new Python code that generates a one-to-many, gapped, multiple structure- or sequence-based sequence alignment (MSSA) given a set of pairwise sequence alignments (which may be structure based). CombAlign has utility in assisting the user in distinguishing structurally conserved versus divergent regions on a reference protein structure relative to other closely related proteins. CombAlign was developed in Python 2.6, and the source code is available for download from the GitHub code repository.Electronic supplementary materialThe online version of this article (doi:10.1186/s13029-015-0039-1) contains supplementary material, which is available to authorized users.

UniAlign: protein structure alignment meets evolution.

During the evolution, functional sites on the surface of the protein as well as the hydrophobic core maintaining the structural integrity are well-conserved. However, available protein structure alignment methods align protein structures based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins. In this article, we propose a new protein pairwise structure alignment algorithm (UniAlign) that incorporates additional evolutionary information captured in the form of sequence similarity, sequence profiles and residue conservation. We define a per-residue score (UniScore) as a weighted sum of these and other features and develop an iterative optimization procedure to search for an alignment with the best overall UniScore. Our extensive experiments on CDD, HOMSTRAD and BAliBASE benchmark datasets show that UniAlign outperforms commonly used structure alignment methods. We further demonstrate UniAlign's ability to develop family-specific models to drastically improve the quality of the alignments. UniAlign is available as a web service at: http://sacan.biomed.drexel.edu/unialign ahmet.sacan@drexel.edu Supplementary data are available at Bioinformatics online.

Multiple 3D RNA Structure Superposition Using Neighbor Joining.

Recent advances in RNA research and the steady growth of available RNA structures call for bioinformatics methods for handling and analyzing RNA structural data. Recently, we introduced SETTER-a fast and accurate method for RNA pairwise structure alignment. In this paper, we describe MultiSETTER, SETTER extension for multiple RNA structure alignment. MultiSETTER combines SETTER's decomposition of RNA structures into non-overlapping structural subunits with the multiple sequence alignment algorithm ClustalW adapted for the structure alignment. The accuracy of MultiSETTER was assessed by the automatic classification of RNA structures and its comparison to SCOR annotations. In addition, MultiSETTER classification was also compared to multiple sequence alignment-based and secondary structure alignment-based classifications provided by LocARNA and RNADistance tools, respectively. MultiSETTER precompiled Windows libraries, as well as the C++ source code, are freely available from http://siret.cz/multisetter.