Approximate Multiple Protein Structure Alignment Using the Sum-of-Pairs Distance

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.

An integrated approach to the analysis and modeling of protein sequences and structures. III. A comparative study of sequence conservation in protein structural families using multiple structural alignments

In this paper, an approach is described that combines multiple structure alignments and multiple sequence alignments to generate sequence profiles for protein families. First, multiple sequence alignments are generated from sequences that are closely related to each sequence of known three-dimensional structure. These alignments then are merged through a multiple structure alignment of family members of known structure. The merged alignment is used to generate a Hidden Markov Model for the family in question. The Hidden Markov Model can be used to search for new family members or to improve alignments for distantly related family members that already have been identified. Application of a profile generated for SH2 domains indicates that the Janus family of nonreceptor protein tyrosine kinases contains SH2 domains. This conclusion is strongly supported by the results of secondary structure-prediction programs, threading calculations, and the analysis of comparative models generated for these domains. One of the Janus kinases, human TYK2, has an SH2 domain that contains a histidine instead of the conserved arginine at the key phosphotyrosine-binding position, betaB5. Calculations of the pK(a) values of the betaB5 arginines in a number of SH2 domains and of the betaB5 histidine in a homology model of TYK2 suggest that this histidine is likely to be neutral around pH 7, thus indicating that it may have lost the ability to bind phosphotyrosine. If this indeed is the case, TYK2 may contain a domain with an SH2 fold that has a modified binding specificity.

While a number of approaches have been geared toward multiple sequence alignments, to date there have been very few approaches to multiple structure alignment and detection of a recurring substructural motif. Among these, none performs both multiple structure comparison and motif detection simultaneously. Further, none considers all structures at the same time, rather than initiating from pairwise molecular comparisons. We present such a multiple structural alignment algorithm. Given an ensemble of protein structures, the algorithm automatically finds the largest common substructure (core) of C(alpha) atoms that appears in all the molecules in the ensemble. The detection of the core and the structural alignment are done simultaneously. Additional structural alignments also are obtained and are ranked by the sizes of the substructural motifs, which are present in the entire ensemble. The method is based on the geometric hashing paradigm. As in our previous structural comparison algorithms, it compares the structures in an amino acid sequence order-independent way, and hence the resulting alignment is unaffected by insertions, deletions and protein chain directionality. As such, it can be applied to protein surfaces, protein-protein interfaces and protein cores to find the optimally, and suboptimally spatially recurring substructural motifs. There is no predefinition of the motif. We describe the algorithm, demonstrating its efficiency. In particular, we present a range of results for several protein ensembles, with different folds and belonging to the same, or to different, families. Since the algorithm treats molecules as collections of points in three-dimensional space, it can also be applied to other molecules, such as RNA, or drugs.

T-Coffee (Tree-based consistency objective function for alignment evaluation) is a versatile multiple sequence alignment (MSA) method suitable for aligning most types of biological sequences. The main strength of T-Coffee is its ability to combine third party aligners and to integrate structural (or homology) information when building MSAs. The series of protocols presented here show how the package can be used to multiply align proteins, RNA and DNA sequences. The protein section shows how users can select the most suitable T-Coffee mode for their data set. Detailed protocols include T-Coffee, the default mode, M-Coffee, a meta version able to combine several third party aligners into one, PSI (position-specific iterated)-Coffee, the homology extended mode suitable for remote homologs and Expresso, the structure-based multiple aligner. We then also show how the T-RMSD (tree based on root mean square deviation) option can be used to produce a functionally informative structure-based clustering. RNA alignment procedures are described for using R-Coffee, a mode able to use predicted RNA secondary structures when aligning RNA sequences. DNA alignments are illustrated with Pro-Coffee, a multiple aligner specific of promoter regions. We also present some of the many reformatting utilities bundled with T-Coffee. The package is an open-source freeware available from http://www.tcoffee.org/.

We have developed a novel method for multiple sequence alignment based on combinatorial selection of similar block candidates. Our method resembles manual multiple alignment performed by biologists. The method is more feasible for nding functional motifs than previous multiple alignment algorithms that are extensions of pairwise alignments. We employed a Hop eld neural network technique so that the method can cope with the combinatorial explosion in examining a large number of \incomplete block candidates.

A large number of methods for multiple sequence alignment are currently available. Recent benchmarking tests demonstrated that strengths and drawbacks of these methods differ substantially. Global strategies can be outperformed by approaches based on local similarities and vice versa, depending on the characteristics of the input sequences. In recent years, mixed approaches that include both global and local features have shown promising results. Herein, we introduce a new algorithm for multiple sequence alignment that integrates the global divide-and-conquer approach with the local segment-based approach, thereby combining the strengths of those two strategies.

Automatic comparison and classification of protein structures

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.

Multiple sequence alignment (MSA) plays a vital role in uncovering similarities among biological sequences such as DNA, RNA, or proteins, providing valuable information about their structural, functional, and evolutionary relationships. However, MSA is a computationally challenging problem, with complexity growing exponentially as the number and length of sequences increase. Currently, standard MSA tools like ClustalW, T-Coffee, and MAFFT, which are based on heuristic algorithms, are widely used but still face many challenges due to the combinatorial explosion. Recent advancements in MSA algorithms have employed reinforcement learning (RL), particularly deep reinforcement learning (DRL), and demonstrated optimized execution time and accuracy with promising results. This is because deep reinforcement learning algorithms update their search policies using gradient descent, instead of exploring the entire solution space making it significantly faster and efficient. In this article, we provide an overview of the recent historical advancements in MSA algorithms, highlighting RL models used to tackle the MSA problem and main challenges and opportunities in this regard.

Multiple sequence alignment (MSA) methods infer the homologous regions within DNA and protein sequences. Among the other algorithms for MSA, the center star method (CSM) is useful when processing a large number of sequences. In this method, the partially conserved regions among the non-center sequences tend to hide in the alignment. Therefore, existence of subsets of similar sequences in the input set leads to a significant accuracy loss. As a solution to the above problem, this research introduces an algorithm for MSA based on a solution proposed by D. Gusfield in 1991. In this research, the sequences are first grouped into subsets, aligned separately using CSM and finally these alignments are merged by applying progressive alignment procedure. The detailed solution for this problem is presented in the following section.

An apparent paradox in computational RNA structure prediction is that many methods, in advance, require a multiple alignment of a set of related sequences, when searching for a common structure between them. However, such a multiple alignment is hard to obtain even for few sequences with low sequence similarity without simultaneously folding and aligning them. Furthermore, it is of interest to conduct a multiple alignment of RNA sequence candidates found from searching as few as two genomic sequences. Here, based on the PMcomp program, we present a global multiple alignment program, foldalignM, which performs especially well on few sequences with low sequence similarity, and is comparable in performance with state of the art programs in general. In addition, it can cluster sequences based on sequence and structure similarity and output a multiple alignment for each cluster. Furthermore, preliminary results with local datasets indicate that the program is useful for post processing foldalign pairwise scans. The program foldalignM is implemented in JAVA and is, along with some accompanying PERL scripts, available at http://foldalign.ku.dk/

2°Struc - the Protein Secondary Structure Analysis Server

The reliability of multiple sequence alignment (MSA) results directly determines the credibility of the conclusions drawn from biological research. However, MSA is inherently an NP-hard problem, making it theoretically impossible to guarantee a globally optimal solution. Consequently, in addition to developing more efficient alignment algorithms, improving the quality of initial alignments through post-processing optimization has become an important strategy for enhancing the overall alignment accuracy. Although post-processing methods have shown potential in improving alignment accuracy, currently, there is a lack of systematic reviews and summaries. In this review, we provide a systematic overview of the development and key research ideas of MSA post-processing methods over the past 3 decades and outline potential directions for future research.

BackgroundStructural alignment of RNAs is becoming important, since the discovery of functional non-coding RNAs (ncRNAs). Recent studies, mainly based on various approximations of the Sankoff algorithm, have resulted in considerable improvement in the accuracy of pairwise structural alignment. In contrast, for the cases with more than two sequences, the practical merit of structural alignment remains unclear as compared to traditional sequence-based methods, although the importance of multiple structural alignment is widely recognized.ResultsWe took a different approach from a straightforward extension of the Sankoff algorithm to the multiple alignments from the viewpoints of accuracy and time complexity. As a new option of the MAFFT alignment program, we developed a multiple RNA alignment framework, X-INS-i, which builds a multiple alignment with an iterative method incorporating structural information through two components: (1) pairwise structural alignments by an external pairwise alignment method such as SCARNA or LaRA and (2) a new objective function, Four-way Consistency, derived from the base-pairing probability of every sub-aligned group at every multiple alignment stage.ConclusionThe BRAliBASE benchmark showed that X-INS-i outperforms other methods currently available in the sum-of-pairs score (SPS) criterion. As a basis for predicting common secondary structure, the accuracy of the present method is comparable to or rather higher than those of the current leading methods such as RNA Sampler. The X-INS-i framework can be used for building a multiple RNA alignment from any combination of algorithms for pairwise RNA alignment and base-pairing probability. The source code is available at the webpage found in the Availability and requirements section.