Quality Of Multiple Sequence Alignment Research Articles

PC_ali: A tool for improved multiple alignments and evolutionary inference based on a hybrid protein sequence and structure similarity score.

Evolutionary inference depends crucially on the quality of multiple sequence alignments (MSA), which is problematic for distantly related proteins. Since protein structure is more conserved than sequence, it seems natural to use structure alignments for distant homologs. However, structure alignments may not be suitable for inferring evolutionary relationships. Here we examined four protein similarity measures that depend on sequence and structure (fraction of aligned residues, sequence identity, fraction of superimposed residues and contact overlap), finding that they are intimately correlated but none of them provides a complete and unbiased picture of conservation in proteins. Therefore, we propose the new hybrid protein sequence and structure similarity score PC_sim based on their main Principal Component. The corresponding divergence measure PC_div shows the strongest correlation with divergences obtained from individual similarities, suggesting that it infers accurate evolutionary divergences. We developed the program PC_ali that constructs protein MSAs either de novo or modifying an input MSA, using a similarity matrix based on PC_sim. tructs a starting MSA based on the maximal cliques of the graph of these PAs and it refines it through progressive alignments along the tree reconstructed with PC_div. Compared with eight state-of-the-art multiple structure or sequence alignment tools, PC_ali achieve higher or equal aligned fraction and structural scores, sequence identity higher than structure aligners although lower than sequence aligners, highest score PC_sim and highest similarity with the MSAs produced by other tools and with the reference MSA Balibase. https://github.com/ugobas/PC_ali. Supplementary data are available at Bioinformatics online.

Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model.

guofei@csu.edu.cn, jj.tang@siat.ac.cn.

Prior knowledge facilitates low homologous protein secondary structure prediction with DSM distillation.

Protein secondary structure prediction (PSSP) is one of the fundamental and challenging problems in the field of computational biology. Accurate PSSP relies on sufficient homologous protein sequences to build the multiple sequence alignment (MSA). Unfortunately, many proteins lack homologous sequences, which results in the low quality of MSA and poor performance. In this article, we propose the novel dynamic scoring matrix (DSM)-Distil to tackle this issue, which takes advantage of the pretrained BERT and exploits the knowledge distillation on the newly designed DSM features. Specifically, we propose the DSM to replace the widely used profile and PSSM (position-specific scoring matrix) features. DSM could automatically dig for the suitable feature for each residue, based on the original profile. Namely, DSM-Distil not only could adapt to the low homologous proteins but also is compatible with high homologous ones. Thanks to the dynamic property, DSM could adapt to the input data much better and achieve higher performance. Moreover, to compensate for low-quality MSA, we propose to generate the pseudo-DSM from a pretrained BERT model and aggregate it with the original DSM by adaptive residue-wise fusion, which helps to build richer and more complete input features. In addition, we propose to supervise the learning of low-quality DSM features using high-quality ones. To achieve this, a novel teacher-student model is designed to distill the knowledge from proteins with high homologous sequences to that of low ones. Combining all the proposed methods, our model achieves the new state-of-the-art performance for low homologous proteins. Compared with the previous state-of-the-art method 'Bagging', DSM-Distil achieves an improvement about 5% and 7.3% improvement for proteins with MSA count ≤30 and extremely low homologous cases, respectively. We also compare DSM-Distil with Alphafold2 which is a state-of-the-art framework for protein structure prediction. DSM-Distil outperforms Alphafold2 by 4.1% on extremely low-quality MSA on 8-state secondary structure prediction. Moreover, we release a large-scale up-to-date test dataset BC40 for low-quality MSA structure prediction evaluation. BC40 dataset: https://drive.google.com/drive/folders/15vwRoOjAkhhwfjDk6-YoKGf4JzZXIMC. HardCase dataset: https://drive.google.com/drive/folders/1BvduOr2b7cObUHy6GuEWk-aUkKJgzTUv. Code: https://github.com/qinwang-ai/DSM-Distil.

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14.

Substantial progresses in protein structure prediction have been made by utilizing deep‐learning and residue‐residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning‐based protein inter‐residue distance predictor to improve template‐free (ab initio) tertiary structure prediction, (b) an enhanced template‐based tertiary structure prediction method, and (c) distance‐based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter‐domain structure prediction. The results demonstrate that the template‐free modeling based on deep learning and residue‐residue distance prediction can predict the correct topology for almost all template‐based modeling targets and a majority of hard targets (template‐free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template‐free modeling performs better than the template‐based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template‐free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at https://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3 and https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0.

Improving deep learning-based protein distance prediction in CASP14.

MotivationAccurate prediction of residue–residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment.ResultsBenchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions [i.e. classifying distances between two residues into two categories: in contact (<8 Angstrom) and not in contact otherwise] and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. Availability and implementationThe software package, source code and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM.Supplementary information Supplementary data are available at Bioinformatics online.

Comparing different machine learning and mathematical regression models to evaluate multiple sequence alignments

The evaluation of multiple sequence alignments (MSAs) is still an open task in bioinformatics. Current MSA scores do not agree about how alignments must be accurately evaluated. Consequently, it is not trivial to know the quality of MSAs when reference alignments are not provided. Recent scores tend to use more complex evaluations adding supplementary biological features. In this work, a set of novel regression approaches are proposed for the MSA evaluation, comparing several supervised learning and mathematical methodologies. Therefore, the following models specifically designed for regression are applied: regression trees, a bootstrap aggregation of regression trees (bagging trees), least-squares support vector machines (LS-SVMs) and Gaussian processes. These algorithms consider a heterogeneous set of biological features together with other standard MSA scores in order to predict the quality of alignments. The most relevant features are then applied to build novel score schemes for the evaluation of alignments. The proposed algorithms are validated by using the BAliBASE benchmark. Additionally, an statistical ANOVA test is performed to study the relevance of these scores considering three alignment factors. According to the obtained results, the four regression models provide accurate evaluations, even outperforming other standard scores such as BLOSUM, PAM or STRIKE.

Evolutionary Distances in the Twilight Zone—A Rational Kernel Approach

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biologically motivated and ignore our knowledge about the evolution of sequences. Thus, it is still a major open question how to define an evolutionary distance metric between divergent sequences that makes use of indel information and known substitution models without the need for a multiple alignment. Here we propose a new evolutionary distance metric to close this gap. It uses finite-state transducers to create a biologically motivated similarity score which models substitutions and indels, and does not depend on a multiple sequence alignment. The sequence similarity score is defined in analogy to pairwise alignments and additionally has the positive semi-definite property. We describe its derivation and show in simulation studies and real-world examples that it is more accurate in reconstructing phylogenies than competing methods. The result is a new and accurate way of determining evolutionary distances in and beyond the twilight zone of sequence alignments that is suitable for large datasets.

DNA^+Pro^: an Improved Progressive Multiple Sequence Alignment Algorithm for Evolutionary Analysis Using Combined DNA-Protein Sequences

AbstractAlignment of DNA and protein sequences is a basic tool in the study of evolutionary, structural and functional relationship among macromolecules. Present sequence alignment methods are somewhat error-prone, often producing systematic bias. Errors in sequence alignments sometimes lead to subsequent misinterpretation of evolutionary, structural and functional information in genes, proteins and genomes. In traditional sequence alignment algorithms, alignments of DNA and protein sequences are conducted separately. It has been long believed that the phylogenetic signal disappears more rapidly from DNA sequences than from encoded proteins. It is therefore generally preferable to align sequences at the amino acid level. Here we present a new method—DNA^+Pro^, which aggregates DNA and protein sequences into combined DNA-protein sequences and align them in a combined fashion. We demonstrate that combining sequences improve the quality of multiple sequence alignment and solve practical evolutionary problems in primate immunodeficiency virus proteins and bacterial restriction enzymes. In addition to increased theoretical information contents, the distance estimations are more biological significant in combined alignment than in protein only or DNA only alignments. By integrating information buried separately in DNA and protein sequences, DNA^+Pro^ improves the accuracy of multiple sequence alignment of closely-related proteins and prevents certain errors that may occur in phylogeny analysis using protein only approaches. The DNA^+Pro^ software and the supplementary data are downloadable free of charge from "our website, http://www.dnapluspro.com":http://www.dnapluspro.com.

BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments

BackgroundThe quality of multiple sequence alignments plays an important role in the accuracy of phylogenetic inference. It has been shown that removing ambiguously aligned regions, but also other sources of bias such as highly variable (saturated) characters, can improve the overall performance of many phylogenetic reconstruction methods. A current scientific trend is to build phylogenetic trees from a large number of sequence datasets (semi-)automatically extracted from numerous complete genomes. Because these approaches do not allow a precise manual curation of each dataset, there exists a real need for efficient bioinformatic tools dedicated to this alignment character trimming step.ResultsHere is presented a new software, named BMGE (Block Mapping and Gathering with Entropy), that is designed to select regions in a multiple sequence alignment that are suited for phylogenetic inference. For each character, BMGE computes a score closely related to an entropy value. Calculation of these entropy-like scores is weighted with BLOSUM or PAM similarity matrices in order to distinguish among biologically expected and unexpected variability for each aligned character. Sets of contiguous characters with a score above a given threshold are considered as not suited for phylogenetic inference and then removed. Simulation analyses show that the character trimming performed by BMGE produces datasets leading to accurate trees, especially with alignments including distantly-related sequences. BMGE also implements trimming and recoding methods aimed at minimizing phylogeny reconstruction artefacts due to compositional heterogeneity.ConclusionsBMGE is able to perform biologically relevant trimming on a multiple alignment of DNA, codon or amino acid sequences. Java source code and executable are freely available at ftp://ftp.pasteur.fr/pub/GenSoft/projects/BMGE/.

Some remarks on evaluating the quality of the multiple sequence alignment based on the BAliBASE benchmark

Some remarks on evaluating the quality of the multiple sequence alignment based on the BAliBASE benchmarkBAliBASE is one of the most widely used benchmarks for multiple sequence alignment programs. The accuracy of alignment methods is measured bybali_score—an application provided together with the database. The standard accuracy measures are the Sum of Pairs (SP) and the Total Column (TC). We have found that, for non-core block columns, results calculated bybali_scoreare different from those obtained on the basis of the formal definitions of the measures. We do not claim that one of these measures is better than the other, but they are definitely different. Such a situation can be the source of confusion when alignments obtained using various methods are compared. Therefore, we propose a new nomenclature for the measures of the quality of multiple sequence alignments to distinguish which one was actually calculated. Moreover, we have found that the occurrence of a gap in some column in the first sequence of the reference alignment causes column discarding.

No so HoT - heads or tails is not able to reliably compare multiple sequence alignments.

Most phylogenetic-tree building applications use multiple sequence alignments as a starting point. A recent meta-level methodology, called Heads or Tails, aims to reveal the quality of multiple sequence alignments by comparing alignments taken in the forward direction with the alignments of the same sequences when the sequences are reversed. Through an examination of a special case for multiple sequence alignment - pair-wise alignments, where an optimal algorithm exists - and the use of a modified global-alignment application, it is shown that the forward and reverse alignments, even when they are the same, do not capture all the possible variations in the alignments and when the forward and reverse alignments differ there may be other alignments that remain unaccounted for. The implication is that comparing just the forward and (biologically irrelevant) reverse alignments is not sufficient to capture the variability in multiple sequence alignments, and the Heads or Tails methodology is therefore not suitable as a method for investigating multiple sequence alignment accuracy. Part of the reason is the inability of individual multiple sequence alignment applications to adequately sample the space of possible alignments. A further implication is that the Hall [Hall, B.G., 2008. Mol. Biol. Evol. 25, 1576-1580] methodology may create optimal synthetic multiple sequence alignments that extant aligners will be unable to completely recover ab initio due to alternative alignments being possible at particular sites. In general, it is shown that more divergent sequences will give rise to an increased number of alternative alignments, so sequence sets with a higher degree of similarity are preferable to sets with lower similarity as the starting point for phylogenetic tree building.  © The Willi Hennig Society 2009.

A statistical score for assessing the quality of multiple sequence alignments

BackgroundMultiple sequence alignment is the foundation of many important applications in bioinformatics that aim at detecting functionally important regions, predicting protein structures, building phylogenetic trees etc. Although the automatic construction of a multiple sequence alignment for a set of remotely related sequences cause a very challenging and error-prone task, many downstream analyses still rely heavily on the accuracy of the alignments.ResultsTo address the need for an objective evaluation framework, we introduce a statistical score that assesses the quality of a given multiple sequence alignment. The quality assessment is based on counting the number of significantly conserved positions in the alignment using importance sampling method in conjunction with statistical profile analysis framework. We first evaluate a novel objective function used in the alignment quality score for measuring the positional conservation. The results for the Src homology 2 (SH2) domain, Ras-like proteins, peptidase M13, subtilase and β-lactamase families demonstrate that the score can distinguish sequence patterns with different degrees of conservation. Secondly, we evaluate the quality of the alignments produced by several widely used multiple sequence alignment programs using a novel alignment quality score and a commonly used sum of pairs method. According to these results, the Mafft strategy L-INS-i outperforms the other methods, although the difference between the Probcons, TCoffee and Muscle is mostly insignificant. The novel alignment quality score provides similar results than the sum of pairs method.ConclusionThe results indicate that the proposed statistical score is useful in assessing the quality of multiple sequence alignments.

Structural clues in the sequences of the aquaporins

The large number of sequences available for the aquaporin family represents a valuable source of information to incorporate into three-dimensional structure determination. Phylogenetic analysis was used to define type sequences to avoid extreme over-representation of some subfamilies, and as a measure of the quality of multiple sequence alignment. Inspection of the sequence alignment suggested eight conserved segments that define the core architecture of six transmembrane helices and two functional loops, B and E, projecting into the plane of the membrane. The sum of the core segments and the minimum lengths of the interlinking loops constitute the 208 residues necessary to satisfy the aquaporin architecture. Analysis of hydrophobic and conservation periodicity and of correlated mutations across the alignment indicated the likely assignment and orientation of the helices in the bilayer. This assignment is examined with respect to the structure of the erythrocyte aquaporin 1 determined by electron crystallography. The aquaporin 1 tetramer is described as three rings of helices, each ring with a different exposure to the lipid environment. The sequence analysis clearly suggests that two helices are exposed along their whole lengths, two helices are exposed only at their N termini, and two helices are not exposed to lipid. It is further proposed that, besides loops B and E, the highly conserved motifs on helices 1 and 4, ExxxTxxF/L, could line the water channel.