Mind the gaps: evidence of bias in estimates of multiple sequence alignments

Multiple sequence alignment (MSA) is a crucial first step in the analysis of genomic and proteomic data. Commonly occurring sequence features, such as deletions and insertions, are known to affect the accuracy of MSA programs, but the extent to which alignment accuracy is affected by the positions of insertions and deletions has not been examined independently of other sources of sequence variation. We assessed the performance of 6 popular MSA programs (ClustalW, DIALIGN-T, MAFFT, MUSCLE, PROBCONS, and T-COFFEE) and one experimental program, PRANK, on amino acid sequences that differed only by short regions of deleted residues. The analysis showed that the absence of residues often led to an incorrect placement of gaps in the alignments, even though the sequences were otherwise identical. In data sets containing sequences with partially overlapping deletions, most MSA programs preferentially aligned the gaps vertically at the expense of incorrectly aligning residues in the flanking regions. Of the programs assessed, only DIALIGN-T was able to place overlapping gaps correctly relative to one another, but this was usually context dependent and was observed only in some of the data sets. In data sets containing sequences with non-overlapping deletions, both DIALIGN-T and MAFFT (G-INS-I) were able to align gaps with near-perfect accuracy, but only MAFFT produced the correct alignment consistently. The same was true for data sets that comprised isoforms of alternatively spliced gene products: both DIALIGN-T and MAFFT produced highly accurate alignments, with MAFFT being the more consistent of the 2 programs. Other programs, notably T-COFFEE and ClustalW, were less accurate. For all data sets, alignments produced by different MSA programs differed markedly, indicating that reliance on a single MSA program may give misleading results. It is therefore advisable to use more than one MSA program when dealing with sequences that may contain deletions or insertions, particularly for high-throughput and pipeline applications where manual refinement of each alignment is not practicable.     

Revisiting the homology modeling of G-protein coupled receptors: β1-adrenoceptor as an example

G-protein coupled receptors (GPCRs) are recognized to constitute the largest family of membrane proteins. Due to the disproportion in the quantity of crystal structures and their amino acid sequences, homology modeling contributes a reasonable and feasible approach to GPCR theoretical coordinates. With the brand new crystal structures resolved recently, herein we deliberated how to designate them as templates to carry out homology modeling in four aspects: (1) various sequence alignment methods; (2) protein weight matrix; (3) different sets of multiple templates; (4) active and inactive state of templates. The accuracy of models was evaluated by comparing the similarity of stereo conformation and molecular docking results between models and the experimental structure of Meleagris gallopavo β(1)-adrenergic receptor (Mg_Adrb1) that we desired to develop as an example. Our results proposed that: (1) Cobalt and MAFFT, two algorithms of sequence alignment, were suitable for single- and multiple-template modeling, respectively; (2) Blosum30 is applicable to align sequences in the case of low sequence identity; (3) multiple-template modeling is not always better than single-template one; (4) the state of template is an influential factor in simulating the GPCR structures as well.     

PROMALS: towards accurate multiple sequence alignments of distantly related proteins

MOTIVATION: Accurate multiple sequence alignments are essential in protein structure modeling, functional prediction and efficient planning of experiments. Although the alignment problem has attracted considerable attention, preparation of high-quality alignments for distantly related sequences remains a difficult task. RESULTS: We developed PROMALS, a multiple alignment method that shows promising results for protein homologs with sequence identity below 10%, aligning close to half of the amino acid residues correctly on average. This is about three times more accurate than traditional pairwise sequence alignment methods. PROMALS algorithm derives its strength from several sources: (i) sequence database searches to retrieve additional homologs; (ii) accurate secondary structure prediction; (iii) a hidden Markov model that uses a novel combined scoring of amino acids and secondary structures; (iv) probabilistic consistency-based scoring applied to progressive alignment of profiles. Compared to the best alignment methods that do not use secondary structure prediction and database searches (e.g. MUMMALS, ProbCons and MAFFT), PROMALS is up to 30% more accurate, with improvement being most prominent for highly divergent homologs. Compared to SPEM and HHalign, which also employ database searches and secondary structure prediction, PROMALS shows an accuracy improvement of several percent. AVAILABILITY: The PROMALS web server is available at: http://prodata.swmed.edu/promals/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

PartTree: an algorithm to build an approximate tree from a large number of unaligned sequences

MOTIVATION: To construct a multiple sequence alignment (MSA) of a large number (> approximately 10,000) of sequences, the calculation of a guide tree with a complexity of O(N2) to O(N3), where N is the number of sequences, is the most time-consuming process. RESULTS: To overcome this limitation, we have developed an approximate algorithm, PartTree, to construct a guide tree with an average time complexity of O(N log N). The new MSA method with the PartTree algorithm can align approximately 60,000 sequences in several minutes on a standard desktop computer. The loss of accuracy in MSA caused by this approximation was estimated to be several percent in benchmark tests using Pfam. AVAILABILITY: The present algorithm has been implemented in the MAFFT sequence alignment package (http://align.bmr.kyushu-u.ac.jp/mafft/software/). SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.     

Local homology recognition and distance measures in linear time using compressed amino acid alphabets

Methods for discovery of local similarities and estimation of evolutionary distance by identifying k-mers (contiguous subsequences of length k) common to two sequences are described. Given unaligned sequences of length L, these methods have O(L) time complexity. The ability of compressed amino acid alphabets to extend these techniques to distantly related proteins was investigated. The performance of these algorithms was evaluated for different alphabets and choices of k using a test set of 1848 pairs of structurally alignable sequences selected from the FSSP database. Distance measures derived from k-mer counting were found to correlate well with percentage identity derived from sequence alignments. Compressed alphabets were seen to improve performance in local similarity discovery, but no evidence was found of improvements when applied to distance estimates. The performance of our local similarity discovery method was compared with the fast Fourier transform (FFT) used in MAFFT, which has O(L log L) time complexity. The method for achieving comparable coverage to FFT is revealed here, and is more than an order of magnitude faster. We suggest using k-mer distance for fast, approximate phylogenetic tree construction, and show that a speed improvement of more than three orders of magnitude can be achieved relative to standard distance methods, which require alignments.     

肺炎克雷伯菌Hfq蛋白特性及对耐药的影响

Hfq(host factor for RNA phage QB replicase)蛋白是一个全局性调节因子,广泛参与细菌生长、趋化、毒力、耐药及应对外界选择压力等方面的调节,但在肺炎克雷伯菌(Klebsiella pneumoniae,KP)中的功能尚不清楚。本研究从临床病例中分离到59株KP,将其hfq基因与11例常见临床感染菌株hfq基因〔从美国国立生物技术信息中心(National Center for Biotechnology Information, NCBI)数据库下载〕进行了比较。所有hfq基因经EMBOSS Transeq翻译成氨基酸序列,用MAFFT软件进行多序列比对,并通过NCBI数据库中的保守结构域预测Hfq蛋白结构域。分别采用ESPript3.0、Phyre2分析Hfq蛋白的二、三级结构。59株KP中仅3株hfq基因的5个密码子位点存在差异,而其蛋白质氨基酸序列完全一致。KP与大肠埃希菌、阴沟肠杆菌、痢疾志贺菌之间,Hfq蛋白的氨基酸序列相似度较高,主要区别在C末端上;与金黄色葡萄球菌、产单核细胞李斯特菌相比,KP Hfq蛋白在N末端和C末端上差别较大;所有菌株C末端均呈酸性。三级结构预测提示68(66.67%)个氨基酸与模板序列一致, 较为保守的功能结构为54-VYKHAI-59序列。采用CRISPR/Cas9同源重组技术敲除KP的hfq基因,并对其进行药物敏感性测试,结果显示,基因敲除菌株对抗生素的耐药性较野生株有显著下降(P<0.05),差异有统计学意义,提示KP的Hfq蛋白氨基酸序列非常保守,可能参与了KP的耐药调节。     

Sequence search on a supercomputer.

A set of programs was developed for searching nucleic acid and protein sequence data bases for sequences similar to a given sequence. The programs, written in FORTRAN 77, were optimized for vector processing on a Hitachi S810-20 supercomputer. A search of a 500-residue protein sequence against the entire PIR data base Ver. 1.0 (1) (0.5 M residues) is carried out in a CPU time of 45 sec. About 4 min is required for an exhaustive search of a 1500-base nucleotide sequence against all mammalian sequences (1.2M bases) in Genbank Ver. 29.0. The CPU time is reduced to about a quarter with a faster version.     

MUSCLE: multiple sequence alignment with high accuracy and high throughput

We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.     

RNA secondary structure, an important bioinformatics tool to enhance multiple sequence alignment: a case study (Sordariomycetes, Fungi)

In a case study of fungi of the class Sordariomycetes, we evaluated the effect of multiple sequence alignment (MSA) on the reliability of the phylogenetic trees, topology and confidence of major phylogenetic clades. We compared two main approaches for constructing MSA based on (1) the knowledge of the secondary (2D) structure of ribosomal RNA (rRNA) genes, and (2) automatic construction of MSA by four alignment programs characterized by different algorithms and evaluation methods, CLUSTAL, MAFFT, MUSCLE, and SAM. In the primary fungal sequences of the two functional rRNA genes, the nuclear small and large ribosomal subunits (18 S and 28 S), we identified four and six, respectively, highly variable regions, which correspond mainly to hairpin loops in the 2D structure. These loops are often positioned in expansion segments, which are missing or are not completely developed in the Archaeal and Eubacterial kingdoms. Proper sorting of these sites was a key for constructing an accurate MSA. We utilized DNA sequences from 28 S as an example for one-gene analysis. Five different MSAs were created and analyzed with maximum parsimony and maximum likelihood methods. The phylogenies inferred from the alignments improved with 2D structure with identified homologous segments, and those constructed using the MAFFT alignment program, with all highly variable regions included, provided the most reliable phylograms with higher bootstrap support for the majority of clades. We illustrate and provide examples demonstrating that re-evaluating ambiguous positions in the consensus sequences using 2D structure and covariance is a promising means in order to improve the quality and reliability of sequence alignments.