Application of multiplexed cysteine‐labeled complex protein sample for 2D electrophoretic gel alignment

The analysis of cellular subproteomes by 2DE is hampered by the difficulty of aligning gel images from samples that have very different protein composition. Here, we present a sensitive and cost‐effective fluorescent labeling method for analyzing protein samples that is not dependent on their composition. The alignment is guided by inclusion of a complex mixture of proteins that is co‐run with the sample. Maleimide‐conjugated fluorescent dyes Dy‐560 and Dy‐635 are used to label the cysteine residues of the sample of interest and the alignment standard, respectively. The two differently labeled mixtures are then combined and separated on a 2D gel and, after selective fluorescence detection, an unsupervised image registration process is used to align the protein patters. In a pilot study, this protocol significantly improved the accuracy of alignment of nuclear proteins with total cellular proteins.     

Structure-based evaluation of sequence comparison and fold recognition alignment accuracy

The biological role, biochemical function, and structure of uncharacterized protein sequences is often inferred from their similarity to known proteins. A constant goal is to increase the reliability, sensitivity, and accuracy of alignment techniques to enable the detection of increasingly distant relationships. Development, tuning, and testing of these methods benefit from appropriate benchmarks for the assessment of alignment accuracy.Here, we describe a benchmark protocol to estimate sequence-to-sequence and sequence-to-structure alignment accuracy. The protocol consists of structurally related pairs of proteins and procedures to evaluate alignment accuracy over the whole set. The set of protein pairs covers all the currently known fold types. The benchmark is challenging in the sense that it consists of proteins lacking clear sequence similarity.Correct target alignments are derived from the three-dimensional structures of these pairs by rigid body superposition. An evaluation engine computes the accuracy of alignments obtained from a particular algorithm in terms of alignment shifts with respect to the structure derived alignments. Using this benchmark we estimate that the best results can be obtained from a combination of amino acid residue substitution matrices and knowledge-based potentials.     

On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins

In this study, we investigate the extent to which techniques for homology modeling that were developed for water-soluble proteins are appropriate for membrane proteins as well. To this end we present an assessment of current strategies for homology modeling of membrane proteins and introduce a benchmark data set of homologous membrane protein structures, called HOMEP. First, we use HOMEP to reveal the relationship between sequence identity and structural similarity in membrane proteins. This analysis indicates that homology modeling is at least as applicable to membrane proteins as it is to water-soluble proteins and that acceptable models (with C alpha-RMSD values to the native of 2 A or less in the transmembrane regions) may be obtained for template sequence identities of 30% or higher if an accurate alignment of the sequences is used. Second, we show that secondary-structure prediction algorithms that were developed for water-soluble proteins perform approximately as well for membrane proteins. Third, we provide a comparison of a set of commonly used sequence alignment algorithms as applied to membrane proteins. We find that high-accuracy alignments of membrane protein sequences can be obtained using state-of-the-art profile-to-profile methods that were developed for water-soluble proteins. Improvements are observed when weights derived from the secondary structure of the query and the template are used in the scoring of the alignment, a result which relies on the accuracy of the secondary-structure prediction of the query sequence. The most accurate alignments were obtained using template profiles constructed with the aid of structural alignments. In contrast, a simple sequence-to-sequence alignment algorithm, using a membrane protein-specific substitution matrix, shows no improvement in alignment accuracy. We suggest that profile-to-profile alignment methods should be adopted to maximize the accuracy of homology models of membrane proteins.     

Core column prediction for protein multiple sequence alignments

Background

In a computed protein multiple sequence alignment, the coreness of a column is the fraction of its substitutions that are in so-called core columns of the gold-standard reference alignment of its proteins. In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted.

Results

We develop for the first time a predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and hence better estimate the alignment’s accuracy. Our approach to predicting coreness is similar to nearest-neighbor classification from machine learning, except we transform nearest-neighbor distances into a coreness prediction via a regression function, and we learn an appropriate distance function through a new optimization formulation that solves a large-scale linear programming problem. We apply our coreness predictor to parameter advising, the task of choosing parameter values for an aligner’s scoring function to obtain a more accurate alignment of a specific set of sequences. We show that for this task, our predictor strongly outperforms other column-confidence estimators from the literature, and affords a substantial boost in alignment accuracy.

     

Globin-like蛋白质折叠类型识别

蛋白质折叠类型识别是蛋白质结构研究的重要内容.以SCOP中的Globin-like折叠为研究对象,选择其中序列同一性小于25%的17个代表性蛋白质为训练集,采用机器和人工结合的办法进行结构比对,产生序列排比,经过训练得到了适合Globin-like折叠的概形隐马尔科夫模型(profile HMM)用于该折叠类型的识别.以Astrall.65中的68057个结构域样本进行检验,识别敏感度为99.64%,特异性100%.在折叠类型水平上,与Pfam和SUPERFAMILY单纯使用序列比对构建的HMM相比,所用模型由多于100个归为一个,仍然保持了很高的识别效果.结果表明:对序列相似度很低但具有相同折叠类型的蛋白质,可以通过引入结构比对的方法建立统一的HMM模型,实现高准确率的折叠类型识别.     

ORFeus: Detection of distant homology using sequence profiles and predicted secondary structure

ORFeus is a fully automated, sensitive protein sequence similarity search server available to the academic community via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/). The goal of the development of ORFeus was to increase the sensitivity of the detection of distantly related protein families. Predicted secondary structure information was added to the information about sequence conservation and variability, a technique known from hybrid threading approaches. The accuracy of the meta profiles created this way is compared with profiles containing only sequence information and with the standard approach of aligning a single sequence with a profile. Additionally, the alignment of meta profiles is more sensitive in detecting remote homology between protein families than if aligning two sequence-only profiles or if aligning a profile with a sequence. The specificity of the alignment score is improved in the lower specificity range compared with the robust sequence-only profiles.     

Improving the alignment quality of consistency based aligners with an evaluation function using synonymous protein words

Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently.In this paper, we present a flexible similarity measure for residue pairs to improve the quality of protein sequence alignment. Our approach, called SymAlign, relies on the identification of conserved words found across a sizeable fraction of the considered dataset, and supported by evolutionary analysis. These words are then used to define a position specific substitution matrix that better reflects the biological significance of local similarity. The experiment results show that the SymAlign scoring scheme can be incorporated within T-Coffee to improve sequence alignment accuracy. We also demonstrate that SymAlign is less sensitive to the presence of structurally non-similar proteins. In the analysis of the relationship between sequence identity and structure similarity, SymAlign can better differentiate structurally similar proteins from non- similar proteins. We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted n-grams. In our analysis of the alignments thus produced, sequence conservation becomes a better indicator of structural similarity. SymAlign also provides alignment visualization that can display sub-optimal alignments on dot-matrices. The visualization makes it easy to identify well-supported alternative alignments that may not have been identified by dynamic programming. SymAlign is available at http://bio-cluster.iis.sinica.edu.tw/SymAlign/.     

The impact of multiple protein sequence alignment on phylogenetic estimation

Multiple sequence alignment is typically the first step in estimating phylogenetic trees, with the assumption being that as alignments improve, so will phylogenetic reconstructions. Over the last decade or so, new multiple sequence alignment methods have been developed to improve comparative analyses of protein structure, but these new methods have not been typically used in phylogenetic analyses. In this paper, we report on a simulation study that we performed to evaluate the consequences of using these new multiple sequence alignment methods in terms of the resultant phylogenetic reconstruction. We find that while alignment accuracy is positively correlated with phylogenetic accuracy, the amount of improvement in phylogenetic estimation that results from an improved alignment can range from quite small to substantial. We observe that phylogenetic accuracy is most highly correlated with alignment accuracy when sequences are most difficult to align, and that variation in alignment accuracy can have little impact on phylogenetic accuracy when alignment error rates are generally low. We discuss these observations and implications for future work.     

MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison

Advances in structural genomics and protein structure prediction require the design of automatic, fast, objective, and well benchmarked methods capable of comparing and assessing the similarity of low-resolution three-dimensional structures, via experimental or theoretical approaches. Here, a new method for sequence-independent structural alignment is presented that allows comparison of an experimental protein structure with an arbitrary low-resolution protein tertiary model. The heuristic algorithm is given and then used to show that it can describe random structural alignments of proteins with different folds with good accuracy by an extreme value distribution. From this observation, a structural similarity score between two proteins or two different conformations of the same protein is derived from the likelihood of obtaining a given structural alignment by chance. The performance of the derived score is then compared with well established, consensus manual-based scores and data sets. We found that the new approach correlates better than other tools with the gold standard provided by a human evaluator. Timings indicate that the algorithm is fast enough for routine use with large databases of protein models. Overall, our results indicate that the new program (MAMMOTH) will be a good tool for protein structure comparisons in structural genomics applications. MAMMOTH is available from our web site at http://physbio.mssm.edu/~ortizg/.     

Evaluation and improvements in the automatic alignment of protein sequences

The accuracy of protein sequence alignment obtained by applying a commonly used global sequence comparison algorithm is assessed. Alignments based on the superposition of the three-dimensional structures are used as a standard for testing the automatic, sequence-based methods. Alignments obtained from the global comparison of five pairs of homologous protein sequences studied gave 54% agreement overall for residues in secondary structures. The inclusion of information about the secondary structure of one of the proteins in order to limit the number of gaps inserted in regions of secondary structure, improved this figure to 68%. A similarity score of greater than six standard deviation units suggests that an alignment which is greater than 75% correct within secondary structural regions can be obtained automatically for the pair of sequences.     

Fast structure alignment for protein databank searching.

A fast method is described for searching and analyzing the protein structure databank. It uses secondary structure followed by residue matching to compare protein structures and is developed from a previous structural alignment method based on dynamic programming. Linear representations of secondary structures are derived and their features compared to identify equivalent elements in two proteins. The secondary structure alignment then constrains the residue alignment, which compares only residues within aligned secondary structures and with similar buried areas and torsional angles. The initial secondary structure alignment improves accuracy and provides a means of filtering out unrelated proteins before the slower residue alignment stage. It is possible to search or sort the protein structure databank very quickly using just secondary structure comparisons. A search through 720 structures with a probe protein of 10 secondary structures required 1.7 CPU hours on a Sun 4/280. Alternatively, combined secondary structure and residue alignments, with a cutoff on the secondary structure score to remove pairs of unrelated proteins from further analysis, took 10.1 CPU hours. The method was applied in searches on different classes of proteins and to cluster a subset of the databank into structurally related groups. Relationships were consistent with known families of protein structure.     

MUSTANG: a multiple structural alignment algorithm

Multiple structural alignment is a fundamental problem in structural genomics. In this article, we define a reliable and robust algorithm, MUSTANG (MUltiple STructural AligNment AlGorithm), for the alignment of multiple protein structures. Given a set of protein structures, the program constructs a multiple alignment using the spatial information of the C(alpha) atoms in the set. Broadly based on the progressive pairwise heuristic, this algorithm gains accuracy through novel and effective refinement phases. MUSTANG reports the multiple sequence alignment and the corresponding superposition of structures. Alignments generated by MUSTANG are compared with several handcurated alignments in the literature as well as with the benchmark alignments of 1033 alignment families from the HOMSTRAD database. The performance of MUSTANG was compared with DALI at a pairwise level, and with other multiple structural alignment tools such as POSA, CE-MC, MALECON, and MultiProt. MUSTANG performs comparably to popular pairwise and multiple structural alignment tools for closely related proteins, and performs more reliably than other multiple structural alignment methods on hard data sets containing distantly related proteins or proteins that show conformational changes.     

SnapDRAGON: a method to delineate protein structural domains from sequence data

We describe a method to identify protein domain boundaries from sequence information alone based on the assumption that hydrophobic residues cluster together in space. SnapDRAGON is a suite of programs developed to predict domain boundaries based on the consistency observed in a set of alternative ab initio three-dimensional (3D) models generated for a given protein multiple sequence alignment. This is achieved by running a distance geometry-based folding technique in conjunction with a 3D-domain assignment algorithm. The overall accuracy of our method in predicting the number of domains for a non-redundant data set of 414 multiple alignments, representing 185 single and 231 multiple-domain proteins, is 72.4 %. Using domain linker regions observed in the tertiary structures associated with each query alignment as the standard of truth, inter-domain boundary positions are delineated with an accuracy of 63.9 % for proteins comprising continuous domains only, and 35.4 % for proteins with discontinuous domains. Overall, domain boundaries are delineated with an accuracy of 51.8 %. The prediction accuracy values are independent of the pair-wise sequence similarities within each of the alignments. These results demonstrate the capability of our method to delineate domains in protein sequences associated with a wide variety of structural domain organisation.     

Information about the protein secondary structure improves quality of an alignment of protein sequences

All popular algorithms of pair-wise alignment of protein primary structures (e.g. Smith-Waterman (SW), FASTA, BLAST, et al.) utilize only amino acid sequences. The SW-algorithm is the most accurate among them, i.e. it produces alignments that are most similar to the alignments obtained by superposition of protein 3D-structures. But even the SW-algorithm is unable to restore the 3D-based alignment if similarity of amino acid sequences (%id) is below 30%. We have proposed a novel alignment method that explicitly takes into account the secondary structure of the compared proteins. We have shown that it creates significantly more accurate alignments compared to SW-algorithm. In particular, for sequences with %id < 30% the average accuracy of the new method is 58% compared to 35% for SW-algorithm (the accuracy of an algorithmic sequence alignment is the part of restored position of a "golden standard" alignment obtained by superposition of corresponding 3D-structures). The accuracy of the proposed method is approximately identical both for experimental, and for theoretically predicted secondary structures. Thus the method can be applied for alignment of protein sequences even if protein 3D-structure is unknown. The program is available at ftp://194.149.64.196/STRUSWER/.     

Protein threading using PROSPECT: design and evaluation

The computer system PROSPECT for the protein fold recognition using the threading method is described and evaluated in this article. For a given target protein sequence and a template structure, PROSPECT guarantees to find a globally optimal threading alignment between the two. The scoring function for a threading alignment employed in PROSPECT consists of four additive terms: i) a mutation term, ii) a singleton fitness term, iii) a pairwise-contact potential term, and iv) alignment gap penalties. The current version of PROSPECT considers pair contacts only between core (alpha-helix or beta-strand) residues and alignment gaps only in loop regions. PROSPECT finds a globally optimal threading efficiently when pairwise contacts are considered only between residues that are spatially close (7 A or less between the C(beta) atoms in the current implementation). On a test set consisting of 137 pairs of target-template proteins, each pair being from the same superfamily and having sequence identity 相似文献   

Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids

Sequence alignment is a common method for finding protein structurally conserved/similar regions. However, sequence alignment is often not accurate if sequence identities between to-be-aligned sequences are less than 30%. This is because that for these sequences, different residues may play similar structural roles and they are incorrectly aligned during the sequence alignment using substitution matrix consisting of 20 types of residues. Based on the similarity of physicochemical features, residues can be clustered into a few groups. Using such simplified alphabets, the complexity of protein sequences is reduced and at the same time the key information encoded in the sequences remains. As a result, the accuracy of sequence alignment might be improved if the residues are properly clustered. Here, by using a database of aligned protein structures (DAPS), a new clustering method based on the substitution scores is proposed for the grouping of residues, and substitution matrices of residues at different levels of simplification are constructed. The validity of the reduced alphabets is confirmed by relative entropy analysis. The reduced alphabets are applied to recognition of protein structurally conserved/similar regions by sequence alignment. The results indicate that the accuracy or efficiency of sequence alignment can be improved with the optimal reduced alphabet with N around 9.     

A contact energy function considering residue hydrophobic environment and its application in protein fold recognition

The three-dimensional （3D） structure prediction of proteins ：is an important task in bioinformatics. Finding energy functions that can better represent residue-residue and residue-solvent interactions is a crucial way to improve the prediction accu- racy. The widely used contact energy functions mostly only consider the contact frequency between different types of residues; however, we find that the contact frequency also relates to the residue hydrophobic environment. Accordingly, we present an improved contact energy function to integrate the two factors, which can reflect the influence of hydrophobic interaction on the stabilization of protein 3D structure more effectively. Furthermore, a fold recognition （threading） approach based on this energy function is developed. The testing results obtained with 20 randomly selected proteins demonstrate that, compared with common contact energy functions, the proposed energy function can improve the accuracy of the fold template prediction from 20% to 50%, and can also improve the accuracy of the sequence-template alignment from 35% to 65%.     

SPSSM8: An accurate approach for predicting eight-state secondary structures of proteins

Protein eight-state secondary structure prediction is challenging, but is necessary to determine protein structure and function. Here, we report the development of a novel approach, SPSSM8, to predict eight-state secondary structures of proteins accurately from sequences based on the structural position-specific scoring matrix (SPSSM). The SPSSM has been successfully utilized to predict three-state secondary structures. Now we employ an eight-state SPSSM as a feature that is obtained from sequence structure alignment against a large database of 9 million sequences with putative structural information. The SPSSM8 uses a low sequence identity dataset (9062 entries) as a training set and conditional random field for the classification algorithm. The SPSSM8 achieved an average eight-state secondary structure accuracy (Q8) of 71.7% (Q3, 81.6%) for an independent testing set (463 entries), which had an improved accuracy of 10.1% and 4.6% compared with SSPro8 and CNF, respectively, and significantly improved the accuracy of eight-state secondary structure prediction. For CASP 9 dataset (92 entries) the SPSSM8 achieved a Q8 accuracy of 80.1% (Q3, 83.0%). The SPSSM8 was confirmed as an outstanding predictor for eight-state secondary structures of proteins. SPSSM8 is freely available at http://cal.tongji.edu.cn/SPSSM8.     

Structure alignment of membrane proteins: Accuracy of available tools and a consensus strategy

Protein structure alignment methods are used for the detection of evolutionary and functionally related positions in proteins. A wide array of different methods are available, but the choice of the best method is often not apparent to the user. Several studies have assessed the alignment accuracy and consistency of structure alignment methods, but none of these explicitly considered membrane proteins, which are important targets for drug development and have distinct structural features. Here, we compared 13 widely used pairwise structural alignment methods on a test set of homologous membrane protein structures (called HOMEP3). Each pair of structures was aligned and the corresponding sequence alignment was used to construct homology models. The model accuracy compared to the known structures was assessed using scoring functions not incorporated in the tested structural alignment methods. The analysis shows that fragment‐based approaches such as FR‐TM‐align are the most useful for aligning structures of membrane proteins. Moreover, fragment‐based approaches are more suitable for comparison of protein structures that have undergone large conformational changes. Nevertheless, no method was clearly superior to all other methods. Additionally, all methods lack a measure to rate the reliability of a position within a structure alignment. To solve both of these problems, we propose a consensus‐type approach, combining alignments from four different methods, namely FR‐TM‐align, DaliLite, MATT, and FATCAT. Agreement between the methods is used to assign confidence values to each position of the alignment. Overall, we conclude that there remains scope for the improvement of structural alignment methods for membrane proteins. Proteins 2015; 83:1720–1732. © 2015 Wiley Periodicals, Inc.