A reduced amino acid alphabet for understanding and designing protein adaptation to mutation

Protein sequence world is considerably larger than structure world. In consequence, numerous non-related sequences may adopt similar 3D folds and different kinds of amino acids may thus be found in similar 3D structures. By grouping together the 20 amino acids into a smaller number of representative residues with similar features, sequence world simplification may be achieved. This clustering hence defines a reduced amino acid alphabet (reduced AAA). Numerous works have shown that protein 3D structures are composed of a limited number of building blocks, defining a structural alphabet. We previously identified such an alphabet composed of 16 representative structural motifs (5-residues length) called Protein Blocks (PBs). This alphabet permits to translate the structure (3D) in sequence of PBs (1D). Based on these two concepts, reduced AAA and PBs, we analyzed the distributions of the different kinds of amino acids and their equivalences in the structural context. Different reduced sets were considered. Recurrent amino acid associations were found in all the local structures while other were specific of some local structures (PBs) (e.g Cysteine, Histidine, Threonine and Serine for the alpha-helix Ncap). Some similar associations are found in other reduced AAAs, e.g Ile with Val, or hydrophobic aromatic residues Trp with Phe and Tyr. We put into evidence interesting alternative associations. This highlights the dependence on the information considered (sequence or structure). This approach, equivalent to a substitution matrix, could be useful for designing protein sequence with different features (for instance adaptation to environment) while preserving mainly the 3D fold.     

Searching for protein signatures using a multilevel alphabet

Short motifs are known to play diverse roles in proteins, such as in mediating the interactions with other molecules, binding to membranes, or conducting a specific biological function. Standard approaches currently employed to detect short motifs in proteins search for enrichment of amino acid motifs considering mostly the sequence information. Here, we presented a new approach to search for common motifs (protein signatures) which share both physicochemical and structural properties, looking simultaneously at different features. Our method takes as an input an amino acid sequence and translates it to a new alphabet that reflects its intrinsic structural and chemical properties. Using the MEME search algorithm, we identified the proteins signatures within subsets of protein which encompass common sequence and structural information. We demonstrated that we can detect enriched structural motifs, such as the amphipathic helix, from large datasets of linear sequences, as well as predicting common structural properties (such as disorder, surface accessibility, or secondary structures) of known functional‐motifs. Finally, we applied the method to the yeast protein interactome and identified novel putative interacting motifs. We propose that our approach can be applied for de novo protein function prediction given either sequence or structural information. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Protein multiple alignment incorporating primary and secondary structure information.

Identifying common local segments, also called motifs, in multiple protein sequences plays an important role for establishing homology between proteins. Homology is easy to establish when sequences are similar (sharing an identity > 25%). However, for distant proteins, it is much more difficult to align motifs that are not similar in sequences but still share common structures or functions. This paper is a first attempt to align multiple protein sequences using both primary and secondary structure information. A new sequence model is proposed so that the model assigns high probabilities not only to motifs that contain conserved amino acids but also to motifs that present common secondary structures. The proposed method is tested in a structural alignment database BAliBASE. We show that information brought by the predicted secondary structures greatly improves motif identification. A website of this program is available at www.stat.purdue.edu/~junxie/2ndmodel/sov.html.     

Stylus: a system for evolutionary experimentation based on a protein/proteome model with non-arbitrary functional constraints

The study of protein evolution is complicated by the vast size of protein sequence space, the huge number of possible protein folds, and the extraordinary complexity of the causal relationships between protein sequence, structure, and function. Much simpler model constructs may therefore provide an attractive complement to experimental studies in this area. Lattice models, which have long been useful in studies of protein folding, have found increasing use here. However, while these models incorporate actual sequences and structures (albeit non-biological ones), they incorporate no actual functions--relying instead on largely arbitrary structural criteria as a proxy for function. In view of the central importance of function to evolution, and the impossibility of incorporating real functional constraints without real function, it is important that protein-like models be developed around real structure-function relationships. Here we describe such a model and introduce open-source software that implements it. The model is based on the structure-function relationship in written language, where structures are two-dimensional ink paths and functions are the meanings that result when these paths form legible characters. To capture something like the hierarchical complexity of protein structure, we use the traditional characters of Chinese origin. Twenty coplanar vectors, encoded by base triplets, act like amino acids in building the character forms. This vector-world model captures many aspects of real proteins, including life-size sequences, a life-size structural repertoire, a realistic genetic code, secondary, tertiary, and quaternary structure, structural domains and motifs, operon-like genetic structures, and layered functional complexity up to a level resembling bacterial genomes and proteomes. Stylus is a full-featured implementation of the vector world for Unix systems. To demonstrate the utility of Stylus, we generated a sample set of homologous vector proteins by evolving successive lines from a single starting gene. These homologues show sequence and structure divergence resembling those of natural homologues in many respects, suggesting that the system may be sufficiently life-like for informative comparison to biology.     

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.     

Measurements of protein sequence-structure correlations

Correlations between protein structures and amino acid sequences are widely used for protein structure prediction. For example, secondary structure predictors generally use correlations between a secondary structure sequence and corresponding primary structure sequence, whereas threading algorithms and similar tertiary structure predictors typically incorporate interresidue contact potentials. To investigate the relative importance of these sequence-structure interactions, we measured the mutual information among the primary structure, secondary structure and side-chain surface exposure, both for adjacent residues along the amino acid sequence and for tertiary structure contacts between residues distantly separated along the backbone. We found that local interactions along the amino acid chain are far more important than non-local contacts and that correlations between proximate amino acids are essentially uninformative. This suggests that knowledge-based contact potentials may be less important for structure predication than is generally believed.     

A test of proposed rules for helix capping: implications for protein design

alpha-helices within proteins are often terminated (capped) by distinctive configurations of the polypeptide chain. Two common arrangements are the Schellman motif and the alternative alpha(L) motif. Rose and coworkers developed stereochemical rules to identify the locations of such motifs in proteins of unknown structure based only on their amino acid sequences. To check the effectiveness of these rules, they made specific predictions regarding the structural and thermodynamic consequences of certain mutations in T4 lysozyme. We have constructed these mutants and show here that they have neither the structure nor the stability that was predicted. The results show the complexity of the protein-folding problem. Comparison of known protein structures may show that a characteristic sequence of amino acids (a sequence motif) corresponds to a conserved structural motif. In any particular protein, however, changes in other parts of the sequence may result in a different conformation. The structure is determined by sequence as a whole, not by parts considered in isolation.     

Evaluation performance of substitution matrices,based on contacts between residue terminal groups

Sequence alignment is a standard method for the estimation of the evolutionary, structural, and functional relationships among amino acid sequences. The quality of alignments depends on the used similarity matrix. Statistical contact potentials (CPs) contain information on contact propensities among residues in native protein structures. Substitution matrices (SMs) based on CPs are applicable for the comparison of distantly related sequences. Here, contact between amino acids was estimated on the basis of the evaluation of the distances between side-chain terminal groups (SCTGs), which are defined as the group of the side-chain heavy atoms with fixed distances between them. In this paper, two new types of CPs and similarity matrices have been constructed: one based on fixed cutoff distance obtained from geometric characteristics of the SCTGs (TGC1), while the other is distance-dependent potential (TGC2). These matrices are compared with other popular SMs. The performance of the matrices was evaluated by comparing sequence with structural alignments. The obtained results show that TGC2 has the best performance among contact-based matrices, but on the whole, contact-based matrices have slightly lower performance than other SMs except fold-level similarity.     

一种新的蛋白质序列的向量表示方法及其应用

为了更多地挖掘隐藏在蛋白质序列中的信息,本研究将20种氨基酸均匀地排列在单位圆周上,得到每种氨基酸对应的二维坐标,再与氨基酸的6个理化指标结合起来,最终用一个八维向量来刻画蛋白质序列。为避免数据极差对分析结果造成的影响,本研究对蛋白质序列所对应的八维向量作归一化处理。基于归一化后的蛋白质序列的向量表示,运用神经网络对蛋白质序列进行分类,并根据向量之间的欧式距离来量化序列之间的相似性。最后,以9个不同物种的ND5蛋白质序列以及8个不同物种的ND6蛋白质序列为例,Clustal W序列比对方法为基准,对本研究的方法与5-字母方法进行验证和比较,结果表明本研的方法是有效的。     

Automated search of natively folded protein fragments for high-throughput structure determination in structural genomics

Structural genomic projects envision almost routine protein structure determinations, which are currently imaginable only for small proteins with molecular weights below 25,000 Da. For larger proteins, structural insight can be obtained by breaking them into small segments of amino acid sequences that can fold into native structures, even when isolated from the rest of the protein. Such segments are autonomously folding units (AFU) and have sizes suitable for fast structural analyses. Here, we propose to expand an intuitive procedure often employed for identifying biologically important domains to an automatic method for detecting putative folded protein fragments. The procedure is based on the recognition that large proteins can be regarded as a combination of independent domains conserved among diverse organisms. We thus have developed a program that reorganizes the output of BLAST searches and detects regions with a large number of similar sequences. To automate the detection process, it is reduced to a simple geometrical problem of recognizing rectangular shaped elevations in a graph that plots the number of similar sequences at each residue of a query sequence. We used our program to quantitatively corroborate the premise that segments with conserved sequences correspond to domains that fold into native structures. We applied our program to a test data set composed of 99 amino acid sequences containing 150 segments with structures listed in the Protein Data Bank, and thus known to fold into native structures. Overall, the fragments identified by our program have an almost 50% probability of forming a native structure, and comparable results are observed with sequences containing domain linkers classified in SCOP. Furthermore, we verified that our program identifies AFU in libraries from various organisms, and we found a significant number of AFU candidates for structural analysis, covering an estimated 5 to 20% of the genomic databases. Altogether, these results argue that methods based on sequence similarity can be useful for dissecting large proteins into small autonomously folding domains, and such methods may provide an efficient support to structural genomics projects.     

Protein sequence randomness and sequence/structure correlations.

We investigated protein sequence/structure correlation by constructing a space of protein sequences, based on methods developed previously for constructing a space of protein structures. The space is constructed by using a representation of the amino acids as vectors of 10 property factors that encode almost all of their physical properties. Each sequence is represented by a distribution of overlapping sequence fragments. A distance between any two sequences can be calculated. By attaching a weight to each factor, intersequence distances can be varied. We optimize the correlation between corresponding distances in the sequence and structure spaces. The optimal correlation between the sequence and structure spaces is significantly better than that which results from correlating randomly generated sequences, having the overall composition of the data base, with the structure space. However, sets of randomly generated sequences, each of which approximates the composition of the real sequence it replaces, produce correlations with the structure space that are as good as that observed for the actual protein sequences. A connection is proposed with previous studies of the protein folding code. It is shown that the most important property factors for the correlation of the sequence and structure spaces are related to helix/bend preference, side chain bulk, and beta-structure preference.     

Exploring an alignment free approach for protein classification and structural class prediction

Alignment free methods based on Chaos Game Representation (CGR), also known as sequence signature approaches, have proven of great interest for DNA sequence analysis. Indeed, they have been successfully applied for sequence comparison, phylogeny, detection of horizontal transfers or extraction of representative motifs in regulation sequences. Transposing such methods to proteins poses several fundamental questions related to representation space dimensionality. Several studies have tackled these points, but none has, so far, brought the application of CGRs to proteins to their fully expected potential. Yet, several studies have shown that techniques based on n-peptide frequencies can be relevant for proteins. Here, we investigate the effectiveness of a strategy based on the CGR approach using a fixed reverse encoding of amino acids into nucleic sequences. We first explore its relevance to protein classification into functional families. We then attempt to apply it to the prediction of protein structural classes. Our results suggest that the reverse encoding approach could be relevant in both cases. We show that it is able to classify functional families of proteins by extracting signatures close to the ProSite patterns. Applied to structural classification, the approach reaches scores of correct classification close to 84%, i.e. close to the scores of related methods in the field. Various optimizations of the approach are still possible, which open the door for future applications.     

基于氨基酸特征序列对人类Rh血型系统的蛋白质结构分析

利用代数学中同态思想和物理中的“粗粒化”思想,以及HP模型,根据a,t,c,g的化学结构分类,提出了DNA序列的特征序列概念（σ-,τ-,σ∩τ-）并推广到蛋白质序列中,从而给出一种数值刻划,将蛋白质序列简化成一个（0,1）序列,基于上述给出特征序列的方法,根据氨基酸分子量与简并度的关系,提出了另外一种DNA序列的特征序列概念（-）并推广到蛋白质序列中,进而给出了另外一种数值刻划,将蛋白质序列简化成一个（0,1,2）序列,通过比较RHD基因和RHCE基因的特征序列的数值刻划图,得出RHD基因和RHCE基因均偏爱使用低分子量且高简并度的氨基酸。     

Discovery of local packing motifs in protein structures

We present a language for describing structural patterns of residues in protein structures and a method for the discovery of such patterns that recur in a set of protein structures. The patterns impose restrictions on the spatial position of each residue, their order along the amino acid chain, and which amino acids are allowed in each position. Unlike other methods for comparing sets of protein structures, our method is not based on the use of pairwise structure comparisons which is often time consuming and can produce inconsistent results. Instead, the method simultaneously takes into account information from all structures in the search for conserved structure patterns which are potential structure motifs. The method is based on describing the spatial neighborhoods of each residue in each structure as a string and applying a sequence pattern discovery method to find patterns common to subsets of these strings. Finally it is checked whether the similarities between the neighborhood strings correspond to spatially similar substructures. We apply the method to analyze sets of very disparate proteins from the four different protein families: serine proteases, cuprodoxins, cysteine proteinases, and ferredoxins. The motifs found by the method correspond well to the site and motif information given in the annotation of these proteins in PDB, Swiss-Prot, and PROSITE. Furthermore, the motifs are confirmed by using the motif data to constrain the structural alignment of the proteins obtained with the program SAP. This gave the best superposition/alignment of the proteins given the motif assignment.     

基于K-mer扭转角偏好的蛋白质结构类型预测

蛋白质的序列、结构和功能多种多样。大量研究表明蛋白质的结构与其氨基酸序列的排序有关,并且局部的氨基酸序列环境对蛋白质的结构具有一定的影响。本文提出一种新的基于5-mer氨基酸扭转角统计偏好的蛋白质结构类型预测方法,该方法通过PDB数据库中5-mer中间氨基酸的扭转角统计偏好来进行结构类型的预测。新方法可以通过计算机仿真实现对新蛋白质序列结构类型的快速预测,并通过两组随机抽取的CATH数据验证了新方法的有效性。     

Word Decoding of Protein Amino Acid Sequences with Availability Analysis: A Linguistic Approach

The amino acid sequences of proteins determine their three-dimensional structures and functions. However, how sequence information is related to structures and functions is still enigmatic. In this study, we show that at least a part of the sequence information can be extracted by treating amino acid sequences of proteins as a collection of English words, based on a working hypothesis that amino acid sequences of proteins are composed of short constituent amino acid sequences (SCSs) or “words”. We first confirmed that the English language highly likely follows Zipf''s law, a special case of power law. We found that the rank-frequency plot of SCSs in proteins exhibits a similar distribution when low-rank tails are excluded. In comparison with natural English and “compressed” English without spaces between words, amino acid sequences of proteins show larger linear ranges and smaller exponents with heavier low-rank tails, demonstrating that the SCS distribution in proteins is largely scale-free. A distribution pattern of SCSs in proteins is similar among species, but species-specific features are also present. Based on the availability scores of SCSs, we found that sequence motifs are enriched in high-availability sites (i.e., “key words”) and vice versa. In fact, the highest availability peak within a given protein sequence often directly corresponds to a sequence motif. The amino acid composition of high-availability sites within motifs is different from that of entire motifs and all protein sequences, suggesting the possible functional importance of specific SCSs and their compositional amino acids within motifs. We anticipate that our availability-based word decoding approach is complementary to sequence alignment approaches in predicting functionally important sites of unknown proteins from their amino acid sequences.     

ChSeq: A database of chameleon sequences

Chameleon sequences (ChSeqs) refer to sequence strings of identical amino acids that can adopt different conformations in protein structures. Researchers have detected and studied ChSeqs to understand the interplay between local and global interactions in protein structure formation. The different secondary structures adopted by one ChSeq challenge sequence‐based secondary structure predictors. With increasing numbers of available Protein Data Bank structures, we here identify a large set of ChSeqs ranging from 6 to 10 residues in length. The homologous ChSeqs discovered highlight the structural plasticity involved in biological function. When compared with previous studies, the set of unrelated ChSeqs found represents an about 20‐fold increase in the number of detected sequences, as well as an increase in the longest ChSeq length from 8 to 10 residues. We applied secondary structure predictors on our ChSeqs and found that methods based on a sequence profile outperformed methods based on a single sequence. For the unrelated ChSeqs, the evolutionary information provided by the sequence profile typically allows successful prediction of the prevailing secondary structure adopted in each protein family. Our dataset will facilitate future studies of ChSeqs, as well as interpretations of the interplay between local and nonlocal interactions. A user‐friendly web interface for this ChSeq database is available at prodata.swmed.edu/chseq .     

Structural characterization of proteins using residue environments

A primary challenge for structural genomics is the automated functional characterization of protein structures. We have developed a sequence-independent method called S-BLEST (Structure-Based Local Environment Search Tool) for the annotation of previously uncharacterized protein structures. S-BLEST encodes the local environment of an amino acid as a vector of structural property values. It has been applied to all amino acids in a nonredundant database of protein structures to generate a searchable structural resource. Given a query amino acid from an experimentally determined or modeled structure, S-BLEST quickly identifies similar amino acid environments using a K-nearest neighbor search. In addition, the method gives an estimation of the statistical significance of each result. We validated S-BLEST on X-ray crystal structures from the ASTRAL 40 nonredundant dataset. We then applied it to 86 crystallographically determined proteins in the protein data bank (PDB) with unknown function and with no significant sequence neighbors in the PDB. S-BLEST was able to associate 20 proteins with at least one local structural neighbor and identify the amino acid environments that are most similar between those neighbors.