Cognate ligand domain mapping for enzymes

Here, we present an automatic assignment of potential cognate ligands to domains of enzymes in the CATH and SCOP protein domain classifications on the basis of structural data available in the wwPDB. This procedure involves two steps; firstly, we assign the binding of particular ligands to particular domains; secondly, we compare the chemical similarity of the PDB ligands to ligands in KEGG in order to assign cognate ligands. We find that use of the Enzyme Commission (EC) numbers is necessary to enable efficient and accurate cognate ligand assignment. The PROCOGNATE database currently has cognate ligand mapping for 3277 (4118) protein structures and 351 (302) superfamilies, as described by the CATH and (SCOP) databases, respectively. We find that just under half of all ligands are only and always bound by a single domain, with 16% bound by more than one domain and the remainder of the ligands showing a variety of binding modes. This finding has implications for domain recombination and the evolution of new protein functions. Domain architecture or context is also found to affect substrate specificity of particular domains, and we discuss example cases. The most popular PDB ligands are all found to be generic components of crystallisation buffers, highlighting the non-cognate ligand problem inherent in the PDB. In contrast, the most popular cognate ligands are all found to be universal cellular currencies of reducing power and energy such as NADH, FADH2 and ATP, respectively, reflecting the fact that the vast majority of enzymatic reactions utilise one of these popular co-factors. These ligands all share a common adenine ribonucleotide moiety, suggesting that many different domain superfamilies have converged to bind this chemical framework.     

Quantifying the similarities within fold space

We have used GRATH, a graph-based structure comparison algorithm, to map the similarities between the different folds observed in the CATH domain structure database. Statistical analysis of the distributions of the fold similarities has allowed us to assess the significance for any similarity. Therefore we have examined whether it is best to represent folds as discrete entities or whether, in fact, a more accurate model would be a continuum wherein folds overlap via common motifs. To do this we have introduced a new statistical measure of fold similarity, termed gregariousness. For a particular fold, gregariousness measures how many other folds have a significant structural overlap with that fold, typically comprising 40% or more of the larger structure. Gregarious folds often contain commonly occurring super-secondary structural motifs, such as beta-meanders, greek keys, alpha-beta plait motifs or alpha-hairpins, which are matching similar motifs in other folds. Apart from one example, all the most gregarious folds matching 20% or more of the other folds in the database, are alpha-beta proteins. They also occur in highly populated architectural regions of fold space, adopting sandwich-like arrangements containing two or more layers of alpha-helices and beta-strands.Domains that exhibit a low gregariousness, are those that have very distinctive folds, with few common motifs or motifs that are packed in unusual arrangements. Most of the superhelices exhibit low gregariousness despite containing some commonly occurring super-secondary structural motifs. In these folds, these common motifs are combined in an unusual way and represent a small proportion of the fold (<10%). Our results suggest that fold space may be considered as continuous for some architectural arrangements (e.g. alpha-beta sandwiches), in that super-secondary motifs can be used to link neighbouring fold groups. However, in other regions of fold space much more discrete topologies are observed with little similarity between folds.     

Customised fragments libraries for protein structure prediction based on structural class annotations

Background

Since experimental techniques are time and cost consuming, in silico protein structure prediction is essential to produce conformations of protein targets. When homologous structures are not available, fragment-based protein structure prediction has become the approach of choice. However, it still has many issues including poor performance when targets’ lengths are above 100 residues, excessive running times and sub-optimal energy functions. Taking advantage of the reliable performance of structural class prediction software, we propose to address some of the limitations of fragment-based methods by integrating structural constraints in their fragment selection process.

Results

Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, we evaluated our proposed pipeline on 70 former CASP targets containing up to 150 amino acids. Using either CATH or SCOP-based structural class annotations, enhancement of structure prediction performance is highly significant in terms of both GDT_TS (at least +2.6, p-values < 0.0005) and RMSD (−0.4, p-values < 0.005). Although CATH and SCOP classifications are different, they perform similarly. Moreover, proteins from all structural classes benefit from the proposed methodology. Further analysis also shows that methods relying on class-based fragments produce conformations which are more relevant to user and converge quicker towards the best model as estimated by GDT_TS (up to 10% in average). This substantiates our hypothesis that usage of structurally relevant templates conducts to not only reducing the size of the conformation space to be explored, but also focusing on a more relevant area.

Conclusions

Since our methodology produces models the quality of which is up to 7% higher in average than those generated by a standard fragment-based predictor, we believe it should be considered before conducting any fragment-based protein structure prediction. Despite such progress, ab initio prediction remains a challenging task, especially for proteins of average and large sizes. Apart from improving search strategies and energy functions, integration of additional constraints seems a promising route, especially if they can be accurately predicted from sequence alone.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0576-2) contains supplementary material, which is available to authorized users.     

Predicting protein structural class by SVM with class-wise optimized features and decision probabilities

Determination of protein structural class solely from sequence information is a challenging task. Several attempts to solve this problem using various methods can be found in literature. We present support vector machine (SVM) approach where probability-based decision is used along with class-wise optimized feature sets. This approach has two distinguishing characteristics from earlier attempts: (1) it uses class-wise optimized features and (2) decisions of different SVM classifiers are coupled with probability estimates to make the final prediction. The algorithm was tested on three datasets, containing 498 domains, 1092 domains and 5261 domains. Ten-fold external cross-validation was performed to assess the performance of the algorithm. Significantly high accuracy of 92.89% was obtained for the 498-dataset. We achieved 54.67% accuracy for the dataset with 1092 domains, which is better than the previously reported best accuracy of 53.8%. We obtained 59.43% prediction accuracy for the larger and less redundant 5261-dataset. We also investigated the advantage of using class-wise features over union of these features (conventional approach) in one-vs.-all SVM framework. Our results clearly show the advantage of using class-wise optimized features. Brief analysis of the selected class-wise features indicates their biological significance.     

DCCP and DICP： Construction and Analyses of Databases for Copper- and Iron-Chelating Proteins

Copper and iron play important roles in a variety of biological processes, especially when being chelated with proteins. The proteins involved in the metal binding, transporting and metabolism have aroused much interest. To facilitate the study on this topic, we constructed two databases （DCCP and DICP） containing the known copper- and iron-chelating proteins~ which are freely available from the website http：//sdbi.sdut.edu.cn/en. Users can conveniently search and browse all of the entries in the databases. Based on the two databases, bioinformatic analyses were performed, which provided some novel insights into metalloproteins.     

基于结构分类的蛋白质折叠模式识别方法

对用于折叠模式识别的蛋白质结构数据库进行结构分类,构建了四个分类库：Ａｌ－α库,Ａｌ－β库,α／β库,α＋β库和一个总库,然后分别统计出不同灵敏度的匹配评估函数（平均势）。对不同的平均势,不同结构类型的蛋白进行的检验发现：来源于α／β库的平均势预测能力最强,来源于Ａｌ－α库的平均势预测能力最弱;对α／β蛋白的预测成功率最高,对Ａｌ－α蛋白的预测成功率最低。这与α／β蛋白结构最规则,Ａｌ－α蛋白未加入辅基不能反映出结构的全部特征是相一致的     

Characterizing the existing and potential structural space of proteins by large-scale multiple loop permutations

Worldwide structural genomics projects are increasing structure coverage of sequence space but have not significantly expanded the protein structure space itself (i.e., number of unique structural folds) since 2007. Discovering new structural folds experimentally by directed evolution and random recombination of secondary-structure blocks is also proved rarely successful. Meanwhile, previous computational efforts for large-scale mapping of protein structure space are limited to simple model proteins and led to an inconclusive answer on the completeness of the existing observed protein structure space. Here, we build novel protein structures by extending naturally occurring circular (single-loop) permutation to multiple loop permutations (MLPs). These structures are clustered by structural similarity measure called TM-score. The computational technique allows us to produce different structural clusters on the same naturally occurring, packed, stable core but with alternatively connected secondary-structure segments. A large-scale MLP of 2936 domains from structural classification of protein domains reproduces those existing structural clusters (63%) mostly as hubs for many nonredundant sequences and illustrates newly discovered novel clusters as islands adopted by a few sequences only. Results further show that there exist a significant number of novel potentially stable clusters for medium-size or large-size single-domain proteins, in particular, > 100 amino acid residues, that are either not yet adopted by nature or adopted only by a few sequences. This study suggests that MLP provides a simple yet highly effective tool for engineering and design of novel protein structures (including naturally knotted proteins). The implication of recovering new-fold targets from critical assessment of structure prediction techniques (CASP) by MLP on template-based structure prediction is also discussed. Our MLP structures are available for download at the publication page of the Web site http://sparks.informatics.iupui.edu.     

The domain structure of talin: Residues 1815-1973 form a five-helix bundle containing a cryptic vinculin-binding site

Talin is a large flexible rod-shaped protein that activates the integrin family of cell adhesion molecules and couples them to cytoskeletal actin. Its rod region consists of a series of helical bundles. Here we show that residues 1815-1973 form a 5-helix bundle, with a topology unique to talin which is optimally suited for formation of a long rod such as talin. This is much more stable than the 4-helix (1843-1973) domain described earlier and as a result its vinculin binding sequence is inaccessible to vinculin at room temperature, with implications for the overall mechanism of the talin-vinculin interaction.

Structured summary

MINT-7722300, MINT-7760951: Talin-1 (uniprotkb:P26039) and Vinculin (uniprotkb:P12003) bind (MI:0407) by molecular sieving (MI:0071)     

Sequence and structure classification of kinases

Kinases are a ubiquitous group of enzymes that catalyze the phosphoryl transfer reaction from a phosphate donor (usually ATP) to a receptor substrate. Although all kinases catalyze essentially the same phosphoryl transfer reaction, they display remarkable diversity in their substrate specificity, structure, and the pathways in which they participate. In order to learn the relationship between structural fold and functional specificities in kinases, we have done a comprehensive survey of all available kinase sequences (>17,000) and classified them into 30 distinct families based on sequence similarities. Of these families, 19, covering nearly 98% of all sequences, fall into seven general structural folds for which three-dimensional structures are known. These fold groups include some of the most widespread protein folds, such as Rossmann fold, ferredoxin fold, ribonuclease H fold, and TIM beta/alpha-barrel. On the basis of this classification system, we examined the shared substrate binding and catalytic mechanisms as well as variations of these mechanisms in the same fold groups. Cases of convergent evolution of identical kinase activities occurring in different folds are discussed.     

基于集成分类器的蛋白质折叠模式识别

蛋白质折叠问题被列为"21世纪的生物物理学"的重要课题,他是分子生物学中心法则尚未解决的一个重大生物学问题,因此预测蛋白质折叠模式是一个复杂、困难、和有挑战性的工作。为了解决该问题,我们引入了分类器集成,本文所采用的是三种分类器(LMT、RandomForest、SMO)进行集成以及188维组合理化特征来对蛋白质类别进行预测。实验证明,该方法可以有效表征蛋白质折叠模式的特性,对蛋白质序列数据实现精确分类;交叉验证和独立测试均证明本文预测准确率超过70%,比前人工作提高近10个百分点。     

Structural classification of small, disulfide-rich protein domains

Disulfide-rich domains are small protein domains whose global folds are stabilized primarily by the formation of disulfide bonds and, to a much lesser extent, by secondary structure and hydrophobic interactions. Disulfide-rich domains perform a wide variety of roles functioning as growth factors, toxins, enzyme inhibitors, hormones, pheromones, allergens, etc. These domains are commonly found both as independent (single-domain) proteins and as domains within larger polypeptides. Here, we present a comprehensive structural classification of approximately 3000 small, disulfide-rich protein domains. We find that these domains can be arranged into 41 fold groups on the basis of structural similarity. Our fold groups, which describe broader structural relationships than existing groupings of these domains, bring together representatives with previously unacknowledged similarities; 18 of the 41 fold groups include domains from several SCOP folds. Within the fold groups, the domains are assembled into families of homologs. We define 98 families of disulfide-rich domains, some of which include newly detected homologs, particularly among knottin-like domains. On the basis of this classification, we have examined cases of convergent and divergent evolution of functions performed by disulfide-rich proteins. Disulfide bonding patterns in these domains are also evaluated. Reducible disulfide bonding patterns are much less frequent, while symmetric disulfide bonding patterns are more common than expected from random considerations. Examples of variations in disulfide bonding patterns found within families and fold groups are discussed.