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Novel feature for catalytic protein residues reflecting interactions with other residues.

ABSTRACT: Owing to their potential for systematic analysis, complex networks have been widely used in proteomics. Representing a protein structure as a topology network provides novel insight into understanding protein folding mechanisms, stability and function. Here, we develop a new feature to reveal correlations between residues using a protein structure network. In an original attempt to quantify the effects of several key residues on catalytic residues, a power function was used to model interactions between residues. The results indicate that focusing on a few residues is a feasible approach to identifying catalytic residues. The spatial environment surrounding a catalytic residue was analyzed in a layered manner. We present evidence that correlation between residues is related to their distance apart most environmental parameters of the outer layer make a smaller contribution to prediction and ii catalytic residues tend to be located near key positions in enzyme folds. Feature analysis revealed satisfactory performance for our features, which were combined with several conventional features in a prediction model for catalytic residues using a comprehensive data set from the Catalytic Site Atlas. Values of 88.6 for sensitivity and 88.4 for specificity were obtained by 10-fold cross-validation. These results suggest that these features reveal the mutual dependence of residues and are promising for further study of structure-function relationship.

SUBMITTER: Li Y

PROVIDER: S-EPMC3066176 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Novel feature for catalytic protein residues reflecting interactions with other residues.

Li Yizhou Y Li Gongbing G Wen Zhining Z Yin Hui H Hu Mei M Xiao Jiamin J Li Menglong M

PloS one 20110329 3

Owing to their potential for systematic analysis, complex networks have been widely used in proteomics. Representing a protein structure as a topology network provides novel insight into understanding protein folding mechanisms, stability and function. Here, we develop a new feature to reveal correlations between residues using a protein structure network. In an original attempt to quantify the effects of several key residues on catalytic residues, a power function was used to model interactions ...[more]

PMID: 21468322

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Project description:Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation.We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods.We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

Project description:BACKGROUND:Protein-protein interactions are crucial for normal biological processes and to regulate cellular reactions that affect gene expression and function. Several previous studies have emphasized the roles of residues at the interface of protein-protein complexes in conferring stability and specificity to the complex. Interface residues in a protein are well known for their interactions with sidechain and main chain atoms with the interacting protein. However, the extent of intra-protein interactions involving interface residues in a protein-protein complex and their relative contribution in comparison to inter-protein interactions are not clearly understood. This paper probes this feature using a dataset of protein-protein complexes of known 3-D structure. RESULTS:We have analysed a dataset of 45 transient protein-protein complex structures with at least one of the interacting proteins with a known structure available also in the unbound form. We observe that a large proportion of interface residues (1608 out of 2137 interface residues, 75%) are involved in intra and inter-protein interactions simultaneously. The amino acid propensities of such interfacial residues involved in bifurcated interactions are found to be highly similar to the general propensities to occur at protein-protein interfaces. Finally, we observe that a majority (83%) of intra-protein interactions of interface residues with bifurcated interactions, are also observed in the protein uncomplexed form. CONCLUSIONS:We have shown, to the best of our knowledge for the first time, that a vast majority of the protein-protein interface residues are involved in extensive intra-protein interactions apart from inter-protein interactions. For a majority of such interface residues the microenvironment in the tertiary structure is pre-formed and retained upon complex formation with its cognate partner during transient interactions. REVIEWERS:This article was reviewed by Arumay Pal and Mallur Madhusudhan.

Project description:The study of protein-protein interactions (PPIs) can be very important for the understanding of biological cellular functions. However, detecting PPIs in the laboratories are both time-consuming and expensive. For this reason, there has been much recent effort to develop techniques for computational prediction of PPIs as this can complement laboratory procedures and provide an inexpensive way of predicting the most likely set of interactions at the entire proteome scale. Although much progress has already been achieved in this direction, the problem is still far from being solved. More effective approaches are still required to overcome the limitations of the current ones. In this study, a novel Multi-scale Local Descriptor (MLD) feature representation scheme is proposed to extract features from a protein sequence. This scheme can capture multi-scale local information by varying the length of protein-sequence segments. Based on the MLD, an ensemble learning method, the Random Forest (RF) method, is used as classifier. The MLD feature representation scheme facilitates the mining of interaction information from multi-scale continuous amino acid segments, making it easier to capture multiple overlapping continuous binding patterns within a protein sequence. When the proposed method is tested with the PPI data of Saccharomyces cerevisiae, it achieves a prediction accuracy of 94.72% with 94.34% sensitivity at the precision of 98.91%. Extensive experiments are performed to compare our method with existing sequence-based method. Experimental results show that the performance of our predictor is better than several other state-of-the-art predictors also with the H. pylori dataset. The reason why such good results are achieved can largely be credited to the learning capabilities of the RF model and the novel MLD feature representation scheme. The experiment results show that the proposed approach can be very promising for predicting PPIs and can be a useful tool for future proteomic studies.

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Novel feature for catalytic protein residues reflecting interactions with other residues.

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Novel feature for catalytic protein residues reflecting interactions with other residues.

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