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A comprehensive analysis of the structure-function relationship in proteins based on local structure similarity.

ABSTRACT: BACKGROUND:Sequence similarity to characterized proteins provides testable functional hypotheses for less than 50% of the proteins identified by genome sequencing projects. With structural genomics it is believed that structural similarities may give functional hypotheses for many of the remaining proteins. METHODOLOGY/PRINCIPAL FINDINGS:We provide a systematic analysis of the structure-function relationship in proteins using the novel concept of local descriptors of protein structure. A local descriptor is a small substructure of a protein which includes both short- and long-range interactions. We employ a library of commonly reoccurring local descriptors general enough to assemble most existing protein structures. We then model the relationship between these local shapes and Gene Ontology using rule-based learning. Our IF-THEN rule model offers legible, high resolution descriptions that combine local substructures and is able to discriminate functions even for functionally versatile folds such as the frequently occurring TIM barrel and Rossmann fold. By evaluating the predictive performance of the model, we provide a comprehensive quantification of the structure-function relationship based only on local structure similarity. Our findings are, among others, that conserved structure is a stronger prerequisite for enzymatic activity than for binding specificity, and that structure-based predictions complement sequence-based predictions. The model is capable of generating correct hypotheses, as confirmed by a literature study, even when no significant sequence similarity to characterized proteins exists. CONCLUSIONS/SIGNIFICANCE:Our approach offers a new and complete description and quantification of the structure-function relationship in proteins. By demonstrating how our predictions offer higher sensitivity than using global structure, and complement the use of sequence, we show that the presented ideas could advance the development of meta-servers in function prediction.

SUBMITTER: Hvidsten TR

PROVIDER: S-EPMC2705683 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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A comprehensive analysis of the structure-function relationship in proteins based on local structure similarity.

Hvidsten Torgeir R TR Laegreid Astrid A Kryshtafovych Andriy A Andersson Gunnar G Fidelis Krzysztof K Komorowski Jan J

PloS one 20090715 7

<h4>Background</h4>Sequence similarity to characterized proteins provides testable functional hypotheses for less than 50% of the proteins identified by genome sequencing projects. With structural genomics it is believed that structural similarities may give functional hypotheses for many of the remaining proteins.<h4>Methodology/principal findings</h4>We provide a systematic analysis of the structure-function relationship in proteins using the novel concept of local descriptors of protein struc ...[more]

PMID: 19603073

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Project description:BackgroundWhen characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.ResultsIn this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an n-gram pattern of amino acids that reflects the sequence variation in a protein's evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.On a large non-redundant dataset of 8,297 protein chains (DsspNr-25), the average Q3 of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (EVA Set_1 and EVA_Set2), the average Q3 of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases.ConclusionsOur experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at http://bio-cluster.iis.sinica.edu.tw/SymPred/.

Project description:Cytoplasmic I?B proteins are primary regulators that interact with NF-?B subunits in the cytoplasm of unstimulated cells. Upon stimulation, these I?B proteins are rapidly degraded, thus allowing NF-?B to translocate into the nucleus and activate the transcription of genes encoding various immune mediators. Subsequent to translocation, nuclear I?B proteins play an important role in the regulation of NF-?B transcriptional activity by acting either as activators or inhibitors. To date, molecular basis for the binding of I?B?, I?B? and I?B? along with their partners is known; however, the activation and inhibition mechanism of the remaining I?B (I?BNS, I?B? and Bcl-3) proteins remains elusive. Moreover, even though I?B proteins are structurally similar, it is difficult to determine the exact specificities of I?B proteins towards their respective binding partners. The three-dimensional structures of I?BNS, I?B? and I?B? were modeled. Subsequently, we used an explicit solvent method to perform detailed molecular dynamic simulations of these proteins along with their known crystal structures (I?B?, I?B? and Bcl-3) in order to investigate the flexibility of the ankyrin repeat domains (ARDs). Furthermore, the refined models of I?BNS, I?B? and Bcl-3 were used for multiple protein-protein docking studies for the identification of I?BNS-p50/p50, I?B?-p50/p65 and Bcl-3-p50/p50 complexes in order to study the structural basis of their activation and inhibition. The docking experiments revealed that I?B? masked the nuclear localization signal (NLS) of the p50/p65 subunits, thereby preventing its translocation into the nucleus. For the Bcl-3- and I?BNS-p50/p50 complexes, the results show that Bcl-3 mediated transcription through its transactivation domain (TAD) while I?BNS inhibited transcription due to its lack of a TAD, which is consistent with biochemical studies. Additionally, the numbers of identified flexible residues were equal in number among all I?B proteins, although they were not conserved. This could be the primary reason for their binding partner specificities.

Project description:Local protein structure representations that incorporate long-range contacts between residues are often considered in protein structure comparison but have found relatively little use in structure prediction where assembly from single backbone fragments dominates. Here, we introduce the concept of local descriptors of protein structure to characterize local neighborhoods of amino acids including short- and long-range interactions. We build a library of recurring local descriptors and show that this library is general enough to allow assembly of unseen protein structures. The library could on average re-assemble 83% of 119 unseen structures, and showed little or no performance decrease between homologous targets and targets with folds not represented among domains used to build it. We then systematically evaluate the descriptor library to establish the level of the sequence signal in sets of protein fragments of similar geometrical conformation. In particular, we test whether that signal is strong enough to facilitate correct assignment and alignment of these local geometries to new sequences. We use the signal to assign descriptors to a test set of 479 sequences with less than 40% sequence identity to any domain used to build the library, and show that on average more than 50% of the backbone fragments constituting descriptors can be correctly aligned. We also use the assigned descriptors to infer SCOP folds, and show that correct predictions can be made in many of the 151 cases where PSI-BLAST was unable to detect significant sequence similarity to proteins in the library. Although the combinatorial problem of simultaneously aligning several fragments to sequence is a major bottleneck compared with single fragment methods, the advantage of the current approach is that correct alignments imply correct long range distance constraints. The lack of these constraints is most likely the major reason why structure prediction methods fail to consistently produce adequate models when good templates are unavailable or undetectable. Thus, we believe that the current study offers new and valuable insight into the prediction of sequence-structure relationships in proteins.

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A comprehensive analysis of the structure-function relationship in proteins based on local structure similarity.

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A comprehensive analysis of the structure-function relationship in proteins based on local structure similarity.

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