Dataset Information

DDOMAIN: Dividing structures into domains using a normalized domain-domain interaction profile.

ABSTRACT: Dividing protein structures into domains is proven useful for more accurate structural and functional characterization of proteins. Here, we develop a method, called DDOMAIN, that divides structure into DOMAINs using a normalized contact-based domain-domain interaction profile. Results of DDOMAIN are compared to AUTHORS annotations (domain definitions are given by the authors who solved protein structures), as well as to popular SCOP and CATH annotations by human experts and automatic programs. DDOMAIN's automatic annotations are most consistent with the AUTHORS annotations (90% agreement in number of domains and 88% agreement in both number of domains and at least 85% overlap in domain assignment of residues) if its three adjustable parameters are trained by the AUTHORS annotations. By comparison, the agreement is 83% (81% with at least 85% overlap criterion) between SCOP-trained DDOMAIN and SCOP annotations and 77% (73%) between CATH-trained DDOMAIN and CATH annotations. The agreement between DDOMAIN and AUTHORS annotations goes beyond single-domain proteins (97%, 82%, and 56% for single-, two-, and three-domain proteins, respectively). For an "easy" data set of proteins whose CATH and SCOP annotations agree with each other in number of domains, the agreement is 90% (89%) between "easy-set"-trained DDOMAIN and CATH/SCOP annotations. The consistency between SCOP-trained DDOMAIN and SCOP annotations is superior to two other recently developed, SCOP-trained, automatic methods PDP (protein domain parser), and DomainParser 2. We also tested a simple consensus method made of PDP, DomainParser 2, and DDOMAIN and a different version of DDOMAIN based on a more sophisticated statistical energy function. The DDOMAIN server and its executable are available in the services section on http://sparks.informatics.iupui.edu.

SUBMITTER: Zhou H

PROVIDER: S-EPMC2206635 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature

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DDOMAIN: Dividing structures into domains using a normalized domain-domain interaction profile.

Zhou Hongyi H Xue Bin B Zhou Yaoqi Y

Protein science : a publication of the Protein Society 20070501 5

Dividing protein structures into domains is proven useful for more accurate structural and functional characterization of proteins. Here, we develop a method, called DDOMAIN, that divides structure into DOMAINs using a normalized contact-based domain-domain interaction profile. Results of DDOMAIN are compared to AUTHORS annotations (domain definitions are given by the authors who solved protein structures), as well as to popular SCOP and CATH annotations by human experts and automatic programs. ...[more]

PMID: 17456745

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Project description:BackgroundThe increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. 1 and more recently by Valdar et al. 2 and Mintseris et al. 3, from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation.ResultsWe have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy.ConclusionWe have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.

Project description:BackgroundWe can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge.ResultsWe present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches.ConclusionsWe have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions.Executable files and examples are given at: http://www.bhsai.org/downloads/padds/

Project description:AimAT-rich interaction domain 1A (ARID1A) encodes a key component of the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complex that participates in gene expression. ARID1A alterations are quite common among cancer patients, although their role remains debated. The aim of this article was to study ARID1A-mutated cancer patients.MethodsMolecular and clinical data of cancer patients evaluated at Phase 1 Unit of Fondazione Policlinico Universitario A. Gemelli IRCCS were collected. Molecular analyses were performed using FoundationOne® CDx (Foundation Medicine Inc., Cambridge, MA, United States). Cancer patients with at least one molecular alteration in ARID1A gene were identified as ARID1A+.ResultsAmong the 270 patients undergoing molecular analysis, we found 25 (9%) with at least one pathogenic alteration in ARID1A. The vast majority of these patients were female (84%). The median age at diagnosis was 59; most of the cancers (15, 60%) were gynecological (especially endometrioid endometrial cancers and clear cell ovarian cancers), diagnosed at an early stage. Frameshift alterations in ARID1A were the most common (19/31, 61%) alterations. The median number of mutations in ARID1A+ population was higher compared to ARID1A- population (6 vs. 4), as well as tumor mutational burden (TMB) [20 mutations/megabase (mut/Mb) vs. 1.26 mut/Mb]. Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), phosphatase and tensin homolog (PTEN), catenin beta 1 (CTNNB1), and lysine methyltransferase 2D (MLL2) mutations were enriched in ARID1A+ population. In this cohort, ARID1A did not display any relation with response to platinum chemotherapy. Cancers with double alterations in ARID1A (ARID1A2+) were all gynecological cancers (83% endometrioid endometrial cancers).ConclusionsThis analysis provides clinical and molecular details about the phenotypes of ARID1A+ cancers, in particular the subgroup of gynecologic cancers. The high frequency of concurrent mutations in the phosphoinositide 3-kinase (PI3K) pathway among endometrioid endometrial cancers may support the proposal of a new treatment strategy based on the combination of ataxia telangiectasia and Rad3-related (ATR) inhibitor and PIK3CA inhibitor.

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DDOMAIN: Dividing structures into domains using a normalized domain-domain interaction profile.

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DDOMAIN: Dividing structures into domains using a normalized domain-domain interaction profile.

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