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A PDB-wide, evolution-based assessment of protein-protein interfaces.

Baskaran K, Duarte JM, Biyani N, Bliven S, Capitani G - BMC Struct. Biol. (2014)

Bottom Line: An automated computational pipeline was developed to run our Evolutionary Protein-Protein Interface Classifier (EPPIC) software on the entire PDB and store the results in a relational database, currently containing > 800,000 interfaces.By comparing our safest predictions to the PDB author annotations, we provide a lower-bound estimate of the error rate of biological unit annotations in the PDB.These tools enable the comprehensive study of several aspects of protein-protein contacts in the PDB and represent a basis for future, even larger scale studies of protein-protein interactions.

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ABSTRACT

Background: Thanks to the growth in sequence and structure databases, more than 50 million sequences are now available in UniProt and 100,000 structures in the PDB. Rich information about protein-protein interfaces can be obtained by a comprehensive study of protein contacts in the PDB, their sequence conservation and geometric features.

Results: An automated computational pipeline was developed to run our Evolutionary Protein-Protein Interface Classifier (EPPIC) software on the entire PDB and store the results in a relational database, currently containing > 800,000 interfaces. This allows the analysis of interface data on a PDB-wide scale. Two large benchmark datasets of biological interfaces and crystal contacts, each containing about 3000 entries, were automatically generated based on criteria thought to be strong indicators of interface type. The BioMany set of biological interfaces includes NMR dimers solved as crystal structures and interfaces that are preserved across diverse crystal forms, as catalogued by the Protein Common Interface Database (ProtCID) from Xu and Dunbrack. The second dataset, XtalMany, is derived from interfaces that would lead to infinite assemblies and are therefore crystal contacts. BioMany and XtalMany were used to benchmark the EPPIC approach. The performance of EPPIC was also compared to classifications from the Protein Interfaces, Surfaces, and Assemblies (PISA) program on a PDB-wide scale, finding that the two approaches give the same call in about 88% of PDB interfaces. By comparing our safest predictions to the PDB author annotations, we provide a lower-bound estimate of the error rate of biological unit annotations in the PDB. Additionally, we developed a PyMOL plugin for direct download and easy visualization of EPPIC interfaces for any PDB entry. Both the datasets and the PyMOL plugin are available at http://www.eppic-web.org/ewui/\#downloads.

Conclusions: Our computational pipeline allows us to analyze protein-protein contacts and their sequence conservation across the entire PDB. Two new benchmark datasets are provided, which are over an order of magnitude larger than existing manually curated ones. These tools enable the comprehensive study of several aspects of protein-protein contacts in the PDB and represent a basis for future, even larger scale studies of protein-protein interactions.

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Author annotation errors in the PDB. Author annotations are compared to to the EPPIC predictions. The comparison is done on a subset of 10,000 interfaces each from the extrema of the core-surface score distribution. The top call in the color legend corresponds to EPPIC and the bottom one to the author annotation.
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Figure 7: Author annotation errors in the PDB. Author annotations are compared to to the EPPIC predictions. The comparison is done on a subset of 10,000 interfaces each from the extrema of the core-surface score distribution. The top call in the color legend corresponds to EPPIC and the bottom one to the author annotation.

Mentions: Figure 7 depicts an interface call comparison between the most robust EPPIC predictions and the author annotation, similar to the interface call comparison between EPPIC and PISA shown in Figure 6. It is known that a certain rate of error affects author biological unit annotations in the PDB. According to Xu and Dunbrack, it is also not uncommon for an author biological unit annotation not to coincide with the biological unit description of the structure in the corresponding publication [11]. Some previous studies have also attempted to estimate this error rate. In an effort in manual annotation aided by automatic homology-based inference, Levy [19] estimates the error rate to be 14.7%. We estimate the error rate of author annotations at interface level to be 6.6%. Our lower figure indicates a baseline level of the most obvious errors, as we intentionally aimed to find the very clear errors, based on our safest predictions.


A PDB-wide, evolution-based assessment of protein-protein interfaces.

Baskaran K, Duarte JM, Biyani N, Bliven S, Capitani G - BMC Struct. Biol. (2014)

Author annotation errors in the PDB. Author annotations are compared to to the EPPIC predictions. The comparison is done on a subset of 10,000 interfaces each from the extrema of the core-surface score distribution. The top call in the color legend corresponds to EPPIC and the bottom one to the author annotation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4274722&req=5

Figure 7: Author annotation errors in the PDB. Author annotations are compared to to the EPPIC predictions. The comparison is done on a subset of 10,000 interfaces each from the extrema of the core-surface score distribution. The top call in the color legend corresponds to EPPIC and the bottom one to the author annotation.
Mentions: Figure 7 depicts an interface call comparison between the most robust EPPIC predictions and the author annotation, similar to the interface call comparison between EPPIC and PISA shown in Figure 6. It is known that a certain rate of error affects author biological unit annotations in the PDB. According to Xu and Dunbrack, it is also not uncommon for an author biological unit annotation not to coincide with the biological unit description of the structure in the corresponding publication [11]. Some previous studies have also attempted to estimate this error rate. In an effort in manual annotation aided by automatic homology-based inference, Levy [19] estimates the error rate to be 14.7%. We estimate the error rate of author annotations at interface level to be 6.6%. Our lower figure indicates a baseline level of the most obvious errors, as we intentionally aimed to find the very clear errors, based on our safest predictions.

Bottom Line: An automated computational pipeline was developed to run our Evolutionary Protein-Protein Interface Classifier (EPPIC) software on the entire PDB and store the results in a relational database, currently containing > 800,000 interfaces.By comparing our safest predictions to the PDB author annotations, we provide a lower-bound estimate of the error rate of biological unit annotations in the PDB.These tools enable the comprehensive study of several aspects of protein-protein contacts in the PDB and represent a basis for future, even larger scale studies of protein-protein interactions.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Thanks to the growth in sequence and structure databases, more than 50 million sequences are now available in UniProt and 100,000 structures in the PDB. Rich information about protein-protein interfaces can be obtained by a comprehensive study of protein contacts in the PDB, their sequence conservation and geometric features.

Results: An automated computational pipeline was developed to run our Evolutionary Protein-Protein Interface Classifier (EPPIC) software on the entire PDB and store the results in a relational database, currently containing > 800,000 interfaces. This allows the analysis of interface data on a PDB-wide scale. Two large benchmark datasets of biological interfaces and crystal contacts, each containing about 3000 entries, were automatically generated based on criteria thought to be strong indicators of interface type. The BioMany set of biological interfaces includes NMR dimers solved as crystal structures and interfaces that are preserved across diverse crystal forms, as catalogued by the Protein Common Interface Database (ProtCID) from Xu and Dunbrack. The second dataset, XtalMany, is derived from interfaces that would lead to infinite assemblies and are therefore crystal contacts. BioMany and XtalMany were used to benchmark the EPPIC approach. The performance of EPPIC was also compared to classifications from the Protein Interfaces, Surfaces, and Assemblies (PISA) program on a PDB-wide scale, finding that the two approaches give the same call in about 88% of PDB interfaces. By comparing our safest predictions to the PDB author annotations, we provide a lower-bound estimate of the error rate of biological unit annotations in the PDB. Additionally, we developed a PyMOL plugin for direct download and easy visualization of EPPIC interfaces for any PDB entry. Both the datasets and the PyMOL plugin are available at http://www.eppic-web.org/ewui/\#downloads.

Conclusions: Our computational pipeline allows us to analyze protein-protein contacts and their sequence conservation across the entire PDB. Two new benchmark datasets are provided, which are over an order of magnitude larger than existing manually curated ones. These tools enable the comprehensive study of several aspects of protein-protein contacts in the PDB and represent a basis for future, even larger scale studies of protein-protein interactions.

Show MeSH
Related in: MedlinePlus