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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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Interface call comparison between EPPIC and PISA. The histogram represents the fraction of convergent and divergent interface calls by EPPIC and PISA as a function of interface area. The top call in the color legend corresponds to EPPIC and the bottom one to PISA. The overall percentages of each call combination are also given.
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Figure 6: Interface call comparison between EPPIC and PISA. The histogram represents the fraction of convergent and divergent interface calls by EPPIC and PISA as a function of interface area. The top call in the color legend corresponds to EPPIC and the bottom one to PISA. The overall percentages of each call combination are also given.

Mentions: Comparing the results of PISA and EPPIC, we found that the two approaches gave the same call for 88% of interfaces. In Figure 6, we show how the fraction of common calls varies as a function of interface area. Unsurprisingly, the lowest agreement is observed in the 600–1200 Å 2 interface area range, where classification is particularly hard. This nicely confirms previous observations based on the analysis of small datasets only [3],[16].


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

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

Interface call comparison between EPPIC and PISA. The histogram represents the fraction of convergent and divergent interface calls by EPPIC and PISA as a function of interface area. The top call in the color legend corresponds to EPPIC and the bottom one to PISA. The overall percentages of each call combination are also given.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Interface call comparison between EPPIC and PISA. The histogram represents the fraction of convergent and divergent interface calls by EPPIC and PISA as a function of interface area. The top call in the color legend corresponds to EPPIC and the bottom one to PISA. The overall percentages of each call combination are also given.
Mentions: Comparing the results of PISA and EPPIC, we found that the two approaches gave the same call for 88% of interfaces. In Figure 6, we show how the fraction of common calls varies as a function of interface area. Unsurprisingly, the lowest agreement is observed in the 600–1200 Å 2 interface area range, where classification is particularly hard. This nicely confirms previous observations based on the analysis of small datasets only [3],[16].

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