PIER: Protein interface recognition for structural proteomics

Scripps Research Institute, La Jolla, California 92037, USA.
Proteins Structure Function and Bioinformatics (Impact Factor: 2.92). 05/2007; 67(2):400-17. DOI: 10.1002/prot.21233
Source: PubMed

ABSTRACT Recent advances in structural proteomics call for development of fast and reliable automatic methods for prediction of functional surfaces of proteins with known three-dimensional structure, including binding sites for known and unknown protein partners as well as oligomerization interfaces. Despite significant progress the problem is still far from being solved. Most existing methods rely, at least partially, on evolutionary information from multiple sequence alignments projected on protein surface. The common drawback of such methods is their limited applicability to the proteins with a sparse set of sequential homologs, as well as inability to detect interfaces in evolutionary variable regions. In this study, the authors developed an improved method for predicting interfaces from a single protein structure, which is based on local statistical properties of the protein surface derived at the level of atomic groups. The proposed Protein IntErface Recognition (PIER) method achieved the overall precision of 60% at the recall threshold of 50% at the residue level on a diverse benchmark of 490 homodimeric, 62 heterodimeric, and 196 transient interfaces (compared with 25% precision at 50% recall expected from random residue function assignment). For 70% of proteins in the benchmark, the binding patch residues were successfully detected with precision exceeding 50% at 50% recall. The calculation only took seconds for an average 300-residue protein. The authors demonstrated that adding the evolutionary conservation signal only marginally influenced the overall prediction performance on the benchmark; moreover, for certain classes of proteins, using this signal actually resulted in a deteriorated prediction. Thorough benchmarking using other datasets from literature showed that PIER yielded improved performance as compared with several alignment-free or alignment-dependent predictions. The accuracy, efficiency, and dependence on structure alone make PIER a suitable tool for automated high-throughput annotation of protein structures emerging from structural proteomics projects.

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    • "Prediction of possible residues of interfaces in each structure was performed by using the CPORT (Concensus Prediction of Interface Residues in Transient) facility (De Vries and Bonvin, 2011). PINuP (Liang et al., 2006), PIER (Kufareva et al., 2007), WHISCY (De Vries et al., 2006), ProMate (Neuvirth et al., 2004), SPPIDER (Porollo and Meller, 2007) and cons-PPISP (Chen and Zhou, 2005) are six interface residues prediction algorithms, which are cumulatively included in CPORT and provide reliable prediction of the interface residues, which can be integrated into the HADDOCK web server as active and passive site residues. The Visual Molecular Dynamics (VMD) software (Humphrey et al., 1996) was used for the protonation and partial charge assignment of the structures, whereas the Molecular Operating Environment (MOE) software (Chemical computing groups, MOE Software, version 2013) (Inc.) was used for calculation of electrostatic charges. "
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    Genetics and molecular research: GMR 04/2015; 14(2):4215-4237. DOI:10.4238/2015.April.28.4 · 0.85 Impact Factor
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    • "The interaction of an antigen and an antibody is a subtype of protein-protein interaction, so some methods that focus on binding sites prediction of protein-protein interaction can be borrowed for conformational B-cell epitopes prediction. Recently, Yao et al. [53] construct a benchmark and evaluate the performance of all existing structure-based B-cell prediction methods, along with 4 binding sites prediction methods: ProMate [84], ConSurf [85], PINUP [86], and PIER [87]. The results "
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    ABSTRACT: Identification of epitopes which invoke strong humoral responses is an essential issue in the field of immunology. Localizing epitopes by experimental methods is expensive in terms of time, cost, and effort; therefore, computational methods feature for its low cost and high speed was employed to predict B-cell epitopes. In this paper, we review the recent advance of bioinformatics resources and tools in conformational B-cell epitope prediction, including databases, algorithms, web servers, and their applications in solving problems in related areas. To stimulate the development of better tools, some promising directions are also extensively discussed.
    Computational and Mathematical Methods in Medicine 07/2013; 2013(1217):943636. DOI:10.1155/2013/943636 · 1.02 Impact Factor
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    • "Linear regression (Kufareva et al., 2007; Li et al., 2006). In this method, Equation (1) is a linear function of input data such as solvent accessibilities, with c as coefficients. "
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    ABSTRACT: MOTIVATION: Proteins function through interactions with other proteins and biomolecules. Protein-protein interfaces hold key information toward molecular understanding of protein function. In the past few years, there have been intensive efforts in developing methods for predicting protein interface residues. A review that presents the current status of interface prediction and an overview of its applications and project future developments is in order. SUMMARY: Interface prediction methods rely on a wide range of sequence, structural and physical attributes that distinguish interface residues from non-interface surface residues. The input data are manipulated into either a numerical value or a probability representing the potential for a residue to be inside a protein interface. Predictions are now satisfactory for complex-forming proteins that are well represented in the Protein Data Bank, but less so for under-represented ones. Future developments will be directed at tackling problems such as building structural models for multi-component structural complexes.
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