Article

PIER: Protein interface recognition for structural proteomics

Scripps Research Institute, La Jolla, California 92037, USA.
Proteins Structure Function and Bioinformatics (Impact Factor: 2.63). 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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    • "An interface propensity is calculated for each feature. The combined score is the product of propensity scores from different properties, which is further smoothed by considering structural neighbors PIER [41] Structure http://abagyan.ucsd.edu/ PIER/ PIER predicts each surface patch as interfacial or not, using PLS (partial least squares) regression on the solvent accessibility values of 12 significantly over-and under-represented atomic groups at the interface Cons-PPISP [7] Structure http://pipe.scs.fsu.edu/ "
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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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    • "SVM algorithm has also been used to predict interaction interface sites9101112and has made different prediction effects on different datasets. Besides, some other machine learning methods have also been used to predict protein-protein interaction interface sites, such as hidden Markov model[13], linear regression[14], score function[15], conditional random fields[16], and random forest[17]. The common points of the previously mentioned methods use one classifier to predict protein interface residues. "
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