A fast method to predict protein interaction sites from sequences

Gallet, Xavier; Charloteaux, Benoît; Thomas, A. G.; Brasseur, Robert

doi:10.1006/jmbi.2000.4092

Cited by 188 publications

(160 citation statements)

References 32 publications

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“…These results highlight the importance of this conserved Arg in protein interaction. It was reported that in a high throughput statistical analysis of amino acid frequencies involved in protein interaction, the arginine is the most frequent residue because of its wider radii of action and more accessible long side chains carrying the charge (59). So the Arg to His mutation may change the favorable amino acid feature for protein interaction or alter the protein structure, resulting in failure of interaction with partners.…”

Section: C-e;mentioning

confidence: 99%

A Single Amino Acid Substitution in IIIf Subfamily of Basic Helix-Loop-Helix Transcription Factor AtMYC1 Leads to Trichome and Root Hair Patterning Defects by Abolishing Its Interaction with Partner Proteins in Arabidopsis

Zhao

Wang

Zhu

et al. 2012

Journal of Biological Chemistry

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Background: IIIf subfamily of bHLH members GL3/EGL3 involved in epidermal cell fate determination through interacting with MYBs/TTG1. Results: A substitution of R173H in the AtMYC1 homolog of GL3/EGL3 abolished its function in trichome and non-hair cell fate control. Conclusion:The functionally conserved Arg residue in AtMYC1/GL3/EGL3 was crucial for protein interaction and proper biological function. Significance: A novel critical and functionally conserved site in IIIf bHLH proteins was identified.

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Section: C-e;mentioning

confidence: 99%

A Single Amino Acid Substitution in IIIf Subfamily of Basic Helix-Loop-Helix Transcription Factor AtMYC1 Leads to Trichome and Root Hair Patterning Defects by Abolishing Its Interaction with Partner Proteins in Arabidopsis

Zhao

Wang

Zhu

et al. 2012

Journal of Biological Chemistry

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“…The important factor influencing the accuracy of interaction predictions is the proper representation of the proteins. There are two classes of input data used in most theoretical methods: sequence profiles (phylogenetic profiles [63], mRNA expression levels [64], the presence of protein domains in analyzed sequences [65], and other approaches [66][67][68]); and three-dimensional structures of interacting partners (prediction methods tested every year in independent computational experiments in the Critical Assessment of PRediction of Interactions, CAPRI [57], methods based on the features of protein surfaces that allow for typing of the protein complexes [58,[69][70][71], the selection of the most important residues and their features on protein interfaces [59,[72][73][74], and other representations of experimental data [6,[75][76][77]). If the complex structure is known, it is possible to select interacting interfaces on the protein surfaces, find the most important residues, and analyze their evolutionary conservation or physico-chemical features [6,58,78,79].…”

Section: Algorithmsmentioning

confidence: 99%

The interactome: Predicting the protein-protein interactions in cells

Plewczyński

Ginalski

2009

Cellular and Molecular Biology Letters

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Abstract:The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

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“…These include methods based on presence of "proline brackets'' (Kini and Evans, 1996), patch analysis using a 6-parameter scoring function Thornton 1997a, 1997b), analysis of hydrophobicity distribution around a target residue (Gallet et al, 2000), multiple sequence alignment (Casarai et al, 1995;Lichtarge et al, 1996;Pazos et al, 1997), structure-based multimeric threading (Lu et al, 2002), analysis of amino acid characteristics of spatial neighbors of a target residue using a neural network (Zhou and Shan,2001;Fariselli et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Identification of Surface Residues Involved in Protein-Protein Interaction — A Support Vector Machine Approach

Yan

Dobbs

Honavar

2003

Intelligent Systems Design and Applications

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SummaryWe describe a machine learning approach for sequence-based prediction of protein-protein interaction sites. A support vector machine (SVM) classifier was trained to predict whether or not a surface residue is an interface residue (i.e., is located in the protein-protein interaction surface) based on the identity of the target residue and its 10 sequence neighbors. Separate classifiers were trained on proteins from two categories of complexes, antibody-antigen and proteaseinhibitor. The effectiveness of each classifier was evaluated using leave-one-out (jack-knife) cross-validation. Interface and non-interface residues were classified with relatively high sensitivity (82.3% and 78.5%) and specificity (81.0% and 77.6%) for proteins in the antigen-antibody and protease inhibitor complexes, respectively. The correlation between predicted and actual labels was 0.430 and 0.462, indicating that the method performs substantially better than chance (zero correlation). Combined with recently developed methods for identification of surface residues from sequence information, this offers a promising approach to prediction of residues involved in protein-protein interaction from sequence information alone.

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A fast method to predict protein interaction sites from sequences

Cited by 188 publications

References 32 publications

A Single Amino Acid Substitution in IIIf Subfamily of Basic Helix-Loop-Helix Transcription Factor AtMYC1 Leads to Trichome and Root Hair Patterning Defects by Abolishing Its Interaction with Partner Proteins in Arabidopsis

A Single Amino Acid Substitution in IIIf Subfamily of Basic Helix-Loop-Helix Transcription Factor AtMYC1 Leads to Trichome and Root Hair Patterning Defects by Abolishing Its Interaction with Partner Proteins in Arabidopsis

The interactome: Predicting the protein-protein interactions in cells

Identification of Surface Residues Involved in Protein-Protein Interaction — A Support Vector Machine Approach

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