Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction

• Published in: PLoS computational biology Vol. 6; no. 11; p. e1001017
• Main Authors: van Dijk, Aalt D J, Morabito, Giuseppa, Fiers, Martijn, van Ham, Roeland C H J, Angenent, Gerco C, Immink, Richard G H
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24-11-2010; Public Library of Science (PLoS)
• Subjects: Amino Acid Motifs; Amino Acid Sequence; Amino acids; arabidopsis; Arabidopsis Proteins - chemistry; Arabidopsis Proteins - genetics; Arabidopsis Proteins - metabolism; Arabidopsis thaliana; Biochemistry/Bioinformatics; Bioinformatics; box genes; Computational biology; Computational Biology/Macromolecular Sequence Analysis; Computational Biology/Sequence Motif Analysis; Computational Biology/Transcriptional Regulation; Databases, Protein; Developmental Biology/Plant Growth and Development; dimerization; Diversification; dna-binding; Evolution; Evolution, Molecular; Evolutionary Biology/Plant Genetics and Gene Expression; Experiments; floral organ; flower development; Genetic aspects; MADS Domain Proteins - chemistry; MADS Domain Proteins - genetics; MADS Domain Proteins - metabolism; Models, Molecular; Models, Statistical; Molecular Biology/Bioinformatics; Molecular Sequence Data; Mutation; novo structure prediction; organ-identity proteins; peptide interactions; Physiological aspects; Plant Biology/Plant Genomes and Evolution; Protein Interaction Domains and Motifs; Protein Interaction Mapping - methods; Protein-protein interactions; Proteins; Reproducibility of Results; Sequence Alignment; signal-transduction; Transcription factors; Netherlands; Amino Acid Sequence; Reproducibility of Results; Models, Molecular; Molecular Sequence Data; Models, Statistical; Protein Interaction Domains & Motifs; Amino Acid Motifs; Protein Interaction Mapping; Sequence Alignment; Arabidopsis Proteins; Databases, Protein; MADS Domain Proteins; Mutation; Evolution, Molecular
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1001017

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution.