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</script>The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain-motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.
Models, Molecular, Binding Sites, QH301-705.5, Molecular Sequence Data, Protein Structure, Tertiary, Structure-Activity Relationship, Models, Chemical, Sequence Analysis, Protein, Protein Interaction Mapping, Feasibility Studies, Computer Simulation, Amino Acid Sequence, Biology (General), Sequence Alignment, Algorithms, Conserved Sequence, Research Article, Protein Binding
Models, Molecular, Binding Sites, QH301-705.5, Molecular Sequence Data, Protein Structure, Tertiary, Structure-Activity Relationship, Models, Chemical, Sequence Analysis, Protein, Protein Interaction Mapping, Feasibility Studies, Computer Simulation, Amino Acid Sequence, Biology (General), Sequence Alignment, Algorithms, Conserved Sequence, Research Article, Protein Binding
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 43 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
