Abstract
The Joint Evolutionary Trees (JET) method detects protein interfaces, the core residues involved in the folding process, and residues susceptible to site-directed mutagenesis and relevant to molecular recognition. The approach, based on the Evolutionary Trace (ET) method, introduces a novel way to treat evolutionary information. Families of homologous sequences are analyzed through a Gibbs-like sampling of distance trees to reduce effects of erroneous multiple alignment and impacts of weakly homologous sequences on distance tree construction. The sampling method makes sequence analysis more sensitive to functional and structural importance of individual residues by avoiding effects of the overrepresentation of highly homologous sequences and improves computational efficiency. A carefully designed clustering method is parametrized on the target structure to detect and extend patches on protein surfaces into predicted interaction sites. Clustering takes into account residues' physical-chemical properties as well as conservation. Large-scale application of JET requires the system to be adjustable for different datasets and to guarantee predictions even if the signal is low. Flexibility was achieved by a careful treatment of the number of retrieved sequences, the amino acid distance between sequences, and the selective thresholds for cluster identification. An iterative version of JET (iJET) that guarantees finding the most likely interface residues is proposed as the appropriate tool for large-scale predictions. Tests are carried out on the Huang database of 62 heterodimer, homodimer, and transient complexes and on 265 interfaces belonging to signal transduction proteins, enzymes, inhibitors, antibodies, antigens, and others. A specific set of proteins chosen for their special functional and structural properties illustrate JET behavior on a large variety of interactions covering proteins, ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf, Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significant improvement in performance and computational efficiency is shown.
Highlights
Interface residues are essential for understanding interaction mechanisms and are often potential drug targets
Interfaces differ from the rest of the protein surface typically because buried interface residues are more conserved than partially buried ones and because the sequences associated with interfaces have undergone few insertions or deletions
Joint Evolutionary Trees (JET) is a method designed to detect very different types of interactions of a protein with another protein, ligands, DNA, and RNA. It uses a carefully designed sampling method, making sequence analysis more sensitive to the functional and structural importance of individual residues, and a clustering method parametrized on the target structure for the detection of patches on protein surfaces and their extension into predicted interaction sites
Summary
Interface residues are essential for understanding interaction mechanisms and are often potential drug targets. Reliable identification of residues that belong to a protein-protein interface typically requires information on protein structures [1] and knowledge of both partners. This information is often unavailable and for this reason, reliable site prediction using a single protein, independently from its partners, becomes valuable. Interfaces differ from the rest of the protein surface typically because buried interface residues are more conserved than partially buried ones and because the sequences associated with interfaces have undergone few insertions or deletions. On average, the most conserved patches of residues overlap only the 37.5% (628%) of the actual protein interface and an analysis of 64 different types of protein interfaces (formed from close homologs/orthologs or from diverse homologs/paralogs) demonstrated that conserved patches cannot clearly discriminate protein interfaces [4]
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