Research Interests

Abstract
Biological processes are based on interactions between proteins, peptides, nucleic acids, lipid bilayers and ligands. The long-term research goal in the lab is to find general principles that both describe the molecular basis underlying these interactions and provide a framework for predicting the functions of the various molecules involved. In recent years, our research has been focused mainly on two routes: (a) membrane protein systems and (b) protein-protein interactions. Our approach is based on combining fundamental physicochemical principles with bioinformatics. Three-dimensional (3D) structures are central to both routes, and we use existing theoretical-computational tools and develop new ones when necessary. Our calculations provide information unobtainable through structural analysis of the proteins alone, and the application of bioinformatics allows us to extend the detailed, quantitative analysis to annotating functions for whole families of proteins.

(A) Structure, function and motion in membrane proteins
About 20-30% of the genes in all organisms code for integral membrane proteins. The overexpression and crystallization of membrane proteins is, however, difficult and thus, 3D structures have been determined for only several of the close to 20,000 sequences of transmembrane (TM) proteins currently available in the SWISS-PROT database. The objective of this research project is to develop and use algorithmic tools to predict structure, function and motion in membrane proteins.
We recently developed a novel method for predicting preferred conformations of pairs of tightly packed TM helices (Fleishman & Ben-Tal, 2002). The method is particularly suitable for cases such as glycophorin A, where packing is mediated by a GxxxG-like motif. The motif allows for two small residues to be on consecutive helix turns in the helix-helix interface. The method was subsequently used in a search for compact conformations in the TM domain of a homodimer of the receptor tyrosine kinase (RTK) erbB2, also known as HER2 (Fleishman et al., 2002). The domain consists of two TM helices, one from each monomer, each of which contains two GxxxG-like motifs, and experiments suggest that both motifs mediate dimerization. The computational search yielded two stable conformations of these helices, corresponding to dimerization via the two motifs, and we hypothesized that they correspond to the basal (inactive) and active states of erbB2. Based on this hypothesis, we explained in molecular detail the effect of the dozen or so available mutations of this receptor, including the constitutively active and transforming mutation denoted as neu*.
We will improve the methodology further to enable structure prediction in other TM proteins. Constraints derived from low-to-medium resolution structural data obtained from cryo-EM or mutagenesis studies, and constraints imposed by the loops connecting pairs of TM helices will be added, and the methodology will be made to deal with more than two helices. The revised methodology will be used to study structure function and motion in TM receptors, transporters and channels. Predictions will be tested in collaboration with experimental labs.

(B) Protein-protein interactions
Experimental approaches for the identification of functionally important regions on the surface of a protein involve mutagenesis, in which exposed residues are replaced one after another while the change in binding to other molecules or changes in activity are recorded. However, practical considerations limit the use of these methods to small-scale studies, precluding a full mapping of all the functionally important residues on the surface of a protein. A main research direction in the lab is the development of alternative approaches, based on the use of evolutionary data on protein families to identify surface patches that are likely to be involved in recognition processes. In parallel, we also characterize inter-protein interfaces using physicochemical and bioinformatics tools. One of our long-term goals is to develop algorithmic tools to identify proteins that may associate with each other, and to dock them to each other if possible.
The rate of evolution is not constant among amino-acid sites; some positions are highly conserved while others vary substantially. These rate variations correspond to different levels of purifying selection acting on these sites. This purifying selection can be the result of geometrical constraints on the folding of the protein into its 3D structure, constraints at amino-acid sites involved in enzymatic activity or in ligand binding or, alternatively, at amino-acid sites that take part in protein-protein interactions.
We developed and tested two new algorithmic tool for mapping amino acid conservation onto the molecular surface of proteins: ConSurf (Armon et al., 2001) and Rate4Site (Pupko et al., 2002). Very recently we developed the ConSurf (http://consurf.tau.ac.il/) web-Server, a web-based-tool that uses the ConSurf and Rate4Site algorithms. Given the 3D-structure of a protein, or preferentially a domain, as an input, the server automatically collects its close sequence-homologues, calculates conservation grades based on the phylogenetic relations among them and maps the grades onto the Van-der-Waals surface of the protein. The protein, with the conservation grades color-coded onto its surface, can finally be visualized on-line (Fig. 1).

Fig. 1. Evolutionary conservation pattern in the potassium ion channel. The channel is represented as a spacefill model, with each atom represented as a sphere. Scores, representing the degree of evolutionary conservation of the amino acids, are color-coded onto the structure of the channel. Evolutionary conserved amino acids are colored maroon, residues of average conservation are white, and variable amino acids are turquoise. A potassium ion is shown in yellow. The residues that are involved in ion binding are highly conserved. The picture was produced using ConSurf (http://consurf.tau.ac.il/).

Selected Publications

1. Kessel, A. and Ben-Tal, N. (2002) Free energy determinants of peptide association with lipid bilayers. Current Topics in Membranes: Peptide-Lipid Interactions 52: 205-253 (Sydney Simon and Thomas McIntosh, Eds.), Academic Press, San Diego.
2. Fleishman, S.J. and Ben-Tal, N. (2002) A novel scoring function for predicting the conformations of tightly packed pairs of transmembrane -helices. J. Mol. Biol. 321: 363-378.
3. Fleishman, S.J., Schlessinger, J. and Ben-Tal, N. (2002) A putative molecular-activation switch in the transmembrane domain of erbB2. Proc. Natl. Acad. Sci. USA 99: 15937-15940.
4. Armon, A., Graur, D. and Ben-Tal, N. (2001) ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface-mapping of phylogenetic information. J. Mol. Biol. 307: 447-463.
5. Pupko, T., Bell, R.E., Mayrose, I., Glaser, F. and Ben-Tal, N. (2002) Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues. Bioinformatics 18 Suppl. 1 S71-S77.