The Ben-Tal Lab of Computational Structural Biology
Welcome to Nir Ben-Tal's lab, member of the Edmond J. Safra Center for Bioinformatics, at the Department of Biochemistry and Molecular Biology, The George S Wise Faculty of Life Sciences , Tel Aviv University.Our research is focused on studying the interplay between protein sequence, structure, motion and function using computational tools. The understanding of these relations provides a molecular dimension to our understanding of protein functions and their involvement in genetic disorders and other diseases. Within the broad fields of structural-bioinformatics and phylogeny, we limit our research to specific niches where structure and motion are difficult to obtain experimentally, and the computations provide data beyond our current knowledge.
The availability of a large number of protein structures enables fundamental questions in evolution to be addressed. For example, how are proteins related to one another? Which physicochemical factors affect protein evolution, and how? Proteins are made up of various combinations of repeating structural domains; therefore, to obtain answers to these questions, it is sufficient to analyze the relationships among such domains. We carry out comparisons within a large and representative set of domains, searching for elongated segments that are similar to one another in sequence and structure and are therefore indicative of common evolutionary origin. The results are represented as a network, the edges of which connect similar domains. The network includes a large connected component, as well as isolated ‘islands’, revealing the complex nature of protein space, which includes continuous and discrete regions. This feature does not depend strongly on the criteria used to define similarity. The network offers a natural means of organizing protein space, and creates opportunities to develop strategies for protein search and design.
Network perspective of the protein universe
Applying an evolution-based approach, we found elements of the protein preserved in different animals, including humans, and used this information to create a conjectured Ca-trace model of hCTR1’s transmembrane domain (left figure, spirals). We then proceeded to calculate the protein’s main modes of motion (Movie 1). Finally, based on the model-structure and predicted motion, a mechanism by which the protein transports the copper ions into the cell was suggested. According to the proposed mechanism, hCTR1 lets the ions through one by one, keeping them under close control (right figure). Careful ion selection and regulation are essential because free copper ions are highly reactive, potentially forming harmful free radicals.
An illustration of one of the main modes of motions predicted to mediate copper transport.
Structural-genomics initiatives aim to produce crystal structures of myriad proteins. This outpouring of structural data is not matched by a similar experimental effort to understand function, and we try to fill this growing gap using phylogenetic analysis. Patches of evolutionarily conserved amino acids that are located in close proximity to each other on the protein surface are often important for biological functions; these patches may mediate the association of the protein with other proteins, RNA, DNA or ligand molecules, and may be involved in enzymatic catalysis. For example, the figure on the right shows a top-down view of the (homo-tetrameric) KcsA potassium channel. The membrane plane aligns with the plane of the slide. A potassium ion (yellow sphere) is trapped in the central pore. The amino acids are color-coded from cyan to maroon based on their ConSurf conservation grades. The amino acids surrounding the ion are highly conserved. This is the selectivity filter, designed to select potassium over other ions (e.g., sodium, which is chemically very similar). Mutations in this region are not tolerated because they imped function.
We continually develop the Rate4Site algorithm and ConSurf web-server for computation of the evolutionary conservation of the amino acids in proteins. We also develop a method for suggesting alternative conformations for proteins of (at least) one known structure.
Rational Drug Design
Traditional high throughput screening is a laborious and costly research, which typically is out of the scope of the academy. We use computational methods to screen large combinatorial libraries for novel drug candidates. The search algorithm finds compounds with good stereochemical fit to the structure of the protein binding site.
An example is our collaboration with the Azem Lab, where we target the Hsp60 chaperonin, which is implicated in multiple cancers. The Azem lab has determined the structure of Hsp60 in complex with its co-chaperonin Hsp10, obtaining a complex with a football-like shape. We screened a database of several million compounds, fitting each compound to the nucleotide binding site of Hsp60 and calculating the binding energies. The different compounds were ranked according to that energy. From the top ranking compounds we selected 10 compounds to be tested in empirical binding assays.
The book that Amit Kessel and Nir Ben-Tal wrote: Introduction to proteins