The Ben-Tal Lab of Computational Structural Biology

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.

Protein space and the emergence of proteins on primordial EarthProteins exhibit evolutionary advantages through the reuse of amino acid segments, particularly within well-characterized structures known as ‘domains,’ typically comprising around 100 amino acids. However, our study explores the possibility of proteins reusing even smaller segments, which we term ‘themes.’ By analyzing two extensive protein sets, we constructed a similarity network, revealing a large interconnected component of domains sharing smaller segments, alongside isolated ‘islands’ representing distinct architectural patterns. This investigation uncovers intricate patterns of theme reuse, suggesting a recursive model of protein evolution, where themes of various lengths navigate between contexts, leaving discernible traces for detection.

Membrane proteins
Our research focuses on membrane proteins, essential for cellular functions and implicated in various diseases, making them prime targets for pharmaceuticals. Currently, we concentrate on Cation/Proton Antiporters (CPAs), a large protein superfamily with over 100,000 entries in UniProt. Through extensive evolutionary analysis of ~6,500 protein homologs, we identified an eight-amino acid sequence motif that distinguishes CPA groups, determines electrogenicity, and influences cation selectivity. This analysis challenges previous classifications and allowed us to modify CPA function, recovering activity in an inactive mutant. Our ongoing goal is to employ simulations and computational techniques to develop a detailed atomic model of the transport mechanism for these intriguing proteins.

Jointly with the Mayrose and Pupko labs, we have been developing ConSurf, an automated tool that accurately predicts the evolutionary conservation of amino acid positions in proteins, and also presents the conservation on the structures of these proteins as a color map (see figure on the right). This type of analysis is of great importance to biologists, as protein positions that are functionally important tend to change much more slowly during evolution compared to those that do not have specific function. Thus, ConSurf can help protein scientists to identify functional positions in new proteins. It can also assist protein engineers finding positions within proteins that can be safely mutated to increase their activity or to change their specificity towards their substrates. While there are other methods that calculate the evolutionary conservation of proteins, ConSurf does it very accurately because it uses statistically robust approaches (Bayesian or maximum likelihood).

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.

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Office: Sherman – Life Sciences, 631

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