אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

The function of a protein is largely determined by its ability to undergo structural changes and shift between various transitional states. These conformational transitions are often induced when proteins interact with their environment, and they can also be induced by mutations to key amino acids (residues) in the protein. In most cases, the majority of changes between transition states can be attributed to a small number of regions in the protein, called "structural hotspots.'' Identifying and studying the changes in the structural hotspots is a key task in understanding the connection between a protein's structure and function. To study the structure of a protein, biologists often rely on 3D structural data obtained from protein crystallography or cryo-EM studies. Such studies are, however, highly tedious, expensive, and time-consuming. Moreover, they are rarely able to capture the different transition states of the studied protein without prior knowledge of the specific stabilizing mutations of each conformation. Additionally, manual inspection of a large number of 3D structures is tedious and error-prone and may be prohibitively time-consuming. In this work, I address this problem in-silico. 

I formally define the problem of hotspot detection, I propose localmatch, an algorithm for detecting a protein's hotspots given a set of 3D structures, and I study the application of this algorithm to the Orai1 calcium channel. To this end, I perform a literature review to collect crystallographic data of different conformations, along with labeled hotspots of the Orai1 protein. I validate my approach by automatically detecting all known hotspots from the literature when running my algorithm on existing crystallographic data in the top 3% of amino acids, significantly outperforming alternative baselines. Furthermore, I demonstrate localmatch's ability to identify the hotspots from the literature based on 3D structures of mutations predicted by AlphaFold, where it detects four of the five hotspots within the top 4% top amino acids.