Navigating Energy Surface of Functional Proteins

2022-2023 Summer
Faculty Department of Project Supervisor: 
Faculty of Engineering and Natural Sciences
Number of Students: 

In 2021, a very exciting development happened in the protein world. AlphaFold2 (AF2) program developed by DeepMind predicted, with unprecedented success, the three-dimensional conformation of proteins using only amino acid sequence information. Although there were those who touted this as a solution to the "Protein Folding Problem", since the method is based on the examination of folded proteins of known structures with advanced machine learning techniques, the underlying physics still maintains its secrets. In this project, we are more concerned with what AF2 cannot do than what it can do.
Our group's research program is based-on predicting the functions of proteins whose three-dimensional structures are known using dynamical information obtained from trajectories and to foretell how the shifts in the environmental conditions will change these functions. Most folded proteins occupy multiple conformations that are close on the conformational space (CS) but are separated by high energy barriers. Computational and experimental methods determine only one or a few of these at best. Moreover, transitions between these occur on the timescales that are beyond the reach of most current computers. To further complicate matters, minor changes in environmental variables also affect the dynamics of transitions between these structures and their populations.
Our ultimateaim is to develop an efficient and integrated method that maps transitions between functional conformations of any given folded protein to its energy surface. To achieve thisaim the first step is to make a comparative study of the methods that disclose reaction coordinates (RC) that manage transitions between different functional structures of proteins. In this project, different approaches such as heuristics, slow modes of motion, perturbation response scanning developed in our group and the advanced method called tICA will each be applied to the same sample protein set; their advantages and disadvantages will be determined systematically. This process will form a part of the larger goal of providing a whole package program for the surveillance of the CS of a protein under a given set of conditions.
In this project, you will learn to run molecular dynamics simulations on open-source software, and you will write your own analysis codes in Tcl and Python. The project will also require you to work with large amounts of data, consisting mainly of the coordinates of the protein systems.

Related Areas of Project: 
Computer Science and Engineering
Molecular Biology, Genetics and Bioengineering
Materials Science ve Nano Engineering