Universality among E. coli DHFR Mutants

*This project will be supervised by Ebru Cetin and Canan Atilgan will co-supervise.
 
Dihydrofolate reductase (DHFR) is an essential enzyme existing in all living organisms and is required for replication of DNA. For suppressing the effect of antibioticsonDHFR,E. coli and other bacteria acquiremutations on the DHFR gene. This is one of the several resistance mechanisms arising in bacteria to keep their life cycle continuing. To understand which mutations are acquired under various selection pressures, our collaborators have devised an instrument called Morbidostat1 which tracks the mutations acquired by bacteria in the presence of the drug; trimethoprim in this case. From these experiments 11 single mutations have beendetected onthe enzyme and their catalytic properties such as Michaelis Constant and catalysis rate have been measured.
 
The timepoint where achemical reaction occurs is arare eventcompared to the life cycle of chemicalsparticipating in the reactions. Thus, a separation of time scales occurs during the process; e.g, the turnover of procust in an enzyme might occur on the time scale of seconds, while the relative motions of the substrate in the binding cavity might take place on sub-nanoseconds.2 While the events that trigger this time separation may well be localized, they are also capable of governing the whole protein dynamics. We have previously shown that DHF binding is an example of such dynamicaleventswhereby bound DHF relaxation times aresynchronized with the average DHFR relaxationprocesses. Examples of this kind of localized motions governing the complex dynamics has been observed for several cases; e.g. in the C=O stretch modulating the catalysis rate at LDH:pyruvate complex;3 or the correlations of bond lengths with reactivity in a variety ofproteins.4,5
 
In this project, we seek to find a relationship betweenthe 11 frequently arising, resistence conferring mutants of DHFR as a function of the enzyme’s catalytic properties. For this aim, the students will analyze the molecular dynamics trajectoriesalready acquiredin our group. They will extract distance matrices of each atom on the sidechains and relate them tothe experimentally measuredcatalytic activity.
 
Good skills of MATLAB or Python is required. Knowledge of Machine Learning tools is optional but will be an advantage.
 
References:
1.         Toprak, E.;  Veres, A.;  Yildiz, S.;  Pedraza, J. M.;  Chait, R.;  Paulsson, J.; Kishony, R., Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nature Protocols 2013, 8 (3), 555-567.
2.         Callender, R.; Dyer, R. B., The Dynamical Nature of Enzymatic Catalysis. Accounts of Chemical Research 2015, 48 (2), 407-413.
3.         Deng, H.;  Zheng, J.;  Clarke, A.;  Holbrook, J. J.;  Callender, R.; Burgner, J. W., Source of Catalysis in the Lactate Dehydrogenase System. Ground-State Interactions in the Enzyme.cntdot.Substrate Complex. Biochemistry 1994, 33 (8), 2297-2305.
4.         Tonge, P. J.; Carey, P. R., Length of the acyl carbonyl bond in acyl-serine proteases correlates with reactivity. Biochemistry 1990, 29 (48), 10723-10727.
5.         Jones, P. G.; Kirby, A. J., Simple correlation between bond length and reactivity. Combined use of crystallographic and kinetic data to explore a reaction coordinate. Journal of the American Chemical Society 1984, 106 (21), 6207-6212.