Modulation of receptor function and enzyme evolution via optimized mutagenesis designs
||Modulation of receptor function and enzyme evolution via optimized mutagenesis designs|
||Hugo Gutierrez de Teran <firstname.lastname@example.org>|
||2023-03-31 – 2023-10-01|
Our group has a number of ongoing projects related to enzyme evolution of cold adaptation (coordinated by Prof. Åqvist). The computational strategy followed has been to perform comparative empirical valence bond (EVB) simulations of cold-adapted enzymes and their warm-adapted homologues with our MD software Q. In the recently granted Berzelius activity, we have improved the computational efficiency of this strategy by adapting and benchmarking the EVB approach in the highly-efficient GROMACS code. We will make intensive use of this approach in the current application by examining different psychrophilic enzymes. The experimental design includes the generation of 3D models of enzymes of unknown structure using AlphaFold-2 deep learning core, and running simulations of several mutants, under different conditions (temperature). The subsequent analysis includes clustering and classification algorithms, prior to training a ML model to relate structural differences between enzymes to the observed temperature dependance of catalysis. For all the above, the Berzelius AI/ML computing cluster is perfectly suited.
A second area of research is on the characterization of signal transduction and chemical modulation of G-protein-coupled receptors (GPCRs, coordinated by associate professor Gutiérrez-de-Terán). In this period, we intend to continue our efforts to identify transmembrane allosteric modulators of class-C mGlu6, as well as novel modulators on yet unknown allosteric sites on class-A A2AAR. On the first case, we will focus on the comparative analysis of classical MD trajectories with both inactive and active forms of photoswitchable ligands. In the second case, we will perform fragment virtual screening on the proposed allosteric sites followed by extensive MD simulations of different setup systems (presence/absence of allosteric and orthosteric ligands, different combinations of mutants, etc.) to determine the allosteric effect of the fragment, before accomplishing the growing of the fragment to a drug-like compound. In the GPCR, we have recently started a drug discovery project project for a class A GPCRs in collaboration with Leiden University, where we propose to generate dynamic protein-compound interaction descriptors extracted from MD simulation trajectories. Such descriptors will feed existing proteochemometric ML models initially developed by the Leiden group, to further improve their predictive power on binding affinity.
Finally we will continue our joint effort of Åqvist and Gutiérrez-de-Terán to develop an efficient large scale virtual screening based on free energy perturbation simulations. The approach herein is to train a ML learning algorithms with relative FEP binding affinity simulation generated for hundreds of compound-pair simulations, to increase the performance and robustness of estimated absolute binding affinities within a compound series.
 Åqvist, J; Isaksen, G.V.; Brandsdal, BØ. "Computation of enzyme cold adaptation." Nature Reviews Chemistry 1.7 (2017): 1-14.
 Wei, S.; Thakur, N.; Ray, A.P.; Jin, B.; Obeng, S.; McCurdy, C.R.; McMahon, L.R.; Gutiérrez-de-Terán, H.; Eddy, M.T.; Lamichhane, R. "Slow Conformational Dynamics of the Human A2A Adenosine Receptor are Temporally Ordered." Structure 30 (2022) 329