Protein flexibility in computer-aided drug discovery: predicting correlated loop conformations and drug metabolism by CYP2C9

Matthew L Danielson, Purdue University

Abstract

Protein flexibility is critical to the molecular recognition process between a protein and a ligand. Flexible loop regions have been shown to play a crucial role in many biological functions such as protein-ligand recognition, enzymatic catalysis, and proteinprotein association. In the context of structure-based drug design, using or predicting an incorrect loop configuration can be detrimental to the study if the loop is capable of interacting with the ligand. To date, most computational loop prediction methods only focus on individual loop regions. However, loop regions are often in close proximity spatially to one another and their mutual interactions stabilize their configuration. We have developed a new method, titled CorLps, that predicts such interacting loop regions. First an ensemble of individual loop conformations is generated for each loop region. The members of the individual ensembles are then combined and are accepted or rejected based on a steric clash filter. After a subsequent side chain optimization step, the resulting interacting loop configurations are initially ranked by the statistical scoring function DFIRE. Our results show that predicting interacting loops with CorLps is superior to sequential prediction of the two interacting loop regions. We applied CorLps to three protein systems, each with at least one flexible loop region capable of interacting with bound ligands; a six residue loop region from phosphoribosylglycinamide formyltransferase (GART), two nine residue loop regions from CYP119, and an eleven residue loop region from enolase were selected for loop prediction. To optimize the accuracy of loop prediction, different scoring functions were used to re-rank the predicted loops. In general, single snapshot MM/GBSA scoring provided the best ranking of native-like loop configurations. Based on the scoring function analyses presented, the optimal ranking of native-like loop configurations is still a difficult challenge and the choice of the “best” scoring function appears to be system dependent. Protein flexibility can also influence the success of site of metabolism (SOM) prediction studies. We have combined molecular dynamics, AutoDock Vina docking, the neighboring atom type (NAT) reactivity model, and a solvent-accessible surface-area term to form a reactivity-accessibility model capable of predicting SOM for cytochrome P450 2C9 substrates. To investigate the importance of protein flexibility during the ligand binding process, the results of SOM prediction using a static protein structure for docking were compared to SOM prediction using multiple protein structures in ensemble docking. Our results indicate that ensemble docking increases the number of ligands that can be docked in a bioactive conformation but only leads to a slight improvement in predicting an experimentally known SOM in the top-1 position for a ligand library of 75 CYP2C9 substrates. However, further classifying the substrate library according to Km values leads to an improvement in SOM prediction for substrates with low Km values. Although the current predictive power of the reactivity-accessibility model still leaves significant room for improvement, the results illustrate the usefulness of this method to identify key protein-ligand interactions and guide structural modifications of the ligand to increase its metabolic stability.

Degree

Ph.D.

Advisors

Lill, Purdue University.

Subject Area

Pharmacology|Biochemistry|Pharmacy sciences|Biophysics

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