<i>In silico</i> screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations

We cover diverse methodologies, computational approaches, and case studies illustrating the ongoing efforts to develop viable drug candidates for treatment of COVID-19.

In silico identification of potent protein inhibitors commonly requires prediction of a ligand binding free energy (BFE). Thermodynamics integration (TI) based on molecular dynamics (MD) simulations is a BFE calculation method capable of acquiring accurate BFE, but it is computationally expensive and time-consuming. In this work, we have developed an efficient automated workflow for identifying compounds with the lowest BFE among thousands of congeneric ligands, which requires only hundreds of TI calculations. Automated machine learning (AutoML) orchestrated by active learning (AL) in an AL-AutoML workflow allows unbiased and efficient search for a small set of best-performing molecules. We have applied this workflow to select inhibitors of the SARS-CoV-2 papain-like protease and were able to find 133 compounds with improved binding affinity, including 16 compounds with better than 100-fold binding affinity improvement. We obtained a hit rate that outperforms that expected of traditional expert medicinal chemist-guided campaigns. Thus, we demonstrate that the combination of AL and AutoML with free energy simulations provides at least 20× speedup relative to the naïve brute force approaches.

Overactivation of the N-methyl-D-aspartate receptor (NMDAR) in postsynaptic neurons leads to glutamate-related excitotoxicity in the central nervous system of mammals. We have built 3-D models of each domain for the universal screening of potential toxicants and their binding mechanisms. Our docking results show that the calculated pK (i) values of glycine and L: -glutamate significantly increase (&gt;1) when the NR1 and NR2A S1S2 domains are closing, respectively. Inversely, D: -cycloserine (DCS) and 5,7-dichlorokynurenic acid (5,7-DCKA) do not show such a dependence on domain closure. Replica exchange molecular dynamics (REMD) confirmed 5 different conformational states of the S1S2 domain along the 308.2 K temperature trajectory. Analysis of residue fluctuations during this temperature trajectory showed that residues in loop 1, loop 2, the amino terminal domain (ATD), and the area linked to ion channel &#x3b1;-helices are involved in this movement. This further implicates the notion that efficacious ligands act through S1S2 lobe movement which can culminate in the opening or closing of the ion channel. We further tested this by docking hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX) to the S1S2 domain. Our results predict that these nitramines are not efficacious and thus do not produce excitoxicity when they bind to the S1S2 domain of the NMDAR.