The SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ∼30 000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ∼200 virtual screenings of compound libraries on selected protein structures, we redefine the protein’s druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

Ligand-gated ion channels conduct currents in response to chemical stimuli, mediating electrochemical signaling in neurons and other excitable cells. For many channels the details of gating remain unclear, partly due to limited structural data and simulation timescales. Here, we used enhanced sampling to simulate the pH-gated channel GLIC, and construct Markov state models (MSMs) of gating. Consistent with new functional recordings we report in oocytes, our analysis revealed differential effects of protonation and mutation on free-energy wells. Clustering of closed- versus open-like states enabled estimation of open probabilities and transition rates, while higher-order clustering affirmed conformational trends in gating. Furthermore, our models uncovered state- and protonation-dependent symmetrization. This demonstrates the applicability of MSMs to map energetic and conformational transitions between ion-channel functional states, and how they reproduce shifts upon activation or mutation, with implications for modeling neuronal function and developing state-selective drugs.

Summary Understanding how proteins transition between different conformers, and how conformers relate to each other in terms of structure and function, is not trivial. Here, we present an online tool for transition pathway generation between two protein conformations using Elastic Network Driven Brownian Dynamics Importance Sampling, a coarse-grained simulation algorithm, which spontaneously predicts transition intermediates trapped experimentally. In addition to path-generation, the server provides an interactive 2D-motion landscape graphical representation of the transitions or any additional conformers to explore their structural relationships. Availability and implementation eBDIMS is available online: http://ebdims.biophysics.se/ or as standalone software: https://github.com/laura-orellana/eBDIMS, https://github.com/cabergh/eBDIMS. Supplementary information Supplementary data are available at Bioinformatics online.