Evaluating the use of absolute binding free energy in the fragment optimization process.

Key to the fragment optimization process is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimization decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman’s r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimization decisions can be supported by the ABFE calculations. Cases that were not supported were often limited by large uncertainties in the free energy estimates, however generally the right direction in ΔΔG is still observed. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better outcomes in ranking binders, improving correlation metrics, although a similar confidence in retrospective synthetic decisions is achieved. Our results indicate that ABFE calculations are currently at the level of accuracy that can be usefully employed to gauge which fragment elaborations are likely to offer the best gains in affinity.

Lipids in the Piezo1 channel pore: gating mechanism, or simulation artifact?

Opening and closure of certain mechanosensitive ion channels have recently been linked with the presence of lipids in or near their pores. Although non-conducting structures of mechanosensitive Piezo channels do not show the presence of lipids in the pore, computational simulations suggest whole phospholipids enter the Piezo1 pore in the closed state. Here, to probe this phenomenon, we conduct coarse-grained (CG) and all-atom (AA) simulations of Piezo1 with different solvation algorithms and equilibrium protocols, including CG-to-AA reverse mapping from Martini CG force field to CHARMM AA force field. Our results show that the lack of initial hydration of the upper pore region, enabled by common CG but not AA solvation algorithms, allows entry of whole lipids through gaps between pore helices during subsequent equilibrium simulations. Absolute binding free energy calculations show that these lipids are thermodynamically unfavorable, indicating they are likely kinetically trapped in the pore during microsecond-long AA simulations. An alternative equilibrium protocol is proposed to avoid such simulation artifact for channels whose pores are walled with transmembrane gaps. This work underscores the notion that, as simulated systems become increasingly complex, interpretation of simulated data in physiological contexts requires extra precautions. When no experimental data is available, free energy approaches such as those implemented here appear as trustworthy validations of results observed from MD trajectories.

Binding free energy predictions in host-guest systems using Autodock4. A retrospective analysis on SAMPL6, SAMPL7 and SAMPL8 challenges

We systematically tested the Autodock4 docking program for absolute binding free energy predictions using the host-guest systems from the recent SAMPL6, SAMPL7 and SAMPL8 challenges. We found that Autodock4 behaves surprisingly well, outperforming in many instances expensive molecular dynamics or quantum chemistry techniques, with an extremely favorable benefit-cost ratio. Some interesting features of Autodock4 predictions are revealed, yielding valuable hints on the overall reliability of docking screening campaigns in drug discovery projects.

Covalent and non-covalent binding free energy calculations for peptidomimetic inhibitors of SARS-CoV-2 main protease

This work employs rigorous absolute binding free energy calculations and QM/MM methods to calculate the total binding energy of two recently crystallized peptidomimetic covalent inhibitors of the SARS-CoV-2 Mpro target.

Absolute Binding Free Energy Calculations for Highly Flexible Protein MDM2 and Its Inhibitors

Reliable prediction of binding affinities for ligand-receptor complex has been the primary goal of a structure-based drug design process. In this respect, alchemical methods are evolving as a popular choice to predict the binding affinities for biomolecular complexes. However, the highly flexible protein-ligand systems pose a challenge to the accuracy of binding free energy calculations mostly due to insufficient sampling. Herein, integrated computational protocol combining free energy perturbation based absolute binding free energy calculation with free energy landscape method was proposed for improved prediction of binding free energy for flexible protein-ligand complexes. The proposed method is applied to the dataset of various classes of p53-MDM2 (murine double minute 2) inhibitors. The absolute binding free energy calculations for MDMX (murine double minute X) resulted in a mean absolute error value of 0.816 kcal/mol while it is 3.08 kcal/mol for MDM2, a highly flexible protein compared to MDMX. With the integration of the free energy landscape method, the mean absolute error for MDM2 is improved to 1.95 kcal/mol.