BFEE2: Automated, Streamlined, and Accurate Absolute Binding Free-Energy Calculations

Abstract

Accurate absolute binding free-energy estimation in silico, following either an alchemical or a geometrical route, involves several subprocesses and requires the introduction of geometric restraints. Human intervention, for instance, to define the necessary collective variables, prepare the input files, monitor the simulation, and perform post-treatments is, however, tedious, cumbersome, and prone to errors. With the aim of automating and streamlining free-energy calculations, especially for nonexperts, version 2.0 of the binding free energy estimator (BFEE2) provides both standardized alchemical and geometrical workflows and obviates the need for extensive human intervention to guarantee complete reproducibility of the results. To achieve the largest gamut of protein−ligand and, more generally, of host−guest complexes, BFEE2 supports most academic force fields, such as CHARMM, Amber, OPLS, and GROMOS. Configurational files are generated in the NAMD and Gromacs formats, and all the post-treatments are performed in an automated fashion. Moreover, convergence of the free-energy calculation can be monitored from the intermediate files generated during the simulation. All in all, BFEE2 is a foolproof, versatile tool for accurate absolute binding freeenergy calculations, assisting the end-user over a broad range of applications.

Uncovering the Mechanism of Drug Resistance Caused by the T790M Mutation in EGFR Kinase From Absolute Binding Free Energy Calculations

“…The binding free energy acts as a useful index to evaluate the binding affinity between mutants and drugs, and can be used as an important indicator of drug resistance ( Zhou et al, 2013 ; Ma et al, 2015 ; Khan et al, 2020 ). In this article, the standard binding free-energy calculations of all systems were performed employing BFEE2 and following a geometrical route ( Gumbart et al, 2013 ; Fu et al, 2021 ; Fu et al, 2022 ). BFEE2, which is a graphical user interface-based software, can automatically set up and analyze absolute binding free-energy calculations carried out with the popular MD engine NAMD ( Fu et al, 2021 ; Fu et al, 2022 ).…”

“…In this article, the standard binding free-energy calculations of all systems were performed employing BFEE2 and following a geometrical route ( Gumbart et al, 2013 ; Fu et al, 2021 ; Fu et al, 2022 ). BFEE2, which is a graphical user interface-based software, can automatically set up and analyze absolute binding free-energy calculations carried out with the popular MD engine NAMD ( Fu et al, 2021 ; Fu et al, 2022 ). The calculation process of each protein-ligand complex was divided into eight independent subprocesses.…”

Evaluation of Inhibitory Activity In Silico of In-House Thiomorpholine Compounds between the ACE2 Receptor and S1 Subunit of SARS-CoV-2 Spike

“…This is why it is necessary to carry out complementary studies that will allow us to have an adequate interpretation or to extrapolate the results obtained in this type of calculation. In the case of the present work, BFEE2 was used to estimate the free energy [40]. The Binding Free Energy Estimator (BFEE2) is Python-based software that automates absolute binding free energy calculations through the alchemical or geometric pathway using molecular dynamics simulations.…”

“…The Binding Free Energy Estimator (BFEE) is a Python-based software that automates the absolute binding free energy calculations through the alchemical or geometric pathway using molecular dynamics simulations. The degrees of freedom of the protein-ligand (or host-host) system are described by a series of geometric variables (or collective variables), as first described by the Karplus group [40]. In BFEE, generalized geometric variables based on the best fit rotation are used, which, in principle, is available for any proteinligand complex.…”

Prediction of Binding Free Energy of Protein–Ligand Complexes with a Hybrid Molecular Mechanics/Generalized Born Surface Area and Machine Learning Method

“…These range from the high-end free-energy methods such as free-energy perturbation (FEP) − and thermodynamic integration (TI), , which are physically rigorous but prohibitively expensive for high-throughput screening, to the low-end empirical scoring functions (SFs). Classical SFs can be typically divided into three classes, namely, force field (FF)-based SFs, − empirical SFs, − and knowledge-based SFs. − FF-based SFs characterize the protein–ligand interactions by linear combination of nonbonding interaction components (such as electrostatic, van der Waals, and hydrogen bonding), while the other more difficult components such as desolvation and entropic contributions are usually simplified or even neglected. Consequently, these SFs show diversified performance in predicting the protein–ligand binding affinity.…”