RevMut-PMX

Documentation Index

Fetch the complete documentation index at: https://docs.revilico.bio/llms.txt

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Why Use This Engine?

In the documentation below, we will use Revilico’s RevMut-PMX engine to predict how a single point mutation in a protein changes its binding affinity for a ligand. This type of calculation is directly applicable to resistance mutation modeling, where a clinical mutation in a drug target reduces binding of an approved inhibitor, and to protein engineering, where beneficial mutations are identified to improve binding of a therapeutic molecule. RevMut-PMX computes the relative binding free energy change (DDG_bind) using PMX hybrid topology generation and GROMACS alchemical free energy perturbation simulations.

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Background

A point mutation changes one amino acid in the protein sequence, which alters the local electrostatic and steric environment of the binding site and thereby affects the binding free energy of any ligand in that site. Directly measuring this effect computationally would require running separate MD simulations of the wild-type and mutant proteins, both free and bound to the ligand, and computing absolute binding free energies for each. This is prohibitively expensive and converges poorly. The thermodynamic cycle approach circumvents this by recognizing that binding free energy differences are path-independent. Rather than simulating the actual mutation event, alchemical free energy perturbation (FEP) simulates the unphysical but thermodynamically valid process of continuously transforming one amino acid residue into another within a single simulation, computing the free energy cost of this transformation both in the presence and absence of the ligand.

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Thermodynamic Cycle

The four corners of the thermodynamic cycle relate wild-type and mutant binding free energies: $$\Delta\Delta G_{\text{bind}} = \Delta G_{\text{mut}}^{\text{complex}} - \Delta G_{\text{mut}}^{\text{apo}}$$ Where $\Delta G_{\text{mut}}^{\text{complex}}$ is the free energy of alchemically mutating the residue with the ligand present, and $\Delta G_{\text{mut}}^{\text{apo}}$ is the same mutation free energy in the protein alone without ligand. Because the thermodynamic cycle is closed, DDG_bind can be obtained from these two alchemical legs without ever computing the physical binding event directly. A negative DDG_bind indicates that the mutation improves ligand binding (stabilizing). A positive DDG_bind indicates that the mutation weakens binding (destabilizing), which is the signature of a resistance mutation.

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Hybrid Topology Generation (PMX)

RevMut-PMX uses PMX to generate a single hybrid topology that simultaneously represents both the wild-type residue (state A, lambda = 0) and the mutant residue (state B, lambda = 1). Atoms shared between the two residues retain their parameters throughout the simulation. Atoms present only in the wild type are converted to dummy atoms that gradually lose their physical interactions as lambda increases from 0 to 1. Atoms present only in the mutant are introduced as dummy atoms at lambda = 0 and gradually acquire their full interactions as lambda approaches 1. A soft-core potential prevents singularities in the van der Waals interactions when atoms appear or disappear: $$V_{\text{sc}}(r) = 4\epsilon \left[ \frac{1}{(\alpha \lambda^p \sigma^6 + r^6)^2} - \frac{1}{\alpha \lambda^p \sigma^6 + r^6} \right]$$ Where alpha = 0.5, p = 1, and sigma = 0.3 nm are the soft-core parameters. Three lambda schedules are available: a standard 11-window schedule for typical mutations, a 17-window charge-changing schedule for mutations that alter the net charge (e.g., Asp to Asn), and a 21-window large-mutation schedule for drastic sidechain changes.

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Simulation Protocol

Each alchemical leg (complex and apo) follows the same simulation pipeline. After building the hybrid topology, the system is solvated in a dodecahedral water box with TIP3P water and 0.15 M NaCl. Energy minimization removes clashes, followed by 100 ps NVT and 100 ps NPT equilibration as described in the RevMD engines. Production FEP simulations run for the user-specified duration (default 5 ns) per lambda window. Both legs and all lambda windows can run in parallel. During each production window, GROMACS computes the derivative of the Hamiltonian with respect to lambda (dH/dlambda) and the reduced potential at all other lambda states at every step, writing these to dhdl.xvg files that are read by the analysis step.

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Free Energy Analysis

After all lambda windows complete, the free energy difference for each leg is estimated by cascading through three estimators in order of accuracy: MBAR (Multistate Bennett Acceptance Ratio) is the primary estimator. It uses the full matrix of reduced potentials across all lambda states simultaneously, extracting the maximum likelihood free energy differences: $$\Delta G = -k_BT \ln \frac{\sum_j \sum_n \frac{e^{-u_{kn}}}{\sum_l N_l e^{f_l - u_{ln}}}}{\sum_j \sum_n \frac{e^{-u_{jn}}}{\sum_l N_l e^{f_l - u_{ln}}}}$$ BAR (Bennett Acceptance Ratio) is used if MBAR does not converge. It computes free energy differences between adjacent lambda windows using the acceptance probability of work values. TI (Thermodynamic Integration) is used as a last resort. It integrates the mean dH/dlambda values across lambda using a trapezoid rule: $$\Delta G = \int_0^1 \left\langle \frac{\partial H}{\partial \lambda} \right\rangle_\lambda \, d\lambda$$ The final DDG_bind is computed as the difference between the complex and apo leg free energies, with uncertainty propagated as: $$\sigma_{\Delta\Delta G} = \sqrt{\sigma_{\text{complex}}^2 + \sigma_{\text{apo}}^2}$$

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Running the Engine

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Inputs

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Outputs

• DDG_bind: Relative binding free energy change in kcal/mol with uncertainty estimate
• DDG_complex: Free energy cost of mutation in the protein-ligand complex
• DDG_apo: Free energy cost of mutation in the apo protein
• Phase timeline: Progress through prepare, setup, running, and analysis phases with timing
• Convergence plots: dH/dlambda or MBAR overlap matrices per leg
• Interpretation: Sign-annotated result with binding stabilizing or destabilizing classification

DDG_bind interpretation: