Protein-Ligand Binding Ranking Using Thermodynamic Cycle Closure
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Solution Overview
Problem
Existing computational methods for calculating protein-ligand binding free energies, such as TI, FEP, and FEP/REST, are limited by high computational resources, sampling timescales, and the need for robust error determination, particularly when assessing congeneric ligand sets.
Innovation Solution
A computer-implemented method for determining relative receptor-ligand binding affinities using cycle closure analysis to probabilistically estimate free energy differences and errors, employing methods like Maximum Likelihood and Bayesian statistics to ensure consistency and reliability of calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If free energy calculations (TI, FEP, lambda dynamics) are used to obtain complete energetic description of binding, then accuracy of binding affinity prediction is improved, but computational resources and time required increase significantly
Solution Approach 1:
The patent applies preliminary action by performing fast end-point methods (MM/GBSA, MM/PBSA) before free energy calculations to pre-screen and identify promising ligands. This preliminary filtering step reduces the number of ligands that require computationally expensive FEP/TI calculations, thereby maintaining accuracy for the most promising candidates while significantly reducing overall computational time and resources.
Solution Approach 2:
The patent segments the ligand set into different groups based on initial screening results, treating congeneric ligands differently from non-congeneric ligands. For congeneric ligands, relative binding free energy calculations are performed which are computationally more efficient than absolute free energy calculations. This segmentation allows the method to optimize computational resources by applying different calculation strategies to different subsets of ligands.
2Measurement precision
If free energy calculations are performed for multiple conformations to improve sampling completeness, then accuracy of binding affinity is improved, but computational resources required increase
Solution Approach 1:
The patent applies partial action by performing free energy calculations on a selected subset of ligands rather than all ligands in the screen. Ligands are prioritized based on their performance in earlier screening stages, and free energy calculations are performed only on those that show promise. This partial application of the computationally intensive method maintains accuracy for the most relevant ligands while reducing overall computational complexity.
3Reliability
If robust error determination is performed for free energy calculations, then reliability of binding affinity predictions is improved, but computational time and resources increase
Solution Approach 1:
The patent implements feedback by using the results from fast end-point methods to guide and inform subsequent free energy calculations. The binding poses and energetics obtained from MM/GBSA or MM/PBSA calculations provide feedback that helps optimize the setup and parameters for FEP/TI calculations. This feedback mechanism improves the reliability of predictions by ensuring that free energy calculations are performed on well-characterized ligand-receptor complexes, while reducing the need for excessive sampling and error analysis.
Data Source
AI summary
Structure-based drug design using a computer, includes: identifying a protein target of pharmacological interest; identifying at least three different ligands for binding to the protein; for each of the ligands, determining a relative strength of binding between the ligand and the protein to form a corresponding complex; ranking the different ligands identified as forming complexes with the protein based on the determined relative binding free energies; and identifying one or more of the ranked ligands as candidates for the drug based on the ranking. Determining the relative strength includes: simulating, using the computer, a set of pairs of different ligands forming at least one closed thermodynamic cycle comprising a plurality of legs linking at least two different ligand pairs to determine multiple relative binding free energy differences for the set of pairs of different ligands; and determining, using the computer, a non-zero hysteresis magnitude associated with each closed thermodynamic cycle by summing the relative binding free energy differences for each of the ligand pairs that form a closed thermodynamic cycle.