Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy

Understanding the thermodynamic signature of protein–peptide binding events is a major challenge in computational chemistry. The complexity generated by both components possessing many degrees of freedom poses a significant issue for methods that attempt to directly compute the enthalpic contribution to binding. Indeed, the prevailing assumption has been that the errors associated with such approaches would be too large for them to be meaningful. Nevertheless, we currently have no indication of how well the present methods would perform in terms of predicting the enthalpy of binding for protein–peptide complexes. To that end, we carefully assembled and curated a set of 11 protein–peptide complexes where there is structural and isothermal titration calorimetry data available and then computed the absolute enthalpy of binding. The initial “out of the box” calculations were, as expected, very modest in terms of agreement with the experiment. However, careful inspection of the outliers allows for the identification of key sampling problems such as distinct conformations of peptide termini not being sampled or suboptimal cofactor parameters. Additional simulations guided by these aspects can lead to a respectable correlation with isothermal titration calorimetry (ITC) experiments ( R 2 of 0.88 and an RMSE of 1.48 kcal/mol overall). Although one cannot know prospectively whether computed ITC values will be correct or not, this work shows that if experimental ITC data are available, then this in conjunction with computed ITC, can be used as a tool to know if the ensemble being simulated is representative of the true ensemble or not. That is important for allowing the correct interpretation of the detailed dynamics of the system with respect to the measured enthalpy. The results also suggest that computational calorimetry is becoming increasingly feasible. We provide the data set as a resource for the community, which could be used as a benchmark to help further progress in this area.

Section: Discussionmentioning

confidence: 99%

Computed Protein–Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems

Çınaroğlu,

Biggin

2023

“…This poses a massive challenge for the prediction accuracy of the barrier height, as already a mis-estimate by a single unit of k B T (or RT , respectively, i.e., 2.5 kJ/mol or 0.6 kcal/mol at physiological temperatures) causes a deviation of a predicted rate from the true rate by a factor of ∼3. For comparison, the average error of current free energy methods is ∼1.6 k B T , i.e., a factor of ∼14 in the error of k . This is a significant deviation compared to the chemically available variation in k within a compound library, which at best covers 4 orders of magnitude (see, e.g., the range of Hsp90-binding compounds available in refs , , , and ) and about 2 orders of magnitude for compounds with the same chemical scaffold for the A 2 adenosine receptor .…”

Section: Current Challenges In Rate Calculationsmentioning

confidence: 99%

Predicting Protein–Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

Wolf

2023

The prediction of drug−target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

“…10−17 A recent statistical analysis suggests that in silico absolute binding free energy calculations in conjunction with recent force fields can approach chemical accuracy, and are, thus, formally ready for blind screening in drug discovery. 18 Despite the many success stories of the methods devised for the calculation of standard protein−ligand binding free energies, achieving accurate estimates of the binding affinity for two proteins remains very challenging, even in less complex cases. 19 One of the reasons is the conformational changes of the binding partners in the course of recognition and association being appreciably more complex than in the case of protein binding of a ligand.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Since the pioneering work of Hermans and Shankar, who, for the first time, introduced a restraining potential to alleviate the difficulty in capturing the large change in configurational entropy as two molecular objects associate, methods to determine the standard protein–ligand binding free energy leaning on the introduction of geometric restraints have been developed at an increasing pace and are now broadly adopted in the fields of physical, biological, and medicinal chemistry. − A recent statistical analysis suggests that in silico absolute binding free energy calculations in conjunction with recent force fields can approach chemical accuracy, and are, thus, formally ready for blind screening in drug discovery …”

Section: Introductionmentioning

confidence: 99%

Achieving Accurate Standard Protein–Protein Binding Free Energy Calculations through the Geometrical Route and Ergodic Sampling

Chipot

Shao

et al. 2023

Self Cite

A new strategy for the prediction of binding free energies of protein–protein complexes is reported in the present article. By combining an ergodic-sampling algorithm with the so-called “geometrical route”, which introduces a series of geometrical restraints as a preamble to the physical separation of the two partners, we achieve accurate binding free energy calculations for medium-sized protein–protein complexes within the microsecond timescale. The ergodic-sampling algorithm, namely, Gaussian-accelerated molecular dynamics (GaMD), implicitly helps explore the conformational change of the two binding partners as they associate reversibly by raising the energy wells. Therefore, independent simulations capturing the isomerization of proteins are no longer needed, reducing both the computational cost and human effort. Numerical applications indicate errors on the order of 0.1 kcal/mol for the Abl-SH3 domain binding a decapeptide, of 2.6 kcal/mol for the barnase–barstar complex, and of 0.2 kcal/mol for human leukocyte elastase binding the third domain of the turkey ovomucoid inhibitor. Compared with the classical geometrical route, which resorts to collective variables to describe the isomerization of proteins, our new strategy possesses remarkable convergence properties and robustness for protein–protein complexes owing to improved ergodic sampling. We are confident that the strategy presented in this study will have a broad range of applications, helping us understand recognition–association phenomena in the areas of physical, biological, and medicinal chemistry.