Enthalpy–Entropy Compensation in Biomolecular Recognition: A Computational Perspective

Peccati, Francesca; Jiménez‐Osés, Gonzalo

doi:10.1021/acsomega.1c00485

Cited by 38 publications

(37 citation statements)

References 28 publications

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“…This could suggest that enthalpy–entropy compensation is important for correctly ranking these outliers. 66 That is, with additional H bonds between the TCR and pHLA, one may expect a more favorable binding enthalpy term, which could be offset to a large degree by a less favorable binding entropy term. There are not enough data points, however, to determine if this is a general trend; we further note that outlier NY-6 did not show an increase in solute–solute hydrogen bonds, indicating that outliers may also be caused through other effects.…”

Section: Resultsmentioning

confidence: 99%

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Crean

Pudney

Cole

et al. 2022

J. Chem. Inf. Model.

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Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

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Section: Resultsmentioning

confidence: 99%

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Crean

Pudney

Cole

et al. 2022

J. Chem. Inf. Model.

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“…We decompose the binding entropy into two terms, ∆S = ∆S conf + ∆S 0 , where ∆S conf is the change in conformational entropy, [41][42][43] which depends on the elastic network topology and spring stiness, and ∆S 0 accounts for other contributions to entropy that are not captured directly by the model (e.g., release of frustrated solvent, ligand conformational change, protein entropy change due to plastic deformation). 24,[44][45][46][47][48][49] We calculate ∆S conf for the elastic network by creating sti bonds of strength K Λ between the protein and ligand binding sites (see Methods). Standard normal mode analysis shows that the resulting entropy change is the sum over the variation in the logarithms of the mode energies λ i before and after binding, ∆S conf = − 1 2 i ∆ ln λ i .…”

Section: Resultsmentioning

confidence: 99%

“…Proteins are most often found to lose entropy upon binding, but many proteins gain entropy due to allosteric conformational change. 105 Moreover, there are other contributions to entropy, such as solvent entropy and ligand entropy, [114][115][116] that are not treated in our model (contributions of translational and rotational entropy do not depend on the internal degrees of freedom and can be included as a constant factor). Incorporating these details is beyond the scope of the model presented here.…”

Section: Methodsmentioning

confidence: 99%

General theory of specific binding: insights from a genetic-mechano-chemical protein model

McBride

Eckmann

Tlusty³

2022

Preprint

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Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. Despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present a model that combines chemistry, mechanics and genetics, and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving discrimination -- by varying degrees of flexibility and shape/chemistry complementarity -- but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is aided by increasing the protein, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. Our model makes testable, specific predictions about the role of flexibility in discrimination, and how to independently tune affinity and specificity. Thus, the proposed theory of molecular discrimination addresses the natural question ``why are proteins so big?'' A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

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“…This could suggest that enthalpy-entropy compensation is important for correctly ranking these outliers. [53] That is, with additional Hbonds between the TCR and pHLA, one may expect a more favorable binding enthalpy term, which could be offset to a large degree by a less favorable binding entropy term.…”

Section: Resultsmentioning

confidence: 99%

Reliable in silico ranking of engineered therapeutic TCR binding affinities with MMPB/GBSA

Kamp

Crean

Cole

et al. 2021

Preprint

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Accurate and efficient in silico ranking of protein-protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics trajectories. This provides a compromise between rapid but approximate scoring functions of single structures and more sophisticated methods such as free energy perturbation. Optimal MMPB/GBSA parameters tend to be system specific. Here, we identify protocols that enable reliable evaluation of the effect of mutations in a T-cell receptor (TCR) in complex with its natural target, the peptide-human leukocyte antigen (pHLA). The development of affinity-enhanced engineered TCRs towards a specific pHLA is of great interest in the field of immunotherapy. Our study highlights the importance of using a higher than default internal dielectric constant, especially in the case of charge changing mutations. Including explicit solvation and/or entropy corrections may deteriorate the ranking of single point variants due to the errors associated with these additions. For multi-point variants, however, these corrections were important for accurate ranking. We also demonstrate how potential outliers could be identified in advance by analyzing changes in the hydrogen bonding networks at the binding interface. Finally, using bootstrapping we show that as few as 5-10 replicas of short (4 ns) MD simulations may be sufficient for reproducible and accurate ranking of candidate TCR variants. Our work demonstrates that reliably ranking TCR variant binding affinities can be achieved at moderate computational cost. The protocols developed here can be applied towards in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

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Enthalpy–Entropy Compensation in Biomolecular Recognition: A Computational Perspective

Cited by 38 publications

References 28 publications

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

General theory of specific binding: insights from a genetic-mechano-chemical protein model

Reliable in silico ranking of engineered therapeutic TCR binding affinities with MMPB/GBSA

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