Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Markthaler, Daniel; Fleck, Maximilian; Stankiewicz, Bartosz; Hansen, Niels

doi:10.1021/acs.jctc.1c01012

Cited by 9 publications

(8 citation statements)

References 119 publications

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“…[ 17 ], convergence in HREM-based approaches was found sluggish even in simple host–guest systems [ 18 ], requiring a “significant number of iterations”. A similar outcome was found in the 2022 study by Markthaler et al [ 19 ], where rather long (more than 100 ns) HREM simulations were required to “remove an observed starting structure dependence to an acceptable results”. In Ref.…”

Section: Introductionsupporting

confidence: 76%

Does Hamiltonian Replica Exchange via Lambda-Hopping Enhance the Sampling in Alchemical Free Energy Calculations?

Procacci

2022

Molecules

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In the context of computational drug design, we examine the effectiveness of the enhanced sampling techniques in state-of-the-art free energy calculations based on alchemical molecular dynamics simulations. In a paradigmatic molecule with competition between conformationally restrained E and Z isomers whose probability ratio is strongly affected by the coupling with the environment, we compare the so-called λ-hopping technique to the Hamiltonian replica exchange methods assessing their convergence behavior as a function of the enhanced sampling protocols (number of replicas, scaling factors, simulation times). We found that the pure λ-hopping, commonly used in solvation and binding free energy calculations via alchemical free energy perturbation techniques, is ineffective in enhancing the sampling of the isomeric states, exhibiting a pathological dependence on the initial conditions. Correct sampling can be restored in λ-hopping simulation by the addition of a “hot-zone” scaling factor to the λ-stratification (FEP+ approach), provided that the additive hot-zone scaling factors are tuned and optimized using preliminary ordinary replica-exchange simulation of the end-states.

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Section: Introductionsupporting

confidence: 76%

Does Hamiltonian Replica Exchange via Lambda-Hopping Enhance the Sampling in Alchemical Free Energy Calculations?

Procacci

2022

Molecules

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“…For quantifying energetics of protein stability and interactions, there are numerous methods for free energy estimation through constrained or otherwise biased MD simulations. These are steered molecular dynamics (SMD) [ 75 ], potential of mean force (PMF) calculations [ 76 , 77 ], alchemical free energy calculations [ 78 , 79 ], and mostly free energy perturbation (FEP) [ 80 , 81 , 82 , 83 , 84 ], including in silico alanine screening [ 85 , 86 , 87 ].…”

Section: Determination Of Protein Stabilitymentioning

confidence: 99%

“…One of the possible ways to relieve computational costs is to transition from all-atom to coarse-grained models of proteins [ 102 , 114 , 126 ]. Other options are the use of accelerated molecular dynamics methods [ 84 , 127 ] and enhanced sampling techniques [ 84 , 128 , 129 , 130 , 131 ], including machine learning approaches [ 132 , 133 , 134 ].…”

Section: Determination Of Protein Stabilitymentioning

confidence: 99%

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

Masson

Lushchekina

2022

Molecules

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The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.

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“…25,26 The proteins of interest were representative β-sheet and α-helix proteins, the WW domain and HP-36. These short proteins have often been the subject of protein folding simulations that use explicit 24,[27][28][29][30][31][32][33][34] and implicit solvents. 7,[35][36][37][38] Subsequently, we identified the critical residues that lead to the differences in solvation models using a recently modified sitedirected thermodynamic analysis method that resolves the solvation free energy G solv and the gas-phase potential energy E u into contributions from the main and side chains.…”

Section: Introductionmentioning

confidence: 99%

Comparing the influence of explicit and implicit solvation models on site‐specific thermodynamic stability of proteins

et al. 2023

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Understanding the molecular basis for protein stability requires a thermodynamic analysis of protein folding. Thermodynamic analysis is often performed by sampling many atomistic conformations using molecular simulations that employ either explicit or implicit water models. However, it remains unclear to what extent thermodynamic results from different solvation models are reliable at the molecular level. In this study, we quantify the influence of both solvation models on folding stability at the individual backbone and side chain resolutions. We assess the residue-specific folding free energy components of a β-sheet protein and a helical protein using trajectories resulting from TIP3P explicit and generalized Born/surface area implicit solvent simulations of model proteins. We found that the thermodynamic discrepancy due to the implicit solvent mostly originates from charged side chains, followed by the under-stabilized hydrophobic ones. In contrast, the contributions of backbone residue in both proteins were comparable for explicit and implicit water models. Our study lays out the foundation for detailed thermodynamic assessment of solvation models in the context of protein simulation.

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Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Cited by 9 publications

References 119 publications

Does Hamiltonian Replica Exchange via Lambda-Hopping Enhance the Sampling in Alchemical Free Energy Calculations?

Does Hamiltonian Replica Exchange via Lambda-Hopping Enhance the Sampling in Alchemical Free Energy Calculations?

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

Comparing the influence of explicit and implicit solvation models on site‐specific thermodynamic stability of proteins

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