Recent developments in methodologies for calculating the entropy and free energy of biological systems by computer simulation

Meirovitch, Hagai

doi:10.1016/j.sbi.2007.03.016

Cited by 125 publications

(94 citation statements)

References 67 publications

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“…Although entropy calculated using normal-mode analysis has been applied to Aβ and α-synuclein monomers, 52−55 our results indicate that Aβ40 has almost the same entropy upon different Zn 2+ binding modes may suggest that it is not an appropriate approach to characterize intrinsically disordered proteins (IDPs) like Aβ. 56 In contrast, results based on the MM/3D-RISM method indicate that Glu 11 is more preferred oxygen ligand (Table 1), and the major contributions arise from the favorable enthalpy (including solvation free energy) and the entropy (Table 2). Moreover, the finding that the distribution of enthalpy can be well approximated by the Gaussian function ( Figure S6, Supporting Information) is in agreement with previous studies of conformational entropy of Aβ42 and its mutants.…”

Section: +mentioning

confidence: 95%

Effects of Zn²⁺ Binding on the Structural and Dynamic Properties of Amyloid Β Peptide Associated with Alzheimer’s Disease: Asp¹ or Glu¹¹?

Wang

2013

ACS Chem. Neurosci.

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Section: +mentioning

confidence: 95%

Effects of Zn²⁺ Binding on the Structural and Dynamic Properties of Amyloid Β Peptide Associated with Alzheimer’s Disease: Asp¹ or Glu¹¹?

Wang

2013

ACS Chem. Neurosci.

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“…Empirical measures of entropies have been obtained from the observed distribution of dihedral angles of main-chains or the number of side-chain rotameric states accessible in the folded and unfolded states (26)(27)(28)(29)(30) from computational simulations or a large database of protein structures. A more detailed review of various methods of entropy estimation can be found in Meirovitch et al (10). Recently, we have shown that by combining statistical potentials with entropy measures obtained from coarse-grained elastic network models (ENMs), an improvement can be achieved, especially in discriminating native protein-protein complexes from docked poses, reflecting the largest scale changes in dynamics (31,32).…”

Section: Significancementioning

confidence: 99%

“…Despite the remarkable advances in energies, predicting the entropies of protein structures has remained largely elusive and relatively little investigated (10). The ability to compute entropies of proteins is crucial not only for protein folding (11) but also for assessing the energetics of conformational changes involved in ligand binding, regulation, and protein-protein interactions (12,13).…”

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confidence: 99%

Knowledge-based entropies improve the identification of native protein structures

Sankar

Jia

Jernigan

2017

Proc. Natl. Acad. Sci. U.S.A.

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Evaluating protein structures requires reliable free energies with good estimates of both potential energies and entropies. Although there are many demonstrated successes from using knowledge-based potential energies, computing entropies of proteins has lagged far behind. Here we take an entirely different approach and evaluate knowledge-based conformational entropies of proteins based on the observed frequencies of contact changes between amino acids in a set of 167 diverse proteins, each of which has two alternative structures. The results show that charged and polar interactions break more often than hydrophobic pairs. This pattern correlates strongly with the average solvent exposure of amino acids in globular proteins, as well as with polarity indices and the sizes of the amino acids. Knowledgebased entropies are derived by using the inverse Boltzmann relationship, in a manner analogous to the way that knowledge-based potentials have been extracted. Including these new knowledge-based entropies almost doubles the performance of knowledge-based potentials in selecting the native protein structures from decoy sets. Beyond the overall energy-entropy compensation, a similar compensation is seen for individual pairs of interacting amino acids. The entropies in this report have immediate applications for 3D structure prediction, protein model assessment, and protein engineering and design.knowledge-based | entropies | free energy | native structure | contact changes K nowledge of a protein's structure is required to understand its dynamics and function; so, improvements in protein structure prediction, especially template-free methods, are essential if the whole protein universe it to be fully comprehended. Computational methods of structure prediction typically yield large numbers of possible structure models (decoys), then require challenging discrimination in determining which of these models is most likely to be the native structure. This well-known bottleneck in protein structure prediction suffers from presentday limitations in structure evaluation. Because the folding of a protein into its native structure is dictated by its free-energy landscape (1), the development of accurate free-energy functions for native structure evaluation is an area of active research. The free energy ΔG of a protein structure can be represented as ΔG = ΔV -TΔS, where ΔV and ΔS represent the energetic (enthalpic) and entropic components, respectively, and T the temperature. In the conventional folding funnel hypothesis of protein folding, the energies and entropies are captured by the depth and by the width of the well (1). Both the energetic and entropic components are combinations of large numbers of contributions, and hence the accurate prediction of free energies is limited by the reliable ability to assess all of these contributions.The energetic contribution to free energy of proteins is usually captured by potential functions, either physics-based or knowledgebased. Physics-based force fields, such as CHARMM, AMBER, GROMOS...

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“…A current challenge is to fully detail the components to the free energy of protein-protein association, and, in particular, the contribution of configurational entropy requires a better description and estimation (11)(12)(13). The three-dimensional structure of the protein-protein complex provides an opportunity to resolve the different energetic components, e.g., the bond lengths, the bond angles, and van der Waals interactions.…”

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confidence: 99%

Entropic contributions and the influence of the hydrophobic environment in promiscuous protein–protein association

Chang

McLaughlin

Baron

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

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The mechanisms by which a promiscuous protein can strongly interact with several different proteins using the same binding interface are not completely understood. An example is protein kinase A (PKA), which uses a single face on its docking/dimerization domain to interact with multiple A-kinase anchoring proteins (AKAP) that localize it to different parts of the cell. In the current study, the configurational entropy contributions to the binding between the AKAP protein HT31 with the D/D domain of RII ␣-regulatory subunit of PKA were examined. The results show that the majority of configurational entropy loss for the interaction was due to decreased fluctuations within rotamer states of the side chains. The result is in contrast to the widely held approximation that the decrease in the number of rotamer states available to the side chains forms the major component. Further analysis showed that there was a direct linear relationship between total configurational entropy and the number of favorable, alternative contacts available within hydrophobic environments. The hydrophobic binding pocket of the D/D domain provides alternative contact points for the side chains of AKAP peptides that allow them to adopt different binding conformations. The increase in binding conformations provides an increase in binding entropy and hence binding affinity. We infer that a general strategy for a promiscuous protein is to provide alternative contact points at its interface to increase binding affinity while the plasticity required for binding to multiple partners is retained. Implications are discussed for understanding and treating diseases in which promiscuous protein interactions are used.energy well ͉ molecular recognition ͉ peptide-protein binding ͉ quasiharmonic approximation ͉ vibrational entropy

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Recent developments in methodologies for calculating the entropy and free energy of biological systems by computer simulation

Cited by 125 publications

References 67 publications

Effects of Zn²⁺ Binding on the Structural and Dynamic Properties of Amyloid Β Peptide Associated with Alzheimer’s Disease: Asp¹ or Glu¹¹?

Effects of Zn²⁺ Binding on the Structural and Dynamic Properties of Amyloid Β Peptide Associated with Alzheimer’s Disease: Asp¹ or Glu¹¹?

Knowledge-based entropies improve the identification of native protein structures

Entropic contributions and the influence of the hydrophobic environment in promiscuous protein–protein association

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Recent developments in methodologies for calculating the entropy and free energy of biological systems by computer simulation

Cited by 125 publications

References 67 publications

Effects of Zn2+ Binding on the Structural and Dynamic Properties of Amyloid Β Peptide Associated with Alzheimer’s Disease: Asp1 or Glu11?

Effects of Zn2+ Binding on the Structural and Dynamic Properties of Amyloid Β Peptide Associated with Alzheimer’s Disease: Asp1 or Glu11?

Knowledge-based entropies improve the identification of native protein structures

Entropic contributions and the influence of the hydrophobic environment in promiscuous protein–protein association

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Effects of Zn²⁺ Binding on the Structural and Dynamic Properties of Amyloid Β Peptide Associated with Alzheimer’s Disease: Asp¹ or Glu¹¹?

Effects of Zn²⁺ Binding on the Structural and Dynamic Properties of Amyloid Β Peptide Associated with Alzheimer’s Disease: Asp¹ or Glu¹¹?