Combining Statistical Potentials with Dynamics-Based Entropies Improves Selection from Protein Decoys and Docking Poses

Zimmermann, Michael T.; Leelananda, Sumudu P.; Kloczkowski, Andrzej; Jernigan, Robert L.

doi:10.1021/jp2120143

Cited by 26 publications

(35 citation statements)

References 59 publications

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“…The potential energy of each structure was estimated as an optimized linear combination of three different knowledge-based potential terms that we previously developed: four-body sequential potential, four-body nonsequential potential, and short-range potentials (50)(51)(52)(53). The potential energy was calculated as V opt = V 4−body seq + 0.28 * V 4−body non−seq + 0.22 * V short range .…”

Section: Methodsmentioning

confidence: 99%

Molecular determinants of cadherin ideal bond formation: Conformation-dependent unbinding on a multidimensional landscape

Manibog

Sankar

Kim

et al. 2016

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

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Classical cadherin cell-cell adhesion proteins are essential for the formation and maintenance of tissue structures; their primary function is to physically couple neighboring cells and withstand mechanical force. Cadherins from opposing cells bind in two distinct trans conformations: strand-swap dimers and X-dimers. As cadherins convert between these conformations, they form ideal bonds (i.e., adhesive interactions that are insensitive to force). However, the biophysical mechanism for ideal bond formation is unknown. Here, we integrate single-molecule force measurements with coarsegrained and atomistic simulations to resolve the mechanistic basis for cadherin ideal bond formation. Using simulations, we predict the energy landscape for cadherin adhesion, the transition pathways for interconversion between X-dimers and strand-swap dimers, and the cadherin structures that form ideal bonds. Based on these predictions, we engineer cadherin mutants that promote or inhibit ideal bond formation and measure their force-dependent kinetics using single-molecule force-clamp measurements with an atomic force microscope. Our data establish that cadherins adopt an intermediate conformation as they shuttle between X-dimers and strandswap dimers; pulling on this conformation induces a torsional motion perpendicular to the pulling direction that unbinds the proteins and forms force-independent ideal bonds. Torsional motion is blocked when cadherins associate laterally in a cis orientation, suggesting that ideal bonds may play a role in mechanically regulating cadherin clustering on cell surfaces.T he formation and maintenance of multicellular structures rely upon specific and robust intercellular adhesion (1). Cell-cell adhesion proteins, such as classical cadherins, are crucial in these processes (2-4). Cadherins are Ca 2+ -dependent transmembrane proteins that mediate the integrity of all soft tissues. One of their principal functions is to bind cells and dampen mechanical perturbations; however, the biophysical mechanism by which cadherins tune adhesion is not understood. Here, we combine predictive simulations with quantitative single-molecule force-clamp measurements to show that E-cadherin (Ecad), a prototypical classical cadherin, dampens the effect of tugging forces by switching conformations and unbinding along a strongly preferred pathway on a multidimensional landscape.Cadherin adhesive function resides in their ectodomain that is comprised of five extracellular (EC) domains arranged in tandem (5-9). Structural studies (10-15) and single-molecule fluorescence measurements (16) show that opposing ectodomains bind in two distinct trans conformations: strand-swap dimers (S-dimers) and X-dimers. S-dimers, which have a higher binding affinity, are formed by the exchange of a conserved tryptophan at position 2 (W2) between binding partners (10, 13, 17-19). In contrast, low-affinity X-dimers are formed by extensive surface interactions around the linker region that connects the two outermost domains (EC1-EC2) (11,12,(14)(15)...

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Section: Methodsmentioning

confidence: 99%

Molecular determinants of cadherin ideal bond formation: Conformation-dependent unbinding on a multidimensional landscape

Manibog

Sankar

Kim

et al. 2016

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

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“…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). This result is based on previous findings that large-scale changes to the dynamics accompany bound states.…”

Section: Significancementioning

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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“…One might assume that implicit solvent models would have overcome this limitation of explicit monopole models of water; unfortunately, they have been calibrated primarily with results from explicit calculations with monopole force fields. One can understand the rise in popularity of statistically based potentials derived from experimental atomic proximities that inherently avoid energetic dichotomies and focus on free energy per se [36,37]. …”

Section: Monopole Versus Multipole Electrostaticsmentioning

confidence: 99%

Limiting assumptions in molecular modeling: electrostatics

Marshall

2013

J Comput Aided Mol Des

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Combining Statistical Potentials with Dynamics-Based Entropies Improves Selection from Protein Decoys and Docking Poses

Cited by 26 publications

References 59 publications

Molecular determinants of cadherin ideal bond formation: Conformation-dependent unbinding on a multidimensional landscape

Molecular determinants of cadherin ideal bond formation: Conformation-dependent unbinding on a multidimensional landscape

Knowledge-based entropies improve the identification of native protein structures

Limiting assumptions in molecular modeling: electrostatics

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