Biomedical subjects

Peter G Wolynes

Publications and source records attributed to Peter G Wolynes.

45 records · Page 3Linked to original sources

PubMed2003

Protein topology determines binding mechanism.

Protein recognition and binding, which result in either transient or long-lived complexes, play a fundamental role in many biological functions, but sometimes also result in pathologic aggregates. We use a simplified simulation model to survey a range of systems where two highly flexible protein chains form a homodimer. In all cases, this model, which corresponds to a perfectly funneled energy landscape for folding and binding, reproduces the macroscopic experimental observations on whether folding and binding are coupled in one step or whether intermediates occur. Owing to the minimal frustration principle, we find that, as in the case of protein folding, the native topology is the major factor that governs the choice of binding mechanism. Even when the monomer is stable on its own, binding sometimes occurs fastest through unfolded intermediates, thus showing the speedup envisioned in the fly-casting scenario for molecular recognition.

Dimerization↗

PubMed2003

Role of water mediated interactions in protein-protein recognition landscapes.

The energy landscape picture of protein folding and binding is employed to optimize a number of pair potentials for direct and water-mediated interactions in protein complex interfaces. We find that water-mediated interactions greatly complement direct interactions in discriminating against various types of trap interactions that model those present in the cell. We highlight the context dependent nature of knowledge-based binding potentials, as contrasted with the situation for autonomous folding. By performing a Principal Component Analysis (PCA) of the corresponding interaction matrixes, we rationalize the strength of the recognition signal for each combination of the contact type and reference trap states using the differential in the idealized "canonical" amino acid compositions of native and trap layers. The comparison of direct and water-mediated contact potential matrixes emphasizes the importance of partial solvation in stabilizing charged groups in the protein interfaces. Specific water-mediated interresidue interactions are expected to influence significantly the kinetics as well as thermodynamics of protein association.

Hydrophobic and Hydrophilic Interactions↗

PubMed2003

Buffed energy landscapes: another solution to the kinetic paradoxes of protein folding.

The energy landscapes of proteins have evolved to be different from most random heteropolymers. Many studies have concluded that evolutionary selection for rapid and reliable folding to a given structure that is stable at biological temperatures leads to energy landscapes having a single dominant basin and an overall funnel topography. We show here that, although such a landscape topography is indeed a sufficient condition for folding, another possibility also exists, giving a previously undescribed class of foldable sequences. These sequences have landscapes that are only weakly funneled in the conventional thermodynamic sense but have unusually low kinetic barriers for reconfigurational motion. Traps have been specifically removed by selection. Here we examine the possibility of folding on these "buffed" landscapes by mapping the determination of statistics of pathways for the heterogeneous nucleation processes involved in escaping from traps to the solution of an imaginary time Schroedinger equation. This equation is solved analytically in adiabatic and "soft-wall" approximations, and numerical results are shown for the general case. The fraction of funneled vs. buffed proteins in sequence space is estimated, suggesting the statistical dominance of the funneling mechanism for achieving foldability.

Biophysical Phenomena↗

PubMed2003

Microscopic theory of network glasses.

A theory of the glass transition of network liquids is developed using self-consistent phonon and liquid state approaches. The dynamical transition and entropy crisis characteristic of random first-order transitions are mapped as a function of the degree of bonding and density. Using a scaling relation for a soft-core model to crudely translate the densities into temperatures, theory predicts that the ratio of the dynamical transition temperature to the laboratory transition temperature rises as the degree of bonding increases, while the Kauzmann temperature falls explaining why highly coordinated liquids are "strong" while van der Waals liquids without coordination are "fragile."

Journal Article↗

PubMed2003

Stochastic gene expression as a many-body problem.

Gene expression has a stochastic component because of the single-molecule nature of the gene and the small number of copies of individual DNA-binding proteins in the cell. We show how the statistics of such systems can be mapped onto quantum many-body problems. The dynamics of a single gene switch resembles the spin-boson model of a two-site polaron or an electron transfer reaction. Networks of switches can be approximately described as quantum spin systems by using an appropriate variational principle. In this way, the concept of frustration for magnetic systems can be taken over into gene networks. The landscape of stable attractors depends on the degree and style of frustration, much as for neural networks. We show the number of attractors, which may represent cell types, is much smaller for appropriately designed weakly frustrated stochastic networks than for randomly connected networks.

Algorithms↗

PubMed2003

The origin of the boson peak and thermal conductivity plateau in low-temperature glasses.

We argue that the intrinsic glassy degrees of freedom in amorphous solids giving rise to the thermal conductivity plateau and the "boson peak" in the heat capacity at moderately low temperatures are directly connected to those motions giving rise to the two-level-like excitations seen at still lower temperatures. These degrees of freedom can be thought of as strongly anharmonic transitions between the local minima of the glassy energy landscape that are accompanied by ripplon-like domain wall motions of the glassy mosaic structure predicted to occur at T(g) by the random first-order transition theory. The energy spectrum of the vibrations of the mosaic depends on the glass transition temperature, the Debye frequency, and the molecular length scale. The resulting spectrum reproduces the experimental low-temperature boson peak. The "nonuniversality" of the thermal conductivity plateau depends on k(B)T(g)omega(D) and arises from calculable interactions with the phonons.

Journal Article↗

PubMed2003

Associative memory Hamiltonians for structure prediction without homology: alpha/beta proteins.

We describe a method for predicting the structure of alpha beta class proteins in the absence of information from homologous structures. The method is based on an associative memory model for short to intermediate range in sequence contacts and a contact potential for long range in sequence contacts. The coefficients in the energy function are chosen to maximize the ratio of the folding temperature to the glass transition temperature. We use the resulting optimized model to predict the structure of three alpha beta protein domains ranging in length from 81 to 115 residues. The resulting predictions align with low rms deviations to large portions of the native state. We have also calculated the free energy as a function of similarity to the native state for one of these three domains, and we show that, as expected from the optimization criteria, the free energy surface resembles a rough funnel to the native state. Finally, we briefly demonstrate the effect of roughness in the energy landscape on the dynamics.

Hydrogen Bonding↗

PubMed2003

The physics and bioinformatics of binding and folding-an energy landscape perspective.

It has been recognized in the last few years that unstructured proteins play an important role in biological organisms, often participating in signal transduction, transcriptional regulation, and a variety of other regulatory activities. Various hypotheses have been put forward for the ubiquity of the unfolded state; rapid turnover, faster or more specific binding kinetics, multifunctionality may all possibly explain apparent ubiquitousness of unfolded proteins in eukaryotic cells. In this paper we extend the energy landscape theory of protein folding to construct an analytical model of how binding and folding are coupled thermodynamically when the energy landscape is partially rugged. To deduce the parameters that enter the theory, which is based on Generalized Random Energy Model, we have analyzed in a bioinformatic sense a large structural database of more than 500 protein complexes. We find that Miyazawa-Jernigan contact potential shows similar energy gaps for folding for both hydrophobic and hydrophilic proteins, but that for binding contacts hydrophobic interfaces turn out to be funneled while hydrophilic ones are antifunneled. This suggests evolution has found a mechanism for avoiding frustration between folding and binding by making use of indirect water-mediated interactions. By juxtaposing the monomeric protein folding free energy profile in the protein complex database with another database consisting of only well-folded monomers, we estimate that at least 15% of monomers in the former database are unfolded in the absence of partner protein interface interactions. When employing the parameters characteristic of these unfolded monomers to construct binding/folding phase diagrams, we find that these monomers would indeed fold if sufficiently stabilizing binding contacts, consistent with that fold, are formed.

Computational Biology↗

PubMed2002

Folding funnels: the key to robust protein structure prediction.

Natural proteins fold because their free energy landscapes are funneled to their native states. The degree to which a model energy function for protein structure prediction can avoid the multiple minima problem and reliably yield at least low-resolution predictions is also dependent on the topography of the energy landscape. We show that the degree of funneling can be quantitatively expressed in terms of a few averaged properties of the landscape. This allows us to optimize simplified energy functions for protein structure prediction even in the absence of homology information. Here we outline the optimization procedure in the context of associative memory energy functions originally introduced for tertiary structure recognition and demonstrate that even partially funneled landscapes lead to qualitatively correct, low-resolution predictions.

Computational Biology↗