Characterisation of the hydrophobic collapse of polystyrene in water using free energy techniques

We characterise the hydrophobic collapse of single polystyrene chains in water using molecular dynamics simulations. Specifically, we calculate the potential of mean force for the collapse of a single polystyrene chain in water using metadynamics, comparing the results between all atomistic with coarse-grained (CG) molecular simulation. We next explore the scaling behaviour of the collapsed globular shape at the minimum energy configuration, characterised by the radius of gyration, as a function of chain length. The exponent is close to one third, consistent with that predicted for a polymer chain in bad solvent. We also explore the scaling behaviour of the solvent accessible surface area (SASA) as a function of chain length, finding a similar exponent for both all atomistic and CG simulations. Furthermore, calculation of the local water density as a function of chain length near the minimum energy configuration suggests that intermediate chain lengths are more likely to form dewetted states, as compared to shorter or longer chain lengths.     

An empirical relationship between rotational correlation time and solvent accessible surface area

Structure–dynamics interrelationships are important in understanding protein function. We have explored the empirical relationship between rotational correlation times (_c and the solvent accessible surface areas (SASA) of 75 proteins with known structures. The theoretical correlation between SASA and _c through the equation SASA = K_rc ^(2/3) is also considered. SASA was determined from the structure, _c ^calc was determined from diffusion tensor calculations, and _c ^expt was determined from NMR backbone¹³ C or ¹⁵N relaxation rate measurements. The theoretical and experimental values of _c correlate with SASA with regression analyses values of K_r as 1696 and 1896 m²s^-(2/3), respectively, and with corresponding correlation coefficients of 0.92 and 0.70.     

Cold unfolding of β-hairpins: a molecular-level rationalization

Isolated β-hairpins in water have a temperature dependence of their conformational stability qualitatively resembling that of globular proteins, showing both cold and hot unfolding transitions. It is shown that a molecular-level rationalization of this cold unfolding can be provided extending the approach devised for globular proteins (Graziano G. Phys Chem Chem Phys 2010; 12:14245-14252). The decrease in the solvent-excluded volume upon folding, measured by the decrease in the solvent accessible surface area, produces a gain in configurational/translational entropy of water molecules that is the main stabilizing contribution of the folded conformation. This always stabilizing Gibbs energy contribution has a parabolic-like temperature dependence in water and is exactly counterbalanced at two temperatures (i.e., the cold and hot unfolding temperatures) by the always destabilizing Gibbs energy contribution due to the loss in conformational degrees of freedom of the peptide chain.     

A Simple Definition of Structural Regions in Proteins and Its Use in Analyzing Interface Evolution

Analysis of proteins commonly requires the partition of their structure into regions such as the surface, interior, or interface. Despite the frequent use of such categorization, no consensus definition seems to exist. This study thus aims at providing a definition that is general, is simple to implement, and yields new biological insights. This analysis relies on 397, 196, and 701 protein structures from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, respectively, and the conclusions are consistent across all three species. A threshold of 25% relative accessible surface area best segregates amino acids at the interior and at the surface. This value is further used to extend the core-rim model of protein-protein interfaces and to introduce a third region called support. Interface core, rim, and support regions contain similar numbers of residues on average, but core residues contribute over two-thirds of the contact surface. The amino acid composition of each region remains similar across different organisms and interface types. The interface core composition is intermediate between the surface and the interior, but the compositions of the support and the rim are virtually identical with those of the interior and the surface, respectively. The support and rim could thus “preexist” in proteins, and evolving a new interaction could require mutations to form an interface core only. Using the interface regions defined, it is shown through simulations that only two substitutions are necessary to shift the average composition of a  1000-Ų surface patch involving ∼ 28 residues to that of an equivalent interface. This analysis and conclusions will help understand the notion of promiscuity in protein-protein interaction networks.     

Dissociation of Abeta(16-22) amyloid fibrils probed by molecular dynamics

The mechanisms of deposition and dissociation are implicated in the assembly of amyloid fibrils. To investigate the kinetics of unbinding of Abeta(16-22) monomers from preformed fibrils, we use molecular dynamics (MD) simulations and the structures for Abeta(16-22) amyloid fibrils. Consistent with experimental studies, the dissociation of Abeta(16-22) peptides involves two main stages, locked and docked, after which peptides unbind. The lifetime of the locked state, in which a peptide retains fibril-like structure and interactions, extends up to 0.5 micros under normal physiological conditions. Upon cooperative rupture of all fibril-like hydrogen bonds (HBs) with the fibril, a peptide enters a docked state. This state is populated by disordered random coil conformations and its lifetime ranges from approximately 10 to 200 ns. The docked state is stabilized by hydrophobic side chain interactions, while the contribution from HBs is small. Our simulations also suggest that the peptides located on fibril edges may form stable beta-strand conformations distinct from the fibril "bulk". We propose that such edge peptides can act as fibril caps, which impede fibril elongation. Our results indicate that the interactions between unbinding peptides constitute the molecular basis for cooperativity of peptide dissociation. The kinetics of fibril growth is reconstructed from unbinding assuming the reversibility of deposition/dissociation pathways. The relation of in silica dissociation kinetics to experimental observations is discussed.     

Molecular modeling of human serum transferrin for rationalizing the changes in its physicochemical properties induced by iron binding. Implication of the mechanism of binding to its receptor

In order to rationalize the physicochemical properties of human serum-transferrin (STf) and the STf-receptor (TfR) recognition process, we have tried to predict the 3D structures of apo- and iron-loaded STf using a homology modeling technique to study the changes in the structural characteristics that take place upon the uptake of iron by STf in solution. The crystal structures of both forms for ovotransferrin were used as templates for the STf modeling. The modeled structure of STf gave a satisfactory interpretation for the typical physicochemical properties such that (1) STf has a negative electrophoretic mobility and its value increases with iron uptake, and (2) the radius of gyration R_g of Tf decreases with iron uptake. It was found that upon iron binding, interdomain closures take place with large movements of the NII and CII subdomains comprising the N- and C-lobes in STf through a hinge-bending motion, accompanied by the opening of the bridge region with a displacement of more than 15 Å. Moreover, in view of the findings from our capillary electrophoresis experiments that the electrostatic interactions significantly contribute to a specific binding of Fe₂-STf with TfR, it is inferred that the connecting (bridge) and its neighboring region associated with a surface exposure of negative charge play an important role in the STf-receptor recognition process.     

分子表面的计算机表示

提出了一种用于生成分子光滑表面的新算法.该算法从分布在一个包含整个分子表面的椭球上的三角网络开始,逐步收缩网络直到所有的三角形最佳贴近分子表面.所使用的收缩包络椭球的技术只要稍加修改就可用于蛋白质空腔的表示.     

How well can we predict native contacts in proteins based on decoy structures and their energies?

One strategy for ab initio protein structure prediction is to generate a large number of possible structures (decoys) and select the most fitting ones based on a scoring or free energy function. The conformational space of a protein is huge, and chances are rare that any heuristically generated structure will directly fall in the neighborhood of the native structure. It is desirable that, instead of being thrown away, the unfitting decoy structures can provide insights into native structures so prediction can be made progressively. First, we demonstrate that a recently parameterized physics-based effective free energy function based on the GROMOS96 force field and a generalized Born/surface area solvent model is, as several other physics-based and knowledge-based models, capable of distinguishing native structures from decoy structures for a number of widely used decoy databases. Second, we observe a substantial increase in correlations of the effective free energies with the degree of similarity between the decoys and the native structure, if the similarity is measured by the content of native inter-residue contacts in a decoy structure rather than its root-mean-square deviation from the native structure. Finally, we investigate the possibility of predicting native contacts based on the frequency of occurrence of contacts in decoy structures. For most proteins contained in the decoy databases, a meaningful amount of native contacts can be predicted based on plain frequencies of occurrence at a relatively high level of accuracy. Relative to using plain frequencies, overwhelming improvements in sensitivity of the predictions are observed for the 4_state_reduced decoy sets by applying energy-dependent weighting of decoy structures in determining the frequency. There, approximately 80% native contacts can be predicted at an accuracy of approximately 80% using energy-weighted frequencies. The sensitivity of the plain frequency approach is much lower (20% to 40%). Such improvements are, however, not observed for the other decoy databases. The rationalization and implications of the results are discussed.     

Membrane protein assembly patterns reflect selection for non-proliferative structures

Membrane proteins that regulate solute movement are often built from multiple copies of an identical polypeptide chain. These complexes represent striking examples of self-assembling systems that recruit monomers only until a prescribed level for function is reached. Here we report that three modes of assembly - distinguished by sequence and stoichiometry - describe all helical membrane protein complexes currently solved to high resolution. Using the 13 presently available non-redundant homo-oligomeric structures, we show that two of these types segregate with protein function: one produces energy-dependent transporters, while the other builds channels for passive diffusion. Given such limited routes to functional complexes, membrane proteins that self-assemble exist on the edge of aggregation, susceptible to mutations that may underlie human diseases.     

Effective prediction model and determination of binding residues influential for inhibitors targeting HIV-1 integrase-LEDGF/p75 interface by employing solvent accessible surface area energy as key determinant

Abstract

Development of a highly accurate prediction model for protein–ligand inhibition has been a major challenge in drug discovery. Herein, we describe a novel predictive model for the inhibition of HIV-1 integrase (IN)-LEDGF/p75 protein-protein interaction. The model was constructed using energy parameters approximated from molecular dynamics (MD) simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations. Chemometric analysis using partial least squares (PLS) regression revealed that solvent accessible surface area energy (ΔG_SASA) is the major determinant parameter contributing greatly to the prediction accuracy. PLS prediction model on the ΔG_SASA values collected from 41 complexes yielded a strong correlation between the predicted and the actual inhibitory activities (R² = 0.9666, RMSEC of pIC₅₀ values = 0.0890). Additionally, for the test set of 14 complexes, the model performed satisfactorily with very low pIC₅₀ errors (Q² = 0.5168, RMSEP = 0.3325). A strong correlation between the buried surface areas on the IN protein, when bound with IN-LEDGF/p75 inhibitors, and the respective ΔG_SASA values was also obtained. Furthermore, the current method could identify ‘hot spots’of amino acid residues highly influential to the inhibitory activity prediction. This could present fruitful implications in binding site determination and future inhibitor developments targeting protein-protein interactions.

Communicated by Ramaswamy H. Sarma