How do side chains orient globally in protein structures?

Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective

Coarse-grained models allow computational investigation of biomolecular processes occurring on long time and length scales, intractable with atomistic simulation. Traditionally, many coarse-grained models rely mostly on pairwise interaction potentials. However, the decimation of degrees of freedom should, in principle, lead to a complex many-body effective interaction potential. In this work, we use experimental data on mutant stability to parametrize coarse-grained models for two proteins with and without many-body terms. We demonstrate that many-body terms are necessary to reproduce quantitatively the effects of point mutations on protein stability, particularly to implicitly take into account the effect of the solvent.

Assessing the predictive capabilities of design heuristics and coarse-grain simulation toward understanding and optimizing site-specific covalent immobilization of β-lactamase

For industrial applications, covalent immobilization of enzymes provides minimum leakage, recoverability, reusability, and high stability. Yet, the suitability of a given site on the enzyme for immobilization remains a trial-and-error procedure. Here, we investigate the reliability of design heuristics and a coarse-grain molecular simulation in predicting the optimum sites for covalent immobilization of TEM-1 β-lactamase. We utilized E. coli-lysate-based cell-free protein synthesis (CFPS) to produce variants containing a site-specific incorporated unnatural amino acid with a unique moiety to facilitate site directed covalent immobilization. To constrain the number of potential immobilization sites, we investigated the predictive capability of several design heuristics. The suitability of immobilization sites was determined by analyzing expression yields, specific activity, immobilization efficiency, and stability of variants. These experimental findings are compared with coarse-grain simulation of TEM-1 domain stability and thermal stability and analyzed for a priori predictive capabilities. This work demonstrates that the design heuristics successfully identify a subset of locations for experimental validation. Specifically, the nucleotide following amber stop codon and domain stability correlate well with the expression yield and specific activity of the variants, respectively. Our approach highlights the advantages of combining coarse-grain simulation and high-throughput experimentation using CFPS to identify optimal enzyme immobilization sites. This article is protected by copyright. All rights reserved.

Prediction of side chain orientations in proteins by statistical machine learning methods

We develop ways to predict the side chain orientations of residues within a protein structure by using several different statistical machine learning methods. Here side chain orientation of a given residue i is measured by an angle Omega(i) between the vector pointing from the center of the protein structure to the C(i)(alpha) atom and the vector pointing from the C(i)(alpha) atom to the center of its side chain atoms. To predict the Omega(i) angles, we construct statistical models by using several different methods such as general linear regression, a regression tree and bagging, a neural network, and a support vector machine. The root mean square errors for the different models range only from 36.67 to 37.60 degrees and the correlation coefficients are all between 30% and 34%. The performances of different models in the test set are, thus, quite similar, and show the relative predictive power of these models to be significant in comparison with random side chain orientations.

Reconstructing protein structure from solvent exposure using tabu search

Background

A new, promising solvent exposure measure, called half-sphere-exposure (HSE), has recently been proposed. Here, we study the reconstruction of a protein's C_α trace solely from structure-derived HSE information. This problem is of relevance for de novo structure prediction using predicted HSE measure. For comparison, we also consider the well-established contact number (CN) measure. We define energy functions based on the HSE- or CN-vectors and minimize them using two conformational search heuristics: Monte Carlo simulation (MCS) and tabu search (TS). While MCS has been the dominant conformational search heuristic in literature, TS has been applied only a few times. To discretize the conformational space, we use lattice models with various complexity.

Results

The proposed TS heuristic with a novel tabu definition generally performs better than MCS for this problem. Our experiments show that, at least for small proteins (up to 35 amino acids), it is possible to reconstruct the protein backbone solely from the HSE or CN information. In general, the HSE measure leads to better models than the CN measure, as judged by the RMSD and the angle correlation with the native structure. The angle correlation, a measure of structural similarity, evaluates whether equivalent residues in two structures have the same general orientation. Our results indicate that the HSE measure is potentially very useful to represent solvent exposure in protein structure prediction, design and simulation.

In quest of an empirical potential for protein structure prediction

Key to successful protein structure prediction is a potential that recognizes the native state from misfolded structures. Recent advances in empirical potentials based on known protein structures include improved reference states for assessing random interactions, sidechain-orientation-dependent pair potentials, potentials for describing secondary or supersecondary structural preferences and, most importantly, optimization protocols that sculpt the energy landscape to enhance the correlation between native-like features and the energy. Improved clustering algorithms that select native-like structures on the basis of cluster density also resulted in greater prediction accuracy. For template-based modeling, these advances allowed improvement in predicted structures relative to their initial template alignments over a wide range of target-template homology. This represents significant progress and suggests applications to proteome-scale structure prediction.