Because a cell must adapt to different stresses and growth rates, its proteostasis system must too. How do cells detect and adjust proteome folding to different conditions? Here, we explore a biophysical cost-benefit principle, namely that the cell should keep its proteome as folded as possible at the minimum possible energy cost. This can be achieved by differential expression of chaperones-balancing foldases (which accelerate folding) against holdases (which act as parking spots). The model captures changes in the foldase-holdase ratio observed both within organisms during aging and across organisms of varying metabolic rates. This work describes a simple biophysical mechanism by which cellular proteostasis adapts to meet the needs of a changing growth environment.

We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network.

It has long been proposed that much of the information encoding how a protein folds is contained locally in the peptide chain. Here we present a large-scale simulation study designed to examine the extent to which conformations of peptide fragments in water predict native conformations in proteins. We perform replica exchange molecular dynamics (REMD) simulations of 872 8-mer, 12-mer, and 16-mer peptide fragments from 13 proteins using the AMBER 96 force field and the OBC implicit solvent model. To analyze the simulations, we compute various contact-based metrics, such as contact probability, and then apply Bayesian classifier methods to infer which metastable contacts are likely to be native vs. non-native. We find that a simple measure, the observed contact probability, is largely more predictive of a peptide's native structure in the protein than combinations of metrics or multi-body components. Our best classification model is a logistic regression model that can achieve up to 63% correct classifications for 8-mers, 71% for 12-mers, and 76% for 16-mers. We validate these results on fragments of a protein outside our training set. We conclude that local structure provides information to solve some but not all of the conformational search problem. These results help improve our understanding of folding mechanisms, and have implications for improving physics-based conformational sampling and structure prediction using all-atom molecular simulations.

Peptides often have conformational preferences. We simulated 133 peptide 8-mer fragments from six different proteins, sampled by replica-exchange molecular dynamics using Amber7 with a GB/SA (generalized-Born/solvent-accessible electrostatic approximation to water) implicit solvent. We found that 85 of the peptides have no preferred structure, while 48 of them converge to a preferred structure. In 85% of the converged cases (41 peptides), the structures found by the simulations bear some resemblance to their native structures, based on a coarse-grained backbone description. In particular, all seven of the beta hairpins in the native structures contain a fragment in the turn that is highly structured. In the eight cases where the bioinformatics-based I-sites library picks out native-like structures, the present simulations are largely in agreement. Such physics-based modeling may be useful for identifying early nuclei in folding kinetics and for assisting in protein-structure prediction methods that utilize the assembly of peptide fragments.

In PLoS Computational Biology, volume 2, issue 4: DOI: 10.1371/journal.pcbi.0020027 The abbreviation GB/SA had an incorrect definition in the Abstract, Introduction, and Abbreviations list. The correct definition of GB/SA is generalized-Born/surface area. Table 4 has several rows that did not appear in bold font in the published article, and Table 5 had four rows with incorrect spacing in the published article. Both tables appear correctly below. Table 4 Ground Mesostrings of β-Sheet Proteins Table 5 Comparison of the Structural Bias with the Native Structure

We present a novel approach for studying the kinetics of protein folding. The framework has evolved from robotics motion planning techniques called probabilistic roadmap methods (prms) that have been applied in many diverse fields with great success. In our previous work, we used a Prm-based technique to study protein folding pathways of several small proteins and obtained encouraging results. In this paper, we describe how our motion planning framework can be used to study protein folding kinetics. In particular, we present a refined version of our Prm-based framework and describe how it can be used to produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap which is computed in a few hours on a desktop PC. Results are presented for 14 proteins. Our ability to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics, such as proteins that exhibit both two-state and three-state kinetics, that are not captured by other theoretical techniques.

We present here the design strategy and performance analysis of a large scale scientific application, for the prediction of 3D Protein structures. The unique challenges which will be investigated are the primary objectives of a reduction in wall clock run time through the parallelization process, and the production of an application capable of running and scaling to a massively parallel configuration (currently 1024 nodes of the Intel Paragon) reliably for many non-contiguous days of supercomputer time. Enough flexibility to be reconfigured for a number of different parallel architectures including the CRAY T3E and IBM SP2 was included through the use of MPI as the parallel software layer. The application, GEOCORE, predicts small ensembles of native-like peptide conformations from amino acid sequences. GEOCORE uses a very simple energy function and an extensive conformational search process. The serial program has been tested on around 20 small peptides and is shown to be capable of discriminating native from non-native structures.

Proteins require specific three-dimensional conforma-tions to function properly. These “native ” conforma-tions result primarily from intramolecular interactions between the atoms in the macromolecule, and also