Using Kinetic Network Models To Probe Non-Native Salt-Bridge Effects on α‑Helix Folding

Salt–bridge interactions play an important role in stabilizing many protein structures, and have been shown to be designable features for protein design. In this work, we study the effects of non-native salt bridges on the folding of a soluble alanine-based peptide (Fs peptide) using extensive all-atom molecular dynamics simulations performed on the Folding@home distributed computing platform. Using Markov State Models, we show how non-native salt-bridges affect the folding kinetics of Fs peptide by perturbing specific conformational states. Furthermore, we present methods for the automatic detection and analysis of such states. These results provide insight into helix folding mechanisms and useful information to guide simulation-based computational protein design

A Maximum-Caliber Approach to Predicting Perturbed Folding Kinetics Due to Mutations

We present a maximum-caliber method for inferring transition rates of a Markov state model (MSM) with perturbed equilibrium populations given estimates of state populations and rates for an unperturbed MSM. It is similar in spirit to previous approaches, but given the inclusion of prior information, it is more robust and simple to implement. We examine its performance in simple biased diffusion models of kinetics and then apply the method to predicting changes in folding rates for several highly nontrivial protein folding systems for which non-native interactions play a significant role, including (1) tryptophan variants of the GB1 hairpin, (2) salt-bridge mutations of the Fs peptide helix, and (3) MSMs built from ultralong folding trajectories of FiP35 and GTT variants of the WW domain. In all cases, the method correctly predicts changes in folding rates, suggesting the wide applicability of maximum-caliber approaches to efficiently predict how mutations perturb protein conformational dynamics

Surprisal Metrics for Quantifying Perturbed Conformational Dynamics in Markov State Models

Markov state models (MSMs), which model conformational dynamics as a network of transitions between metastable states, have been increasingly used to model the thermodynamics and kinetics of biomolecules. In considering perturbations to molecular dynamics induced by sequence mutations, chemical modifications, or changes in external conditions, it is important to assess how transition rates change, independent of changes in metastable state definitions. Here, we present a surprisal metric to quantify the difference in metastable state transitions for two closely related MSMs, taking into account the statistical uncertainty in observed transition counts. We show that the surprisal is a relative entropy metric closely related to the Jensen–Shannon divergence between two MSMs, which can be used to identify conformational states most affected by perturbations. As examples, we apply the surprisal metric to a two-dimensional lattice model of a protein hairpin with mutations to hydrophobic residues, all-atom simulations of the Fs peptide α-helix with a salt-bridge mutation, and a comparison of protein G β-hairpin with its trpzip4 variant. Moreover, we show that surprisal-based adaptive sampling is an efficient strategy to reduce the statistical uncertainty in the Jensen–Shannon divergence, which could be a useful strategy for molecular simulation-based <i>ab initio</i> design

Insights into Peptoid Helix Folding Cooperativity from an Improved Backbone Potential

Peptoids (N-substituted oligoglycines) are biomimetic polymers that can fold into a variety of unique structural scaffolds. Peptoid helices, which result from the incorporation of bulky chiral side chains, are a key peptoid structural motif whose formation has not yet been accurately modeled in molecular simulations. Here, we report that a simple modification of the backbone φ-angle potential in GAFF is able to produce well-folded <i>cis</i>-amide helices of (<i>S</i>)-<i>N</i>-(1-phenyl­ethyl)­glycine (Nspe), consistent with experiment. We validate our results against both QM calculations and NMR experiments. For this latter task, we make quantitative comparisons to sparse NOE data using the Bayesian Inference of Conformational Populations (BICePs) algorithm, a method we have recently developed for this purpose. We then performed extensive REMD simulations of Nspe oligomers as a function of chain length and temperature to probe the molecular forces driving cooperative helix formation. Analysis of simulation data by Lifson–Roig helix–coil theory show that the modified potential predicts much more cooperative folding for Nspe helices. Unlike peptides, per-residue entropy changes for helix nucleation and extension are mostly positive, suggesting that steric bulk provides the main driving force for folding. We expect these results to inform future work aimed at predicting and designing peptoid peptidomimetics and tertiary assemblies of peptoid helices

Diverted Total Synthesis of Carolacton-Inspired Analogs Yields Three Distinct Phenotypes in <i>Streptococcus mutans</i> Biofilms

The oral microbiome is a dynamic environment inhabited by both commensals and pathogens. Among these is Streptococcus mutans, the causative agent of dental caries, the most prevalent childhood disease. Carolacton has remarkably specific activity against S. mutans, causing acid-mediated cell death during biofilm formation; however, its complex structure limits its utility. Herein, we report the diverted total synthesis and biological evaluation of a rationally designed library of simplified analogs that unveiled three unique biofilm phenotypes further validating the role of natural product synthesis in the discovery of new biological phenomena