Modeling of Dynamic Allostery in Proteins Enabled by Machine Learning

Regulation of protein activity is essential for normal cell functionality. Many proteins are regulated allosterically, that is, with spatial gaps between stimulation and active sites. Biological stimuli that regulate proteins allosterically include, for example, ions and small molecules, post-translational modifications, and intensive state-variables like temperature and pH. These effectors can not only switch activities on-and-off, but also fine-tune activities. Understanding the underpinnings of allostery, that is, how signals are propagated between distant sites, and how transmitted signals manifest themselves into regulation of protein activity, has been one of the central foci of biology for over 50 years. Today, the importance of such studies goes beyond basic pedagogical interests as bioengineers seek design features to control protein function for myriad purposes, including design of nano-biosensors, drug delivery vehicles, synthetic cells and organic-synthetic interfaces. The current phenomenological view of allostery is that signaling and activity control occur via effector-induced changes in protein conformational ensembles. If the structures of two states of a protein differ from each other significantly, then thermal fluctuations can be neglected and an atomically detailed model of regulation can be constructed in terms of how their minimum-energy structures differ between states. However, when the minimum-energy structures of states differ from each other only marginally and the difference is comparable to thermal fluctuations, then a mechanistic model cannot be constructed solely on the basis of differences in protein structure. Understanding the mechanism of dynamic allostery requires not only assessment of high-dimensional conformational ensembles of the various individual states, including inactive, transition and active states, but also relationships between them. This challenge faces many diverse protein families, including G-protein coupled receptors, immune cell receptors, heat shock proteins, nuclear transcription factors and viral attachment proteins, whose mechanisms, despite numerous studies, remain poorly understood. This dissertation deals with the development of new methods that significantly boost the applicability of molecular simulation techniques to probe dynamic allostery in these proteins. Specifically, it deals with two different methods, one to obtain quantitative estimates for subtle differences between conformational ensembles, and the other to relate conformational ensemble differences to allosteric signal communication. Both methods are enabled by a new application of the mathematical framework of machine learning. These methods are applied to (a) identify specific effects of employed force fields on conformational ensembles, (b) compare multiple ensembles against each other for determination of common signaling pathways induced by different effectors, (c) identify the effects of point mutations on conformational ensemble shifts in proteins, and (d) understand the mechanism of dynamic allostery in a PDZ domain. These diverse applications essentially demonstrate the generality of the developed approaches, and specifically set the foundation for future studies on PDZ domains and viral attachment proteins

Application of Bayesian ANN and RJMCMC to predict the grain size of hot strip low carbon steels

Artificial Neural Network (ANN) and Reversible Jump Markov Chain Monte Carlo (RJMCMC) are used to predict the grain size of hot strip low carbon steels, as a function of steel composition. Results show a good agreement with experimental data taken from Mobarakeh Steel Company (MSC). The developed model is capable of recognizing the role and importance of elements in grain refinement. Furthermore, effects of these elements including manganese, silicon and vanadium are investigated in the present study, which are in good agreement with the literature

Machine Learning Approaches to Evaluate Correlation Patterns in Allosteric Signaling: A Case Study of the PDZ2 Domain

Many proteins are regulated by dynamic allostery wherein regulator-induced changes in structure are comparable with thermal fluctuations. Consequently, understanding their mechanisms requires assessment of relationships between and within conformational ensembles of different states. Here we show how machine learning based approaches can be used to simplify this high-dimensional data mining task and also obtain mechanistic insight. In particular, we use these approaches to investigate two fundamental questions in dynamic allostery. First, how do regulators modify inter-site correlations in conformational fluctuations (Cij)? Second, how are regulator-induced shifts in conformational ensembles at two different sites in a protein related to each other? We address these questions in the context of the human protein tyrosine phosphatase 1E’s PDZ2 domain, which is a model protein for studying dynamic allostery. We use molecular dynamics to generate conformational ensembles of the PDZ2 domain in both the regulator-bound and regulator-free states. The employed protocol reproduces methyl deuterium order parameters from NMR. Results from unsupervised clustering of Cij combined with flow analyses of weighted graphs of Cij show that regulator binding significantly alters the global signaling network in the protein; however, not by altering the spatial arrangement of strongly interacting amino acid clusters but by modifying the connectivity between clusters. Additionally, we find that regulator-induced shifts in conformational ensembles, which we evaluate by repartitioning ensembles using supervised learning, are, in fact, correlated. This correlation Δij is less extensive compared to Cij, but in contrast to Cij, Δij depends inversely on the distance from the regulator binding site. Assuming that Δij is an indicator of the transduction of the regulatory signal leads to the conclusion that the regulatory signal weakens with distance from the regulatory site. Overall, this work provides new approaches to analyze high-dimensional molecular simulation data and also presents applications that yield new insight into dynamic allostery