Predicting Experimental Quantities in Protein Folding Kinetics Using Stochastic Roadmap Simulation

Chiang, Tsung-Han; Apaydın, Mehmet Serkan; Brutlag, Douglas L.; Hsu, David; Latombe, Jean‐Claude

doi:10.1007/11732990_34

Cited by 6 publications

(8 citation statements)

References 19 publications

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“…The mean absolute error of our predictions is 1.55. In comparison, the mean errors reported for two different techniques on a similar, but not identical, set of proteins in [8] was 2.77 and 3.42, respectively.…”

Section: Experiments and Resultsmentioning

confidence: 90%

“…In this section we describe our model of protein folding; it is identical to that used in [16] and very similar to those reported elsewhere [1,8,11].…”

Section: A Simplified Model Of Protein (Un)foldingmentioning

confidence: 95%

“…In practice, it is common to sample from the set of possible configurations. The algorithms reported in [1,8,11,16], for example, operate on state space ranging in size from 10 4 to 10 9 configurations. In contrast, we seek to consider the entire space of binary strings by adopting symbolic techniques from the field of model checking.…”

Section: Kineticsmentioning

confidence: 99%

“…Obviously, this is a highly simplified theory of folding. Nevertheless, this theory has been shown capable of making accurate quantitative predictions regarding the kinetics of folding (e.g., [1,8,11,16]). …”

Section: Introductionmentioning

confidence: 99%

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Predicting Protein Folding Kinetics Via Temporal Logic Model Checking

Langmead¹,

Jha²

2007

Lecture Notes in Computer Science

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We present a novel approach for predicting protein folding kinetics using techniques from the field of model checking. This represents the first time model checking has been applied to a problem in the field of structural biology. The protein's energy landscape is encoded symbolically using Binary decision diagrams and related data structures. Questions regarding the kinetics of folding are encoded as formulas in the temporal logic CTL. Model checking algorithms are then used to make quantitative predictions about the kinetics of folding. We show that our approach scales to state spaces as large as 10 23 when using exact algorithms for model checking. This is at least 14 orders of magnitude larger than the number of configurations considered by comparable techniques. Furthermore, our approach scales to state spaces at least as large as 10 32 unique configurations when using approximation algorithms for model checking. We tested our method on 19 test proteins. The quantitative predictions regarding folding rates for these test proteins are in good agreement with experimentally measured values, achieving a correlation coefficient of 0.87.

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Section: Experiments and Resultsmentioning

confidence: 90%

“…In this section we describe our model of protein folding; it is identical to that used in [16] and very similar to those reported elsewhere [1,8,11].…”

Section: A Simplified Model Of Protein (Un)foldingmentioning

confidence: 95%

Section: Kineticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Protein Folding Kinetics Via Temporal Logic Model Checking

Langmead¹,

Jha²

2007

Lecture Notes in Computer Science

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“…Second, while our focus is on studying the transition process, their focus has been to compare the prm approach with other computational methods such as Monte Carlo simulation. More recently, Cortes and Simeon used a prm-based approach to model long loops in proteins [13,12], and Chiang et al [10] applied prms to calculate quantities related to protein folding kinetics such as P f old and Φ-value analysis.…”

mentioning

confidence: 99%

A Motion Planning Approach to Studying Molecular Motions

Tapia¹,

Thomas²,

Amato³

2010

Communications in Information and Systems

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Abstract. While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are strongly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer's disease are associated with protein misfolding and aggregation. Similarly, RNA folding velocity may regulate the plasmid copy number, and RNA folding kinetics can regulate gene expression at the translational level. Knowledge of the stability, folding, kinetics and detailed mechanics of the folding process may help provide insight into how proteins and RNAs fold. In this paper, we present an overview of our work with a computational method we have adapted from robotic motion planning to study molecular motions. We have validated against experimental data and have demonstrated that our method can capture biological results such as stochastic folding pathways, population kinetics of various conformations, and relative folding rates. Thus, our method provides both a detailed view (e.g., individual pathways) and a global view (e.g., population kinetics, relative folding rates, and reaction coordinates) of energy landscapes of both proteins and RNAs. We have validated these techniques by showing that we observe the same relative folding rates as shown in experiments for structurally similar protein molecules that exhibit different folding behaviors. Our analysis has also been able to predict the same relative gene expression rate for wild-type MS2 phage RNA and three of its mutants.

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Roadmap Methods for Protein Folding

Moll

Schwarz²,

Kavraki³

2008

Protein Structure Prediction

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Protein folding refers to the process whereby a protein assumes its intricate three-dimensional shape. This chapter reviews a class of methods for studying the folding process called roadmap methods. The goal of these methods is not to predict the folded structure of a protein, but rather to analyze the folding kinetics. It is assumed that the folded state is known. Roadmap methods maintain a graph representation of sampled conformations. By analyzing this graph one can predict structure formation order, the probability of folding, and get a coarse view of the energy landscape.

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Predicting Experimental Quantities in Protein Folding Kinetics Using Stochastic Roadmap Simulation

Cited by 6 publications

References 19 publications

Predicting Protein Folding Kinetics Via Temporal Logic Model Checking

Predicting Protein Folding Kinetics Via Temporal Logic Model Checking

A Motion Planning Approach to Studying Molecular Motions

Roadmap Methods for Protein Folding

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