PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes

Li, Yunqi; Fang, Jianwen

doi:10.1371/journal.pone.0047247

Cited by 47 publications

(33 citation statements)

References 53 publications

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“…Various prediction algorithms have been developed to compute the effect of mutations on thermodynamic stability (ΔΔ G ) based on three‐dimensional protein structures . In general, these methods can predict trends fairly well, although they tend to deviate on the specific ΔΔ G value for each single mutation .…”

Section: Methods To Identify Stabilizing/compensatory Mutationsmentioning

confidence: 99%

Modulating protein stability – directed evolution strategies for improved protein function

Socha

Tokuriki

2013

The FEBS Journal

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Protein engineering is widely used to generate proteins with novel or enhanced function. However, manipulating protein function in the laboratory can prove laborious, protracted and challenging. Recent developments in the understanding of protein evolutionary dynamics have unveiled the full extent by which the evolution of function is limited by protein stability ‐ a revelation that may be applied to protein engineering on a whole. Thus, strategies that modulate protein stability and reduce its constraining effects may facilitate the engineering of protein function. A combinatorial approach involving the introduction of compensatory mutations and manipulation of the stability threshold by chaperone buffering during directed evolution can improve the functional adaptation of a protein, thereby fostering our ability to attain ever‐more ambitious protein functions in the laboratory.

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Section: Methods To Identify Stabilizing/compensatory Mutationsmentioning

confidence: 99%

Modulating protein stability – directed evolution strategies for improved protein function

Socha

Tokuriki

2013

The FEBS Journal

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“…All of the following approaches employ machine learning to predict protein stability upon mutation. These approaches include neural networks [16], [17], random forests [18]- [20], decision trees, [21], [22] and support vector machines [23]- [26]. In a study by Jia et al [27], five supervised machine learning methods (support vector machines, random forests, neural networks, the naive Bayes classifier, and K-nearest neighbours) along with partial least squares regression were benchmarked for performance in predictive modelling of protein stability.…”

Section: Approaches To Predict Protein Stabilitymentioning

confidence: 99%

Predicting Protein Thermostability Upon Mutation Using Molecular Dynamics Timeseries Data

Fleming

Kinsella

Ing

2016

Preprint

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A large number of human diseases result from disruptions to protein structure and function caused by missense mutations. Computational methods are frequently employed to assist in the prediction of protein stability upon mutation. These methods utilize a combination of protein sequence data, protein structure data, empirical energy functions, and physicochemical properties of amino acids. In this work, we present the first use of dynamic protein structural features in order to improve stability predictions upon mutation. This is achieved through the use of a set of timeseries extracted from microsecond timescale atomistic molecular dynamics simulations of proteins. Standard machine learning algorithms using mean, variance, and histograms of these timeseries were found to be 60-70% accurate in stability classification based on experimental G or protein-chaperone interaction measurements. A recurrent neural network with full treatment of timeseries data was found to be 80% accurate according the F1 score. The performance of our models was found to be equal or better than two recently developed machine learning methods for binary classification as well as two industry-standard stability prediction algorithms. In addition to classification, understanding the molecular basis of protein stability disruption due to disease-causing mutations is a significant challenge that impedes the development of drugs and therapies that may be used treat genetic diseases. The use of dynamic structural features allows for novel insight into the molecular basis of protein disruption by mutation in a diverse set of soluble proteins. To assist in the interpretation of machine learning results, we present a technique for determining the importance of features to a recurrent neural network using Garson's method. We propose a novel extension of neural interpretation diagrams by implementing Garson's method to scale each node in the neural interpretation diagram according to its relative importance to the network.

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“…Hence, researchers have long sought to develop tools for accurate in silico prediction of enzyme thermostability. Accordingly, many tools have been developed over the past two decades to predict the enzyme melting temperature (Tm), 1-3 the change in thermodynamic stability (∆∆G) upon point mutations, [4][5][6][7][8][9][10][11][12] or the optimal growth temperature (OGT) of the source organism. [13][14][15][16][17][18][19][20][21] Unfortunately, for prediction purposes, higher OGT or thermal stability do not necessarily indicate substantial catalytic activity at high temperatures.…”

Section: Introductionmentioning

confidence: 99%

Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning

Gado

Beckham

Payne

2020

Preprint

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Accurate prediction of the optimal catalytic temperature (Topt) of enzymes is vital in biotechnology, as enzymes with high Topt values are desired for enhanced reaction rates. Recently, a machine-learning method (TOME) for predicting Topt was developed. TOME was trained on a normally-distributed dataset with a median Topt of 37˚C and less than five percent of Topt values above 85˚C, limiting the method's predictive capabilities for thermostable enzymes. Due to the distribution of the training data, the mean squared error on Topt values greater than 85°C is nearly an order of magnitude higher than the error on values between 30 and 50°C. In this study, we apply ensemble learning and resampling strategies that tackle the data imbalance to significantly decrease the error on high Topt values (>85˚C) by 60% and increase the overall R 2 value from 0.527 to 0.632. The revised method, TOMER, and the resampling strategies applied in this work are freely available to other researchers as a Python package on GitHub.

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PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes

Cited by 47 publications

References 53 publications

Modulating protein stability – directed evolution strategies for improved protein function

Modulating protein stability – directed evolution strategies for improved protein function

Predicting Protein Thermostability Upon Mutation Using Molecular Dynamics Timeseries Data

Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning

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