Predicting protein stability and solubility changes upon mutations: data perspective

Mazurenko, Stanislav

doi:10.1002/cctc.202000933

Cited by 26 publications

(23 citation statements)

References 89 publications

(140 reference statements)

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“…The development of novel algorithms and soware tools for rational engineering of protein loops is highly desirable, but still challenging. New experimental data 51 and better understanding of structure-stability relationships are also essential premises for developing more reliable predictive models by machine learning. 52…”

Section: Discussionmentioning

confidence: 99%

Decoding the intricate network of molecular interactions of a hyperstable engineered biocatalyst

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Decoding the intricate network of molecular interactions of a hyperstable engineered biocatalyst

et al. 2020

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“…Several types of search queries may be of interest to the users. The first one relates to data filtering by values ( 10 ). Typically, software developers filter out the data collected at extreme pH (<6 or >8) due to changes in charged states for ionizable residues.…”

Section: Resultsmentioning

confidence: 99%

FireProtDB: database of manually curated protein stability data

Štourač

Dúbrava

Musil

et al. 2020

Nucleic Acids Research

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The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProtDB. The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb.

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“…Overall we confirmed the presence of the well-known mutation-type imbalances in all three datasets including the symmetric and curated datasets. Apparently, compilation of a balanced dataset requires attention to a list of features, 48 otherwise ensuring a symmetric distribution and/or removing of large redundancies does not necessarily solve all of the quality issues.…”

Section: Resultsmentioning

confidence: 99%

Towards Compilation of Balanced Protein Stability Datasets: Flattening the ΔΔG Curve through Systematic Under-sampling

Kebabci¹,

Timuçin

2021

Preprint

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Protein stability datasets contain neutral mutations that are highly concentrated in a much narrower ΔΔG range than destabilizing and stabilizing mutations. Notwithstanding their high density, often studies analyzing stability datasets and/or predictors ignore the neutral mutations and use a binary classification scheme labeling only destabilizing and stabilizing mutations. Recognizing that highly concentrated neutral mutations would affect the quality of stability datasets, we have explored three protein stability datasets; S2648, PON-tstab and the symmetric Ssym that differ in size and quality. A characteristic leptokurtic shape in the ΔΔG distributions of all three datasets including the curated and symmetric ones were reported due to concentrated neutral mutations. To further investigate the impact of neutral mutations on ΔΔG predictions, we have comprehensively assessed the performance of eleven predictors on the PON-tstab dataset. Correlation and error analyses showed that all of the predictors performed the best on the neutral mutations while their performance became gradually worse as the ΔΔG of the mutations departed further from the neutral zone regardless of the direction, implying a bias towards dense mutations. To this end, after unraveling the role of concentrated neutral mutations in biases of stability datasets, we described a systematic under-sampling approach to balance the ΔΔG distributions. Before under-sampling, mutations were clustered based on their biochemical and/or structural features and then three mutations were systematically selected from every 2 kcal/mol of each cluster. Upon implementation of this approach by distinct clustering schemes, we generated five subsets varying in size and ΔΔG distributions. All subsets notably showed amelioration of not only the shape of ΔΔG distributions but also other pre-existing imbalances in the frequency distributions. We also reported differences in the performance of the predictors between the parent and under-sampled subsets due to the enrichment of previously under-represented mutations in the subsets. Altogether, this study not only elaborated the pivotal role of concentrated mutations in the dataset biases but also contemplated and realized a rational strategy to tackle this and other forms of biases. Under-sampling code is available (https://github.com/narodkebabci/gRoR).

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Predicting protein stability and solubility changes upon mutations: data perspective

Abstract: This publication is part of a Special Collection on "Data Science in Catalysis" Please check the ChemCatChem homepage for more articles in the collection.

Cited by 26 publications

References 89 publications

Decoding the intricate network of molecular interactions of a hyperstable engineered biocatalyst

Decoding the intricate network of molecular interactions of a hyperstable engineered biocatalyst

FireProtDB: database of manually curated protein stability data

Towards Compilation of Balanced Protein Stability Datasets: Flattening the ΔΔG Curve through Systematic Under-sampling

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