Classifying disease-associated variants using measures of protein activity and stability

Jepsen, Michael Maglegaard; Fowler, Douglas M.; Hartmann‐Petersen, Rasmus; Stein, Amelie; Lindorff‐Larsen, Kresten

doi:10.1016/b978-0-12-819132-3.00005-1

Cited by 26 publications

(48 citation statements)

References 37 publications

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“…Despite recent advances in proteome-wide experiments ( Després et al, 2020 ), it is still not possible to probe all possible variants in all proteins experimentally, and thus computational methods remain an important supplement to predict and understand variant effects. Experimental data from MAVEs are thus increasingly used to benchmark prediction methods, as they provide a broad view of the effect of amino acid substitutions in proteins ( Hopf et al, 2017 ; Jepsen et al, 2020 ; Livesey and Marsh, 2020 ; Reeb et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“…Recently we exploited the two different MAVEs for PTEN to analyse a small number of pathogenic variants together with variants that have been observed in a broader analysis of the human population ( Jepsen et al, 2020 ). Specifically, we compared the abundance-based (VAMP-seq) and activity-based multiplexed data to two computational methods aimed at capturing either (i) specifically protein stability or (ii) function more broadly.…”

Section: Resultsmentioning

confidence: 99%

“…The outcome of the VAMP-seq experiment is not substantially affected by the fusion with full-length GFP and correlates with in vitro measurements of thermal stability ( Matreyek et al, 2018 ), but importantly also captures other effects that might affect protein abundance and which could be relevant for function, evolution and disease. Our analysis here covers the 56% of all possible single amino acid variants in PTEN for which we have measurements for both the activity and abundance, and thus complements our recent analysis of a small number of disease variants in PTEN ( Jepsen et al, 2020 ).…”

Section: Introductionmentioning

confidence: 87%

“…While these experiments only probe one function of PTEN and might be affected also e.g. by expression levels, it has been shown that the resulting data accurately classifies the pathogenicity of PTEN variants ( Mighell et al, 2018 ; Jepsen et al, 2020 ). We complement these data with results from a different MAVE in which variant effects on cellular abundance are determined in an experiment termed ‘variant abundance by massively parallel sequencing’ (VAMP-seq) ( Matreyek et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance

Cagiada

Johansson

Valančiūtė

et al. 2020

Preprint

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Understanding and predicting how amino acid substitutions affect proteins is key to practical uses of proteins, and to our basic understanding of protein function and evolution. Amino acid changes may affect protein function in a number of ways including direct perturbations of activity or indirect effects on protein folding and stability. We have analysed 6749 experimentally determined variant effects from multiplexed assays on abundance and activity in two proteins (NUDT15 and PTEN) to quantify these effects, and find that a third of the variants cause loss of function, and about half of loss-of-function variants also have low cellular abundance. We analyse the structural and mechanistic origins of loss of function, and use the experimental data to find residues important for enzymatic activity. We performed computational analyses of protein stability and evolutionary conservation and show how we may predict positions where variants cause loss of activity or abundance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance

Cagiada

Johansson

Valančiūtė

et al. 2020

Preprint

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“…The calculations were repeated on five independent runs, whose results were then averaged to obtain the final values reported in the manuscript. The resulting difference in stability was multiplied by 1.44 to bring the DDG values from Rosetta energy units onto a scale corresponding to kJ/mol (Jepsen et al, 2020).…”

Section: Computational Prediction Of Stability and Dnak Bindingmentioning

confidence: 99%

Synergistic stabilization of a double mutant in CI2 from an in-cell library screen

Hamborg

Granata

Olsen

et al. 2020

Preprint

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Most single point mutations destabilize folded proteins. Mutations that stabilize a protein typically only have a small effect and multiple mutations are often needed to substantially increase the stability. Multiple point mutations may act synergistically on the stability, and it is not straightforward to predict their combined effect from the individual contributions. Here, we have applied an efficient in-cell assay to select variants of the barley chymotrypsin inhibitor 2 with increased stability. We find two variants that are more than 3.8 kJ/mol more stable than the wild-type. In one case the increased stability is the effect of the single substitution D55G. The other case is a double mutant, L49I/I57V, which is 5.1 kJ/mol more stable than the sum of the effects of the individual mutations. In addition to demonstrating the strength of our selection system for finding stabilizing mutations, our work also demonstrate how subtle conformational effects may modulate stability.

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RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

et al. 2022

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Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and proteinprotein interaction. Advances in experimental mutational scans allow highthroughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-Valentina Sora and Adrian Otamendi Laspiur equally contributed to this study.

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Classifying disease-associated variants using measures of protein activity and stability

Cited by 26 publications

References 37 publications

Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance

Understanding the origins of loss of protein function by analyzing the effects of thousands of variants on activity and abundance

Synergistic stabilization of a double mutant in CI2 from an in-cell library screen

RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

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