Evolution of protein phosphorylation across 18 fungal species

Studer, Romain A.; Rodriguez‐Mias, Ricard A.; Haas, Kelsey M.; Hsu, Joanne I.; Viéitez, Cristina; Solé, Carme; Swaney, Danielle L.; Stanford, Lindsay; Liachko, Ivan; Böttcher, René; Dunham, Maitreya J.; Nadal, Eulàlia de; Posas, Francesc; Beltrão, Pedro; Villén, Judit

doi:10.1126/science.aaf2144

Cited by 95 publications

(117 citation statements)

References 56 publications

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“…As an illustration of this, recent benchmarking on a large blind subset of the recently released SKEMPI 2.0 (487 mutations in 56 complexes), which none of the predictors had previously seen, showed that iSEE, FoldX, mCSM, and BindProfX did not perform as well as on the training sets they originally used with PCC values all below 0.4. Even so, we have seen some promising contributions of such predictors to practical problems, for example, References .…”

Section: Discussionmentioning

confidence: 99%

“…From 2001, CAPRI has been playing a central role in stimulating the progress of docking algorithms and scoring functions as described in literature. 15,[101][102][103][104][105][106][107][108] In the 5th edition, CAPRI was extended to assess the methods to predict the impact of mutations for two designed influenza hemagglutinin binders based on yeast display enrichment data obtained using deep sequencing. 15,40,104 This assessment reported severe difficulties of docking scoring functions for predicting the impact of mutations on PPIs.…”

Section: Blind Challenges As Catalysts Of Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng

Roel‐Touris

et al. 2019

WIREs Comput Mol Sci

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Predicting the structure and thermodynamics of protein–protein interactions (PPIs) are key to a proper understanding and modulation of their function. Since experimental methods might not be able to catch up with the fast growth of genomic data, computational alternatives are therefore required. We present here a review dealing with various aspects of predicting binding affinity changes upon mutations (ΔΔG). We focus on predictors that consider three‐dimensional structure information to estimate the impact of mutations on the binding affinity of a protein–protein complex, excluding the rigorous free energy perturbation methods. Training and evaluation, ΔΔG databases, data selection, and existing ΔΔG predictors are specially emphasized. We also establish the parallel with scoring functions used in docking since those share many similar PPI features with ΔΔG predictors. The field has seen a common evolution of ΔΔG predictors and scoring functions over time, transforming from purely energetic functions to statistical energy‐based and further to machine learning‐based functions. As machine learning has come to age, limitations in terms of quantity, quality and variety of the available data become the bottlenecks for the future development of these computational methods. This can be alleviated by building infrastructures for data generation, collection and sharing. Further developments can be catalyzed by conducting community‐wide blind challenges for method assessment. This article is categorized under: Structure and Mechanism > Molecular Structures Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions

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Section: Discussionmentioning

confidence: 99%

Section: Blind Challenges As Catalysts Of Developmentmentioning

confidence: 99%

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Geng

Roel‐Touris

et al. 2019

WIREs Comput Mol Sci

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“…Our own literature mining revealed (at that time) that the phosphoproteomic data available for fungi other than S. cerevisiae were rather limited, although very recently a comparative phosphoproteomic analysis has been performed in 18 fungi (Studer et al 2016). The only other closely related ascomycete for which sufficient phosphoproteomic data were available to allow meaningful comparative analyses was C. albicans , with two datasets comprising 9438 nonredundant p-sites.…”

Section: Resultsmentioning

confidence: 99%

The Pivotal Role of Protein Phosphorylation in the Control of Yeast Central Metabolism

Vlastaridis

Papakyriakou

Chaliotis

et al. 2017

G3 Genes|Genomes|Genetics

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Protein phosphorylation is the most frequent eukaryotic post-translational modification and can act as either a molecular switch or rheostat for protein functions. The deliberate manipulation of protein phosphorylation has great potential for regulating specific protein functions with surgical precision, rather than the gross effects gained by the over/underexpression or complete deletion of a protein-encoding gene. In order to assess the impact of phosphorylation on central metabolism, and thus its potential for biotechnological and medical exploitation, a compendium of highly confident protein phosphorylation sites (p-sites) for the model organism Saccharomyces cerevisiae has been analyzed together with two more datasets from the fungal pathogen Candida albicans. Our analysis highlights the global properties of the regulation of yeast central metabolism by protein phosphorylation, where almost half of the enzymes involved are subject to this sort of post-translational modification. These phosphorylated enzymes, compared to the nonphosphorylated ones, are more abundant, regulate more reactions, have more protein–protein interactions, and a higher fraction of them are ubiquitinated. The p-sites of metabolic enzymes are also more conserved than the background p-sites, and hundreds of them have the potential for regulating metabolite production. All this integrated information has allowed us to prioritize thousands of p-sites in terms of their potential phenotypic impact. This multi-source compendium should enable the design of future high-throughput (HTP) mutation studies to identify key molecular switches/rheostats for the manipulation of not only the metabolism of yeast, but also that of many other biotechnologically and medically important fungi and eukaryotes.

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“…Looking for phosphosites, where phosphate groups are added or removed, the mass spectrometry data highlights specific phosphosites that enjoy high degree of conservation; for example, at protein interfaces necessary for protein-protein interactions. 1 Subsequent assessment of the extent of binding affinity between kinase motif and profiled phosphosite gave an understanding of the functional importance of individual phosphosite assayed. Finally, biochemical assays were employed to provide a view towards how different peptide libraries correlate with kinase activity level.…”

Section: Perspectivementioning

confidence: 99%

Understanding evolutionary dynamics of phosphorylation with fungal species as model system

2017

Preprint

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Movement in proteins requires energy. To this end, natural selection has selected phosphate as the principal energy currency in cells. On the other hand, depicting the state of transcription could come in the form of epigenetic markers, which are modifications on nucleotide residues. But, what are the deeper evolutionary forces that underpin the selection of phosphorylation as a key process for translating molecular information of cellular state into specific phenotype in movement and metabolism at the cellular level? From another perspective, what are the factors that guide the selection of particular phosphosites as principal phosphorylation sites? Seeking answers to the latter question, Villen and coworkers ("Evolution of protein phosphorylation across 18 fungal species", Science, Link) used mass spectrometry to profile the phosphoproteome of 18 fungal species and employed discovery science approaches to elucidate specific phosphosite highly conserved for particular functions such as transcription and translation. Using histone protein (H2) and transcription initiator factor (eIF4E) as model proteins for gaining a deeper understanding of the evolutionary forces that shape the annotation of specific phosphosite (from a large library of possible phosphosites) as key molecular effectors of cellular processes such as conformational changes in enzymes and ion channels. Results obtained suggests possible selection forces that define particular phosphosite for function, which are corroborated through assessing kinase motif usage and biochemical assays for the binding affinity between peptide libraries and cell lysates. Looking at a broader landscape of protein phosphorylation, the paper, however, does not yield sufficient insights to answer questions such as how protein phosphorylation first emerged as a defining mechanism for translating stored cellular energy in phosphate groups into movement necessary for protein function and, by extension, that of the cell. Understanding the evolutionary processes that first potentiated protein phosphorylation as well as the specific natural selection factors that resulted in the definition of specific phosphosite for particular function are fundamental questions important to understanding how biology utilizes chemical and physical principles for powering life.

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Evolution of protein phosphorylation across 18 fungal species

Cited by 95 publications

References 56 publications

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

The Pivotal Role of Protein Phosphorylation in the Control of Yeast Central Metabolism

Understanding evolutionary dynamics of phosphorylation with fungal species as model system

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