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Many plant transcription factors (TFs) are multifunctional and regulate growth and development in more than one tissue. These TFs can generally associate with different protein partners depending on the tissue type, thereby regulating tissue-specific target gene sets. However, how interaction specificity is ensured is still largely unclear. Here, we examine protein-protein interaction specificity using subfunctionalized co-orthologs of the FRUITFULL (FUL) subfamily of MADS-domain TFs. In Arabidopsis, FUL is multifunctional, playing important roles in flowering and fruiting, whereas these functions have partially been divided in the tomato co-orthologs FUL1 and FUL2. By linking protein sequence and function, we discovered a key amino acid motif that determines interaction specificity of MADS-domain TFs, which in Arabidopsis FUL determines the interaction with AGAMOUS and SEPALLATA proteins, linked to the regulation of a subset of targets. This insight offers great opportunities to dissect the biological functions of multifunctional MADS TFs., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

Phytoplasmas are pathogenic bacteria that reprogram plant host development for their own benefit. Previous studies have characterized a few different phytoplasma effector proteins that destabilize specific plant transcription factors. However, these are only a small fraction of the potential effectors used by phytoplasmas; therefore, the molecular mechanisms through which phytoplasmas modulate their hosts require further investigation. To obtain further insights into the phytoplasma infection mechanisms, we generated a protein-protein interaction network between a broad set of phytoplasma effectors and a large, unbiased collection of Arabidopsis thaliana transcription factors and transcriptional regulators. We found widespread, but specific, interactions between phytoplasma effectors and host transcription factors, especially those related to host developmental processes. In particular, many unrelated effectors target specific sets of TCP transcription factors, which regulate plant development and immunity. Comparison with other host-pathogen protein interaction networks shows that phytoplasma effectors have unusual targets, indicating that phytoplasmas have evolved a unique and unusual infection strategy. This study contributes a rich and solid data source that guides further investigations of the functions of individual effectors, as demonstrated for some herein. Moreover, the dataset provides insights into the underlying molecular mechanisms of phytoplasma infection., (© 2023 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

Protein degradation is a key component of the regulation of gene expression and is at the center of several pathogenic processes. Proteins are regularly degraded, but there is large variation in their lifetimes, and the kinetics of protein degradation are not well understood. Many different factors can influence protein degradation rates, painting a highly complex picture. This has been partially unravelled in recent years thanks to invaluable advances in proteomics techniques. In this Mini-Review, we give a global vision of the determinants of protein degradation rates with the backdrop of the current understanding of proteolytic systems to give a contemporary view of the field., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

Motivation: Polyketide synthases (PKSs) are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular PKSs, which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein-protein interactions (PPIs). The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome., Results: Here, we introduce PKSpop, which uses a coevolution-based PPI algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for PPIs than coevolution between ketosynthase and acyl carrier protein domains., Availability and Implementation: The code is available on http://www.bif.wur.nl/ (under 'Software')., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The causative agent of the coronavirus disease 2019 (COVID-19) pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected millions and killed hundreds of thousands of people worldwide, highlighting an urgent need to develop antiviral therapies. Here we present a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in Vero E6 cells, revealing dramatic rewiring of phosphorylation on host and viral proteins. SARS-CoV-2 infection promoted casein kinase II (CK2) and p38 MAPK activation, production of diverse cytokines, and shutdown of mitotic kinases, resulting in cell cycle arrest. Infection also stimulated a marked induction of CK2-containing filopodial protrusions possessing budding viral particles. Eighty-seven drugs and compounds were identified by mapping global phosphorylation profiles to dysregulated kinases and pathways. We found pharmacologic inhibition of the p38, CK2, CDK, AXL, and PIKFYVE kinases to possess antiviral efficacy, representing potential COVID-19 therapies., Competing Interests: Declaration of Interests The Krogan laboratory has received research support from Vir Biotechnology and F. Hoffmann-La Roche. K.M.S. has consulting agreements for the following companies involving cash and/or stock compensation: Black Diamond Therapeutics, BridGene Biosciences, Denali Therapeutics, Dice Molecules, eFFECTOR Therapeutics (zotatifin and tomivosertib), Erasca, Genentech/Roche, Janssen Pharmaceuticals, Kumquat Biosciences, Kura Oncology, Merck, Mitokinin, Petra Pharma, Qulab Inc. Revolution Medicines (WDB002), Type6 Therapeutics, Venthera, and Wellspring Biosciences (Araxes Pharma)., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Motivation: Predicting residue-residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance., Results: We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus non-interaction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. This requires no training labels concerning interactions or contacts. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions., Availability and Implementation: Source code and test data are available at www.bif.wur.nl/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Protein turnover is a key aspect of cellular homeostasis and proteome dynamics. However, there is little consensus on which properties of a protein determine its lifetime in the cell. In this work, we exploit two reliable datasets of experimental protein degradation rates to learn models and uncover determinants of protein degradation, with particular focus on properties that can be derived from the sequence. Our work shows that simple sequence features suffice to obtain predictive models of which the output correlates reasonably well with the experimentally measured values. We also show that intrinsic disorder may have a larger effect than previously reported, and that the effect of PEST regions, long thought to act as specific degradation signals, can be better explained by their disorder. We also find that determinants of protein degradation depend on the cell types or experimental conditions studied. This analysis serves as a first step towards the development of more complex, mature computational models of degradation of proteins and eventually of their full life cycle. Proteins 2017; 85:1593-1601. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)