An integrative epigenome-based strategy for unbiased functional profiling of clinical kinase inhibitors
Molecular Systems Biology
by Francesco Gualdrini, Stefano Rizzieri, Sara Polletti, Francesco Pileri, Yinxiu Zhan, Alessandro Cuomo, Gioacchino Natoli
14h ago
Unbiased genome-wide analyses of epigenomic alterations induced by clinical kinase inhibitors (CKIs) in macrophages activated by inflammatory stimuli allow identifying insofar unknown similarities and differences among CKIs, improving their annotations and showing opportunities for repurposing.AbstractMore than 500 kinases are implicated in the control of most cellular process in mammals, and deregulation of their activity is linked to cancer and inflammatory disorders. 80 clinical kinase inhibitors (CKIs) have been approved for clinical use and hundreds are in various stages of development. H ..read more
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Systematic identification of structure-specific protein–protein interactions
Molecular Systems Biology
by Aleš Holfeld, Dina Schuster, Fabian Sesterhenn, Alison K Gillingham, Patrick Stalder, Walther Haenseler, Inigo Barrio-Hernandez, Dhiman Ghosh, Jane Vowles, Sally A Cowley, Luise Nagel, Basavraj Khanppnavar, Tetiana Serdiuk, Pedro Beltrao, Volodymyr M Korkhov, Sean Munro, Roland Riek, Natalie de Souza, Paola Picotti
6d ago
A structural proteomics approach for identifying candidate interactors of any protein within a complex lysate is presented. It is applied to identify conformation-specific interactors of Rab GTPases and of the monomeric and fibrillar forms of the disease-associated protein alpha-synuclein.AbstractThe physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific ..read more
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Development and validation of AI/ML derived splice-switching oligonucleotides
Molecular Systems Biology
by Alyssa D Fronk, Miguel A Manzanares, Paulina Zheng, Adam Geier, Kendall Anderson, Shaleigh Stanton, Hasan Zumrut, Sakshi Gera, Robin Munch, Vanessa Frederick, Priyanka Dhingra, Gayatri Arun, Martin Akerman
2w ago
Machine learning models are trained to identify functional splice-switching oligonucleotides (SSOs) and to predict the splicing factors (SFs) inhibited. SSOs are developed for a novel target in Triple Negative Breast Cancer.AbstractSplice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRN ..read more
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Identification of type VI secretion system effector-immunity pairs using structural bioinformatics
Molecular Systems Biology
by Alexander M Geller, Maor Shalom, David Zlotkin, Noam Blum, Asaf Levy
2w ago
Structural bioinformatic tools were utilized for the discovery of novel specialized Type VI Secretion System (T6SS) effectors and their cognate immunity proteins, highlighting their utility over standard sequence-based tools. The effector predictions were supported by experimental results.AbstractThe type VI secretion system (T6SS) is an important mediator of microbe–microbe and microbe–host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity prot ..read more
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Genome-wide CRISPR screens identify novel regulators of wild-type and mutant p53 stability
Molecular Systems Biology
by YiQing Lü, Tiffany Cho, Saptaparna Mukherjee, Carmen Florencia Suarez, Nicolas S Gonzalez-Foutel, Ahmad Malik, Sebastien Martinez, Dzana Dervovic, Robin Hyunseo Oh, Ellen Langille, Khalid N Al-Zahrani, Lisa Hoeg, Zhen Yuan Lin, Ricky Tsai, Geraldine Mbamalu, Varda Rotter, Patricia Ashton-Prolla, Jason Moffat, Lucia Beatriz Chemes, Anne-Claude Gingras, Moshe Oren, Daniel Durocher, Daniel Schramek
1M ago
Genome-wide p53 protein stability screens provide a comprehensive network view of the processes regulating wildtype and mutant p53 and uncover potential targets for reinforcing wild-type p53 or targeting mutant p53 in cancer.AbstractTumor suppressor p53 (TP53) is frequently mutated in cancer, often resulting not only in loss of its tumor-suppressive function but also acquisition of dominant-negative and even oncogenic gain-of-function traits. While wild-type p53 levels are tightly regulated, mutants are typically stabilized in tumors, which is crucial for their oncogenic properties. Here, we s ..read more
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Interrogation of RNA-protein interaction dynamics in bacterial growth
Molecular Systems Biology
by Mie Monti, Reyme Herman, Leonardo Mancini, Charlotte Capitanchik, Karen Davey, Charlotte S Dawson, Jernej Ule, Gavin H Thomas, Anne E Willis, Kathryn S Lilley, Eneko Villanueva
1M ago
A dynamic analysis of RNA-protein interaction rewiring across growth phases detects extensive reorganisation of the RBPome and reveals the RNA binding properties for 17 unannotated E. coli proteins and their differential impact on cell growth and evolutionary conservation.AbstractCharacterising RNA–protein interaction dynamics is fundamental to understand how bacteria respond to their environment. In this study, we have analysed the dynamics of 91% of the Escherichia coli expressed proteome and the RNA-interaction properties of 271 RNA-binding proteins (RBPs) at different growth phases. We fin ..read more
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Mutational biases favor complexity increases in protein interaction networks after gene duplication
Molecular Systems Biology
by Angel F Cisneros, Lou Nielly-Thibault, Saurav Mallik, Emmanuel D Levy, Christian R Landry
1M ago
Duplicated self-interacting proteins can interact with themselves (homomers) or one another (heteromers). To understand whether natural selection is required to keep homomers over heteromers (or vice versa), the evolution of such duplicate proteins is simulated in the absence of new functions.AbstractBiological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following ..read more
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Cellular energy regulates mRNA degradation in a codon-specific manner
Molecular Systems Biology
by Pedro Tomaz da Silva, Yujie Zhang, Evangelos Theodorakis, Laura D Martens, Vicente A Yépez, Vicent Pelechano, Julien Gagneur
1M ago
Analysis of GTEx data and perturbation experiments in yeast show that cellular energy regulates the effect of optimal codon usage on mRNA stability.AbstractCodon optimality is a major determinant of mRNA translation and degradation rates. However, whether and through which mechanisms its effects are regulated remains poorly understood. Here we show that codon optimality associates with up to 2-fold change in mRNA stability variations between human tissues, and that its effect is attenuated in tissues with high energy metabolism and amplifies with age. Mathematical modeling and perturbation dat ..read more
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PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data
Molecular Systems Biology
by Anastasia Razdaibiedina, Alexander Brechalov, Helena Friesen, Mojca Mattiazzi Usaj, Myra Paz David Masinas, Harsha Garadi Suresh, Kyle Wang, Charles Boone, Jimmy Ba, Brenda Andrews
2M ago
PIFiA is a self-supervised deep-learning approach for protein functional annotation from single-cell images. It generates feature profiles from images of the yeast ORF-GFP collection that can be used in downstream analyses.AbstractFluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant featu ..read more
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AI-guided pipeline for protein–protein interaction drug discovery identifies a SARS-CoV-2 inhibitor
Molecular Systems Biology
by Philipp Trepte, Christopher Secker, Julien Olivet, Jeremy Blavier, Simona Kostova, Sibusiso B Maseko, Igor Minia, Eduardo Silva Ramos, Patricia Cassonnet, Sabrina Golusik, Martina Zenkner, Stephanie Beetz, Mara J Liebich, Nadine Scharek, Anja Schütz, Marcel Sperling, Michael Lisurek, Yang Wang, Kerstin Spirohn, Tong Hao, Michael A Calderwood, David E Hill, Markus Landthaler, Soon Gang Choi, Jean-Claude Twizere, Marc Vidal, Erich E Wanker
2M ago
A new pipeline for prioritizing protein-protein interactions (PPIs) for drug discovery, combines machine learning-based scoring of quantitative PPI data, protein complex structure prediction and virtual drug screening.AbstractProtein–protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have devel ..read more
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