Tracking chromatin state changes using μMap photo-proximity labeling

Interactions between biomolecules, particularly proteins, underlie all cellular processes, and ultimately control cell fate. Perturbation of native interactions through mutation, changes in expression levels, or external stimuli leads to altered cellular physiology and can result in either disease or therapeutic effects. Mapping these interactions and determining how they respond to stimulus is the genesis of many drug development efforts, leading to new therapeutic targets and improvements in human health. However, in the complex environment of the nucleus it is challenging to determine protein-protein interactions due to low abundance, transient or multi-valent binding, and a lack of technologies that are able to interrogate these interactions without disrupting the protein binding surface under study. Chromatin remodelers, modifying enzymes, interactors, and transcription factors can all be redirected by subtle changes to the microenvironment, causing global changes in protein expression levels and subsequent physiology. Here, we describe the Chroma-μMap method for the traceless incorporation of Ir-photosensitizers into the nuclear microenvironment using engineered split inteins. These Ir-catalysts can activate diazirine warheads to form reactive carbenes within a ~10 nm radius, cross-linking with proteins within the immediate microenvironment for analysis via quantitative chemoproteomics. We demonstrate this concept on nine different nuclear proteins with varied function and in each case, elucidating their microenvironments. Additionally, we show that this short-range proximity labelling method can reveal the critical changes in interactomes in the presence of cancer-associated mutations, as well as treatment with small-molecule inhibitors. Chroma-μMap improves our fundamental understanding of nuclear protein-protein interactions, as well as the effects that small molecule therapeutics have on the local chromatin environment, and in doing so is expected to have a significant impact on the field of epigenetic drug discovery in both academia and industry.

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Tracking chromatin state changes using µMap photo-proximity labeling

10.21203/rs.3.rs-955662/v1 ◽

2021 ◽

Author(s):

Tom Muir ◽

Ciaran Seath ◽

David MacMillan ◽

Antony Burton

Keyword(s):

Cell Fate ◽

Small Molecule ◽

Protein Interactions ◽

Control Cell ◽

Global Changes ◽

Therapeutic Effects ◽

Protein Protein Interactions ◽

Chromatin Remodelers ◽

Expression Levels ◽

Epigenetic Drug

Abstract Interactions between biomolecules, particularly proteins, underlie all cellular processes, and ultimately control cell fate. Perturbation of native interactions through mutation, changes in expression levels, or external stimuli leads to altered cellular physiology and can result in either disease or therapeutic effects. Mapping these interactions and determining how they respond to stimulus is the genesis of many drug development efforts, leading to new therapeutic targets and improvements in human health. However, in the complex environment of the nucleus it is challenging to determine protein-protein interactions due to low abundance, transient or multi-valent binding, and a lack of technologies that are able to interrogate these interactions without disrupting the protein binding surface under study. Chromatin remodelers, modifying enzymes, interactors, and transcription factors can all be redirected by subtle changes to the microenvironment, causing global changes in protein expression levels and subsequent physiology. Here, we describe the Chroma-µMap method for the traceless incorporation of Ir-photosensitizers into the nuclear microenvironment using engineered split inteins. These Ir-catalysts can activate diazirine warheads to form reactive carbenes within a ~10 nm radius, cross-linking with proteins within the immediate microenvironment for analysis via quantitative chemoproteomics. We demonstrate this concept on nine different nuclear proteins with varied function and in each case, elucidating their microenvironments. Additionally, we show that this short-range proximity labeling method can reveal the critical changes in interactomes in the presence of cancer-associated mutations, as well as treatment with small-molecule inhibitors. Chroma-µMap improves our fundamental understand-ing of nuclear protein-protein interactions, as well as the effects that small molecule therapeutics have on the local chromatin environment, and in doing so is expected to have a significant impact on the field of epigenetic drug discovery in both academia and industry.

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Faculty Opinions recommendation of Importance of Rigidity in Designing Small Molecule Drugs To Tackle Protein-Protein Interactions (PPIs) through Stabilization of Desired Conformers.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732129727.793539183 ◽

2017 ◽

Author(s):

John Lowe

Keyword(s):

Small Molecule ◽

Protein Interactions ◽

Protein Protein Interactions ◽

Small Molecule Drugs

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Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

Current Topics in Medicinal Chemistry ◽

10.2174/1568026618666181025114903 ◽

2018 ◽

Vol 18 (20) ◽

pp. 1719-1736 ◽

Cited By ~ 3

Author(s):

Sharanya Sarkar ◽

Khushboo Gulati ◽

Manikyaprabhu Kairamkonda ◽

Amit Mishra ◽

Krishna Mohan Poluri

Keyword(s):

Small Molecule ◽

Protein Interactions ◽

Small Molecule Inhibitors ◽

Computational Techniques ◽

Protein Protein Interactions ◽

Computational Tools ◽

Computational Approaches ◽

Protein Protein Interaction ◽

Wide Range ◽

Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

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