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A Chemical Approach for Programmable Protein Outputs Based on Engineered Cell Interactions.

ABSTRACT: Cell-cell interactions and communication are crucial to the proper function of complex mammalian physiology including neurocognitive and immune system functions. While many tools are available for observing and perturbing intracellular processes, relatively few exist to probe intercellular processes. Current techniques for studying interactions often rely on direct protein contact, and few can manipulate diverse, functional outputs with tunable protein expression. To address these limitations, we have developed a small-molecule approach based on a trimethoprim prodrug-enzyme pair capable of reporting the presence of two different engineered cell populations with programmable protein outputs. The approach relies on bacterial nitroreductase enzyme catalysis, which is orthogonal to normal mammalian biology, and diffusion of trimethoprim from "activator" cells to "receiver" cells. We test this strategy, which can theoretically regulate many different types of proteins, using biochemical and in vitro culture assays with optical and cytokine protein readouts. This describes the first small-molecule approach capable of detecting and controlling engineered cell-cell outputs, and we anticipate future applications that are especially relevant to the field of immuno-oncology.

SUBMITTER: Jacome DA

PROVIDER: S-EPMC8324041 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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A Chemical Approach for Programmable Protein Outputs Based on Engineered Cell Interactions.

Jacome Daniel A DA Northrup Justin D JD Ruff Andrew J AJ Reilly Sean W SW Lee Iris K IK Blizard Gabrielle S GS Sellmyer Mark A MA

ACS chemical biology 20201222 1

Cell-cell interactions and communication are crucial to the proper function of complex mammalian physiology including neurocognitive and immune system functions. While many tools are available for observing and perturbing intracellular processes, relatively few exist to probe intercellular processes. Current techniques for studying interactions often rely on direct protein contact, and few can manipulate diverse, functional outputs with tunable protein expression. To address these limitations, w ...[more]

PMID: 33351606

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Project description:BackgroundKnowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins.ResultsDomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms.ConclusionWe envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.

Dataset Information

A Chemical Approach for Programmable Protein Outputs Based on Engineered Cell Interactions.

Publications

A Chemical Approach for Programmable Protein Outputs Based on Engineered Cell Interactions.

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