The mechanistic Target of Rapamycin Complex 1 (mTORC1) is a key regulator of cell growth and proliferation. Upon activation in a favorable, nutrient-rich environment, mTORC1 triggers anabolic reactions and inhibits catabolism. Nutrient signals, especially amino acid signals, are transmitted to mTORC1 through a series of protein complexes, which ultimately converge on the Rag GTPases, a heterodimeric GTPase that directly recruits mTORC1 to the lysosomal surface. Recent discovery of the Rag GTPases and their regulators has revealed a key intermediate between amino acid sufficiency and mTORC1 activation. However, the molecular mechanisms of how these protein machineries collaborate to transmit the amino acid signal are still elusive. Understanding the mechanistic details of this pathway will require: (1) determination of the structures of key protein components to reveal domains and residues that are critical for their biological functions, and (2) biochemical analyses to define protein functions at the mechanistic level and quantify the effect of specific perturbations. In this proposal, we aim to develop biochemical and biophysical tools to study Rag-dependent amino acid sensing at the molecular level. Specifically, we aim to use structural biology tools to directly visualize the protein complexes that mediate this process (Aim 1), and enzymatic kinetics assays to quantify the functions of the Rag GTPases and their regulators (Aim 2). Further, we plan to reconstitute an in vitro system to recapitulate Rag-dependent amino acid sensing (Aim 3), and directly visualize the organization of these protein machines at the single-molecule level (Aim 4). The approaches developed here will provide a unified model and yield novel insights into this important biological process.
To adapt to environmental conditions, cells need to sense the absence or presence of amino acids to regulate their growth. The proposed research aims to understand the mechanism of mediating amino acid sensing upstream of mTORC1 at the molecular level. By combining various techniques, we plan to build a comprehensive model for this important biological process, which will predict the behavior of mutants in this pathway.
