Protein Structure, Stability, and Amyloid Formation

Division Of Basic Sciences - Nci

Investigators

Linked publications, trials & patents

Abstract

the oncogenic network of Ras signaling. We have been seeking to decipher the mechanisms of the proteins, their autoinhibition and activation, at the detailed conformational level, aiming to understand and to discover their pharmacological susceptibilities. Our broad outlook further aims to uncover their detailed interactions with other proteins and with the cell membrane, and (felled) regulation. We have also been interested in their signaling pathways. We aim to determine the scenarios of exactly how key signaling nodes are activated (or repressed, in repressors) by oncogenic driver mutations, and regulated under normal control, and decipher the hallmarks of the propagation of their signaling to the cell cycle. Uncontrolled cell proliferation is a hallmark of cancer leading us to ask what the determinants that influence signaling strength are. For cell proliferation, signaling should be sufficiently strong. This wide-ranging scope motivates us to investigate protein kinases in solution, lipid kinase (at the membrane), adaptor proteins, and their dynamic associations. To address this daunting task, we focus on two major Ras pathways that feed into the cell cycle, MAPK and PI3K/mTOR. MAPK acts in cell division; PI3K/mTOR in cell growth. Both are required in proliferation. We aim to unravel allosteric activation and inhibition mechanisms of oncogenic proteins, allosteric therapeutics and signaling. Protein activation and inhibition involve conformational changes, which are the hallmarks of allostery. We seek to understand how allostery controls physiological function; how it can play a role in cancer, and how it can be harnessed by drugs. We consider the conformational ensembles of the proteins in solution and when membrane-anchored, and their assemblies. We also ask: What is productive signaling? How to define it, how to measure it, and what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream protein in a specific cell such that it leads to cell proliferation? These questions have either not been considered or not resolved. Recently we also took up the compelling question of neurodevelopmental disorders (NDDs) and their connection to cancer. We ask the puzzling questions of how same-gene mutations can drive both cancer and NDDs and why individuals with NDD have a higher risk of cancer. Ras, MEK, PI3K, PTEN, and SHP2 are among the oncogenic proteins that can harbor mutations that encode diseases other than cancer. Understanding why some of their mutations can promote cancer, whereas others promote NDDs, and why even the same mutations may promote both phenotypes, has important clinical ramifications. Our thesis is that the immune and nervous systems co-evolve as the embryo develops. Immunity can release cytokines that activate MAPK signaling in neural cells. In specific embryonic brain cell types, dysregulated signaling that results from germline or embryonic mutations can promote changes in chromatin organization and gene accessibility, and thus expression levels of essential genes in neurodevelopment. In cancer, dysregulated signaling can emerge from sporadic somatic mutations during human life. NDDs and cancer share similarities. We suggested that in NDDs, immunity, and cancer, there appears an almost invariable involvement of small GTPases (e.g., Ras, RhoA, Rac) and their pathways. TLRs, IL-1, GIT1, and FGFR signaling pathways, all can be dysregulated in NDDs and cancer. While there are signaling similarities, decisive differentiating factors are timing windows, and cell type specific perturbation levels, pointing to chromatin reorganization. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. We explore the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We suggest that target combinations should preferentially be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs. We aim to predict which proteins can interact and how, through a structure-based interface mimicry strategy. Efficient and reliable prediction of new interactions can allow identification of potential targets. Powerful protein-protein interaction prediction tools can map interactions and predict how pathogens can hijack signaling in the host cell, which can be tested by experiment. Available experimental structural data are scant, and the combinatorial landscape of host protein-pathogen interactions is vast. We are working to further enhance our server to include more interactions and modeled proteins with AI-adopted prediction methods.