Metalloallostery and Transition Metal Signaling: Bioinorganic Copper Chemistry Beyond Active Sites

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Abstract

Transition metal chemistry is essential to life, where metal binding to DNA, RNA, and proteins underpins all facets of the central dogma of biology. In this context, metals in proteins are typically studied as static active site cofactors. However, the emergence of transition metal signaling, where mobile metal pools can transiently bind to biological targets beyond active sites, is expanding this conventional view of bioinorganic chemistry. This Minireview focuses on the concept of metalloallostery, using copper as a canonical example of how metals can regulate protein function by binding to remote allosteric sites (e.g., exosites). We summarize advances in and prospects for the field, including imaging dynamic transition metal signaling pools, allosteric inhibition or activation of protein targets by metal binding, and metal‐dependent signaling pathways that underlie nutrient vulnerabilities in diseases spanning obesity, fatty liver disease, cancer, and neurodegeneration.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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Ferroptosis: an iron-dependent form of nonapoptotic cell death.

Scott J. Dixon, Kathryn Lemberg, Michael Lamprecht … (2012)

Nonapoptotic forms of cell death may facilitate the selective elimination of some tumor cells or be activated in specific pathological states. The oncogenic RAS-selective lethal small molecule erastin triggers a unique iron-dependent form of nonapoptotic cell death that we term ferroptosis. Ferroptosis is dependent upon intracellular iron, but not other metals, and is morphologically, biochemically, and genetically distinct from apoptosis, necrosis, and autophagy. We identify the small molecule ferrostatin-1 as a potent inhibitor of ferroptosis in cancer cells and glutamate-induced cell death in organotypic rat brain slices, suggesting similarities between these two processes. Indeed, erastin, like glutamate, inhibits cystine uptake by the cystine/glutamate antiporter (system x(c)(-)), creating a void in the antioxidant defenses of the cell and ultimately leading to iron-dependent, oxidative death. Thus, activation of ferroptosis results in the nonapoptotic destruction of certain cancer cells, whereas inhibition of this process may protect organisms from neurodegeneration. Copyright © 2012 Elsevier Inc. All rights reserved.

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Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes.

Zobair Younossi, Aaron Koenig, Dinan Abdelatif … (2016)

Nonalcoholic fatty liver disease (NAFLD) is a major cause of liver disease worldwide. We estimated the global prevalence, incidence, progression, and outcomes of NAFLD and nonalcoholic steatohepatitis (NASH). PubMed/MEDLINE were searched from 1989 to 2015 for terms involving epidemiology and progression of NAFLD. Exclusions included selected groups (studies that exclusively enrolled morbidly obese or diabetics or pediatric) and no data on alcohol consumption or other liver diseases. Incidence of hepatocellular carcinoma (HCC), cirrhosis, overall mortality, and liver-related mortality were determined. NASH required histological diagnosis. All studies were reviewed by three independent investigators. Analysis was stratified by region, diagnostic technique, biopsy indication, and study population. We used random-effects models to provide point estimates (95% confidence interval [CI]) of prevalence, incidence, mortality and incidence rate ratios, and metaregression with subgroup analysis to account for heterogeneity. Of 729 studies, 86 were included with a sample size of 8,515,431 from 22 countries. Global prevalence of NAFLD is 25.24% (95% CI: 22.10-28.65) with highest prevalence in the Middle East and South America and lowest in Africa. Metabolic comorbidities associated with NAFLD included obesity (51.34%; 95% CI: 41.38-61.20), type 2 diabetes (22.51%; 95% CI: 17.92-27.89), hyperlipidemia (69.16%; 95% CI: 49.91-83.46%), hypertension (39.34%; 95% CI: 33.15-45.88), and metabolic syndrome (42.54%; 95% CI: 30.06-56.05). Fibrosis progression proportion, and mean annual rate of progression in NASH were 40.76% (95% CI: 34.69-47.13) and 0.09 (95% CI: 0.06-0.12). HCC incidence among NAFLD patients was 0.44 per 1,000 person-years (range, 0.29-0.66). Liver-specific mortality and overall mortality among NAFLD and NASH were 0.77 per 1,000 (range, 0.33-1.77) and 11.77 per 1,000 person-years (range, 7.10-19.53) and 15.44 per 1,000 (range, 11.72-20.34) and 25.56 per 1,000 person-years (range, 6.29-103.80). Incidence risk ratios for liver-specific and overall mortality for NAFLD were 1.94 (range, 1.28-2.92) and 1.05 (range, 0.70-1.56).