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.
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.
more » « less- PAR ID:
- 10399231
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Angewandte Chemie
- Volume:
- 135
- Issue:
- 11
- ISSN:
- 0044-8249
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, Active and Regulatory site Prediction (AR‐Pred), which supplements protein geometry, evolutionary, and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. As the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median area under the curve (AUC) of 91% and Matthews correlation coefficient (MCC) of 0.68, whereas the less well‐defined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR‐Pred is available as a free downloadable package at
https://github.com/sambitmishra0628/AR-PRED_source . -
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Abstract Allostery governing two conformational states is one of the proposed mechanisms for catch‐bond behavior in adhesive proteins. In FimH, a catch‐bond protein expressed by pathogenic bacteria, separation of two domains disrupts inhibition by the pilin domain. Thus, tensile force can induce a conformational change in the lectin domain, from an inactive state to an active state with high affinity. To better understand allosteric inhibition in two‐domain FimH (H2 inactive), we use molecular dynamics simulations to study the lectin domain alone, which has high affinity (HL active), and also the lectin domain stabilized in the low‐affinity conformation by an Arg‐60‐Pro mutation (HL mutant). Because ligand‐binding induces an allostery‐like conformational change in HL mutant, this more experimentally tractable version has been proposed as a “minimal model” for FimH. We find that HL mutant has larger backbone fluctuations than both H2 inactive and HL active, at the binding pocket and allosteric interdomain region. We use an internal coordinate system of dihedral angles to identify protein regions with differences in backbone and side chain dynamics beyond the putative allosteric pathway sites. By characterizing HL mutant dynamics for the first time, we provide additional insight into the transmission of allosteric information across the lectin domain and build upon structural and thermodynamic data in the literature to further support the use of HL mutant as a “minimal model.” Understanding how to alter protein dynamics to prevent the allosteric conformational change may guide drug development to prevent infection by blocking FimH adhesion.
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Abstract Motivation Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages.
Results Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein’s global dynamics.
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