Finding Our Way in the Dark Proteome

Bhowmick, Asmit; Brookes, David H.; Yost, Shane R.; Dyson, H. Jane; Forman-Kay, Julie D.; Gunter, Dan; Head‐Gordon, Martin; Hura, Greg L.; Pande, Vijay S.; Wemmer, David E.; Wright, Peter E.; Head‐Gordon, Teresa

doi:10.1021/jacs.6b06543

Cited by 113 publications

(100 citation statements)

References 177 publications

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“…Firstly, the relative populations of the monomeric metastable states are not well‐converged across different studies. From the methodological standpoint of computational ensemble modeling, a persistent problem discussed in a very recent proteomics review (Bhowmick et al, ) stems from the limited amount of experimental data available for individual IDPs, which makes it extremely difficult to arrive at a unique solution with distinguishable features between close ensembles. The latest Bayesian formalism‐based ensemble building methods hold great potential to address this issue, but are currently in their infancy (Brookes & Head‐Gordon, ; Gurry et al, ).…”

Section: Resultsmentioning

confidence: 99%

Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment

Bhattacharya

Thompson

2018

WIREs Comput Mol Sci

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Neurodegenerative amyloidogenesis begins with the aggregation of intrinsically disordered proteins (IDPs), which is the first step in a cascade of assembly events that can lead to insoluble fibrous deposits in brain tissue. IDP conformations that promote formation of toxic oligomers remain poorly understood, and are the most fundamental target of putative treatments for neurodegenerative disease. Rapid advances in theory, simulation and experimental methods, hold the promise of reversing protein aggregation by identifying and developing inhibitors of the transient amyloidogenic IDP conformations. To make meaningful progress it is important to appreciate the benefits and limitations of the latest developments in computational methods of conformational and ensemble modeling, and their integration and validation with experiments. Integrated studies are beginning to provide significant conceptual and mechanistic insights, including identification of the properties of amyloidogenic IDPs in their free, unbound form. At the same time, contradicting viewpoints have emerged concerning convergence of IDP ensemble signatures and properties from parallel studies, and there also remains a pressing need to develop physical models that can deliver reliable predictions across different IDP families. Focussing on the four most common amyloidogenic IDPs of Amyloid β, Tau, α‐synuclein and Prions, improvements are proposed for next‐generation models and experiments that can potentially identify drug treatments for neurodegenerative disease via incorporation of the extended cellular environment. This article is categorized under: Molecular and Statistical Mechanics > Molecular Mechanics Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Molecular Structures

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Section: Resultsmentioning

confidence: 99%

Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment

Bhattacharya

Thompson

2018

WIREs Comput Mol Sci

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“…Ordered proteins have well‐defined 3D structures and exhibit small‐scale structural fluctuations under physiological conditions. On the contrary, intrinsically disordered proteins could sample an ensemble of conformations which may be compact (molten globule‐like) or extended (coil‐like or pre‐molten globule‐like) . Furthermore, proteins can be entirely disordered polypeptides (IDPs) or a combination of disordered regions (IDRs) and ordered domains …”

Section: Introductionmentioning

confidence: 99%

Features of molecular recognition of intrinsically disordered proteins via coupled folding and binding

et al. 2019

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The sequence–structure–function paradigm of proteins has been revolutionized by the discovery of intrinsically disordered proteins (IDPs) or intrinsically disordered regions (IDRs). In contrast to traditional ordered proteins, IDPs/IDRs are unstructured under physiological conditions. The absence of well‐defined three‐dimensional structures in the free state of IDPs/IDRs is fundamental to their function. Folding upon binding is an important mode of molecular recognition for IDPs/IDRs. While great efforts have been devoted to investigating the complex structures and binding kinetics and affinities, our knowledge on the binding mechanisms of IDPs/IDRs remains very limited. Here, we review recent advances on the binding mechanisms of IDPs/IDRs. The structures and kinetic parameters of IDPs/IDRs can vary greatly, and the binding mechanisms can be highly dependent on the structural properties of IDPs/IDRs. IDPs/IDRs can employ various combinations of conformational selection and induced fit in a binding process, which can be templated by the target and/or encoded by the IDP/IDR. Further studies should provide deeper insights into the molecular recognition of IDPs/IDRs and enable the rational design of IDP/IDR binding mechanisms in the future.

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“…Due to the highly dynamic nature, IDP cannot be described by a single or a few well‐defined structures . Instead, the accurate characterization of IDP required the structural ensemble, which includes a limited number of low free energy states and the statistic weights corresponding to these states . Therefore, precisely determining the structure ensembles of IDPs is crucial for the understanding of IDP functions.…”

Section: Introductionmentioning

confidence: 99%

All‐atom structure ensembles of islet amyloid polypeptides determined by enhanced sampling and experiment data restraints

Wang

Liu

et al. 2019

Proteins

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Exploring the accurate structure ensembles are critical to understand the functions of intrinsically disordered proteins (IDPs). As a well‐known IDP, islet amyloid polypeptide (IAPP) plays important roles in the development of human type II diabetes (T2D). The toxicity of human IAPP (hIAPP) is induced by the amyloidosis of the peptide, however, its aggregation mechanism remains ambiguous. The characterization of structure ensemble of hIAPP, as well as the differences between hIAPP and its non‐amyloidogenic homologous such as rat IAPP (rIAPP), would greatly help to illuminate the amyloidosis mechanism of IAPP. In this study, the atomic structure ensembles of hIAPP and rIAPP were characterized by all‐atom molecular dynamics (MD) simulations combined with enhanced sampling technology and experiment data restraints. The obtained structure ensembles were firstly compared with those determined by the conventional MD (cMD) and enhanced sampling without experiment data restraints. The results showed that the enhanced sampling and experiment data restraints would improve the simulation accuracy. The transient N‐terminal α‐helix structures were adopted by the sub‐states of both hIAPP and rIAPP, however, the C‐terminal helical structures were only present on hIAPP. The hydrophobic residues in the amyloid‐core region of hIAPP are exposed to the solvent. The structure ensemble differences between hIAPP and rIAPP revealed in this work provide potential explain to the amyloidogenic mechanism and would be helpful for the design of drugs to combat T2D.

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Finding Our Way in the Dark Proteome

Cited by 113 publications

References 177 publications

Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment

Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment

Features of molecular recognition of intrinsically disordered proteins via coupled folding and binding

All‐atom structure ensembles of islet amyloid polypeptides determined by enhanced sampling and experiment data restraints

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