scholarly journals Mechanistic Insights on ATP’s role as Hydrotrope

2021 ◽  
Author(s):  
Susmita Sarkar ◽  
Jagannath Mondal
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Polymer Surface ◽  
Disordered Proteins ◽  
Aggregation Process ◽  
Single Chain ◽  
Aggregation Propensity ◽  
Intrinsically Disordered ◽  
Amphiphilic Molecules ◽  
Preferential Binding ◽  
Self Aggregation

AbstractHydrotropes are small amphiphilic molecules which help in solubilizing hydrophobic entities in aqueous medium. Recent experimental investigation has provided convincing evidences that, adenosine triphosphate (ATP), besides being an energy currency of cell, also can act as hydrotrope to inhibit the formation of protein condensates. In this work, we have designed computer simulations of prototypical macromolecules in aqueous ATP solution to dissect the molecular mechanism underlying ATP’s newly discovered role as a hydrotrope. The simulation demonstrates that ATP can unfold a single-chain of hydrophobic macromolecule as well as can disrupt the aggregation process of a hydrophobic assembly. Moreover, the introduction of charges in the macromolecule is found to reinforce ATP’s disaggregation effects in a synergistic fashion, a behaviour reminiscent of recent experimental observation of pronounced hydrotropic action of ATP in intrinsically disordered proteins. A molecular analysis indicates that this new-found ability of ATP are ingrained in its propensity of preferential binding to the polymer surface, which gets fortified in presence of charges. The investigation also renders evidence that the key to the ATP’s superior hydrotropic role over chemical hydrotrope (Sodium xylene sulfonate, NaXS) may lie in its inherent self-aggregation propensity. Overall, via employing a bottom-up approach the current investigation provides fresh mechanistic insights into the dual solubilizing and denaturing abilities of ATP.

Current Challenges and Limitations in the Studies of Intrinsically Disordered Proteins in Neurodegenerative Diseases by Computer Simulations

2020 ◽  
Vol 17 ◽  
Author(s):  
Ibrahim Yagiz Akbayrak ◽  
Sule Irem Caglayan ◽  
Zilan Ozcan ◽  
Vladimir N. Uversky ◽  
Orkid Coskuner-Weber
Keyword(s):  
Neurodegenerative Diseases ◽  
Computer Simulations ◽  
Conformational Changes ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Computational Studies ◽  
Accurate Data ◽  
Aggregation Propensity ◽  
Intrinsically Disordered ◽  
Future Developments

: Experiments face challenges in the analysis of intrinsically disordered proteins in solution due to fast conformational changes and enhanced aggregation propensity. Computational studies complement experiments, being widely used in the analyses of intrinsically disordered proteins, especially those positioned at the centers of neurodegenerative diseases. However, recent investigations – including our own – revealed that computer simulations face significant challenges and limitations themselves. In this review, we introduced and discussed some of the scientific challenges and limitations of computational studies conducted on intrinsically disordered proteins. We also outlined the importance of future developments in the areas of computational chemistry and computational physics that would be needed for generating more accurate data for intrinsically disordered proteins from computer simulations. Additional theoretical strategies that can be developed are discussed herein.

scholarly journals Accurate model of liquid-liquid phase behaviour of intrinsically-disordered proteins from data-driven optimization of single-chain properties

2021 ◽  
Author(s):  
Giulio Tesei ◽  
Thea K. Schulze ◽  
Ramon Crehuet ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Liquid Phase ◽  
Intrinsically Disordered Proteins ◽  
Molecular Simulations ◽  
Phase Behaviour ◽  
Data Driven ◽  
Coarse Grained ◽  
Disordered Proteins ◽  
Model Parameters ◽  
Single Chain ◽  
Intrinsically Disordered

Many intrinsically disordered proteins (IDPs) may undergo liquid-liquid phase separation (LLPS) and participate in the formation of membraneless organelles in the cell, thereby contributing to the regulation and compartmentalisation of intracellular biochemical reactions. The phase behaviour of IDPs is sequence-dependent, and its investigation through molecular simulations requires protein models that combine computational efficiency with an accurate description of intra- and intermolecular interactions. We developed a general coarse-grained model of IDPs, with residue-level detail, based on an extensive set of experimental data on single-chain properties. Ensemble-averaged experimental observables are predicted from molecular simulations, and a data-driven parameter-learning procedure is used to identify the residue-specific model parameters that minimize the discrepancy between predictions and experiments. The model accurately reproduces the experimentally observed conformational propensities of a set of IDPs. Through two-body as well as large-scale molecular simulations, we show that the optimization of the intramolecular interactions results in improved predictions of protein self-association and LLPS.

scholarly journals Connecting coil-to-globule transitions to full phase diagrams for intrinsically disordered proteins

2020 ◽  
Author(s):  
X. Zeng ◽  
A. S. Holehouse ◽  
T. Mittag ◽  
A. Chilkoti ◽  
R. V. Pappu
Keyword(s):  
Phase Separation ◽  
Phase Diagrams ◽  
Intrinsically Disordered Proteins ◽  
Driving Forces ◽  
Temporal Organization ◽  
Disordered Proteins ◽  
Single Chain ◽  
Cluster Theory ◽  
Intrinsically Disordered ◽  
Specific Phase

ABSTRACTPhase separation is thought to underlie spatial and temporal organization that is required for controlling biochemical reactions in cells. Multivalence of interaction motifs also known as stickers is a defining feature of proteins that drive phase separation. Intrinsically disordered proteins with stickers uniformly distributed along the linear sequence can serve as scaffold molecules that drive phase separation. The sequence-intrinsic contributions of disordered proteins to phase separation can be discerned by computing or measuring sequence-specific phase diagrams. These help to delineate the combinations of protein concentration and a suitable control parameter such as temperature that support phase separation. Here, we present an approach that combines detailed simulations with a numerical adaptation of an analytical Gaussian cluster theory to enable the calculation of sequence-specific phase diagrams. Our approach leverages the known equivalence between the driving forces for single chain collapse in dilute solutions and the driving forces for phase separation in concentrated solutions. We demonstrate the application of the theory-aided computations through calculation of phase diagrams for a set of archetypal intrinsically disordered low complexity domains.STATEMENT OF SIGNIFICANCEIntrinsically disordered proteins that have the requisite valence of adhesive linear motifs can drive phase separation and give rise to membraneless biomolecular condensates. Knowledge of how phase diagrams vary with amino acid sequence and changes to solution conditions is essential for understanding how proteins contribute to condensate assembly and dissolution. In this work, we introduce a new two-pronged computational approach to predict sequence-specific phase diagrams. This approach starts by extracting key parameters from simulations of single-chain coil-to-globule transitions. We use these parameters in our numerical implementation of the Gaussian cluster theory (GCT) for polymer solutions to construct sequences-specific phase diagrams. The method is efficient and demonstrably accurate and should pave the way for high-throughput assessments of phase behavior.

scholarly journals Accurate model of liquid–liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties

2021 ◽  
Vol 118 (44) ◽  
pp. e2111696118
Author(s):  
Giulio Tesei ◽  
Thea K. Schulze ◽  
Ramon Crehuet ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Liquid Phase ◽  
Phase Behavior ◽  
Intrinsically Disordered Proteins ◽  
Molecular Simulations ◽  
Intramolecular Interactions ◽  
Coarse Grained ◽  
Disordered Proteins ◽  
Model Parameters ◽  
Single Chain ◽  
Intrinsically Disordered

Many intrinsically disordered proteins (IDPs) may undergo liquid–liquid phase separation (LLPS) and participate in the formation of membraneless organelles in the cell, thereby contributing to the regulation and compartmentalization of intracellular biochemical reactions. The phase behavior of IDPs is sequence dependent, and its investigation through molecular simulations requires protein models that combine computational efficiency with an accurate description of intramolecular and intermolecular interactions. We developed a general coarse-grained model of IDPs, with residue-level detail, based on an extensive set of experimental data on single-chain properties. Ensemble-averaged experimental observables are predicted from molecular simulations, and a data-driven parameter-learning procedure is used to identify the residue-specific model parameters that minimize the discrepancy between predictions and experiments. The model accurately reproduces the experimentally observed conformational propensities of a set of IDPs. Through two-body as well as large-scale molecular simulations, we show that the optimization of the intramolecular interactions results in improved predictions of protein self-association and LLPS.

scholarly journals Investigating the Conformational Ensembles of Intrinsically-Disordered Proteins with a Simple Physics-Based Model

2020 ◽  
Author(s):  
Yani Zhao ◽  
Robinson Cortes-Huerto ◽  
Kurt Kremer ◽  
Joseph F. Rudzinski
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Conformational Dynamics ◽  
Coarse Grained ◽  
Disordered Proteins ◽  
Side Chain ◽  
Conformational Ensemble ◽  
Single Chain ◽  
Conformational Ensembles ◽  
Intrinsically Disordered ◽  
The Impact

Intrinsically disordered proteins (IDPs) play an important role in an array of biological processes but present a number of fundamental challenges for computational modeling. Recently, simple polymer models have re-gained popularity for interpreting the experimental characterization of IDPs. Homopolymer theory provides a strong foundation for understanding generic features of phenomena ranging from single-chain conformational dynamics to the properties of entangled polymer melts, but is difficult to extend to the copolymer context. This challenge is magnified for proteins due to the variety of competing interactions and large deviations in side-chain properties. In this work, we apply a simple physics-based coarse-grained model for describing largely disordered conformational ensembles of peptides, based on the premise that sampling sterically-forbidden conformations can compromise the faithful description of both static and dynamical properties. The Hamiltonian of the employed model can be easily adjusted to investigate the impact of distinct interactions and sequence specificity on the randomness of the resulting conformational ensemble. In particular, starting with a bead-spring-like model and then adding more detailed interactions one by one, we construct a hierarchical set of models and perform a detailed comparison of their properties. Our analysis clarifies the role of generic attractions, electrostatics and side-chain sterics, while providing a foundation for developing efficient models for IDPs that retain an accurate description of the hierarchy of conformational dynamics, which is nontrivially influenced by interactions with surrounding proteins and solvent molecules.

Sequence Specificity Despite Intrinsic Disorder: How a Disease-Associated Val/Met Polymorphism Shifts Tertiary Interactions in a Long Disordered Protein

2019 ◽  
Author(s):  
Ruchi Lohia ◽  
Reza Salari ◽  
Grace Brannigan
Keyword(s):  
Secondary Structure ◽  
Electrostatic Interactions ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Sequence Specificity ◽  
Intrinsically Disordered ◽  
Specific Effects ◽  
Heterogeneous Polymer ◽  
Non Local ◽  
Dynamics Simulations

<div>The role of electrostatic interactions and mutations that change charge states in intrinsically disordered proteins (IDPs) is well-established, but many disease-associated mutations in IDPs are charge-neutral. The Val66Met single nucleotide polymorphism (SNP) encodes a hydrophobic-to-hydrophobic mutation at the midpoint of the prodomain of precursor brain-derived neurotrophic factor (BDNF), one of the earliest SNPs to be associated with neuropsychiatric disorders, for which the underlying molecular mechanism is unknown. Here we report on over 250 μs of fully-atomistic, explicit solvent, temperature replica exchange molecular dynamics simulations of the 91 residue BDNF prodomain, for both the V66 and M66 sequence.</div><div>The simulations were able to correctly reproduce the location of both local and non-local secondary changes due to the Val66Met mutation when compared with NMR spectroscopy. We find that the local structure change is mediated via entropic and sequence specific effects. We show that the highly disordered prodomain can be meaningfully divided into domains based on sequence alone. Monte Carlo simulations of a self-excluding heterogeneous polymer, with monomers representing each domain, suggest the sequence would be effectively segmented by the long, highly disordered polyampholyte near the sequence midpoint. This is qualitatively consistent with observed interdomain contacts within the BDNF prodomain, although contacts between the two segments are enriched relative to the self-excluding polymer. The Val66Met mutation increases interactions across the boundary between the two segments, due in part to a specific Met-Met interaction with a Methionine in the other segment. This effect propagates to cause the non-local change in secondary structure around the second methionine, previously observed in NMR. The effect is not mediated simply via changes in inter-domain contacts but is also dependent on secondary structure formation around residue 66, indicating a mechanism for secondary structure coupling in disordered proteins. </div>

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