Computational Models for the Study of Protein Aggregation

Co, Nguyen Truong; Li, Mai Suan; Krupa, Paweł

doi:10.1007/978-1-0716-1546-1_4

Cited by 4 publications

(4 citation statements)

References 218 publications

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“…Molecular-simulation methods have long been used to study peptide aggregation, including both all-atom [ 16 , 17 , 18 ] and coarse-grained (CG) methodologies [ 19 ]. Of those, the methods based on CG models offer much longer time- and size-scales, including the possibility of simulating aggregation from scratch, even though this extension is achieved at the inevitable expense of modeling accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

et al. 2022

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The UNited RESidue (UNRES) model of polypeptide chains was applied to study the association of 20 peptides with sizes ranging from 6 to 32 amino-acid residues. Twelve of those were potentially aggregating hexa- or heptapeptides excised from larger proteins, while the remaining eight contained potentially aggregating sequences, functionalized by attaching larger ends rich in charged residues. For 13 peptides, the experimental data of aggregation were used. The remaining seven were synthesized, and their properties were measured in this work. Multiplexed replica-exchange simulations of eight-chain systems were conducted at 12 temperatures from 260 to 370 K at concentrations from 0.421 to 5.78 mM, corresponding to the experimental conditions. The temperature profiles of the fractions of monomers and octamers showed a clear transition corresponding to aggregate dissociation. Low simulated transition temperatures were obtained for the peptides, which did not precipitate after incubation, as well as for the H-GNNQQNY-NH2 prion–protein fragment, which forms small fibrils. A substantial amount of inter-strand β-sheets was found in most of the systems. The results suggest that UNRES simulations can be used to assess peptide aggregation except for glutamine- and asparagine-rich peptides, for which a revision of the UNRES sidechain–sidechain interaction potentials appears necessary.

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Section: Introductionmentioning

confidence: 99%

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

et al. 2022

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“…While MD simulation based on the all-atom models has the advantage of analyzing phenomena at the atomic level, it is computationally time-consuming. Since protein aggregation simulations are particularly computationally demanding, simulation studies using implicit solvent models, such as the GB/SA model [196][197][198], and coarse-grained models, such as the AWSEM [199], MARTINI [200,201], and UNRES force fields [202,203], are also being conducted [204,205]. The implicit solvent models used to be employed often [33,34,51,96] but now are not often used for all-atom simulations because it is known that the interaction between water and solutes plays an important role in the aggregation [206,207] and disaggregation of the amyloid fibrils [80,84].…”

Section: Discussionmentioning

confidence: 99%

Frontiers in Protein Folding and Related Areas – in Memory of Professor Sir Christopher M. Dobson (1949–2019)

2023

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“…It has been experimentally demonstrated that the residues with higher β-sheet propensities impart positive effect on aggregate formation in proteins (Chiti and Dobson, 2006;Chiti and Dobson, 2009;Co, et al, 2022;Cukalevski, et al, 2012;Ebo, et al, 2020;Hartl, 2017). It is also reported that sometimes the unstable α-helices assumes β-sheet confirmation as an effect of change in environment (O'Donnell, et al, 2011;Ono and Watanabe-Nakayama, 2021;Smith and Shell, 2017).…”

Section: Secondary Structural Compatibility (Css) Score For β-Sheet P...mentioning

confidence: 99%

“…Various mathematical and machine learning approaches, including position-specific scoring matrices approach, Bayesian classifier and weighted decision tree approach, statistical mechanics algorithm, etc., are being implemented on the knowledge derived from protein sequence and structural properties to identify and score aggregation prone regions in proteins. Some of these properties include the extent of hydrophobicity and residue composition of small segments, residue pair preferences, β-strand propensity, and solvent accessibility/burial of amino acid residues in protein structures (Beerten, et al, 2015; Co, et al, 2022; Conchillo-Solé, et al, 2007;Fernandez-Escamilla, et al, 2004; Garbuzynskiy, et al, 2010; O’Donnell, et al, 2011; Smith and Shell, 2017; Walsh, et al, 2014; Zhang, et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Decoding Protein Aggregation through Computational Approach: Identification and Scoring of Aggregation-Prone Regions in Protein Sequences

Kaushik,

Launey

2024

Preprint

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Protein aggregation is a critical phenomenon associated with numerous neurodegenerative and systemic diseases. Understanding the propensity of proteins to aggregate is essential for unraveling the molecular basis of these disorders and for design and engineering of novel proteins or modulating the activity/stability of enzymatic proteins. Here, we present APR-Score, a novel machine-learning based computational method designed to identify aggregation-prone regions within protein sequences. ARP-Score leverages a combination of sequence-based features to predict regions of proteins that are prone to aggregate. The APR-Score harnessed the information ingrained in the compiled sequence and structural features to provide state-of-the-art accuracy. The APR-Score is assessed by conducting rigorous cross-validation experiments on the training dataset and further validated on an independent test dataset. The APR-Score prediction models demonstrated robustness and reliability in discriminating aggregation-prone regions from non-aggregating ones on an independent dataset, achieving Mathew’s correlation coefficient (MCC) 0.81, precision 0.89, and F1-Score 0.91. The APR-Score offers a valuable tool for researchers investigating protein aggregation-related diseases, as it can expedite the identification of aggregation-prone regions, aiding in the development of targeted therapies and diagnostic tools. The computational protein design and engineering regimes can be facilitated through APR-Score based identification and screening of aggregation prone protein sequences.

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Computational Models for the Study of Protein Aggregation

Cited by 4 publications

References 218 publications

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

Frontiers in Protein Folding and Related Areas – in Memory of Professor Sir Christopher M. Dobson (1949–2019)

Decoding Protein Aggregation through Computational Approach: Identification and Scoring of Aggregation-Prone Regions in Protein Sequences

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