Rationalization of the effects of mutations on peptide andprotein aggregation rates

By Fabrizio Chiti, M. Stefani, Niccolò Taddei, G. Ramponi, Christopher M. Dobson. Nature, 2003

1,049 citations recorded by the source corpus.

Subfields: Molecular Biology, Physiology

View Paper: https://www.semanticscholar.org/paper/495a17ad2e92a6a8d9b4f98b6067b9abab1f18cb

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In Molecular Biology

1. Yaoqi Zhou
2. Yuedong Yang
3. Zhixiu Li
4. Jian Zhan
5. Liang Dai
6. David Baker
7. Ron Jacak
8. Andrew Leaver-Fay

In Physiology

1. Murielle A. Watzky
2. Michele Vendruscolo
3. Aimee M Morris
4. Ò. Conchillo-Solé
5. Xavier Daura
6. Francesc X. Avilés
7. Salvador Ventura
8. J. Kelly

Citations in the corpus

• Improving computational protein design by using structure-derived sequence profile (2010, background): “More polar residues on the surface of the proteins for the sequences designed by RosettaDesign-SR will make them less likely to aggregate, 78 , 79 a significant problem in designed sequences.”
• Functional Amyloid Formation within Mammalian Tissue (2005, background): “We are unaware of any other protein nearly so amyloidogenic [26], consistent”
• Computational Protein Design with Explicit Consideration of Surface Hydrophobic Patches (2011, result): “In agreement with these findings, Chiti et al 8 found that the rate of aggregation of proteins and peptides increases as the amount of exposed hydrophobic surface area increases.”
• Protein aggregation kinetics, mechanism, and curve-fitting: a review of the literature. (2009, background): “It was shown that the hydrophobicity and hydrophobic/hydrophilic patterns (or the propensity to convert from α-helix to β-sheet) contributed positively to the aggregation rate (A, BN0), while charge contributed negatively (Cb0), see entry 18 of Table 2 [135].”
• Intrinsically Semi-disordered State and Its Role in Induced Folding and Protein Aggregation (2013, background): “the semi-disordered state with the models based on physicochemical properties, neural networks, and structural profiles [57–61] will likely lead to further improvement in accuracy of predicting amyloidogenic regions.”
• AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides (2007, positive): “At the same time, it has been shown that the same physicochemical principles underlie the aggregation propensities of different polypeptides from unfolded states [14].”