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Can Kayatekin

Researcher at Massachusetts Institute of Technology

Publications -  28
Citations -  1773

Can Kayatekin is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Protein folding & Population. The author has an hindex of 16, co-authored 26 publications receiving 1515 citations. Previous affiliations of Can Kayatekin include Sanofi S.A. & Howard Hughes Medical Institute.

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Quantitative Analysis of Hsp90-Client Interactions Reveals Principles of Substrate Recognition

TL;DR: In this paper, the authors systematically and quantitatively surveyed most human kinases, transcription factors, and E3 ligases for interaction with HSP90 and its cochaperone CDC37.
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Luminidependens (LD) is an Arabidopsis protein with prion behavior

TL;DR: The results suggest that prion-like conformational switches are evolutionarily conserved and might function in a wide variety of normal biological processes.
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Zinc binding modulates the entire folding free energy surface of human Cu,Zn superoxide dismutase.

TL;DR: The approximately 100-fold increase in the rate of folding of SOD in the presence of micromolar concentrations of zinc demonstrates a significant role for a preorganized zinc-binding loop in the transition-state ensemble for the rate-limiting monomer folding reaction in this beta-barrel protein.
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Microsecond acquisition of heterogeneous structure in the folding of a TIM barrel protein.

TL;DR: The earliest kinetic folding events for (βα)8 barrels reflect the appearance of off-pathway intermediates and ready access to locally folded, stable substructures may be a hallmark of repeat-module proteins and the source of early kinetic traps in these very common motifs.
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Microsecond Hydrophobic Collapse in the Folding of Escherichia coli Dihydrofolate Reductase, an α/β-Type Protein

TL;DR: The protein folding trajectories constructed by comparing the development of the compactness and the secondary structure suggest that the specific hydrophobic collapse model rather than the framework model better explains the experimental observations.