E
Eugene I. Shakhnovich
Researcher at Harvard University
Publications - 473
Citations - 26389
Eugene I. Shakhnovich is an academic researcher from Harvard University. The author has contributed to research in topics: Protein folding & Folding (chemistry). The author has an hindex of 82, co-authored 454 publications receiving 24773 citations. Previous affiliations of Eugene I. Shakhnovich include University of Pennsylvania & Russian Academy of Sciences.
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Protein homeostasis imposes a barrier on functional integration of horizontally transferred genes in bacteria
Shimon Bershtein,Adrian W.R. Serohijos,Sanchari Bhattacharyya,Michael Manhart,Jeong-Mo Choi,Wanmeng Mu,Jingwen Zhou,Eugene I. Shakhnovich +7 more
TL;DR: By apparently distinguishing between self and non-self proteins, protein homeostasis imposes an immediate and global barrier to the functional integration of foreign genes by decreasing the intracellular abundance of their products.
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Native atom types for knowledge-based potentials: application to binding energy prediction.
TL;DR: A physically motivated approach to optimizing knowledge-based potentials for binding energy prediction that can be integrated into a variety of stages within a lead discovery protocol is described.
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A tale of two tails: The importance of unstructured termini in the aggregation pathway of β2-microglobulin
Rui J. S. Loureiro,Diogo Vila-Viçosa,Miguel Machuqueiro,Eugene I. Shakhnovich,Patrícia F. N. Faísca +4 more
TL;DR: The analysis carried out here recapitulates the importance of the DE‐loop in HB2m self‐association at a neutral pH and predicts a leading role of the C‐terminus and the adjacent G‐strand in the dimerization process under acidic conditions.
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Cooperativity and Stability in a Langevin Model of Protein Folding
TL;DR: In this article, two simplified models of protein dynamics based on Langevin's equation of motion in a viscous medium are presented, and the effect of the potential energy function's symmetry on the kinetics and thermodynamics of simulated folding are explored.
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Accelerating high-throughput virtual screening through molecular pool-based active learning
TL;DR: In this paper, a surrogate structure-property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation.