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Discrete elements for 3D microfluidics
Discrete elements for 3D microfluidics
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Discrete elements for 3D microfluidics

Bhargava, Krisna C. ; Thompson, Bryant ; Malmstadt, Noah

Proceedings of the National Academy of Sciences - PNAS, 2014-10, Vol.111 (42), p.15013-15018 [Periódico revisado por pares]

United States: National Academy of Sciences

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A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures
A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures
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A general-purpose protein design framework based on mining sequence–structure relationships in known protein structures

Zhou, Jianfu ; Panaitiu, Alexandra E. ; Grigoryan, Gevorg

Proceedings of the National Academy of Sciences - PNAS, 2020-01, Vol.117 (2), p.1059-1068 [Periódico revisado por pares]

United States: National Academy of Sciences

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3
Concern over differences between experimental design and data analyses in Jones et al./Reply to Charles V. Vorhees: Aspartame and anxiety-like behavior
Concern over differences between experimental design and data analyses in Jones et al./Reply to Charles V. Vorhees: Aspartame and anxiety-like behavior
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Concern over differences between experimental design and data analyses in Jones et al./Reply to Charles V. Vorhees: Aspartame and anxiety-like behavior

Vorhees, Charles V ; Jones, Sara K ; McCarthy, Deirdre M ; Vied, Cynthia ; Stanwood, Gregg D ; Schatschneider, Chris ; Bhide, Pradeep G

Proceedings of the National Academy of Sciences - PNAS, 2023-06, Vol.120 (24) [Periódico revisado por pares]

Washington: National Academy of Sciences

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4
Massively parallel, computationally guided design of a proenzyme
Massively parallel, computationally guided design of a proenzyme
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Massively parallel, computationally guided design of a proenzyme

Yachnin, Brahm J ; Azouz, Laura R ; White, 3rd, Ralph E ; Minetti, Conceição A S A ; Remeta, David P ; Tan, Victor M ; Drake, Justin M ; Khare, Sagar D

Proceedings of the National Academy of Sciences - PNAS, 2022-04, Vol.119 (15), p.e2116097119-e2116097119 [Periódico revisado por pares]

United States: National Academy of Sciences

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5
Iterative approach to computational enzyme design
Iterative approach to computational enzyme design
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Iterative approach to computational enzyme design

Privett, Heidi K ; Kiss, Gert ; Lee, Toni M ; Blomberg, Rebecca ; Chica, Roberto A ; Thomas, Leonard M ; Hilvert, Donald ; Houk, Kendall N ; Mayo, Stephen L

Proceedings of the National Academy of Sciences - PNAS, 2012-03, Vol.109 (10), p.3790-3795 [Periódico revisado por pares]

United States: National Academy of Sciences

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6
Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1
Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1
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Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1

Mulligan, Vikram Khipple ; Workman, Sean ; Sun, Tianjun ; Rettie, Stephen ; Li, Xinting ; Worrall, Liam J ; Craven, Timothy W ; King, Dustin T ; Hosseinzadeh, Parisa ; Watkins, Andrew M ; Renfrew, P Douglas ; Guffy, Sharon ; Labonte, Jason W ; Moretti, Rocco ; Bonneau, Richard ; Strynadka, Natalie C J ; Baker, David

Proceedings of the National Academy of Sciences - PNAS, 2021-03, Vol.118 (12) [Periódico revisado por pares]

United States: National Academy of Sciences

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7
Computational design of a symmetric homodimer using β-strand assembly
Computational design of a symmetric homodimer using β-strand assembly
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Computational design of a symmetric homodimer using β-strand assembly

Stranges, P. Benjamin ; Machius, Mischa ; Miley, Michael J ; Tripathy, Ashutosh ; Kuhlman, Brian

Proceedings of the National Academy of Sciences - PNAS, 2011-12, Vol.108 (51), p.20562-20567 [Periódico revisado por pares]

United States: National Academy of Sciences

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8
In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models
In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models
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In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models

Goudy, Odessa J ; Nallathambi, Amrita ; Kinjo, Tomoaki ; Randolph, Nicholas Z ; Kuhlman, Brian

Proceedings of the National Academy of Sciences - PNAS, 2023-12, Vol.120 (49), p.e2307371120-e2307371120 [Periódico revisado por pares]

United States: National Academy of Sciences

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9
Soft, skin-interfaced microfluidic systems with integrated immunoassays, fluorometric sensors, and impedance measurement capabilities
Soft, skin-interfaced microfluidic systems with integrated immunoassays, fluorometric sensors, and impedance measurement capabilities
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Soft, skin-interfaced microfluidic systems with integrated immunoassays, fluorometric sensors, and impedance measurement capabilities

Kim, Sungbong ; Lee, Boram ; Reeder, Jonathan T. ; Seo, Seon Hee ; Lee, Sung-Uk ; Hourlier-Fargette, Aurélie ; Shin, Joonchul ; Sekine, Yurina ; Jeong, Hyoyoung ; Oh, Yong Suk ; Aranyosi, Alexander J. ; Lee, Stephen P. ; Model, Jeffrey B. ; Lee, Geumbee ; Seo, Min-Ho ; Kwak, Sung Soo ; Jo, Seongbin ; Park, Gyungmin ; Han, Sunghyun ; Park, Inkyu ; Jung, Hyo-Il ; Ghaffari, Roozbeh ; Koo, Jahyun ; Braun, Paul V. ; Rogers, John A.

Proceedings of the National Academy of Sciences - PNAS, 2020-11, Vol.117 (45), p.27906-27915 [Periódico revisado por pares]

United States: National Academy of Sciences

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10
Effective design principles for leakless strand displacement systems
Effective design principles for leakless strand displacement systems
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Effective design principles for leakless strand displacement systems

Wang, Boya ; Thachuk, Chris ; Ellington, Andrew D. ; Winfree, Erik ; Soloveichik, David

Proceedings of the National Academy of Sciences - PNAS, 2018-12, Vol.115 (52), p.E12182-E12191 [Periódico revisado por pares]

United States: National Academy of Sciences

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