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Bruce Tidor
Researcher at Massachusetts Institute of Technology
Publications - 180
Citations - 19350
Bruce Tidor is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Protease & Binding site. The author has an hindex of 60, co-authored 179 publications receiving 17680 citations. Previous affiliations of Bruce Tidor include National University of Singapore & Harvard University.
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Book ChapterDOI
Towards a multi-scale model of cartilage: coarse-graining glycosaminoglycans
TL;DR: In this paper, a coarse-grained model for the computation of structural and thermodynamic properties of anionic glycosaminoglycans, the molecular building blocks of aggrecan, is presented.
Journal ArticleDOI
Entropy of Two-Molecule Correlated Translational-Rotational Motions Using the kth Nearest Neighbor Method.
TL;DR: The entropy associated with rotations, translations, and their coupled motions provides an important contribution to the free energy of many physicochemical processes such as association and solvatization.
Journal ArticleDOI
X-ray structural and simulation analysis of a protein mutant: The value of a combined approach
TL;DR: Comparisons between the experimental and calculated values for the difference between the free energy of denaturation of the mutant and the wild type show the importance of the combined use of simulations and crystallography for interpreting the effects of mutations on the energetics of the system.
Substrate Envelope-Designed Potent HIV-1 Protease Inhibitors to Avoid Drug Resistance
Madhavi N. L. Nalam,Akbar Ali,G. S. Kiran Kumar Reddy,Hong Cao,Saima Ghafoor Anjum,Michael D. Altman,Nese Kurt Yilmaz,Bruce Tidor,Tariq M. Rana,Celia A. Schiffer +9 more
TL;DR: In this paper, the authors designed highly potent HIV-1 protease inhibitors using the substrate envelope model, which confines inhibitors within the consensus volume of natural substrates, providing inhibitors less susceptible to resistance because a mutation affecting such inhibitors will simultaneously affect viral substrate processing.
Journal ArticleDOI
Cellular level models as tools for cytokine design
TL;DR: A simple kinetic model is abstracted that captures relevant features from cytokine systems as well as related growth factor systems and provides insight into how binding kinetics affect ligand potency, and reveals the basis for tensions among a number of different network characteristics.