Literature DB >> 35600324

How to Model for a Living: The CSGF as a Catalyst for Supermodels.

M L Radhakrishnan1.   

Abstract

Models are ubiquitous and uniting tools for computational scientists across disciplines. As a computational biophysical chemist, I apply multiple models to understand and predict how molecules recognize and interact with each other in complex, dynamic biological environments. The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) cultivates interest in engaging in models from an multidisciplinary perspective and enables junior scientists to see how computational modeling is a creative and collaborative process. Below, I describe ways, based in part on my own experiences as a CSGF recipient, in which modeling can be used both to understand the molecular world and to excite others about computational science.

Entities:  

Year:  2021        PMID: 35600324      PMCID: PMC9119097          DOI: 10.1109/mcse.2021.3119764

Source DB:  PubMed          Journal:  Comput Sci Eng        ISSN: 1521-9615            Impact factor:   2.152


  9 in total

1.  Barstar is electrostatically optimized for tight binding to barnase.

Authors:  L P Lee; B Tidor
Journal:  Nat Struct Biol       Date:  2001-01

2.  Specificity in molecular design: a physical framework for probing the determinants of binding specificity and promiscuity in a biological environment.

Authors:  Mala L Radhakrishnan; Bruce Tidor
Journal:  J Phys Chem B       Date:  2007-11-03       Impact factor: 2.991

3.  Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems.

Authors:  Mala L Radhakrishnan; Bruce Tidor
Journal:  J Chem Inf Model       Date:  2008-05-27       Impact factor: 4.956

4.  Macromolecular crowding effects on electrostatic binding affinity: Fundamental insights from theoretical, idealized models.

Authors:  Rachel Kim; Mala L Radhakrishnan
Journal:  J Chem Phys       Date:  2021-06-14       Impact factor: 3.488

5.  Computationally Modeling Electrostatic Binding Energetics in a Crowded, Dynamic Environment: Physical Insights from a Peptide-DNA System.

Authors:  Carla P Perez; Donald E Elmore; Mala L Radhakrishnan
Journal:  J Phys Chem B       Date:  2019-12-10       Impact factor: 2.991

6.  Multiple drugs and multiple targets: an analysis of the electrostatic determinants of binding between non-nucleoside HIV-1 reverse transcriptase inhibitors and variants of HIV-1 RT.

Authors:  Mona S Minkara; Pamela H Davis; Mala L Radhakrishnan
Journal:  Proteins       Date:  2011-11-17

7.  Analysis of fast boundary-integral approximations for modeling electrostatic contributions of molecular binding.

Authors:  Amelia B Kreienkamp; Lucy Y Liu; Mona S Minkara; Matthew G Knepley; Jaydeep P Bardhan; Mala L Radhakrishnan
Journal:  Mol Based Math Biol       Date:  2013-06

8.  A dual receptor crosstalk model of G-protein-coupled signal transduction.

Authors:  Patrick Flaherty; Mala L Radhakrishnan; Tuan Dinh; Robert A Rebres; Tamara I Roach; Michael I Jordan; Adam P Arkin
Journal:  PLoS Comput Biol       Date:  2008-09-26       Impact factor: 4.475

9.  The effect of macromolecular crowding on the electrostatic component of barnase-barstar binding: a computational, implicit solvent-based study.

Authors:  Helena W Qi; Priyanka Nakka; Connie Chen; Mala L Radhakrishnan
Journal:  PLoS One       Date:  2014-06-10       Impact factor: 3.240

  9 in total

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