Literature DB >> 28475332

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery.

Francesca Spyrakis1, Mostafa H Ahmed2, Alexander S Bayden3, Pietro Cozzini4, Andrea Mozzarelli5,6, Glen E Kellogg2.   

Abstract

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

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Year:  2017        PMID: 28475332     DOI: 10.1021/acs.jmedchem.7b00057

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  31 in total

1.  Computing Ligands Bound to Proteins Using MELD-Accelerated MD.

Authors:  Cong Liu; Emiliano Brini; Alberto Perez; Ken A Dill
Journal:  J Chem Theory Comput       Date:  2020-09-23       Impact factor: 6.006

Review 2.  An outlook on using serial femtosecond crystallography in drug discovery.

Authors:  Alexey Mishin; Anastasiia Gusach; Aleksandra Luginina; Egor Marin; Valentin Borshchevskiy; Vadim Cherezov
Journal:  Expert Opin Drug Discov       Date:  2019-06-11       Impact factor: 6.098

3.  Utilizing Grand Canonical Monte Carlo Methods in Drug Discovery.

Authors:  Michael S Bodnarchuk; Martin J Packer; Alexe Haywood
Journal:  ACS Med Chem Lett       Date:  2019-12-11       Impact factor: 4.345

4.  Discovering New Casein Kinase 1d Inhibitors with an Innovative Molecular Dynamics Enabled Virtual Screening Workflow.

Authors:  Simone Sciabola; Paolo Benedetti; Giulia D'Arrigo; Rubben Torella; Massimo Baroni; Gabriele Cruciani; Francesca Spyrakis
Journal:  ACS Med Chem Lett       Date:  2018-12-13       Impact factor: 4.345

5.  Inhibitors of the M2 Proton Channel Engage and Disrupt Transmembrane Networks of Hydrogen-Bonded Waters.

Authors:  Jessica L Thomaston; Nicholas F Polizzi; Athina Konstantinidi; Jun Wang; Antonios Kolocouris; William F DeGrado
Journal:  J Am Chem Soc       Date:  2018-09-12       Impact factor: 15.419

6.  Pyridinylimidazoles as GSK3β Inhibitors: The Impact of Tautomerism on Compound Activity via Water Networks.

Authors:  Fabian Heider; Tatu Pantsar; Mark Kudolo; Francesco Ansideri; Angela De Simone; Letizia Pruccoli; Taiane Schneider; Marcia Inês Goettert; Andrea Tarozzi; Vincenza Andrisano; Stefan A Laufer; Pierre Koch
Journal:  ACS Med Chem Lett       Date:  2019-08-26       Impact factor: 4.345

7.  Outliers in SAR and QSAR: 3. Importance of considering the role of water molecules in protein-ligand interactions and quantitative structure-activity relationship studies.

Authors:  Ki Hwan Kim
Journal:  J Comput Aided Mol Des       Date:  2021-03-13       Impact factor: 3.686

Review 8.  Novel insights in linking solvent relaxation dynamics and protein conformations utilizing red edge excitation shift approach.

Authors:  Rupasree Brahma; H Raghuraman
Journal:  Emerg Top Life Sci       Date:  2021-05-14

Review 9.  Water in protein hydration and ligand recognition.

Authors:  Manuela Maurer; Chris Oostenbrink
Journal:  J Mol Recognit       Date:  2019-08-27       Impact factor: 2.891

10.  Prediction of n-octanol/water partition coefficients and acidity constants (pKa) in the SAMPL7 blind challenge with the IEFPCM-MST model.

Authors:  Antonio Viayna; Silvana Pinheiro; Carles Curutchet; F Javier Luque; William J Zamora
Journal:  J Comput Aided Mol Des       Date:  2021-07-10       Impact factor: 3.686

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