The Impact of Structural Proteomics on Drug Design

2008 ◽  
pp. 347-360
Author(s):  
Yuan-Ping Pang
Keyword(s):  
Drug Design ◽  
Structural Proteomics ◽  
The Impact

THE IMPACT OF STRUCTURAL PROTEOMICS ON BIOTECHNOLOGY

2012 ◽  
pp. 353-370
Author(s):  
Babu A. Manjasetty ◽  
Andrew P. Turnbull ◽  
Santosh Panjikar
Keyword(s):  
Structural Proteomics ◽  
The Impact

scholarly journals The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design

2020 ◽  
Author(s):  
Oleg Y. Borbulevych ◽  
Roger I. Martin ◽  
Lance M. Westerhoff
Keyword(s):  
Drug Design ◽  
Critical Role ◽  
Pharmaceutical Research ◽  
Energy Functional ◽  
Structural Refinement ◽  
Structure Based Drug Design ◽  
X Ray ◽  
X Ray Crystallography ◽  
Refinement Method ◽  
The Impact

Abstract Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule—along with any bound ligand(s)—within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method—coupled with our XModeScore method for experimental tautomer/protomer state determination—to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

The impact of genomics on drug design

1996 ◽  
Vol 1 (11) ◽  
pp. 474-480 ◽  
Author(s):  
Lee J. Beeley ◽  
D. Malcolm Duckworth
Keyword(s):  
Drug Design ◽  
The Impact

scholarly journals The Effects of Combinatorial Chemistry and Technologies on Drug Discovery and Biotechnology – a Mini Review

2014 ◽  
Vol 13 (2) ◽  
pp. 87-108 ◽  
Author(s):  
Pierfausto Seneci ◽  
Giorgio Fassina ◽  
Vladimir Frecer ◽  
Stanislav Miertus
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Expert Opinion ◽  
High Throughput ◽  
Combinatorial Chemistry ◽  
Rational Design ◽  
Combinatorial Libraries ◽  
Computer Assisted ◽  
Future Trends ◽  
The Impact

Abstract The review will focus on the aspects of combinatorial chemistry and technologies that are more relevant in the modern pharmaceutical process. An historical, critical introduction is followed by three chapters, dealing with the use of combinatorial chemistry/high throughput synthesis in medicinal chemistry; the rational design of combinatorial libraries using computer-assisted combinatorial drug design; and the use of combinatorial technologies in biotechnology. The impact of “combinatorial thinking” in drug discovery in general, and in the examples reported in details, is critically discussed. Finally, an expert opinion on current and future trends in combinatorial chemistry and combinatorial technologies is provided.

scholarly journals Data-Driven Molecular Dynamics: A Multifaceted Challenge

2020 ◽  
Vol 13 (9) ◽  
pp. 253
Author(s):  
Mattia Bernetti ◽  
Martina Bertazzo ◽  
Matteo Masetti
Keyword(s):  
Molecular Dynamics ◽  
Big Data ◽  
Drug Discovery ◽  
Drug Design ◽  
Md Simulations ◽  
Biomolecular Systems ◽  
Biomolecular Simulations ◽  
The Impact ◽  
Opening Up ◽  
Computational Drug Discovery

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

scholarly journals The impact of Structural Proteomics on Biotechnology

2009 ◽  
Vol 26 (1) ◽  
pp. 353-370 ◽  
Author(s):  
Babu A. Manjasetty ◽  
Andrew P. Turnbull ◽  
Santosh Panjikar
Keyword(s):  
Structural Proteomics ◽  
The Impact
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