scholarly journals Molecular insights into the improved clinical performance of PEGylated interferon therapeutics: a molecular dynamics perspective

RSC Advances ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 2315-2322 ◽  
Author(s):  
Dong Xu ◽  
Nikolai Smolin ◽  
Rance K. Shaw ◽  
Samuel R. Battey ◽  
Aoxiang Tao ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Drug Design ◽  
Therapeutic Efficacy ◽  
Clinical Performance ◽  
Pegylated Interferon ◽  
Computational Approach ◽  
Molecular Evidence ◽  
Protein Drug ◽  
Pegylated Protein

We discovered molecular evidence that links PEGylation to improved clinical performance, yet at the expense of decreased bioactivity. Our computational approach will facilitate PEGylated protein drug design and optimize its overall therapeutic efficacy.

Molecular dynamics in drug design

2015 ◽  
Vol 91 ◽  
pp. 4-14 ◽  
Author(s):  
Hongtao Zhao ◽  
Amedeo Caflisch
Keyword(s):  
Molecular Dynamics ◽  
Drug Design

scholarly journals In vitro and In vivo Molecular Evidence for Better Therapeutic Efficacy of ABT-627 and Taxotere Combination in Prostate Cancer

2007 ◽  
Vol 67 (8) ◽  
pp. 3818-3826 ◽  
Author(s):  
Sanjeev Banerjee ◽  
Maha Hussain ◽  
Zhiwei Wang ◽  
Allen Saliganan ◽  
Mingxin Che ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Therapeutic Efficacy ◽  
Molecular Evidence

A computational study on cannabinoid receptors and potent bioactive cannabinoid ligands: homology modeling, docking, de novo drug design and molecular dynamics analysis

2009 ◽  
Vol 14 (2) ◽  
pp. 257-276 ◽  
Author(s):  
Serdar Durdagi ◽  
Manthos G. Papadopoulos ◽  
Panagiotis G. Zoumpoulakis ◽  
Catherine Koukoulitsa ◽  
Thomas Mavromoustakos
Keyword(s):  
Molecular Dynamics ◽  
Drug Design ◽  
Homology Modeling ◽  
Cannabinoid Receptors ◽  
De Novo ◽  
Computational Study ◽  
Dynamics Analysis ◽  
De Novo Drug Design

Epitopes based drug design for dengue virus envelope protein: A computational approach

2017 ◽  
Vol 71 ◽  
pp. 152-160 ◽  
Author(s):  
Abdul Wadood ◽  
Aamir Mehmood ◽  
Huma Khan ◽  
Muhammad Ilyas ◽  
Ayaz Ahmad ◽  
...  
Keyword(s):  
Drug Design ◽  
Dengue Virus ◽  
Envelope Protein ◽  
Protein A ◽  
Computational Approach ◽  
Virus Envelope ◽  
Virus Envelope Protein

scholarly journals Rethinking protein drug design with highly accurate structure prediction of anti-CRISPR proteins

2021 ◽  
Author(s):  
Ho-min Park ◽  
Yunseol Park ◽  
Joris Vankerschaver ◽  
Arnout Van Messem ◽  
Wesley De Neve ◽  
...  
Keyword(s):  
Protein Structure ◽  
Drug Design ◽  
Structure Prediction ◽  
Modern Medicine ◽  
Protein Therapeutics ◽  
Protein Drug ◽  
Host Proteins ◽  
Small Proteins ◽  
Small Molecule Therapeutics ◽  
Host Parasite

Protein therapeutics play an important role in controlling the functions and activities of disease-causing proteins in modern medicine. Despite protein therapeutics having several advantages over traditional small-molecule therapeutics, further development has been hindered by drug complexity and delivery issues. However, recent progress in deep learning-based protein structure prediction approaches such as AlphaFold opens new opportunities to exploit the complexity of these macro-biomolecules for highly-specialised design to inhibit, regulate or even manipulate specific disease-causing proteins. Anti-CRISPR proteins are small proteins from bacteriophages that counter-defend against the prokaryotic adaptive immunity of CRISPR-Cas systems. They are unique examples of natural protein therapeutics that have been optimized by the host-parasite evolutionary arms race to inhibit a wide variety of host proteins. Here, we show that these Anti-CRISPR proteins display diverse inhibition mechanisms through accurate structural prediction and functional analysis. We find that these phage-derived proteins are extremely distinct in structure, some of which have no homologues in the current protein structure domain. Furthermore, we find a novel family of Anti-CRISPR proteins which are structurally homologous to the recently-discovered mechanism of manipulating host proteins through enzymatic activity, rather than through direct inference. Using highly accurate structure prediction, we present a wide variety of protein-manipulating strategies of anti-CRISPR proteins for future protein drug design.

scholarly journals Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

2021 ◽  
Vol 14 (10) ◽  
pp. 1062
Author(s):  
Tomasz Róg ◽  
Mykhailo Girych ◽  
Alex Bunker
Keyword(s):  
Molecular Dynamics ◽  
Drug Design ◽  
Active Site ◽  
Lipid Membranes ◽  
Md Simulation ◽  
Lipid Membrane ◽  
Pharmaceutical Research ◽  
Specific Protein ◽  
Design Tool ◽  
Drug Molecules

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard “lock and key” paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

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