Protein structure and computational drug discovery

2018 ◽  
Vol 46 (5) ◽  
pp. 1367-1379 ◽  
Author(s):  
Tracy L. Nero ◽  
Michael W. Parker ◽  
Craig J. Morton
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Protein Function ◽  
Weak Interactions ◽  
Protein Structures ◽  
Three Dimensional ◽  
Specific Protein ◽  
Enzymatic Reactions ◽  
Computer Aided ◽  
Small Molecule Ligands

The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.

Smart, Innovative and Intelligent Technologies Used in Drug Designing

2014 ◽  
pp. 285-301
Author(s):  
S. Deshpande ◽  
S. K. Basu ◽  
X. Li ◽  
X. Chen
Keyword(s):  
Drug Discovery ◽  
Protein Structures ◽  
Three Dimensional ◽  
New Drugs ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Mathematical Methods ◽  
Structure Based Drug Design ◽  
Recent Developments ◽  
Therapeutic Compounds

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

scholarly journals Smart, Innovative and Intelligent Technologies Used in Drug Designing

2017 ◽  
pp. 1175-1191
Author(s):  
S. Deshpande ◽  
S. K. Basu ◽  
X. Li ◽  
X. Chen
Keyword(s):  
Drug Discovery ◽  
Protein Structures ◽  
Three Dimensional ◽  
New Drugs ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Mathematical Methods ◽  
Structure Based Drug Design ◽  
Recent Developments ◽  
Therapeutic Compounds

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

scholarly journals The Druggable Genome as Seen from the Protein Data Bank

2020 ◽  
Author(s):  
Jiayan Wang ◽  
Setayesh Yazdani ◽  
Ana Han ◽  
Matthieu Schapira
Keyword(s):  
Human Genome ◽  
Small Molecule ◽  
Data Bank ◽  
Specific Protein ◽  
Genome Project ◽  
Protein Families ◽  
Small Molecule Ligands ◽  
Human Proteins ◽  
Small Molecule Modulators ◽  
Druggable Genome

AbstractAlmost twenty years after the human genome was sequenced, the wealth of data produced by the international human genome project has not translated into a significantly improved drug discovery enterprise. This is in part because small molecule modulators that could be used to explore the cellular function of their target proteins and to discover new therapeutic opportunities are only available for a limited portion of the human proteome. International efforts are underway to develop such chemical tools for a few, specific protein families, and a “Target 2035” call to enable, expand and federate these efforts towards a comprehensive chemical coverage of the druggable genome was recently announced. But what is the druggable genome? Here, we systematically review structures of human proteins bound to drug-like ligands available from the protein databank (PDB) and use ligand desolvation upon binding as a druggability metric to draw a landscape of the human druggable genome. We show that the vast majority of druggable protein families, including some highly populated and deeply associated with cancer according to genomic screens, are almost orphan of small molecule ligands, and propose a list of 46 druggable domains representing 3440 human proteins that could be the focus of large chemical probe discovery efforts.

scholarly journals Recent Advances in In Silico Target Fishing

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5124 ◽  
Author(s):  
Salvatore Galati ◽  
Miriana Di Stefano ◽  
Elisa Martinelli ◽  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Biological Activities ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Specific Protein ◽  
Protein Targets ◽  
Drug Candidates ◽  
Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

scholarly journals Ab Initio Modelling the Structure of Proton-Sensing G-Protein Coupled Receptor GPR151

2020 ◽  
Author(s):  
Wei Li
Keyword(s):  
Ab Initio ◽  
G Protein ◽  
Protein Function ◽  
Protein Structures ◽  
Three Dimensional ◽  
Data Bank ◽  
Dimensional Structure ◽  
G Protein Coupled Receptor ◽  
Great Excess ◽  
G Protein Coupled

Protein is the proteios building block of life. Evolutionarily, its sequence is not as conserved as its structure, making it more reasonable for protein structure, instead of protein sequence, to be the descriptor of protein function. Yet, in the National Center for Biotechnology Information (NCBI) database, the number of experimentally identified protein sequences is in great excess of that of experimentally determined protein structures inside the almost-half-a-century old Protein Data Bank (PDB). For instance, GPR151 is an proton-sensing G-protein coupled receptor (GPCR) originally identified as homologous to galanin receptors. As of March 19, 2020, GPR151’s structure has not been experimentally determined and deposited in PDB yet. Thus, an ab initio modelling approach was employed here to build a three-dimensional structure of GPR151. Overall, the ab initio GPR151 model presented herein constitutes the first structural hypothesis of GPR151 to be experimentally tested in future with previously published, currently ongoing and future GPR151 studies.

scholarly journals Assessment of AI-based Protein Structure Prediction for the NLRP3 Target

2021 ◽  
Author(s):  
Jian Yin ◽  
Jialin Yu ◽  
Weiren Cui ◽  
Junkun Lei ◽  
Wenhua Chen ◽  
...  
Keyword(s):  
Protein Structure ◽  
Drug Discovery ◽  
Small Molecule ◽  
Structure Prediction ◽  
3D Model ◽  
Protein Structures ◽  
Binding Modes ◽  
Careful Evaluation ◽  
Small Molecule Drug ◽  
Recent Success

The recent success of AlphaFold and RoseTTAFold has demonstrated the values of AI methods in predicting highly accurate protein structures. Despite the advances, their roles in the context of small-molecule drug discovery need to be thoroughly explored. In this study, we evaluated the possibility whether the AI-based models can lead to reliable 3D structures of protein-ligand complexes. With the AI-generated protein structure, we were able to confidently predict the binding modes of small-molecule inhibitors for NLRP3, a challenging protein target in terms of obtaining the 3D model both experimentally and computationally. We therefore concluded that through careful evaluation, AI-predicted protein structures can be trusted and useful for small-molecule drug discovery projects.

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