scholarly journals Multi omics by LC-MS/MS to search small molecule ligands of nuclear receptors to control transcription of pharmaceutical active proteins for drug discovery

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Zenzaburo Tozuka ◽  
Akihiro Kunisawa
Keyword(s):  
Drug Discovery ◽  
Nuclear Receptors ◽  
Small Molecule ◽  
Small Molecule Ligands

scholarly journals Distribution patterns of small-molecule ligands in the protein universe and implications for origin of life and drug discovery

2007 ◽  
Vol 8 (8) ◽  
pp. R176 ◽  
Author(s):  
Hong-Fang Ji ◽  
De-Xin Kong ◽  
Liang Shen ◽  
Ling-Ling Chen ◽  
Bin-Guang Ma ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Origin Of Life ◽  
Small Molecule ◽  
Distribution Patterns ◽  
Small Molecule Ligands ◽  
Protein Universe

scholarly journals Augmented base pairing networks encode RNA-small molecule binding preferences

2019 ◽  
Author(s):  
Carlos Oliver ◽  
Vincent Mallet ◽  
Roman Sarrazin Gendron ◽  
Vladimir Reinharz ◽  
William L. Hamilton ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Binding Sites ◽  
Rna Binding ◽  
Graph Representation ◽  
Learning Approaches ◽  
Base Pairing ◽  
Binding Prediction ◽  
Structural Patterns ◽  
Small Molecule Ligands

AbstractMotivationThe binding of small molecules to RNAs is an important mechanism which can stabilize 3D structures or activate key molecular functions. To date, computational and experimental efforts toward small molecule binding prediction have primarily focused on protein targets. Considering that a very large portion of the genome is transcribed into non-coding RNAs but only few regions are translated into proteins, successful annotations of RNA elements targeted by small-molecule would likely uncover a vast repertoire of biological pathways and possibly lead to new therapeutic avenues.ResultsOur work is a first attempt at bringing machine learning approaches to the problem of RNA drug discovery. RNAmigos takes advantage of the unique structural properties of RNA to predict small molecule ligands for unseen binding sites. A key feature of our model is an efficient representation of binding sites as augmented base pairing networks (ABPNs) aimed at encoding important structural patterns. We subject our ligand predictions to two virtual screen settings and show that we are able to rank the known ligand on average in the 73rd percentile, showing a significant improvement over several baselines. Furthermore, we observe that graphs which are augmented with non-Watson Crick (a.k.a non-canonical) base pairs are the only representation which is able to retrieve a significant signal, suggesting that non-canonical interactions are an necessary source of binding specificity in RNAs. We also find that an auxiliary graph representation task significantly boosts performance by providing efficient structural embeddings to the low data setting of ligand prediction. RNAmigos shows that RNA binding data contains structural patterns with potential for drug discovery, and provides methodological insights which can be applied to other structure-function learning tasks.AvailabilityCode and data is freely available at http://csb.cs.mcgill.ca/[email protected]

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.

Quantitative Ranking of Ligand Binding Kinetics with a Multiscale Milestoning Simulation Approach

2018 ◽  
Author(s):  
Benjamin R. Jagger ◽  
Christoper T. Lee ◽  
Rommie Amaro
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Small Molecule ◽  
Brownian Dynamics ◽  
Model Simulation ◽  
Computational Cost ◽  
Lead Selection ◽  
Delta G ◽  
Dynamics Simulations

<p>The ranking of small molecule binders by their kinetic (kon and koff) and thermodynamic (delta G) properties can be a valuable metric for lead selection and optimization in a drug discovery campaign, as these quantities are often indicators of in vivo efficacy. Efficient and accurate predictions of these quantities can aid the in drug discovery effort, acting as a screening step. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict kon’s, koff’s, and G’s. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, -cyclodextrin, based on predicted kon’s and koff’s. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long-timescale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by koff and G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish “best practices” for its future use.</p>

Growing by Sharing - Story of Ryvu and Selvita

2020 ◽  
Vol 7 (1) ◽  
pp. 33-47
Author(s):  
Magdalena Marciniak
Keyword(s):  
Drug Discovery ◽  
Business Model ◽  
Small Molecule ◽  
Contract Research ◽  
Industrial Enterprises ◽  
Strong Position ◽  
Small Molecule Therapeutics

Ryvu Therapeutics and Selvita originated in 2007, a time when drug discovery in Poland was still not pursued by industrial enterprises. For many years, both entities operated one company and were known under a common name Selvita S.A., combining their efforts on both innovative small-molecule therapeutics for oncology and expertise in Contract Research Services (CRO). Following more than a decade of such a hybrid business model, Selvita established a strong position in the field of drug discovery and built trust among partners, clients, and investors globally. This encouraged the leaders of the company to separate the two divisions into fully autonomous units, which in fact, had already been operating quite independently and both were successful in diverse areas of drug discovery activities. At the beginning of October 2019, two new companies were established and both parts were given independence and more opportunities for growth. Discovery and development engine was named as Ryvu Therapeutics, and the CRO part of the company remained with the name Selvita. To reach this stage, both the divisions went through an interesting journey together, supporting and strengthening each other for the benefit of both.

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