scholarly journals The effect of obstacles in multi-site protein target search with DNA looping

2020 ◽  
Vol 152 (2) ◽  
pp. 025101 ◽  
Author(s):  
Cayke Felipe ◽  
Jaeoh Shin ◽  
Yulia Loginova ◽  
Anatoly B. Kolomeisky
Keyword(s):  
Dna Looping ◽  
Target Search ◽  
Protein Target

DNA Looping and DNA Conformational Fluctuations Can Accelerate Protein Target Search

2021 ◽  
Vol 125 (7) ◽  
pp. 1727-1734
Author(s):  
Cayke Felipe ◽  
Jaeoh Shin ◽  
Anatoly B. Kolomeisky
Keyword(s):  
Dna Looping ◽  
Target Search ◽  
Protein Target

scholarly journals Modeling protein target search in human chromosomes

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Markus Nyberg ◽  
Tobias Ambjörnsson ◽  
Per Stenberg ◽  
Ludvig Lizana
Keyword(s):  
Human Chromosomes ◽  
Target Search ◽  
Protein Target

scholarly journals DNA Internal Motion Likely Accelerates Protein Target Search in a Packed Nucleoid

2017 ◽  
Vol 112 (11) ◽  
pp. 2261-2270 ◽  
Author(s):  
Edmond Chow ◽  
Jeffrey Skolnick
Keyword(s):  
Internal Motion ◽  
Target Search ◽  
Protein Target

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

2020 ◽  
Author(s):  
Lewis Mervin ◽  
Avid M. Afzal ◽  
Ola Engkvist ◽  
Andreas Bender
Keyword(s):  
Target Prediction ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Protein Target ◽  
Bioactivity Prediction ◽  
Vector Machines ◽  
Scaling Methods ◽  
Data Points ◽  
Compound Target

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

Binding Mechanism and Structural Insights into the Identified Protein Target of Covid-19 with In-Vitro Effective Drug Ivermectin

2020 ◽  
Author(s):  
Parth Sarthi Sen Gupta ◽  
Satyaranjan Biswal ◽  
Saroj Kumar Panda ◽  
Abhik Kumar Ray ◽  
Malay Kumar Rana
Keyword(s):  
Ternary Complex ◽  
Bound State ◽  
Molecular Target ◽  
Cooperative Interaction ◽  
Effective Drug ◽  
Rna Dependent Rna Polymerase ◽  
Protein Target ◽  
Ternary Complex Formation ◽  
Structural Insights

<p>While an FDA approved drug Ivermectin was reported to dramatically reduce the cell line of SARS-CoV-2 by ~5000 folds within 48 hours, the precise mechanism of action and the COVID-19 molecular target involved in interaction with this in-vitro effective drug are unknown yet. Among 12 different COVID-19 targets studied here, the RNA dependent RNA polymerase (RdRp) with RNA and Helicase NCB site show the strongest affinity to Ivermectin amounting -10.4 kcal/mol and -9.6 kcal/mol, respectively. Molecular dynamics of corresponding protein-drug complexes reveals that the drug bound state of RdRp with RNA has better structural stability than the Helicase NCB site, with MM/PBSA free energy of -135.2 kJ/mol, almost twice that of Helicase (-76.6 kJ/mol). The selectivity of Ivermectin to RdRp is triggered by a cooperative interaction of RNA-RdRp by ternary complex formation. Identification of the target and its interaction profile with Ivermectin can lead to more powerful drug designs for COVID-19 and experimental exploration. </p>

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