scholarly journals Physical, Modular and Articulated Interface for Interactive Molecular Manipulation

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5415
Author(s):  
Bastien Vincke ◽  
Mohamed Anis Ghaoui ◽  
Nicolas Férey ◽  
Xavier Martinez
Keyword(s):  
Degrees Of Freedom ◽  
Direct Interaction ◽  
Rational Drug Design ◽  
Embedded Sensors ◽  
Detailed Knowledge ◽  
Interactive Tools ◽  
Innovative Methodology ◽  
Rational Drug ◽  
Molecular Manipulation ◽  
Interaction Approach

Rational drug design is an approach based on detailed knowledge of molecular interactions and dynamic of bio-molecules. This approach involves designing new digital and interactive tools including classical desktop interaction devices as well as advanced ones such as haptic arms or virtual reality devices. These approaches however struggle to deal with flexibility of bio-molecules by simultaneously steering the numerous degrees of freedom. We propose a new method that follows a direct interaction approach by implementing an innovative methodology benefiting from a physical, modular and articulated molecular interface augmented by wireless embedded sensors. The goal is to create, design and steer its in silico twin virtual model and better interact with dynamic molecular models.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

The Influence of Loading Position in A Priori High Stress Detection using Mode Superposition

2020 ◽  
Vol 1 (1) ◽  
pp. 93-102
Author(s):  
Carsten Strzalka ◽  
◽  
Manfred Zehn ◽  
Keyword(s):  
Finite Element ◽  
Degrees Of Freedom ◽  
Equations Of Motion ◽  
A Priori ◽  
High Stress ◽  
Von Mises Stress ◽  
Detailed Knowledge ◽  
Variable Loading ◽  
Numerical Effort ◽  
Von Mises

For the analysis of structural components, the finite element method (FEM) has become the most widely applied tool for numerical stress- and subsequent durability analyses. In industrial application advanced FE-models result in high numbers of degrees of freedom, making dynamic analyses time-consuming and expensive. As detailed finite element models are necessary for accurate stress results, the resulting data and connected numerical effort from dynamic stress analysis can be high. For the reduction of that effort, sophisticated methods have been developed to limit numerical calculations and processing of data to only small fractions of the global model. Therefore, detailed knowledge of the position of a component’s highly stressed areas is of great advantage for any present or subsequent analysis steps. In this paper an efficient method for the a priori detection of highly stressed areas of force-excited components is presented, based on modal stress superposition. As the component’s dynamic response and corresponding stress is always a function of its excitation, special attention is paid to the influence of the loading position. Based on the frequency domain solution of the modally decoupled equations of motion, a coefficient for a priori weighted superposition of modal von Mises stress fields is developed and validated on a simply supported cantilever beam structure with variable loading positions. The proposed approach is then applied to a simplified industrial model of a twist beam rear axle.

Rational Drug Design Approach of Receptor Tyrosine Kinase Type III Inhibitors

2020 ◽  
Vol 26 (42) ◽  
pp. 7623-7640 ◽  
Author(s):  
Cheolhee Kim ◽  
Eunae Kim
Keyword(s):  
Drug Design ◽  
Tyrosine Kinase ◽  
Receptor Tyrosine Kinase ◽  
Synergistic Effects ◽  
Rational Drug Design ◽  
Computational Techniques ◽  
Biological Targets ◽  
Type Iii ◽  
Theoretical Approaches ◽  
Rational Drug

: Rational drug design is accomplished through the complementary use of structural biology and computational biology of biological macromolecules involved in disease pathology. Most of the known theoretical approaches for drug design are based on knowledge of the biological targets to which the drug binds. This approach can be used to design drug molecules that restore the balance of the signaling pathway by inhibiting or stimulating biological targets by molecular modeling procedures as well as by molecular dynamics simulations. Type III receptor tyrosine kinase affects most of the fundamental cellular processes including cell cycle, cell migration, cell metabolism, and survival, as well as cell proliferation and differentiation. Many inhibitors of successful rational drug design show that some computational techniques can be combined to achieve synergistic effects.

Recent Advances in the Rational Drug Design Based on Multi-target Ligands

2020 ◽  
Vol 27 (28) ◽  
pp. 4720-4740 ◽  
Author(s):  
Ting Yang ◽  
Xin Sui ◽  
Bing Yu ◽  
Youqing Shen ◽  
Hailin Cong
Keyword(s):  
Drug Resistance ◽  
Clinical Practice ◽  
Side Effects ◽  
Drug Design ◽  
Rational Drug Design ◽  
Health Conditions ◽  
Pharmacokinetic Parameters ◽  
Single Target ◽  
Rational Drug ◽  
Huge Challenge

Multi-target drugs have gained considerable attention in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance. Single-target drugs, although highly selective, may not necessarily have better efficacy or fewer side effects. Therefore, more attention is being paid to developing drugs that work on multiple targets at the same time, but developing such drugs is a huge challenge for medicinal chemists. Each target must have sufficient activity and have sufficiently characterized pharmacokinetic parameters. Multi-target drugs, which have long been known and effectively used in clinical practice, are briefly discussed in the present article. In addition, in this review, we will discuss the possible applications of multi-target ligands to guide the repositioning of prospective drugs.

Rational Drug Design Paradigms: The Odyssey for Designing Better Drugs

2015 ◽  
Vol 18 (3) ◽  
pp. 238-256 ◽  
Author(s):  
Tahsin Kellici ◽  
Dimitrios Ntountaniotis ◽  
Eleni Vrontaki ◽  
George Liapakis ◽  
Panagiota Moutevelis-Minakakis ◽  
...  
Keyword(s):  
Drug Design ◽  
Rational Drug Design ◽  
Rational Drug

scholarly journals Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

2021 ◽  
Author(s):  
Teresa Danielle Bergazin ◽  
Nicolas Tielker ◽  
Yingying Zhang ◽  
Junjun Mao ◽  
M. R. Gunner ◽  
...  
Keyword(s):  
Free Energy ◽  
Partition Coefficients ◽  
Physical Property ◽  
Rational Drug Design ◽  
Log P ◽  
Prediction Methods ◽  
Research Groups ◽  
Reasonable Accuracy ◽  
Statistical Assessment ◽  
Rational Drug

AbstractThe Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

scholarly journals Direct Interaction of Polar Scaffolding Protein Wag31 with Nucleoid-Associated Protein Rv3852 Regulates Its Polar Localization

Cells ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1558
Author(s):  
Rajni Garg ◽  
Chinmay Anand ◽  
Sohini Ganguly ◽  
Sandhya Rao ◽  
Rinkee Verma ◽  
...  
Keyword(s):  
Cell Wall ◽  
Cell Envelope ◽  
Transmembrane Helix ◽  
Ectopic Expression ◽  
Direct Interaction ◽  
Fine Tuning ◽  
Scaffolding Protein ◽  
Physical Interaction ◽  
Cell Wall Synthesis ◽  
Interaction Approach

Rv3852 is a unique nucleoid-associated protein (NAP) found exclusively in Mycobacterium tuberculosis (Mtb) and closely related species. Although annotated as H-NS, we showed previously that it is very different from H-NS in its properties and is distinct from other NAPs, anchoring to cell membrane by virtue of possessing a C-terminal transmembrane helix. Here, we investigated the role of Rv3852 in Mtb in organizing architecture or synthesis machinery of cell wall by protein–protein interaction approach. We demonstrated a direct physical interaction of Rv3852 with Wag31, an important cell shape and cell wall integrity determinant essential in Mtb. Wag31 localizes to the cell poles and possibly acts as a scaffold for cell wall synthesis proteins, resulting in polar cell growth in Mtb. Ectopic expression of Rv3852 in M. smegmatis resulted in its interaction with Wag31 orthologue DivIVAMsm. Binding of the NAP to Wag31 appears to be necessary for fine-tuning Wag31 localization to the cell poles, enabling complex cell wall synthesis in Mtb. In Rv3852 knockout background, Wag31 is mislocalized resulting in disturbed nascent peptidoglycan synthesis, suggesting that the NAP acts as a driver for localization of Wag31 to the cell poles. While this novel association between these two proteins presents one of the mechanisms to structure the elaborate multi-layered cell envelope of Mtb, it also exemplifies a new function for a NAP in mycobacteria.

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