ProtMD: Incorporating Conformation Flexibility for Drug Binding via Pretraining on Molecular Dynamics Simulations | Fang Wu
Molecular Modelling and Drug Discovery26 Tammi 2023

ProtMD: Incorporating Conformation Flexibility for Drug Binding via Pretraining on Molecular Dynamics Simulations | Fang Wu

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.

Also, consider joining the M2D2 Slack.

Abstract: The latest biological findings observe that the motionless lock-and-key theory is no longer applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. We present a novel spatial-temporal pre-training method based on the modified Equivariant Graph Matching Networks (EGMN), dubbed ProtMD. It has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside MD trajectories with very fine temporal resolutions. More importantly, we investigate the underlying mechanism behind the success of ProtMD, and further demonstrate a tight correlation between the magnitude of spatial motion of conformation and the extent to which the ligand and the receptor bind with each other.

Speaker: Fang Wu

Twitter - Prudencio

Twitter - Therence

Twitter - Jonny

Twitter - Valence Discovery

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(60)

Structure-Independent Peptide Binder Design via Generative Language Models | Pranam Chatterjee

Structure-Independent Peptide Binder Design via Generative Language Models | Pranam Chatterjee

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplifie...

20 Kesä 20231h

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics | Albert Musaelian

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics | Albert Musaelian

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplif...

13 Kesä 20231h 9min

Multimodal Deep Learning for Protein Engineering | Kevin K. Yang

Multimodal Deep Learning for Protein Engineering | Kevin K. Yang

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplifie...

7 Kesä 20231h 2min

Systematic Analysis of Biomolecular Conformational Ensembles with PENSA | Martin Vögele

Systematic Analysis of Biomolecular Conformational Ensembles with PENSA | Martin Vögele

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplifies ...

30 Touko 202350min

Training Neural Network Potentials: Bayesian and Simulation-based Approaches | Stephan Thaler

Training Neural Network Potentials: Bayesian and Simulation-based Approaches | Stephan Thaler

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplifies mo...

16 Touko 20231h 3min

Accelerating Cryptic Pocket Discovery Using Alphafold and Markov State Modelling | Soumendranath Bhakat

Accelerating Cryptic Pocket Discovery Using Alphafold and Markov State Modelling | Soumendranath Bhakat

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplifies mole...

9 Touko 202331min

Machine Learning Molecules | Gianni De Fabritiis

Machine Learning Molecules | Gianni De Fabritiis

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplifies mole...

25 Huhti 202357min

Protein Representation Learning by Geometric Structure Pretraining | Zuobai Zhang

Protein Representation Learning by Geometric Structure Pretraining | Zuobai Zhang

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplifies molecu...

19 Huhti 202353min

Suosittua kategoriassa Tiede

rss-mita-tulisi-tietaa
rss-hereilla
rss-poliisin-mieli
tiedekulma-podcast
docemilia
radio-antro
rss-duodecim-lehti
utelias-mieli
filocast-filosofian-perusteet
rss-radplus
rss-tiedetta-vai-tarinaa
rss-opeklubi
rss-lihavuudesta-podcast
rss-kasvikutsut
rss-totuuden-liepeilla