Link Your Sites (LYS) Scripts: Automated Search of Protein Structures and Mapping of Sites Under Positive Selection Detected by PAML

Autor: Rute R. da Fonseca, Lys Sanz Moreta

Publikováno v: Evolutionary Biology. 47:240-245

The visualization of the molecular context of an amino acid mutation in a protein structure is crucial for the assessment of its functional impact and to understand its evolutionary implications. Currently, searches for fast evolving amino acid posit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d73bd75380c13af72b5223c5010d960
https://doi.org/10.1007/s11692-020-09507-9

Bayesian protein superposition using Hamiltonian Monte Carlo

Autor: Thomas Hamelryck, Ahmad Salim Al-Sibahi, Lys Sanz Moreta

Publikováno v: BIBE

Optimally superimposing protein structures is essential to study their structure, function, dynamics and evolution. We present THESEUS NUTS (No U-Turn Sampler), a Bayesian version of the THESEUS model [1] –[3] which relies on maximum likelihood est

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2e9aa342ef7e2d9f2401e5e9ea2ee1c0
https://doi.org/10.1109/bibe50027.2020.00009

Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model

Autor: Anders Bundgård Sørensen, Christian S. Steenmanns, Lys Sanz Moreta, Christian B. Thygesen, Thomas Hamelryck, Ahmad Salim Al-Sibahi

Publikováno v: University of Copenhagen
Thygesen, C B, Al-Sibahi, A S, Steenmanns, C S, Sanz Moreta, L, Sørensen, A B & Hamelryck, T W 2021, Efficient Generative Modelling of Protein Structure Fragments using a Deep Markov Model . in International Conference on Machine Learning, 18-24 July 2021, Virtual . PMLR, Proceedings of Machine Learning Research, vol. 139, pp. 10258-10267, 38th International Conference on Machine Learning, 18/07/2021 . < https://proceedings.mlr.press/v139/thygesen21a.html >

Fragment libraries are often used in protein structure prediction, simulation and design as a means to significantly reduce the vast conformational search space. Current state-of-the-art methods for fragment library generation do not properly account

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f92320426b7f08ca592d192186f8c42e
https://curis.ku.dk/portal/en/publications/efficient-generative-modelling-of-protein-structure-fragments-using-a-deep-markov-model(8a5efeb8-a3bf-48a3-bd47-1577e4464536).html