Zobrazeno 1 - 10
of 36
pro vyhledávání: '"M. Kandathil"'
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract The AlphaFold Protein Structure Database, containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the database is prec
Externí odkaz:
https://doaj.org/article/f8022b5cbb9142df8d7dd4df27731493
Autor:
George M. Kandathil
This monograph narrates the decade-long struggle of workers, unions, and management in transforming one of the largest ailing family-owned jute businesses in India, into a sustainable worker-owned and governed cooperative. It focuses on the variation
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Prediction of protein structures on the scale of genomes remains a challenge. Here the authors introduce a protein structure prediction method that uses deep learning to predict inter-atomic distances, torsion angles and hydrogen bonds, and apply it
Externí odkaz:
https://doaj.org/article/ccdc53de641e49ecb1dcddc994cc5c94
The AlphaFold Protein Structure Database (AFDB), containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the information within
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::061d438d38177771fa0dd77c172ca3d8
https://doi.org/10.1101/2023.02.19.529114
https://doi.org/10.1101/2023.02.19.529114
Publikováno v:
Nature Reviews Molecular Cell Biology. 23:40-55
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models
Publikováno v:
Biomolecules, Vol 9, Iss 10, p 612 (2019)
Our previous work with fragment-assembly methods has demonstrated specific deficiencies in conformational sampling behaviour that, when addressed through improved sampling algorithms, can lead to more reliable prediction of tertiary protein structure
Externí odkaz:
https://doaj.org/article/568aaa3f81874cfdb5bd924fc8a873ec
The design of novel protein sequences is providing paths towards the development of novel therapeutics and materials. At the forefront is the challenging field of de novo protein design, which looks to design protein sequences unlike those found in n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::87a2120847d7519c5c4cba2f5ce3edc2
https://doi.org/10.1101/2022.01.27.478087
https://doi.org/10.1101/2022.01.27.478087
Publikováno v:
PNAS
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Significance We present a deep learning-based predictor of protein tertiary structure that uses only a multiple sequence alignment (MSA) as input. To date, most emphasis has been on the accuracy of such deep learning methods, but here we show that ac
Publikováno v:
Nature reviews. Molecular cell biology. 23(1)
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Nature Communications
Nature Communications
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even f