Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1016/j.crmeth.2021.100014 http://scihub22266oqcxt.onion/10.1016/j.crmeth.2021.100014 34355210!8336924!34355210     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cell+Rep+Methods 2021 ; 1 (3): ä Nephropedia Template TP {{tp|p=34355210|t=2021. Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations |pdf=|usr=}}{{34355210}} gab.com Text Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations JO: Cell Rep Methods http://www.kidney.de/pget.php?&tw=1&pmid=34355210 #FOAvip Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction. Twit Text FOAVip Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations ????Cell Rep Methods http://www.kidney.de/pget.php?&tw=1&pmid=34355210 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34355210 #FOAvip English Wikipedia <ref>{{Cite pmid|34355210}}</ref> Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations #MMPMID34355210 Zheng W; Zhang C; Li Y; Pearce R; Bell EW; Zhang Y Cell Rep Methods 2021[Jul]; 1 (3): ä PMID34355210show ga Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction. |*COVID-19[MESH] |*Deep Learning[MESH] |Algorithms[MESH] |Computational Biology/methods[MESH] |Humans[MESH] |Models, Molecular[MESH] |Protein Conformation[MESH] |Proteins/genetics[MESH]Folding non-homologous proteins by coupling deep-learning contact maps(epmc).pdf DeepDyve Pubget Overpricing