Cargando…

Predicting Protein Folds with Fold-Specific PSSM Libraries

Accurately assigning folds for divergent protein sequences is a major obstacle to structural studies. Herein, we outline an effective method for fold recognition using sets of PSSMs, each of which is constructed for different protein folds. Our analyses demonstrate that FSL (Fold-specific Position S...

Descripción completa

Detalles Bibliográficos
Autores principales: Hong, Yoojin, Chintapalli, Sree Vamsee, Ko, Kyung Dae, Bhardwaj, Gaurav, Zhang, Zhenhai, van Rossum, Damian, Patterson, Randen L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116844/
https://www.ncbi.nlm.nih.gov/pubmed/21698189
http://dx.doi.org/10.1371/journal.pone.0020557
_version_ 1782206286735081472
author Hong, Yoojin
Chintapalli, Sree Vamsee
Ko, Kyung Dae
Bhardwaj, Gaurav
Zhang, Zhenhai
van Rossum, Damian
Patterson, Randen L.
author_facet Hong, Yoojin
Chintapalli, Sree Vamsee
Ko, Kyung Dae
Bhardwaj, Gaurav
Zhang, Zhenhai
van Rossum, Damian
Patterson, Randen L.
author_sort Hong, Yoojin
collection PubMed
description Accurately assigning folds for divergent protein sequences is a major obstacle to structural studies. Herein, we outline an effective method for fold recognition using sets of PSSMs, each of which is constructed for different protein folds. Our analyses demonstrate that FSL (Fold-specific Position Specific Scoring Matrix Libraries) can predict/relate structures given only their amino acid sequences of highly divergent proteins. This ability to detect distant relationships is dependent on low-identity sequence alignments obtained from FSL. Results from our experiments demonstrate that FSL perform well in recognizing folds from the “twilight-zone” SABmark dataset. Further, this method is capable of accurate fold prediction in newly determined structures. We suggest that by building complete PSSM libraries for all unique folds within the Protein Database (PDB), FSL can be used to rapidly and reliably annotate a large subset of protein folds at proteomic level. The related programs and fold-specific PSSMs for our FSL are publicly available at: http://ccp.psu.edu/download/FSLv1.0/.
format Online
Article
Text
id pubmed-3116844
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-31168442011-06-22 Predicting Protein Folds with Fold-Specific PSSM Libraries Hong, Yoojin Chintapalli, Sree Vamsee Ko, Kyung Dae Bhardwaj, Gaurav Zhang, Zhenhai van Rossum, Damian Patterson, Randen L. PLoS One Research Article Accurately assigning folds for divergent protein sequences is a major obstacle to structural studies. Herein, we outline an effective method for fold recognition using sets of PSSMs, each of which is constructed for different protein folds. Our analyses demonstrate that FSL (Fold-specific Position Specific Scoring Matrix Libraries) can predict/relate structures given only their amino acid sequences of highly divergent proteins. This ability to detect distant relationships is dependent on low-identity sequence alignments obtained from FSL. Results from our experiments demonstrate that FSL perform well in recognizing folds from the “twilight-zone” SABmark dataset. Further, this method is capable of accurate fold prediction in newly determined structures. We suggest that by building complete PSSM libraries for all unique folds within the Protein Database (PDB), FSL can be used to rapidly and reliably annotate a large subset of protein folds at proteomic level. The related programs and fold-specific PSSMs for our FSL are publicly available at: http://ccp.psu.edu/download/FSLv1.0/. Public Library of Science 2011-06-16 /pmc/articles/PMC3116844/ /pubmed/21698189 http://dx.doi.org/10.1371/journal.pone.0020557 Text en Hong et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Hong, Yoojin
Chintapalli, Sree Vamsee
Ko, Kyung Dae
Bhardwaj, Gaurav
Zhang, Zhenhai
van Rossum, Damian
Patterson, Randen L.
Predicting Protein Folds with Fold-Specific PSSM Libraries
title Predicting Protein Folds with Fold-Specific PSSM Libraries
title_full Predicting Protein Folds with Fold-Specific PSSM Libraries
title_fullStr Predicting Protein Folds with Fold-Specific PSSM Libraries
title_full_unstemmed Predicting Protein Folds with Fold-Specific PSSM Libraries
title_short Predicting Protein Folds with Fold-Specific PSSM Libraries
title_sort predicting protein folds with fold-specific pssm libraries
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116844/
https://www.ncbi.nlm.nih.gov/pubmed/21698189
http://dx.doi.org/10.1371/journal.pone.0020557
work_keys_str_mv AT hongyoojin predictingproteinfoldswithfoldspecificpssmlibraries
AT chintapallisreevamsee predictingproteinfoldswithfoldspecificpssmlibraries
AT kokyungdae predictingproteinfoldswithfoldspecificpssmlibraries
AT bhardwajgaurav predictingproteinfoldswithfoldspecificpssmlibraries
AT zhangzhenhai predictingproteinfoldswithfoldspecificpssmlibraries
AT vanrossumdamian predictingproteinfoldswithfoldspecificpssmlibraries
AT pattersonrandenl predictingproteinfoldswithfoldspecificpssmlibraries