Literature DB >> 26831093

Global informatics and physical property selection in protein sequences.

Harold A Scheraga1, S Rackovsky2.   

Abstract

The degree of informatic independence between the physical properties of amino acids as encoded in actual protein sequences is calculated. It is shown that no physical property can be identified that carries significantly less information than others and that the information overlap between different properties and different length scales along the sequence is essentially zero. These observations suggest that bioinformatic models based on arbitrarily selected sets of physical properties are inherently deficient.

Keywords:  Fourier analysis; information theory; physical properties; protein bioinformatics

Mesh:

Substances:

Year:  2016        PMID: 26831093      PMCID: PMC4763726          DOI: 10.1073/pnas.1525745113

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  10 in total

1.  Global characteristics of protein sequences and their implications.

Authors:  S Rackovsky
Journal:  Proc Natl Acad Sci U S A       Date:  2010-04-26       Impact factor: 11.205

2.  Nonlinearities in protein space limit the utility of informatics in protein biophysics.

Authors:  S Rackovsky
Journal:  Proteins       Date:  2015-09-10

3.  Characterization of architecture signals in proteins.

Authors:  S Rackovsky
Journal:  J Phys Chem B       Date:  2006-09-28       Impact factor: 2.991

4.  Sequence determinants of protein architecture.

Authors:  S Rackovsky
Journal:  Proteins       Date:  2013-08-13

5.  Sequence physical properties encode the global organization of protein structure space.

Authors:  S Rackovsky
Journal:  Proc Natl Acad Sci U S A       Date:  2009-08-12       Impact factor: 11.205

6.  "Hidden" sequence periodicities and protein architecture.

Authors:  S Rackovsky
Journal:  Proc Natl Acad Sci U S A       Date:  1998-07-21       Impact factor: 11.205

7.  Homolog detection using global sequence properties suggests an alternate view of structural encoding in protein sequences.

Authors:  Harold A Scheraga; S Rackovsky
Journal:  Proc Natl Acad Sci U S A       Date:  2014-03-24       Impact factor: 11.205

8.  On the nature of the protein folding code.

Authors:  S Rackovsky
Journal:  Proc Natl Acad Sci U S A       Date:  1993-01-15       Impact factor: 11.205

9.  ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins.

Authors:  Yasser B Ruiz-Blanco; Waldo Paz; James Green; Yovani Marrero-Ponce
Journal:  BMC Bioinformatics       Date:  2015-05-16       Impact factor: 3.169

10.  CATH: comprehensive structural and functional annotations for genome sequences.

Authors:  Ian Sillitoe; Tony E Lewis; Alison Cuff; Sayoni Das; Paul Ashford; Natalie L Dawson; Nicholas Furnham; Roman A Laskowski; David Lee; Jonathan G Lees; Sonja Lehtinen; Romain A Studer; Janet Thornton; Christine A Orengo
Journal:  Nucleic Acids Res       Date:  2014-10-27       Impact factor: 19.160

  10 in total
  6 in total

1.  ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins.

Authors:  Sandra Romero-Molina; Yasser B Ruiz-Blanco; James R Green; Elsa Sanchez-Garcia
Journal:  Protein Sci       Date:  2019-09       Impact factor: 6.725

2.  Sequence-specific dynamic information in proteins.

Authors:  H A Scheraga; S Rackovsky
Journal:  Proteins       Date:  2019-06-11

3.  Sequence-, structure-, and dynamics-based comparisons of structurally homologous CheY-like proteins.

Authors:  Yi He; Gia G Maisuradze; Yanping Yin; Khatuna Kachlishvili; S Rackovsky; Harold A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  2017-01-31       Impact factor: 11.205

4.  The structure of protein dynamic space.

Authors:  S Rackovsky; Harold A Scheraga
Journal:  Proc Natl Acad Sci U S A       Date:  2020-08-05       Impact factor: 11.205

5.  Dynamic and conformational switching in proteins.

Authors:  H A Scheraga; S Rackovsky
Journal:  Biopolymers       Date:  2020-12-03       Impact factor: 2.505

6.  Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.

Authors:  Sutanu Nandi; Piyali Ganguli; Ram Rup Sarkar
Journal:  PLoS One       Date:  2020-11-30       Impact factor: 3.240

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.