We are analyzing https://link.springer.com/article/10.1186/1471-2105-4-52.

Title:
LEAping to conclusions: A computational reanalysis of late embryogenesis abundant proteins and their possible roles | BMC Bioinformatics
Description:
Background The late embryogenesis abundant (LEA) proteins cover a number of loosely related groups of proteins, originally found in plants but now being found in non-plant species. Their precise function is unknown, though considerable evidence suggests that LEA proteins are involved in desiccation resistance. Using a number of statistically-based bioinformatics tools the classification of a large set of LEA proteins, covering all Groups, is reexamined together with some previous findings. Searches based on peptide composition return proteins with similar composition to different LEA Groups; keyword clustering is then applied to reveal keywords and phrases suggestive of the Groups
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Keywords {🔍}

lea, group, proteins, protein, groups, sequence, table, google, scholar, article, sequences, found, pubmed, stress, superfamily, cas, expression, plant, cold, set, number, highly, significant, amino, class, learning, legoshi, acid, popp, content, related, desiccation, consensus, evidence, based, structure, peptides, keywords, popps, full, machine, application, cluster, motif, families, rules, values, data, composition, clustering,

Topics {✒️}

/research/compbio/sam_t02/t02-query emphasising alpha-helical structure high alpha-helical content alpha-helical antifreeze protein uk/~phiwww/prof/] karplus late embryogenesis abundant uk/software/emboss/] rost machine-learning algorithms-supervised web-based application takes supervised machine-learning rules open access license literally late embryogenesis full access secondary structure prediction poly-lysine stutters totalling unsupervised machine learning unsupervised machine-learning protein annotators' assistant abscisic acid-modulated genes article download pdf minimising inter-cluster scores predicted secondary structure ignoring low-complexity domains statistically-based bioinformatics tools sam-t02[http protein structure prediction secondary structure predictors article wise possibly coiled-coil structures privacy choices/manage cookies galau ga binomial distribution statistic machine learning methods balance accuracy/correct-predictions stress-induced gene expression uncharacterised lea proteins low complexity protein machine learning applied combining local-structure predicted loop content supervised learning application hidden markov model supervised classification experiments high helix content low complexity regions high sequence complexity pfam family pf02987 low-complexity proteins low complexity proteins canonical lea protein

Schema {🗺️}

WebPage:
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         headline:LEAping to conclusions: A computational reanalysis of late embryogenesis abundant proteins and their possible roles
         description:The late embryogenesis abundant (LEA) proteins cover a number of loosely related groups of proteins, originally found in plants but now being found in non-plant species. Their precise function is unknown, though considerable evidence suggests that LEA proteins are involved in desiccation resistance. Using a number of statistically-based bioinformatics tools the classification of a large set of LEA proteins, covering all Groups, is reexamined together with some previous findings. Searches based on peptide composition return proteins with similar composition to different LEA Groups; keyword clustering is then applied to reveal keywords and phrases suggestive of the Groups' properties. Previous research has suggested that glycine is characteristic of LEA proteins, but it is only highly over-represented in Groups 1 and 2, while alanine, thought characteristic of Group 2, is over-represented in Group 3, 4 and 6 but under-represented in Groups 1 and 2. However, for LEA Groups 1 2 and 3 it is shown that glutamine is very significantly over-represented, while cysteine, phenylalanine, isoleucine, leucine and tryptophan are significantly under-represented. There is also evidence that the Group 4 LEA proteins are more appropriately redistributed to Group 2 and Group 3. Similarly, Group 5 is better found among the Group 3 LEA proteins. There is evidence that Group 2 and Group 3 LEA proteins, though distinct, might be related. This relationship is also evident in the overlapping sets of keywords for the two Groups, emphasising alpha-helical structure and, at a larger scale, filaments, all of which fits well with experimental evidence that proteins from both Groups are natively unstructured, but become structured under stress conditions. The keywords support localisation of LEA proteins both in the nucleus and associated with the cytoskeleton, and a mode of action similar to chaperones, perhaps the cold shock chaperones, via a role in DNA-binding. In general, non-globular and low-complexity proteins, such as the LEA proteins, pose particular challenges in determining their functions and modes of action. Rather than masking off and ignoring low-complexity domains, novel tools and tool combinations are needed which are capable of analysing such proteins in their entirety.
         datePublished:2003-10-29T00:00:00Z
         dateModified:2003-10-29T00:00:00Z
         pageStart:1
         pageEnd:19
         sameAs:https://doi.org/10.1186/1471-2105-4-52
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            Late Embryogenesis Abundant
            Late Embryogenesis Abundant Protein
            Pfam Family
            Unsupervised Machine Learning
            Bioinformatics
            Microarrays
            Computational Biology/Bioinformatics
            Computer Appl. in Life Sciences
            Algorithms
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ScholarlyArticle:
      headline:LEAping to conclusions: A computational reanalysis of late embryogenesis abundant proteins and their possible roles
      description:The late embryogenesis abundant (LEA) proteins cover a number of loosely related groups of proteins, originally found in plants but now being found in non-plant species. Their precise function is unknown, though considerable evidence suggests that LEA proteins are involved in desiccation resistance. Using a number of statistically-based bioinformatics tools the classification of a large set of LEA proteins, covering all Groups, is reexamined together with some previous findings. Searches based on peptide composition return proteins with similar composition to different LEA Groups; keyword clustering is then applied to reveal keywords and phrases suggestive of the Groups' properties. Previous research has suggested that glycine is characteristic of LEA proteins, but it is only highly over-represented in Groups 1 and 2, while alanine, thought characteristic of Group 2, is over-represented in Group 3, 4 and 6 but under-represented in Groups 1 and 2. However, for LEA Groups 1 2 and 3 it is shown that glutamine is very significantly over-represented, while cysteine, phenylalanine, isoleucine, leucine and tryptophan are significantly under-represented. There is also evidence that the Group 4 LEA proteins are more appropriately redistributed to Group 2 and Group 3. Similarly, Group 5 is better found among the Group 3 LEA proteins. There is evidence that Group 2 and Group 3 LEA proteins, though distinct, might be related. This relationship is also evident in the overlapping sets of keywords for the two Groups, emphasising alpha-helical structure and, at a larger scale, filaments, all of which fits well with experimental evidence that proteins from both Groups are natively unstructured, but become structured under stress conditions. The keywords support localisation of LEA proteins both in the nucleus and associated with the cytoskeleton, and a mode of action similar to chaperones, perhaps the cold shock chaperones, via a role in DNA-binding. In general, non-globular and low-complexity proteins, such as the LEA proteins, pose particular challenges in determining their functions and modes of action. Rather than masking off and ignoring low-complexity domains, novel tools and tool combinations are needed which are capable of analysing such proteins in their entirety.
      datePublished:2003-10-29T00:00:00Z
      dateModified:2003-10-29T00:00:00Z
      pageStart:1
      pageEnd:19
      sameAs:https://doi.org/10.1186/1471-2105-4-52
      keywords:
         Cold Stress
         Late Embryogenesis Abundant
         Late Embryogenesis Abundant Protein
         Pfam Family
         Unsupervised Machine Learning
         Bioinformatics
         Microarrays
         Computational Biology/Bioinformatics
         Computer Appl. in Life Sciences
         Algorithms
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         issn:
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            affiliation:
                  name:Department of Genetics Cambridge University Cambridge U.K
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                     name:Department of Genetics Cambridge University Cambridge U.K, UK
                     type:PostalAddress
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PostalAddress:
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