PMC:7278709 / 59555-61621 JSON TXT

Annnotations TAB JSON ListView MergeView

LitCovid_Glycan-Motif-Structure

{"project":"LitCovid_Glycan-Motif-Structure","denotations":[{"id":"T105","span":{"begin":347,"end":358},"obj":"https://glytoucan.org/Structures/Glycans/G81533KY"},{"id":"T106","span":{"begin":1468,"end":1479},"obj":"https://glytoucan.org/Structures/Glycans/G81533KY"},{"id":"T107","span":{"begin":1526,"end":1537},"obj":"https://glytoucan.org/Structures/Glycans/G81533KY"},{"id":"T108","span":{"begin":1701,"end":1713},"obj":"https://glytoucan.org/Structures/Glycans/G81533KY"}],"text":" A simple predictive model was developed based on optimized parameters. If a simpler method works as well as a more rigorous approach, it can sometimes provide insight and help build even better rigorous approaches. The method used in the present case was initially based on the GOR method [20] for protein secondary structure prediction in which sialic acid glycan binding state of residues replaced α-helix, β-sheet, and coil (or loop) states of residues. However, it was found that the results were essentially reproduced by a simpler model. This is primarily because the directional effects (in terms of N-terminal direction or C-terminal direction along the amino acid residue sequence) was found to be equivalent (i.e., symmetrical) and to persist for some 8 residues in both directions, very like the influence of alanine on α-helix formation in the GOR method [20]. Consequently the final method used in the present study consisted of just two changes to that used in previous Results Section 4.3 above, the second being described in point (vii) below. The qualitative scores as parameters to types of amino acid residue as shown in Table 1 were based on visual observations by the present author and a survey of sites in the literature, and this was improved by optimization on essentially the same set of proteins examined. The 20 amino acids were initially assigned the qualitative parameters of Table 1, then these 20 parameters were optimized to optimize sialic acid glycan binding predictions as positive for 20 sialic acid glycan binding proteins, and negative for 10 proteins not considered as binding sugars, and 10 proteins such as lectins that bind other sugars that do not contain sialic acids. This gave as before glycine (G), alanine (A) aspartate (D), asparagine (N), serine (S), threonine (T), cystine/cysteine (C) each with a score of 1, and the rest assigned 0, except for tyrosine (Y phenylalanine (F) and histidine (H) that were now assigned larger parameter values of 2 and tryptophan (W) that was assigned a larger parameter value of 4."}

LitCovid-PMC-OGER-BB

{"project":"LitCovid-PMC-OGER-BB","denotations":[{"id":"T1104","span":{"begin":11,"end":58},"obj":"GO:0005840"},{"id":"T1105","span":{"begin":380,"end":382},"obj":"PR:000012269"},{"id":"T1106","span":{"begin":634,"end":655},"obj":"SO:0001114"},{"id":"T1107","span":{"begin":804,"end":856},"obj":"CHEBI:26707;CHEBI:26707"},{"id":"T1108","span":{"begin":1048,"end":1082},"obj":"CHEBI:17234;CHEBI:17234"},{"id":"T1109","span":{"begin":1353,"end":1374},"obj":"SO:0000417"},{"id":"T1110","span":{"begin":1496,"end":1552},"obj":"GO:0065007"}],"text":" A simple predictive model was developed based on optimized parameters. If a simpler method works as well as a more rigorous approach, it can sometimes provide insight and help build even better rigorous approaches. The method used in the present case was initially based on the GOR method [20] for protein secondary structure prediction in which sialic acid glycan binding state of residues replaced α-helix, β-sheet, and coil (or loop) states of residues. However, it was found that the results were essentially reproduced by a simpler model. This is primarily because the directional effects (in terms of N-terminal direction or C-terminal direction along the amino acid residue sequence) was found to be equivalent (i.e., symmetrical) and to persist for some 8 residues in both directions, very like the influence of alanine on α-helix formation in the GOR method [20]. Consequently the final method used in the present study consisted of just two changes to that used in previous Results Section 4.3 above, the second being described in point (vii) below. The qualitative scores as parameters to types of amino acid residue as shown in Table 1 were based on visual observations by the present author and a survey of sites in the literature, and this was improved by optimization on essentially the same set of proteins examined. The 20 amino acids were initially assigned the qualitative parameters of Table 1, then these 20 parameters were optimized to optimize sialic acid glycan binding predictions as positive for 20 sialic acid glycan binding proteins, and negative for 10 proteins not considered as binding sugars, and 10 proteins such as lectins that bind other sugars that do not contain sialic acids. This gave as before glycine (G), alanine (A) aspartate (D), asparagine (N), serine (S), threonine (T), cystine/cysteine (C) each with a score of 1, and the rest assigned 0, except for tyrosine (Y phenylalanine (F) and histidine (H) that were now assigned larger parameter values of 2 and tryptophan (W) that was assigned a larger parameter value of 4."}

LitCovid-PD-FMA-UBERON

LitCovid-PD-UBERON

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T25","span":{"begin":403,"end":408},"obj":"Body_part"},{"id":"T26","span":{"begin":834,"end":839},"obj":"Body_part"}],"attributes":[{"id":"A25","pred":"uberon_id","subj":"T25","obj":"http://purl.obolibrary.org/obo/UBERON_0002488"},{"id":"A26","pred":"uberon_id","subj":"T26","obj":"http://purl.obolibrary.org/obo/UBERON_0002488"}],"text":" A simple predictive model was developed based on optimized parameters. If a simpler method works as well as a more rigorous approach, it can sometimes provide insight and help build even better rigorous approaches. The method used in the present case was initially based on the GOR method [20] for protein secondary structure prediction in which sialic acid glycan binding state of residues replaced α-helix, β-sheet, and coil (or loop) states of residues. However, it was found that the results were essentially reproduced by a simpler model. This is primarily because the directional effects (in terms of N-terminal direction or C-terminal direction along the amino acid residue sequence) was found to be equivalent (i.e., symmetrical) and to persist for some 8 residues in both directions, very like the influence of alanine on α-helix formation in the GOR method [20]. Consequently the final method used in the present study consisted of just two changes to that used in previous Results Section 4.3 above, the second being described in point (vii) below. The qualitative scores as parameters to types of amino acid residue as shown in Table 1 were based on visual observations by the present author and a survey of sites in the literature, and this was improved by optimization on essentially the same set of proteins examined. The 20 amino acids were initially assigned the qualitative parameters of Table 1, then these 20 parameters were optimized to optimize sialic acid glycan binding predictions as positive for 20 sialic acid glycan binding proteins, and negative for 10 proteins not considered as binding sugars, and 10 proteins such as lectins that bind other sugars that do not contain sialic acids. This gave as before glycine (G), alanine (A) aspartate (D), asparagine (N), serine (S), threonine (T), cystine/cysteine (C) each with a score of 1, and the rest assigned 0, except for tyrosine (Y phenylalanine (F) and histidine (H) that were now assigned larger parameter values of 2 and tryptophan (W) that was assigned a larger parameter value of 4."}

LitCovid-PD-CLO

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T562","span":{"begin":1,"end":2},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T563","span":{"begin":75,"end":76},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T564","span":{"begin":109,"end":110},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T565","span":{"begin":528,"end":529},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T566","span":{"begin":663,"end":681},"obj":"http://purl.obolibrary.org/obo/CHEBI_33708"},{"id":"T567","span":{"begin":663,"end":681},"obj":"http://purl.obolibrary.org/obo/PR_000036907"},{"id":"T568","span":{"begin":1110,"end":1128},"obj":"http://purl.obolibrary.org/obo/CHEBI_33708"},{"id":"T569","span":{"begin":1110,"end":1128},"obj":"http://purl.obolibrary.org/obo/PR_000036907"},{"id":"T570","span":{"begin":1209,"end":1210},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T571","span":{"begin":1757,"end":1758},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T572","span":{"begin":1849,"end":1850},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T573","span":{"begin":2036,"end":2037},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":" A simple predictive model was developed based on optimized parameters. If a simpler method works as well as a more rigorous approach, it can sometimes provide insight and help build even better rigorous approaches. The method used in the present case was initially based on the GOR method [20] for protein secondary structure prediction in which sialic acid glycan binding state of residues replaced α-helix, β-sheet, and coil (or loop) states of residues. However, it was found that the results were essentially reproduced by a simpler model. This is primarily because the directional effects (in terms of N-terminal direction or C-terminal direction along the amino acid residue sequence) was found to be equivalent (i.e., symmetrical) and to persist for some 8 residues in both directions, very like the influence of alanine on α-helix formation in the GOR method [20]. Consequently the final method used in the present study consisted of just two changes to that used in previous Results Section 4.3 above, the second being described in point (vii) below. The qualitative scores as parameters to types of amino acid residue as shown in Table 1 were based on visual observations by the present author and a survey of sites in the literature, and this was improved by optimization on essentially the same set of proteins examined. The 20 amino acids were initially assigned the qualitative parameters of Table 1, then these 20 parameters were optimized to optimize sialic acid glycan binding predictions as positive for 20 sialic acid glycan binding proteins, and negative for 10 proteins not considered as binding sugars, and 10 proteins such as lectins that bind other sugars that do not contain sialic acids. This gave as before glycine (G), alanine (A) aspartate (D), asparagine (N), serine (S), threonine (T), cystine/cysteine (C) each with a score of 1, and the rest assigned 0, except for tyrosine (Y phenylalanine (F) and histidine (H) that were now assigned larger parameter values of 2 and tryptophan (W) that was assigned a larger parameter value of 4."}

LitCovid-PD-CHEBI

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T27","span":{"begin":840,"end":849},"obj":"http://purl.obolibrary.org/obo/GO_0009058"}],"text":" A simple predictive model was developed based on optimized parameters. If a simpler method works as well as a more rigorous approach, it can sometimes provide insight and help build even better rigorous approaches. The method used in the present case was initially based on the GOR method [20] for protein secondary structure prediction in which sialic acid glycan binding state of residues replaced α-helix, β-sheet, and coil (or loop) states of residues. However, it was found that the results were essentially reproduced by a simpler model. This is primarily because the directional effects (in terms of N-terminal direction or C-terminal direction along the amino acid residue sequence) was found to be equivalent (i.e., symmetrical) and to persist for some 8 residues in both directions, very like the influence of alanine on α-helix formation in the GOR method [20]. Consequently the final method used in the present study consisted of just two changes to that used in previous Results Section 4.3 above, the second being described in point (vii) below. The qualitative scores as parameters to types of amino acid residue as shown in Table 1 were based on visual observations by the present author and a survey of sites in the literature, and this was improved by optimization on essentially the same set of proteins examined. The 20 amino acids were initially assigned the qualitative parameters of Table 1, then these 20 parameters were optimized to optimize sialic acid glycan binding predictions as positive for 20 sialic acid glycan binding proteins, and negative for 10 proteins not considered as binding sugars, and 10 proteins such as lectins that bind other sugars that do not contain sialic acids. This gave as before glycine (G), alanine (A) aspartate (D), asparagine (N), serine (S), threonine (T), cystine/cysteine (C) each with a score of 1, and the rest assigned 0, except for tyrosine (Y phenylalanine (F) and histidine (H) that were now assigned larger parameter values of 2 and tryptophan (W) that was assigned a larger parameter value of 4."}

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T355","span":{"begin":72,"end":215},"obj":"Sentence"},{"id":"T356","span":{"begin":216,"end":457},"obj":"Sentence"},{"id":"T357","span":{"begin":458,"end":544},"obj":"Sentence"},{"id":"T358","span":{"begin":545,"end":873},"obj":"Sentence"},{"id":"T359","span":{"begin":874,"end":1060},"obj":"Sentence"},{"id":"T360","span":{"begin":1061,"end":1333},"obj":"Sentence"},{"id":"T361","span":{"begin":1334,"end":1714},"obj":"Sentence"},{"id":"T362","span":{"begin":1715,"end":2066},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":" A simple predictive model was developed based on optimized parameters. If a simpler method works as well as a more rigorous approach, it can sometimes provide insight and help build even better rigorous approaches. The method used in the present case was initially based on the GOR method [20] for protein secondary structure prediction in which sialic acid glycan binding state of residues replaced α-helix, β-sheet, and coil (or loop) states of residues. However, it was found that the results were essentially reproduced by a simpler model. This is primarily because the directional effects (in terms of N-terminal direction or C-terminal direction along the amino acid residue sequence) was found to be equivalent (i.e., symmetrical) and to persist for some 8 residues in both directions, very like the influence of alanine on α-helix formation in the GOR method [20]. Consequently the final method used in the present study consisted of just two changes to that used in previous Results Section 4.3 above, the second being described in point (vii) below. The qualitative scores as parameters to types of amino acid residue as shown in Table 1 were based on visual observations by the present author and a survey of sites in the literature, and this was improved by optimization on essentially the same set of proteins examined. The 20 amino acids were initially assigned the qualitative parameters of Table 1, then these 20 parameters were optimized to optimize sialic acid glycan binding predictions as positive for 20 sialic acid glycan binding proteins, and negative for 10 proteins not considered as binding sugars, and 10 proteins such as lectins that bind other sugars that do not contain sialic acids. This gave as before glycine (G), alanine (A) aspartate (D), asparagine (N), serine (S), threonine (T), cystine/cysteine (C) each with a score of 1, and the rest assigned 0, except for tyrosine (Y phenylalanine (F) and histidine (H) that were now assigned larger parameter values of 2 and tryptophan (W) that was assigned a larger parameter value of 4."}

2_test

{"project":"2_test","denotations":[{"id":"32658736-8743705-23109494","span":{"begin":291,"end":293},"obj":"8743705"},{"id":"32658736-8743705-23109495","span":{"begin":869,"end":871},"obj":"8743705"}],"text":" A simple predictive model was developed based on optimized parameters. If a simpler method works as well as a more rigorous approach, it can sometimes provide insight and help build even better rigorous approaches. The method used in the present case was initially based on the GOR method [20] for protein secondary structure prediction in which sialic acid glycan binding state of residues replaced α-helix, β-sheet, and coil (or loop) states of residues. However, it was found that the results were essentially reproduced by a simpler model. This is primarily because the directional effects (in terms of N-terminal direction or C-terminal direction along the amino acid residue sequence) was found to be equivalent (i.e., symmetrical) and to persist for some 8 residues in both directions, very like the influence of alanine on α-helix formation in the GOR method [20]. Consequently the final method used in the present study consisted of just two changes to that used in previous Results Section 4.3 above, the second being described in point (vii) below. The qualitative scores as parameters to types of amino acid residue as shown in Table 1 were based on visual observations by the present author and a survey of sites in the literature, and this was improved by optimization on essentially the same set of proteins examined. The 20 amino acids were initially assigned the qualitative parameters of Table 1, then these 20 parameters were optimized to optimize sialic acid glycan binding predictions as positive for 20 sialic acid glycan binding proteins, and negative for 10 proteins not considered as binding sugars, and 10 proteins such as lectins that bind other sugars that do not contain sialic acids. This gave as before glycine (G), alanine (A) aspartate (D), asparagine (N), serine (S), threonine (T), cystine/cysteine (C) each with a score of 1, and the rest assigned 0, except for tyrosine (Y phenylalanine (F) and histidine (H) that were now assigned larger parameter values of 2 and tryptophan (W) that was assigned a larger parameter value of 4."}

LitCovid-PubTator

PMC:7278709 / 59555-61621 JSONTXT

Annnotations TAB JSON ListView MergeView

PMC:7278709 / 59555-61621 JSON TXT