<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Ye N</submitter><funding>National Natural Science Foundation of China</funding><funding>Nanhu Scholars Program for Young Scholars of Xinyang Normal University</funding><funding>Key Scientific Research Project of Colleges and Universities in Henan Province</funding><funding>Science and Technology Department, Henan Province</funding><pagination>8965712</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8989566</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>2022</volume><pubmed_abstract>Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation </pubmed_abstract><journal>BioMed research international</journal><pubmed_title>A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level.</pubmed_title><pmcid>PMC8989566</pmcid><funding_grant_id>61802329</funding_grant_id><funding_grant_id>22A170019</funding_grant_id><funding_grant_id>212102210392</funding_grant_id><funding_grant_id>62002307</funding_grant_id><pubmed_authors>Ye N</pubmed_authors><pubmed_authors>Liang X</pubmed_authors><pubmed_authors>Fan J</pubmed_authors><pubmed_authors>Zhang J</pubmed_authors><pubmed_authors>Zhou F</pubmed_authors><pubmed_authors>Li B</pubmed_authors><pubmed_authors>Chai H</pubmed_authors></additional><is_claimable>false</is_claimable><name>A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level.</name><description>Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation </description><dates><release>2022-01-01T00:00:00Z</release><publication>2022</publication><modification>2026-06-01T03:06:01.317Z</modification><creation>2024-11-20T08:53:27.695Z</creation></dates><accession>S-EPMC8989566</accession><cross_references><pubmed>35402609</pubmed><doi>10.1155/2022/8965712</doi></cross_references></HashMap>