{"id":643,"date":"2020-08-04T01:35:37","date_gmt":"2020-08-04T05:35:37","guid":{"rendered":"https:\/\/cib.udp.cl\/cms\/publicacion\/bioinformatics-approaches-for-predicting-kinase-substrate-relationships\/"},"modified":"2020-09-01T16:17:00","modified_gmt":"2020-09-01T20:17:00","slug":"bioinformatics-approaches-for-predicting-kinase-substrate-relationships","status":"publish","type":"publicacion","link":"https:\/\/cib.udp.cl\/en\/publicacion\/bioinformatics-approaches-for-predicting-kinase-substrate-relationships\/","title":{"rendered":"Bioinformatics Approaches for Predicting Kinase\u2013Substrate Relationships"},"content":{"rendered":"<p>Protein phosphorylation, catalyzed by protein kinases, is the main posttranslational modification in eukaryotes, regulating essential aspects of cellular function. Using mass spectrometry techniques, a profound knowledge has been achieved in the localization of phosphorylated residues at proteomic scale. Although it is still largely unknown, the protein kinases are responsible for such modifications. To fill this gap, many computational algorithms have been developed, which are capable to predict kinase\u2013substrate relationships. The greatest difficulty for these approaches is to model the complex nature that determines kinase\u2013substrate specificity. The vast majority of predictors is based on the linear primary sequence pattern that surrounds phosphorylation sites. However, in the intracellular environment the protein kinase specificity is influenced by contextual factors, such as protein\u2013protein interactions, substrates co-expression patterns, and subcellular localization. Only recently, the development of phosphorylation predictors has begun to incorporate these variables, significantly improving specificity of these methods. An accurate modeling of kinase\u2013substrate relationships could be the greatest contribution of bioinformatics to understand physiological cell signaling and its pathological impairment.<\/p>\n","protected":false},"featured_media":899,"template":"","class_list":["post-643","publicacion","type-publicacion","status-publish","has-post-thumbnail","hentry","lineas-bioinformatica-de-kinasas-de-proteinas","revista-intechopen"],"acf":[],"_links":{"self":[{"href":"https:\/\/cib.udp.cl\/en\/wp-json\/wp\/v2\/publicacion\/643","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cib.udp.cl\/en\/wp-json\/wp\/v2\/publicacion"}],"about":[{"href":"https:\/\/cib.udp.cl\/en\/wp-json\/wp\/v2\/types\/publicacion"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cib.udp.cl\/en\/wp-json\/wp\/v2\/media\/899"}],"wp:attachment":[{"href":"https:\/\/cib.udp.cl\/en\/wp-json\/wp\/v2\/media?parent=643"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}