{"doi":"10.1142/9789811270611_0008","title":"Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites","abstract":"Protein phosphorylation is a key post-translational modification that plays a central role in many cellular processes. With recent advances in biotechnology, thousands of phosphorylated sites can be identified and quantified in a given sample, enabling proteome-wide screening of cellular signaling. However, for most (> 90%) of the phosphorylation sites that are identified in these experiments, the kinase(s) that target these sites are unknown. To broadly utilize available structural, functional, evolutionary, and contextual information in predicting kinase-substrate associations (KSAs), we develop a network-based machine learning framework. Our framework integrates a multitude of data sources to characterize the landscape of functional relationships and associations among phosphosites and kinases. To construct a phosphosite-phosphosite association network, we use sequence similarity, shared biological pathways, co-evolution, co-occurrence, and co-phosphorylation of phosphosites across different biological states. To construct a kinase-kinase association network, we integrate protein-protein interactions, shared biological pathways, and membership in common kinase families. We use node embeddings computed from these heterogeneous networks to train machine learning models for predicting kinase-substrate associations. Our systematic computational experiments using the PhosphositePLUS database shows that the resulting algorithm, NetKSA, outperforms two state-of-the-art algorithms, including KinomeXplorer and LinkPhinder, in overall KSA prediction. By stratifying the ranking of kinases, NetKSA also enables annotation of phosphosites that are targeted by relatively less-studied kinases.Availability: The code and data are available at compbio.case.edu/NetKSA/.","journal":"PubMed","year":2022,"id":299626,"datarank":0.30762632358243797,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.06621063671732287,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.06621063671732287,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":4,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9465,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":557764,"name":"Serhan Yılmaz","orcid":"0000-0003-4669-9593","position":1,"is_corresponding":false},{"id":903803,"name":"Filipa Blasco Tavares Pereira Lopes","orcid":"0000-0002-2859-1506","position":2,"is_corresponding":false},{"id":355234,"name":"Mark R. Chance","orcid":"0000-0002-2991-6405","position":3,"is_corresponding":false},{"id":350713,"name":"Mehmet Koyutürk","orcid":"0000-0002-3434-5512","position":4,"is_corresponding":false},{"id":447801,"name":"Marzieh Ayati","orcid":"0000-0002-5280-5738","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:31:44.904250Z","pmid":"36540966","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}