{"doi":"10.31487/j.cor.2020.08.12","title":"Identifying Biomolecular Targets of the Anticancer Vitamin-E-δ-Tocotrienol Using a Computational Approach: Virtual Target Screening","abstract":"In recent years, evidence has mounted that a particular form of vitamin E (its δ-tocotrienol variant) may have cellular functions beyond that of an antioxidant, a role commonly ascribed to the tocotrienol class of compounds. In particular, numerous studies of δ-tocotrienol’s effect on cancer cells have identified it as a potent anticancer and antitumor agent. However, this important revelation of potential therapeutic use poses a series of new challenges, with arguably the most important being the elucidation of the precise mechanism of action responsible for the anticancer activity of δ-tocotrienol. As an initial step to address this question, we have used a computational tool, Virtual Target Screening (a molecular docking-based tool that identifies potential binding partners for small molecules), to identify potential biomolecular targets of δ-tocotrienol. Then, to gain a consensus as to the type of biomolecular entity that could be a target for δ-tocotrienol, we utilized PharmMapper and PASS (a ligand-based chemoinformatic approach), and ProBiS (a tool that analyses binding site similarities across known proteins). The results of our multipronged computational consensus-seeking approach showed that such a strategy can identify potential cellular targets of small molecules. This is evidenced by our identification of estrogen receptor-beta, a protein that has been previously shown to bind δ-tocotrienol, which elicited a cellular response. This study supports the use of such a computational approach as an initial step in target identification to avoid time-consuming, costly large-scale experimental screening, greatly reducing the experimental work to just one or a few candidate proteins.","journal":"Clinical Oncology and Research","year":2020,"id":117141,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9521,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":546678,"name":"Yuri Pevzner","orcid":"0000-0002-6274-8765","position":1,"is_corresponding":false},{"id":547281,"name":"Elza Pevzner","orcid":null,"position":2,"is_corresponding":false},{"id":547282,"name":"Kenyon G. Daniel","orcid":null,"position":3,"is_corresponding":false},{"id":546679,"name":"Wayne C. Guida","orcid":"0000-0003-3353-4853","position":4,"is_corresponding":false},{"id":377241,"name":"Mokenge P. Malafa","orcid":"0000-0003-2716-4315","position":6,"is_corresponding":false},{"id":546677,"name":"Wesley H. Brooks","orcid":"0000-0002-5604-3769","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-18T23:13:51.309289Z","pmid":null,"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":[]}