دورية أكاديمية
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Comprehensive Research on Druggable Proteins: From PSSM to Pre-Trained Language Models.

التفاصيل البيبلوغرافية
العنوان: Comprehensive Research on Druggable Proteins: From PSSM to Pre-Trained Language Models.
المؤلفون: Chu H; College of Information Technology, Shanghai Ocean University, Shanghai 201306, China., Liu T; College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
المصدر: International journal of molecular sciences [Int J Mol Sci] 2024 Apr 19; Vol. 25 (8). Date of Electronic Publication: 2024 Apr 19.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067 (Electronic) Linking ISSN: 14220067 NLM ISO Abbreviation: Int J Mol Sci Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI, [2000-
مواضيع طبية MeSH: Proteins*/metabolism, Computational Biology/methods ; Drug Discovery/methods ; Position-Specific Scoring Matrices ; Databases, Protein ; Humans ; Algorithms
مستخلص: Identification of druggable proteins can greatly reduce the cost of discovering new potential drugs. Traditional experimental approaches to exploring these proteins are often costly, slow, and labor-intensive, making them impractical for large-scale research. In response, recent decades have seen a rise in computational methods. These alternatives support drug discovery by creating advanced predictive models. In this study, we proposed a fast and precise classifier for the identification of druggable proteins using a protein language model (PLM) with fine-tuned evolutionary scale modeling 2 (ESM-2) embeddings, achieving 95.11% accuracy on the benchmark dataset. Furthermore, we made a careful comparison to examine the predictive abilities of ESM-2 embeddings and position-specific scoring matrix (PSSM) features by using the same classifiers. The results suggest that ESM-2 embeddings outperformed PSSM features in terms of accuracy and efficiency. Recognizing the potential of language models, we also developed an end-to-end model based on the generative pre-trained transformers 2 (GPT-2) with modifications. To our knowledge, this is the first time a large language model (LLM) GPT-2 has been deployed for the recognition of druggable proteins. Additionally, a more up-to-date dataset, known as Pharos, was adopted to further validate the performance of the proposed model.
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فهرسة مساهمة: Keywords: ESM-2; PSSM; deep learning; druggable protein; machine learning
المشرفين على المادة: 0 (Proteins)
تواريخ الأحداث: Date Created: 20240427 Date Completed: 20240427 Latest Revision: 20240429
رمز التحديث: 20240429
مُعرف محوري في PubMed: PMC11049818
DOI: 10.3390/ijms25084507
PMID: 38674091
قاعدة البيانات: MEDLINE
الوصف
تدمد:1422-0067
DOI:10.3390/ijms25084507