دورية أكاديمية
ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.
| العنوان: | ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes. |
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| المؤلفون: | Bogdanova EA; Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia., Novoseletsky VN; Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen, China. |
| المصدر: | Proteins [Proteins] 2024 Sep; Vol. 92 (9), pp. 1127-1136. Date of Electronic Publication: 2024 May 09. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Wiley-Liss Country of Publication: United States NLM ID: 8700181 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0134 (Electronic) Linking ISSN: 08873585 NLM ISO Abbreviation: Proteins Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York, NY : Wiley-Liss Original Publication: New York : Alan R. Liss, c1986- |
| مواضيع طبية MeSH: | Proteins*/chemistry , Proteins*/metabolism , Neural Networks, Computer* , Protein Binding* , Algorithms*, Thermodynamics ; Databases, Protein ; Computational Biology/methods ; Binding Sites ; Protein Interaction Mapping/methods |
| مستخلص: | Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN. (© 2024 Wiley Periodicals LLC.) |
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| معلومات مُعتمدة: | Non-commercial Foundation for the Advancement of Science and Education INTELLECT |
| فهرسة مساهمة: | Keywords: interaction energy; multiprotein complexes; neural networks; protein binding; structure‐based features |
| المشرفين على المادة: | 0 (Proteins) |
| تواريخ الأحداث: | Date Created: 20240509 Date Completed: 20240806 Latest Revision: 20240806 |
| رمز التحديث: | 20240806 |
| DOI: | 10.1002/prot.26700 |
| PMID: | 38722047 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1097-0134 |
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| DOI: | 10.1002/prot.26700 |