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Machine Learning Methods for Predicting 3D Structure of Proteins

Student: Gleb Chistiakov

Supervisor: Mikhail Posypkin

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 8

Year of Graduation: 2020

Predicting the 3D structure of proteins is one of the most important issues in bioinformatics. Having knowledge about protein conformation, it becomes possible to study the interaction of several proteins and consequently invent new medicines. With the development of machine learning algorithms, folding prediction has reached a new level, since experimental methods require a lot of time and money. This paper describes application of ML methods to predicting the class of dihedral angles α of a coarse-grained protein model, which can be further used as an initial approximation for constructing the 3D structure of the amino acid sequence. The achieved results are 0.88 F1-score for the binary classification, 0.79 for the three-class classification, 0.75 for the four-class classification.

Full text (added May 19, 2020)

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