• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
For visually-impairedUser profile (HSE staff only)SearchMenu

Refinement of Antibody Structure Prediction with Deep Learing

Student: Pavel Solikov

Supervisor: Denis Moskvin

Faculty: St. Petersburg School of Physics, Mathematics, and Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 9

Year of Graduation: 2020

Prediction of the 3D tertiary structure of the most variable region of antibody (H3 loop) based on its amino-acid sequence is an important unsolved problem in bioinformatics. Existing methods rarely provide high quality results and, on average, their prediction accuracy remains extremely low. One of the problems is that existing solutions do not use deep learning methods, which have proved to outperform classic algorithms and have become the standard solution in areas such as computer vision, natural language processing, etc. In this thesis, a method for predicting the structure of the H3 loop using only deep learning was proposed. Several methods for encoding amino acid sequences, endowing them with structural and semantic information, as well as enabling them to overcome the problem of small amounts of data, were considered. Finally, several different deep neural network architectures, which have shown outstanding results in natural language processing, were applied in this thesis. The best model obtained was tested on a previously unknown data set and showed results similar to those of existing methods.

Full text (added May 28, 2020)

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses