Probabilistic Assessment of a Pentapeptide Composition Influence on Its Stability

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

The influence of the arrangement of amino acid residues in a pentapeptide on its stability is being studied. A forecast of pentapeptide stability is made using the gradient boosting method, which allows one to evaluate the influence of each feature on the stability of the pentapeptide. Combinations of amino acid arrangements in the pentapeptide have been identified that make a significant contribution to its stability. It has been shown that the use
of such combinations reduces the amount of data required to obtain a reliable prediction of pentapeptide stability.

About the authors

A. I. Mikhal'skiy

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: ipuran@yandex.ru
Moscow, Russia

Zh. A. Novosel'tseva

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences

Email: novoselc.janna@yandex.ru
Moscow, Russia

A. A. Anashkina

Engelgardt Institute of Molecular Biology, Russian Academy of Sciences

Email: a_anastasya@inbox.ru
Moscow, Russia

A. N. Nekrasov

Shemyakin–Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences

Author for correspondence.
Email: a_nnekrasov@mail.ru
Moscow, Russia

References

  1. Senior A.W., Evans R., Jumper J. et al. Improved protein structure prediction using potentials from deep learning // Nature. 2020. V. 577. P. 706-710.
  2. Pereira J., Simpkin A.J., Hartmann M.D. et al. High accuracy protein structure prediction in CASP14 // Proteins Structure Function and Bioinformatics. 2021. V. 89. No. 12. P. 1687-1699. https://doi.org/10.1002/prot.26171
  3. Nekrasov A.N., Kozmin Yu.P., Kozyrev S.V. et al. Hierarchical structure of protein sequence // Int. J. Mol. Sci. 2021. V. 22. No. 15. 8339. https://doi.org/10.3390/ijms22158339
  4. Anashkina A.A., Nekrasov A.N., Alekseeva L.G. et al. A minimum set of stable blocks for rational design of polypeptide chains // Biochimie. 2019. V. 160. P. 88-92.
  5. Ke G., Meng Q., Finley T., Wang T. et al. A Highly Efficient Gradient Boosting Decision Tree // Proc. 31st Conference on Neural Information Processing Systems (NIPS). Long Beach. 2017. P. 3149-3157.
  6. Bergstra J., Yamins D., Cox D.D. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures // Proc. of the 30th International Conference on Machine Learning (ICML). 2013. P. 115-123.
  7. Lundberg S.M., Lee S.I. A unified approach to interpreting model predictions // Proc. 31st Conference on Neural Information Processing Systems (NIPS). Long Beach. 2017. P. 4765-4774.
  8. Mikhalskii A.I., Petrov I.V., Tsurko V.V., Anashkina A.A. et al. Application of mutual information estimation for prediction the structural stability of pentapeptides // Rus. J. Numer. Anal. Math. Model. 2020. V. 35. No. 5. P. 263-271.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2023 The Russian Academy of Sciences