Skip to main content
Log in

Probabilistic Assessment of a Pentapeptide Composition Influence on Its Stability

  • THEMATIC ISSUE
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.

REFERENCES

  1. Senior, A.W., Evans, R., Jumper J., et al., Improved Protein Structure Prediction Using Potentials from Deep Learning, Nature, 2020, vol. 577, pp. 706–710.

    Article  Google Scholar 

  2. Pereira, J., Simpkin, A.J., Hartmann, M.D., et al., High Accuracy Protein Structure Prediction in CASP14, Proteins Structure Function and Bioinformatics, 2021, vol. 89, no. 12, pp. 1687–1699. https://doi.org/10.1002/prot.26171

    Article  Google Scholar 

  3. Nekrasov, A.N., Kozmin, Yu.P., Kozyrev, S.V., et al., Hierarchical Structure of Protein Sequence, Int. J. Mol. Sci., 2021, vol. 22, no. 15, 8339. https://doi.org/10.3390/ijms22158339

    Article  Google Scholar 

  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, vol. 160, pp. 88–92.

    Article  Google Scholar 

  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, pp. 3149–3157.

  6. Bergstra, J., Yamins, D., and 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, pp. 115–123.

  7. Lundberg, S.M. and Lee, S.I., A Unified Approach to Interpreting Model Predictions, Proc. 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, 2017, pp. 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, Russ. J. Numer. Anal. Math. Model., 2020, vol. 35, no. 5, pp. 263–271.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to A. I. Mikhalskii, J. A. Novoseltseva, A. A. Anashkina or A. N. Nekrasov.

Additional information

This paper was recommended for publication by A.A. Galyaev, a member of the Editorial Board

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mikhalskii, A.I., Novoseltseva, J.A., Anashkina, A.A. et al. Probabilistic Assessment of a Pentapeptide Composition Influence on Its Stability. Autom Remote Control 84, 1275–1282 (2023). https://doi.org/10.1134/S0005117923120032

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117923120032

Keywords:

Navigation