Abstract
Introduction. The article is devoted to the theoretical prediction of the spatial structure of proteins of protein three-dimensional structures, with a focus on the specifics of modeling chemical compounds containing a significant number of amino acid residues. The relevance of the study is driven by the need to develop methods capable of producing reliable structural models without resorting to complex and resource-intensive experimental procedures. This is particularly important given the growing volume of genetic data and the need to interpret it at the level of the spatial organization of protein molecules.
The aim of the work was to analyze the possibilities of using ring-shaped models of amino acids to determine the spatial structure of proteins, to evaluate their effectiveness and applicability in problems of theoretical modeling, bioinformatics and structural chemistry.
Material and Methods. Modeling is performed using ring-polyhedral models of amino acids, which represent polyhedra formed from cyclic structures. These models impose constraints on the variability of bond angles and the possible positions of side chains, significantly reducing the number of permissible configurations and simplifying computational procedures. Algorithms are applied that allow determination of protein three-dimensional structures solely from the nucleotide coding sequence (CDS), without relying on additional experimental data, including X-ray crystallography and spectroscopic methods.
Results. The possibility of determining the forms of chemical compounds using ring-shaped models is demonstrated using the example of the amino acids serine (C3H7NO3) and leucine (C6H12NO2), as well as the standard structural element of proteins – the alpha helix. The results show consistency of the proposed approach with fundamental concepts of protein spatial organization and confirm its applicability for analyzing typical structural motifs.
Conclusion. The use of ring-shaped models of amino acids allows us to limit the space of possible structural configurations, increase the predictability of modeling, and can be considered as a promising approach to the theoretical study of proteins, especially when experimental data are limited.
Authors and affiliations
https://elibrary.ru/zrikgz
D.N. Kozhevnikov(1), A.Yu. Kushelev(2)
1-Federal State Autonomous Educational Institution of Higher Education “State University of Education”,
V. Voloshinoy St., 24, Mytishchi, Moscow Region, 141014, Russian Federation; 2P-ublic Laboratory “Nanomir”,
Averyanova Microdistrict, 16, Apt. 69, Dmitrov, Moscow Region, 141800, Russian Federation
References
Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K., Bates R., Žídek A., Potapenko A., Bridgland A., Meyer C., Kohl S.A.A., Ballard A.J., Cowie A., Romera-Paredes B., Nikolov S., Jain R., Adler J., Back T., Petersen S., Reiman D., Clancy E., Zielinski M., Steinegger M., Pacholska M., Berghammer T., Bodenstein S., Silver D., Vinyals O., Senior A.W., Kavukcuoglu K., Kohli P., Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature. 2021; 596 (7873): 583–9. DOI: 10.1038/s41586-021-03819-2
2. Varadi M., Anyango S., Deshpande M., Nair S., Natassia C., Yordanova G., Yuan D., Stroe O., Wood G., Laydon A., Žídek A., Green T., Tunyasuvunakool K., Petersen S., Jumper J., Clancy E., Green R., Vora A., Lutfi M., Figurnov M., Cowie A., Hobbs N., Kohli P., Kleywegt G., Birney E., Hassabis D., Velankar S. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022; 50 (D1): 439–44. DOI: 10.1093/nar/gkab1061
Abramson J., Balsiger N., Bates R., Bridgland A., Clancy E., Figurnov M., Green T., Hassabis D., Jumper J., Kohl S.A.A., Meyer C., Nair S., Potapenko A., Ronneberger O., Tunyasuvunakool K., Vinyals O., Žídek A. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024; 630 (8016): 493–500. DOI: 10.1038/s41586-024-07487-w
. Woods H., Leman J.K., Meiler J. Modeling membrane geometries implicitly in Rosetta. Protein Sci. 2024; 33 (3): e4908. DOI: 10.1002/pro.4908
. Harmalkar A., Bryant P., Coombe D., Deshmukh S., Meiler J., Noé F., Uddin M. Reliable protein-protein docking with AlphaFold, Rosetta, and replica exchange. eLife. 2025; 13: RP94029. DOI: 10.7554/eLife.94029
. Borges-Araújo L., Coelho R., Silva T., Gomes J., Oliveira H., Rocha M., Pereira P. Pragmatic Coarse-Graining of Proteins: Models and Applications. J. Chem Theory Comput. 2023; 19 (20): 7112–35. DOI: 10.1021/acs.jctc.3c00733
Uddin M., Coombe D. A Coarse-Grained Simulation Approach for Protein Molecular Conformation Dynamics. J. Phys Chem A. 2025; 129 (2): 607–22. DOI: 10.1021/acs.jpca.4c06977
Irbäck A., Nilsson M., Sandelin E., Larsson P. Folding lattice proteins confined on minimal grids using a quantum-inspired encoding. Phys Rev E. 2025; 112 (4–2): 045302. DOI: 10.1103/PhysRevE.112.045302
Niazi S.K. Critical assessment of AI-based protein structure prediction: Fundamental challenges and future directions in drug discovery. Comput Struct Biotechnol Rep. 2025; 2: 100064. DOI: 10.1016/j.csbr.2025.100064
Bryant P., Noé F. Structure prediction of alternative protein conformations. Nat Commun. 2024; 15 (1): 7328. DOI: 10.1038/s41467-024-51507-2
Wayment-Steele H.K., Ojoawo A., Otten R., Meiler J., Noé F., Jumper J., Clancy E., Green T. Predicting multiple conformations via sequence clustering and AlphaFold2. Nature. 2024; 625 (7996): 832–9. DOI: 10.1038/s41586-023-06832-9
. Rosenberg A.A., Marx A., Bronstein A.M. Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon. Nat Commun. 2022; 13 (1): 2815. DOI: 10.1038/s41467-022-30390-9
Кушелев А.Ю. Определение вторичной структуры белка с использованием генетического кода. Медицина. Социология. Философия. Прикладные исследования. 2025; 1: 70–8. [Kushelev A.Y. Opredelenie vtorichnoy struktury belka s ispol’zovaniem geneticheskogo koda [Determination of protein secondary structure using the genetic code]. Meditsina. Sotsiologiya. Filosofiya. Prikladnye issledovaniya. 2025; 1: 70–8. Available from: https://medsociofil.ru/archive/?ELEMENT_ID=61275 (in Russian)].
Snelson K. Portrait of an atom. Exhibition booklet. Baltimore (MD): Maryland Science Center. 1981.
Bergman D.L. Spinning Charge Ring Model of Elementary Particles. Galilean Electrodynamics. 1991; 2 (2): 30–2.
Власов А.Д. Атом Шредингера. УФН. 1993; 163 (2): 97–103. [Vlasov A.D. Atom Shredingera [Schrödinger’s atom]. UFN. 1993; 163 (2): 97–103 (in Russian)].
. Lucas J. A physical model for atoms and nuclei. Galilean Electrodynamics. 1996; 7 (1): 3–12.
. Kozhevnikov D.N. Models of molecules with ring-shaped faces. Translated from Zhurnal Fizicheskoi Khimii. Russian J. Phys Chem. 1996; 70 (6): 1057–1059.
. Кожевников Д.Н., Кушелев А.Ю. Моделирование форм электронных поверхностей химических соединений с различными ковалентными связями в виде полиэдров из колец (кольцегранников). Медицина. Социология. Философия. Прикладные исследования. 2025; 2: 151–6. [Kozhevnikov D.N., Kushelev A.Y. Modeling electron surface shapes of chemical compounds with various covalent bonds as polyhedra of rings (ring-polyhedra). Meditsina. Sotsiologiya. Filosofiya. Prikladnye issledovaniya. 2025; 2: 151–6 (in Russian)].
. Taxawa tesnikishii (cellular organisms) UPI00255DB334 [Internet]. Available from: https://www.uniprot.org/uniparc?query=KAJ9625621. Accessed 15 Jul 2025.
. Gekko japonicus (cellular organisms) UPI00074FD5FA [Internet]. Available from: https://www.uniprot.org/uniparc?query=XP_015279638. Accessed 15 Jul 2025.