Danil D. Kotelnikov

Danil D. Kotelnikov

Computational protein design, molecular docking, and the pipelines that run them.

Research assistant at a biomedical research institute, designing single-domain antibodies against viral antigens. Reading for an MSc in Applied Mathematics and Informatics on the programme “Bioinformatics in Agriculture”. Fifteen papers across protein design, molecular modelling, plant genomics and molecular diagnostics.

  • Schematic of the five-stage de novo nanobody design pipeline and its re-assembled redesign variant.

    NanoDeNovo: nanobodies designed from the antigen alone

    A five-stage cascade that samples nanobody scaffolds, folds them, docks against the poliovirus type 1 VP3 capsid protein, redesigns the CDR loops on the survivors and humanises what remains. No immunisation and no display library. Each stage answers a different question, so the errors of the five filters are less likely to align than five variants of one confidence metric. Cost per candidate rises by three orders of magnitude between folding and Rosetta relaxation, which is what fixes the order they run in.

    Fig. 8. Kotelnikov, Tatarinova, Zhdanov, 2025. doi:10.3390/ijms26199262. CC BY 4.0.
  • Map of regulatory non-coding RNA classes, their mechanisms of action and molecular targets linked to eusociality in Hymenoptera.

    Non-coding RNA and the eusocial transition

    A synthesis of what the regulatory RNA families do during caste differentiation, developmental plasticity and behavioural modulation in bees, ants and wasps. The review maps each class to a mechanism of action and to the genes it regulates, then asks how far expression profiles track the transition from facultative to obligate sociality, and whether they contribute to the point at which that transition stops being reversible.

    Fig. 1. Lebedev, Smutin, Timkin, Kotelnikov, Taldaev, Panushev, Adonin, 2025. doi:10.1016/j.ncrna.2024.10.007. CC BY 4.0.
  • Top mismatch cluster discrimination score by locus Cyathostomum nassatus COX1 17.86, Fusarium culmorum RPB2 8.44, F. culmorum TEF1 6.50, F. culmorum TUB2 5.00. C. nassatus COX1 17.86 F. culmorum RPB2 8.44 F. culmorum TEF1 6.50 F. culmorum TUB2 5.00

    Primery: primers placed on mismatch clusters

    Discriminatory nucleotide positions are located by BLAST before design, grouped into primer-length landing zones and scored by how many non-target species they exclude and how consistently. Primer3 then designs inside those zones under the constraints of the chosen chemistry, and the result is validated back against the database with amplicon classification. Where a locus cannot support species-level discrimination, the pipeline reports genus-level instead of shipping a primer that will cross-amplify.

    Top cluster discrimination score per benchmark locus. Manuscript in preparation.
  • Metal complexes as biological agents: structure and binding

    Gold(III) complexes with doubly protonated phenanthrolines, and binuclear copper(II) furancarboxylates carrying 5-nitro-1,10-phenanthroline. Crystal structures were resolved by X-ray diffraction and the copper coordination environment found to be a square pyramid with a coordination number of five, stabilised supramolecularly through hydrogen bonding and stacking between aromatic rings. The computational side covered that stabilisation and the modelling of copper binding to mycobacterial protein targets, where histidine- and glutamate-containing sites dominate. Both copper complexes suppressed viability in an ovarian adenocarcinoma line.

    Neither article is openly licensed, so no figure is reproduced here.
  • Two-dimensional ligand interaction diagrams and three-dimensional binding poses for glucocorticoids in the TRPM8 pocket, computed with AutoDock and with MOE.

    Cross-checked ligand docking on an ion channel

    A panel of synthetic glucocorticoids screened against a predicted TRPM8 structure taken from a structure database rather than from crystallography. Each pose was computed twice in independent docking engines and the interaction maps compared residue by residue, with agreement between the two treated as the acceptance test rather than the score from either alone. One compound engaged the residue set of interest; the rest formed stable complexes elsewhere on the channel.

    Ligand interaction diagrams. Timkin, Kotelnikov, Timofeev, Naumov, Borodin, 2024. doi:10.20538/1682-0363-2024-4-136-144. CC BY 4.0.
  • 2023 – Research assistant Laboratory of protein biochemistry and chemical pathology, biomedical research institute
  • 2025 – 2026 Bioinformatician Laboratory of molecular genetic studies of plants, agricultural university
  • 2024 – 2026 Researcher Laboratory of biotechnology, crop research institute
Structure prediction
AlphaFold2, AlphaFold-Multimer, AlphaFold3, tFold-Ab, Chai-1, Boltz-1, Protenix
Docking
AutoDock, AutoDock Vina, VinaGPU, MOE, Glide, Rosetta3, ClusPro, ReplicaDock, HDOCK, ZDOCK
Molecular dynamics
GROMACS with CHARMM36, AMBER14/19, OPLS-AA; CHARMM-GUI; CGenFF, OpenFF, GAFF, MCPB.py
Free energy
Uni-GBSA, gmx_MMPBSA; RMSD, RMSF, hydrogen bonds, PLIP
Assembly
ABySS2, SPAdes, MaSuRCA, MEGAHIT, RagTag, TGSGapCloser; KMC, Jellyfish, GenomeScope, smudgeplot
Language
Python with Biopython, NumPy, SciPy; PySide6 for interfaces; Linux throughout
Hardware
Intel Xeon Platinum 8368, 512 GB RAM

All notes

E-mail
danil.kotelnikov.02@gmail.com
Telegram
@yourlilygarden
ORCID
0009-0003-5159-5796
Availability
Open to relocation, EU and US