In the first stage, we will develop a structure-based CBLB-specific QSAR model for fast ligand-based screening based on the publicly available data of 3D CLBL-ligand structures and binding compounds. The distinguishing feature of the QSAR model is the graph neural network architecture coupled with Behler-Parrinello symmetry functions in the representation of protein-ligand complexes [1]. We will apply the derived QSAR model to billion-size REAL Enamine and obtain a focused chemical library to be tested with more computationally expensive approaches, as it follows.

In the second stage, we will compose a representative conformational ensemble of the CBLB TKB domain. We will consider available experimental structures as well as construct theoretical models using molecular mechanics approaches, such as molecular dynamics and non-linear normal mode analysis. We will analyze the obtained conformations using our deep learning binding profiling approach [2], resulting in the most promising binding site conformations for the structure-based virtual ligand screening.

In the third stage, given the focused chemical library and representative conformations obtained in the first and second stages, respectively, we will run 4D molecular docking using ICM-Pro docking suite (www.molsoft.com). We will use semi-empirical quantum mechanics calculations to generate starting 3D conformers of the focused chemical library and our custom protein preparation scripts to prepare the docking project [3]. We will use our deep learning-based scoring function [1] to estimate the binding affinity from the docked compounds. Thus, we will obtain the ranked list of the compounds, from which we will select the 100 hit candidates based on the score, chemical scaffold, and manual inspection of the binding poses.

[1] Karlov, Dmitry S., et al. "graphDelta: MPNN scoring function for the affinity prediction of protein–ligand complexes." ACS omega 5.10 (2020): 5150-5159.

[2] Kozlovskii, Igor, and Petr Popov. "Spatiotemporal identification of druggable binding sites using deep learning." Communications biology 3.1 (2020): 1-12.

[3] Grudinin, Sergei, et al. "Predicting binding poses and affinities in the CSAR 2013–2014 docking exercises using the knowledge-based Convex-PL potential." Journal of Chemical Information and Modeling 56.6 (2016): 1053-1062.

We developed deep learning approaches for i) protein-ligand scoring functions to estimate binding affinity, and ii) explicit generation of conformational ensembles for the 4D molecular docking.

ICM-Pro

BiteNet

GraphDelta

Gromacs

RDkit

Deepchem

Smina

Gnina

Tensorflow

Pytorch

Karlov, Dmitry S., et al. "Biphenyl scaffold for the design of NMDA-receptor negative modulators: molecular modeling, synthesis, and biological activity." RSC Medicinal Chemistry13.7 (2022): 822-830.

Kozlovskii, Igor, and Petr Popov. "Structure-based deep learning for binding site detection in nucleic acid macromolecules." NAR genomics and bioinformatics 3.4 (2021): lqab111.

Kozlovskii, Igor, and Petr Popov. "Protein–peptide binding site detection using 3D convolutional neural networks." Journal of chemical information and modeling 61.8 (2021): 3814-3823.

Kozlovskii, Igor, and Petr Popov. "Spatiotemporal identification of druggable binding sites using deep learning." Communications biology 3.1 (2020): 1-12.

Karlov, Dmitry S., et al. "graphDelta: MPNN scoring function for the affinity prediction of protein–ligand complexes." ACS omega 5.10 (2020): 5150-5159.

Gusach, Anastasiia, et al. "Structural basis of ligand selectivity and disease mutations in cysteinyl leukotriene receptors." Nature communications 10.1 (2019): 1-9.

Luginina, Aleksandra, et al. "Structure-based mechanism of cysteinyl leukotriene receptor inhibition by antiasthmatic drugs." Science advances 5.10 (2019): eaax2518.