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Project titleUnderstanding of biased agonism and search of biased agonists for angeotensin receptor type 1 by means of structural bioinformatics and machine learning.
Project LeadPopov Petr
AffiliationMoscow Institute of Physics and Technology,
Implementation period2018 - 2019
Research area 04 - BIOLOGY AND LIFE SCIENCES, 04-202 - Proteomics; structure and functions of proteins
Keywordsbiased agonists, GPCR, machine learning, statistical scoring function, molecular modeling
Nowadays one needs methods for rational design of drugs with minimal side effects. One of the important pharmacological targets is angiotensin receptor type 1 (AT1R). This receptor is involved in cardiovascular diseases, such as vasoconstriction, hypertension, etc, resulting in daily loss of many human lives. In the case of AT1R receptor blockers, existing drugs possess crucial side effects, such as heart failure and apoptosis of cardiac cells. These are caused by uncontrolled signaling simultaneously through several intermediary proteins, including various types of G-proteins, as well as beta-arestines. Therefore, it is necessary to create new types of chemical compounds - biased agonists, which selectively activate only one signaling pathway and block another, in order to eliminate possible side effects. However, experimental study and validation chemical compounds for receptors similar to AT1R is extremely difficult, and there are no methods for rational design of biased agonists. This project aims to rationalize the design of biased AT1R receptor agonists by development of computational approaches for predicting the pharmacological role and affinity of the chemical compound for the receptor. In contrast to the existing methods, the proposed approaches are based on structural information about the receptor, which is further processed with the machine learning algorithms in order to obtain predictive computational models. This will help to develop novel drugs with minimal side effects.
To date, the problem of design of biased agonist for the AT1R receptor has not yet been solved. Also, despite the big efforts and research, molecular mechanisms of the action of biased agonists remain unknown. For this project a library of chemical compounds with known affinity and pharmacological role for the AT1R receptor will be collected based on data published in scientific articles and public databases such as Chembl, DrugBank, Zink and others. Using molecular docking tools and binding sites for ligands ZD7155 and Benicar in crystallographic structures of the AT1R receptor, the structural models of the AT1R receptor in complex with various chemical compounds from the collected library will be obtained. By carrying out the structural-activity-relationship of the obtained models and comparing the determinants of the binding of agonists, antagonists and biased agonists (TRV120027), key features of the biased agonists with respect to the other compounds will be revealed. Thus, the mechanism of action of biased agonists will be investigated and proposed using structural bioinformatics tools. At the next step, structural descriptors for the obtained structural models of the AT1R ligand complex will be calculated, and a training matrix will be constructed for machine learning using the corresponding data on the pharmacological role and affinity of the ligand. Using machine learning a prediction model will be derived to evaluate the pharmacological role of the ligand and its affinity for the AT1R receptor given a conformation of the AT1R-ligand complex. Structural models of the AT1R receptor in complex with chemical compounds from accessible chemical libraries will be obtained, and the most promising candidate-biased agonists for the AT1R receptor will be identified using the derived prediction model. Thus, for the first time a prediction model will be constructed for the bioinformatical search of biased agonists for the AT1R receptor. The developed methods will form an algorithmic base that can be adapted and applied to other GPCRs, which will help in the structure-based drug design.
Annotation of the results obtained in 2018
Angiotensin II is a regulator of a number of important processes of the cardiovascular system, including blood pressure. It interacts with the first type of renin-angiotensin system receptor (AT1R). Blocking this receptor with drugs based on low molecular weight AT1R ligands, such as losartan, is used to treat cardiovascular diseases. Currently, the ligands used in pharmaceuticals completely block the functional activity of the receptor (antagonists). However, the maximum positive effect could be reached with biased ligands, that selectively block one of the signalling pathways. Thus, finding new high-affinity ligands to AT1R is an urgent and important problem. This project aims to develop a methodology for assessing the binding of low molecular weight compounds to the AT1R receptor by means of structural bioinformatics and machine learning algorithms. To this end, at the first stage, a library of low-molecular compounds was assembled with experimentally established binding constants with AT1R. The resulting library is a basis for the compilation of a training set for solving the problem of classification - so, depending on the value of the compound binding constant in the library, they are divided into positive and negative examples. Then, using molecular modeling methods (homology modeling, structural optimization, molecular docking, and other ICM-Pro software modules), structural models of molecular complexes of low molecular weight compounds and AT1R were obtained, as well as analysis of AT1R interaction interfaces with agonists, antagonists and biased agonists for determining key amino acid residues. Further, for the numerical description of the intermolecular interactions of low-molecular compounds with the receptor, a software library was developed for calculating molecular descriptors. The molecular descriptor consists of four comprehensive SIF, SPLIF, RDF and PHAP3PT3D vectors, thus reflecting the intermolecular interaction between the low molecular weight compound and the receptor in a high-dimensional Euclidean space. At the next stage, a statistical analysis of the distributions of the molecular descriptors obtained, data augmentation, as well as the formulation and solution of the machine learning problem in a given equal space will be carried out. The obtained results and the developed libraries were actively used to study the structural and functional properties of AT1R and other GPCRs. Several abstracts for conferences and two scientific publications (Q1) were prepared and published (https://doi.org/10.1016/j.cell.2018.12.011 and https://doi.org/10.1016/j.sbi. 2019.02.010). Also, some results were published in the media with proper RSF funding information: https://mipt.ru/news/sozdan_tochnyy_vychislitelnyy_metod_dlya_stabilizatsii_lekarstvennykh_misheney , https://mipt.ru/news/vtoroy_kannabinoidnyy_retseptor_vydal_svoyu_strukturu.
1. Попов П.А., Катрич В.Ю. CompoMug for prediction of stabilizing mutations in GPCRs Journal of Bioenergetics and Biomembranes, - (year - 2018).
2. Романовская Д.Д., Попов П.А. Numerical representation of GPCR–ligand interactions for machine learning Journal of Bioenergetics and Biomembranes, - (year - 2018).
3. Зарецкий М.С., Катрич В.Ю., Попов П.А. Sequence-based analysis of sodium binding pocket in GPCRs Journal of Bioenergetics and Biomembranes, - (year - 2018).
4. - Создан точный вычислительный метод для стабилизации лекарственных мишеней МФТИ/Импульс, - (year - ).
5. - Второй каннабиноидный рецептор выдал свою структуру МФТИ/Импульс, - (year - ).
6. Ли К, Хуа Т, Вемури К, Хо ДжХ, Ву Ю, Ву Л, Попов П, Бенчама О. Звонок Н, Ку Л, Хан ГВ, Луер МР, Синар Р, Коффей НДж, Вэнг Дж, Катрич В, Хао С, Кунос Г, Бон ЛМ, Макриянис А, Стивенс РС, Лиу ЗДж. Crystal Structure of the Human Cannabinoid Receptor CB2 Cell, том 176, выпуск 3, страницы 459-467.e13 (year - 2019).
7. Попов П, Козловский И, Катрич В. Computational design for thermostabilization of GPCRs Current Opinion in Structural Biology, Том 55, страницы 25-33 (year - 2019).