Impact of cancer-associated mutations in CC chemokine receptor 2 on receptor function and antagonism

Hollander, L S den; Béquignon, Olivier J. M.; Wang, X; Wezel, K van; Broekhuis, J; González, Marina Gorostiola; Visser, Karin E. de; Ijzerman, A. P.; Westen, Gerard J. P. van; Heitman, Laura H.

doi:10.1016/j.bcp.2022.115399

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…In the last decades, the scientific community has seen an increasing interest in the dynamic aspects of GPCRs, resulting in community efforts such as the GPCRmd database, where curated GPCR MD simulations are publicly available [23]. Simultaneously, GPCR research in the context of oncological therapies is gaining momentum [24], with several in vitro studies showing how cancer-related somatic mutations affect receptor function and/or pharmacological intervention [25][26][27]. Some of the physiological effects observed in mutants have been associated with changes in receptor dynamics thanks to MD simulations [28].…”

Section: Introductionmentioning

confidence: 99%

3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

Gorostiola González,

van den Broek,

Braun

et al. 2023

J Cheminform

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Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive models. State-of-the-art protein descriptors, however, are calculated from the protein sequence and neglect the dynamic nature of proteins. This dynamic nature can be computationally simulated with molecular dynamics (MD). Here, novel 3D dynamic protein descriptors (3DDPDs) were designed to be applied in bioactivity prediction tasks with PCM models. As a test case, publicly available G protein-coupled receptor (GPCR) MD data from GPCRmd was used. GPCRs are membrane-bound proteins, which are activated by hormones and neurotransmitters, and constitute an important target family for drug discovery. GPCRs exist in different conformational states that allow the transmission of diverse signals and that can be modified by ligand interactions, among other factors. To translate the MD-encoded protein dynamics two types of 3DDPDs were considered: one-hot encoded residue-specific (rs) and embedding-like protein-specific (ps) 3DDPDs. The descriptors were developed by calculating distributions of trajectory coordinates and partial charges, applying dimensionality reduction, and subsequently condensing them into vectors per residue or protein, respectively. 3DDPDs were benchmarked on several PCM tasks against state-of-the-art non-dynamic protein descriptors. Our rs- and ps3DDPDs outperformed non-dynamic descriptors in regression tasks using a temporal split and showed comparable performance with a random split and in all classification tasks. Combinations of non-dynamic descriptors with 3DDPDs did not result in increased performance. Finally, the power of 3DDPDs to capture dynamic fluctuations in mutant GPCRs was explored. The results presented here show the potential of including protein dynamic information on machine learning tasks, specifically bioactivity prediction, and open opportunities for applications in drug discovery, including oncology.

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