Biology subjects

IJzerman, A. P.

Publications and source records attributed to IJzerman, A. P..

5 recordsLinked to original sources

bioRxiv2024

A patient-centric knowledge graph approach to prioritize mutants for selective anti-cancer targeting

Personalized oncology has revolutionized cancer treatment by targeting specific genetic aberrations in tumors. However, the identification of suitable targets for anti-cancer therapies remains a challenge. In this study, we introduce a knowledge graph approach to prioritize cancer mutations with clinical, functional, and structural significance as potential therapeutic targets. Focusing on the human kinome, we integrate protein-protein interaction and patient-centric mutation networks to construct a comprehensive network enriched with transcriptomic, structural, and drug response data, together covering five layers of information. Moreover, we make the constructed knowledge graph publicly available, along with a plethora of scripts to facilitate further annotation and expansion of the network. Interactive visualization resources are also provided, ensuring accessibility for researchers regardless of computational expertise and enabling detailed analysis by cancer type and individual layers of information. This comprehensive resource has the potential to identify relevant mutations for targeted therapeutic interventions, thereby advancing personalized oncology and improving patient outcomes.

bioinformatics↗

bioRxiv2024

The solute carrier superfamily interactome

Solute carrier (SLC) transporters form a protein superfamily that enables transmembrane transport of diverse substrates including nutrients, ions and drugs. There are about 450 different SLCs, residing in a variety of subcellular membranes. Loss-of-function of an unusually high proportion of SLC transporters is genetically associated with a plethora of human diseases, making SLCs a rapidly emerging but challenging drug target class. Knowledge of their protein environment may elucidate the molecular basis for their functional integration with metabolic and cellular pathways and help conceive pharmacological interventions based on modulating proteostatic regulation. We aimed at obtaining a global survey of the SLC protein interaction landscape and mapped the protein-protein interactions of 396 SLCs by interaction proteomics. We employed a functional assessment based on RNA interference of interactors in combination with measurement of protein stability and localization. As an example, we detail the role of a SLC16A6 phospho-degron, and the contributions of PDZ-domain proteins LIN7C and MPP1 to the trafficking of SLC43A2. Overall, our work offers a resource for SLC-protein interactions for the scientific community.

systems biology↗

bioRxiv2021

Pan-cancer in silico analysis of somatic mutations in G-protein coupled receptors: The effect of evolutionary conservation and natural variance

G protein-coupled receptors (GPCRs) form the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used an aggregated dataset of mutations found in cancer patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 Genomes project. While the location of these mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment was observed in cancer patients among conserved residues in GPCRs such as the "DRY" motif. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). The validity of our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, and we identified novel GPCRs that had not been directly linked to cancer before such as the P2Y Receptor 10 (P2RY10). As a proof of concept, we projected the structurally investigated mutations in the crystal structure of the C-C Chemokine (CCR) 5 receptor, one of the high-ranking GPCRs previously linked to cancer. Several positions were pinpointed that relate to either structural integrity or endogenous and synthetic ligand binding, providing a rationale to their mechanism of influence in cancer. In conclusion, this study identifies a list of GPCRs that are prioritized for experimental follow up characterization to elucidate their role in cancer. The computational approach here described can be adapted to investigate the roles in cancer of any protein family. Author summaryDespite cancer being one of the most studied diseases due to its high mortality rate, one underexplored aspect is the association of certain protein families with tumor pathogenicity. We focused hereon the G protein-coupled receptorsfamily for three reasons. Firstly, it has been shown that this is the second most mutated class of proteins in cancer following kinases. Secondly, this family has been extensively studied resulting in a wide availability of experimental data for these proteins. Finally, more than 30 % of the drugs currently in the market targetitsmembers.For these receptors, weexplored the mutational landscape across cancer patients compared to healthy individuals. Our findings show the existence of cancer-related alteration patterns thatoccuratconserved positions. Additionally, we computationally ranked these G protein-coupled receptors on their importance in the pathogenesis of cancer based on multiple objectives. The result is a list of recommendations on where to focus next. These results suggest that there is room for repurposing existing therapies for cancer treatment while also highlighting the risk of potential interactions between cancer treatmentsand common drugs. All in all, we present a window of opportunity for new targeting strategies in oncology for G protein-coupled receptors.

bioinformatics↗

bioRxiv2021

Deciphering conformational selectivity in the A2A adenosine G protein-coupled receptor by Free Energy simulations

Transmembranal G Protein-Coupled Receptors (GPCRs) transduce extracellular chemical signals to the cell, via conformational change from a resting (inactive) to an active (canonically bound to a G-protein) conformation. Receptor activation is normally modulated by extracellular ligand binding, but mutations in the receptor can also shift this equilibrium by stabilizing different conformational states. In this work, we built structure-energetic relationships of receptor activation based on original thermodynamic cycles that represent the conformational equilibrium of the prototypical A2A adenosine receptor (AR). These cycles were solved with efficient free energy perturbation (FEP) protocols, allowing to distinguish the pharmacological profile of different series of A2AAR agonists with different efficacies. The modulatory effects of point mutations on the basal activity of the receptor or on ligand efficacies could also be detected. This methodology can guide GPCR ligand design with tailored pharmacological properties, or allow the identification of mutations that modulate receptor activation with potential clinical implications. Author SummaryThe design of new ligands as chemical modulators of G protein-coupled receptors (GPCRs) has benefited considerably during the last years of advances in both the structural and computational biology disciplines. Within the last, area, the use of free energy calculation methods has arisen as a computational tool to predict ligand affinities to explain structure-affinity relationships and guide lead optimization campaigns. However, our comprehension of the structural determinants of ligands with different pharmacological profile is scarce, and knowledge of the chemical modifications associated with an agonistic or antagonistic profile would be extremely valuable. We herein report an original implementation of the thermodynamic cycles associated with free energy perturbation (FEP) simulations, to mimic the conformational equilibrium between active and inactive GPCRs, and establish a framework to describe pharmacological profiles as a function of the ligands selectivity for a given receptor conformation. The advantage of this method resides into its simplicity of use, and the only consideration of active and inactive conformations of the receptor, with no simulation of the transitions between them. This model can accurately predict the pharmacological profile of series of full and partial agonists as opposed to antagonists of the A2A adenosine receptor, and moreover, how certain mutations associated with modulation of basal activity can influence this pharmacological profiles, which enables our understanding of such clinically relevant mutations.

biophysics↗

bioRxiv2017

Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics.\n\nIn this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naive Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution.\n\nDeep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method ( DNN_PCM) performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized DNN_PCM).\n\nHere, a standardized set to test and evaluate different machine learning algorithms in the context of multitask learning is offered by providing the data and the protocols.

bioinformatics↗