Biology subjects

van Westen, G. J. P.

Publications and source records attributed to van Westen, G. J. P..

5 recordsLinked to original sources

bioRxiv2025

Identification of determinants of high-fidelity DNA synthesis in M. smegmatis DnaE1 through in silico and in vivo approaches

Drug resistance in Mycobacterium tuberculosis presents a major challenge in tuberculosis treatment, highlighting the need to understand the underlying mechanisms. DNA replication plays an important role in the acquisition of drug resistance and the expression of the DNA polymerase DnaE2 during adverse conditions has been associated with increased mutation rates. Here we investigate the functional differences between the high fidelity replicative DNA polymerase DnaE1 and the predicted error-prone DNA polymerase DnaE2, focusing on which amino acid changes affect polymerase fidelity. For this we identify potential fidelity-altering positions using a two-entropies sequence analysis combined with experimental validation to test whether changes of these positions affect the mutation rates. We find that a double mutation in the palm domain of M. smegmatis DnaE1: D431S/R432D, increases mutation frequencies both in vivo and in vitro. The location of these two residues adjacent to the DNA backbone of the template strand suggests that the amino acid change results in a looser grip on the DNA, allowing for the incorporation of incorrect nucleotides. These insights improve our understanding of the mechanisms underlying drug resistance in M. tuberculosis and could help in the development of future strategies to combat it.

molecular biology↗

bioRxiv2025

PyVADesign: a Python-based cloning tool for one-step generation of large mutant libraries

MotivationThe generation and analysis of diverse mutants of a protein is a powerful tool for understanding protein function. However, generating such mutants can be time-consuming, while the commercial option of buying a series of mutant plasmids can be expensive. In contrast, the insertion of a synthesized double stranded DNA (dsDNA) fragment into a plasmid is a fast and low-cost method to generate a large library of mutants with one or more point mutations, insertions, or deletions. ResultsTo aid in the design of these DNA fragments we have developed PyVADesign: a Python package that makes the design and ordering of dsDNA fragments straight-forward and cost-effective. In PyVADesign, the mutations of interest are clustered in different cloning groups for efficient exchange into the target plasmid. Additionally, primers that prepare the target plasmid for insertion of the dsDNA fragment, as well as primers for sequencing, are automatically designed within the same program. AvailabilityPyVADesign is open source and available at https://github.com/CDDLeiden/PyVADesign/. Contactgerard@lacdr.leidenuniv.nl

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↗

bioRxiv2019

Chemical genetics strategy to profile kinase target engagement reveals role of FES in neutrophil phagocytosis via SYK activation

Chemical tools and methods that report on target engagement of endogenously expressed protein kinases by small molecules in human cells are highly desirable. Here, we describe a chemical genetics strategy that allows the study of non-receptor tyrosine kinase FES, a promising therapeutic target for cancer and immune disorders. Precise gene editing was used in combination with a rationally designed, complementary fluorescent probe to visualize endogenous FES kinase in HL-60 cells. We replaced a single oxygen atom by a sulphur in a serine residue at the DFG-1 position of the ATP-binding pocket in an endogenously expressed kinase, thereby sensitizing the engineered protein towards covalent labeling and inactivation by a fluorescent probe. The temporal control offered by this strategy allows acute inactivation of FES activity both during myeloid differentiation and in terminally differentiated neutrophils. Our results show that FES activity is dispensable for differentiation of HL-60 cells towards macrophages. Instead, FES plays a key role in neutrophil phagocytosis by activation of SYK kinase, a central regulator of immune function in neutrophils. This strategy holds promise as a target validation method for kinases.

physiology↗

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↗