Biology subjects

Andersen, L.

Publications and source records attributed to Andersen, L..

5 recordsLinked to original sources

bioRxiv2025

BindFlow: a free, user-friendly pipeline for absolute binding free energy calculations using free energy perturbation or MM(PB/GB)SA

We present BindFlow, a Python-based software for automated absolute binding free energy (ABFE) calculations at the free energy perturbation (FEP) or at the molecular mechanics Poisson-Boltzmann/generalized Born surface area [MM(PB/GB)SA] level of theory. BindFlow is free, open-source, user-friendly, easily customizable, runs on work-stations or distributed computing platforms, and provides extensive documentation and tutorials. BindFlow uses GROMACS as molecular dynamics engine and provides built-in support for the small-molecule force fields GAFF, OpenFF, and Espaloma. We test BindFlow by computing affinities for 139 ligand/target pairs, involving eight different targets including six soluble proteins, one membrane protein and one non-protein host- guest system. Quantified by Pearson, Kendall, and Spearman correlations coefficients, we find that the agreement of BindFlow predictions with experiments are overall similar to gold standards in the field. Interestingly, we find that MM(PB/GB)SA achieves correlations that, for some systems and force fields, approach those obtained with FEP, while requiring only a fraction of the computational cost. This study establishes BindFlow as a validated and accessible tool for ABFE calculations.

biophysics↗

bioRxiv2025

Accelerating ligand discovery by combining Bayesian optimization with MMGBSA-based binding affinity calculations

Predicting protein-ligand binding affinity with high accuracy is critical in structure-based drug discovery. While docking methods offer computational efficiency, they often lack the precision required for reliable affinity ranking. In contrast, molecular dynamics (MD)-based approaches such as MMGBSA provide more accurate binding free energy estimates but are computationally intensive, limiting their scalability. To address this trade-off, we introduce an active learning framework that automates molecule selection for docking and MD simulations, replacing manual expert-driven decisions with a data-efficient, model-guided strategy. Our approach integrates fixed -- partly pre-trained deep learning -- molecular embeddings (MolFormer, ChemBERTa-2, and Morgan fingerprints) with adaptive regression models (e.g. Bayesian Ridge and Random Forest) to iteratively improve binding affinity predictions. We evaluate this approach retro-spectively on a new dataset of 59,356 chemically diverse compounds from ZINC-22 targeting the MCL1 protein using both AutoDock Vina and MMGBSA binding free energy scores. Our results show that incorporating MMGBSA scores into the active learning loop significantly enhances performance, recovering 79.9% of the top 1% binders in the whole dataset, compared to only 6.7% when using docking scores alone. Notably, MMGBSA exhibits a stronger correlation with experimental binding affinities than AutoDock Vina on our dataset and enables more accurate ranking of candidate compounds in a runtime efficient way. Furthermore, we demonstrate that a one-at-a-time acquisition active learning strategy consistently outperforms traditional batched acquisition, the latter achieving just 78.4% recovery with MolFormer and Bayesian Ridge. These findings underscore the potential of integrating deep learning-based molecular representations with MD-level accuracy in an active learning framework, offering a scalable and efficient path to accelerate virtual screening and improve hit identification in drug discovery.

bioinformatics↗

bioRxiv2025

Somatosensory timing and cerebellar-basal ganglia beta-band interactions in Parkinson's disease

Rhythmic cognition relies on the interplay between endogenous and exogenous temporal structures, allowing organisms to anticipate and adapt to events in time. Neural oscillations, particularly in the beta-band (14-30 Hz), have been implicated in this predictive capacity, coordinating sensorimotor networks that support timing and rhythm perception. Parkinson's disease (PD), a movement disorder characterized by disrupted basal ganglia function, offers a unique framework to probe the neural mechanisms underlying rhythmic cognition. We estimated beta-band source activity and connectivity across the cerebellum, basal ganglia, thalamus, and motor cortex during a rhythmic somatosensory timing task to compare brain activity between participants with PD (n=24) and age-matched controls (n=25). Using magnetoencephalography, participants received tactile stimuli in regular (non-jittered) and irregular (jittered) sequences, with the final stimulus omitted to probe prediction before and evaluation after the omission. We found prediction-related differences between participants with PD and controls in the cerebellum and basal ganglia before the omissions, while replicating earlier findings in controls, consistent with cerebellar evaluation responses after the omissions. In PD, basal ganglia activity showed greater activity for jittered over non-jittered somatosensory rhythms, opposite to controls. Altered cerebellar beta-band responses correlated with PD symptom severity. Connectivity analyzes showed group-by-regularity interactions in a sensory-integration network. These findings suggest that temporal prediction and evaluation in rhythmic cognition rely on coordinated beta-band dynamics across the cerebello-striatal-thalamo-cortical network. In PD, disrupted basal ganglia function may be associated with increased cerebellar engagement and altered network dynamics under irregular temporal conditions, potentially reflecting differences in the processing of rhythmic structure.

neuroscience↗

bioRxiv2022

The miR-15a/16-1 and miR-15b/16-2 clusters regulate early B cell development by limiting IL-7 receptor expression

Pleiotropic functions of miRNAs as transcriptional repressors have been reported for multiple biological processes. One prominent miRNA family is the miR-15 family, which is a well-established tumor-suppressor in B-cell chronic lymphocytic leukemia (CLL). The miR-15 family consists of three bicistronic clusters, miR-15a/16-1, miR-15b/16-2 and miR-497/195, all sharing the same seed sequence suggesting that loss one cluster can be functionally compensated by the remaining miR-15 family members. Thus, a combined deletion may be necessary to reveal its physiological function in vivo. A combined knockout of the most prominent miR-15 clusters, miR-15a/16-1 and miR-15b/16-2 in the hematopoietic system reveals a novel role of the miR-15 family in early B cell development highlighted by an increase of the pro-B cell compartment. Mechanistically, this effect is mediated by enhanced IL-7 receptor expression, which we identified as direct miR-15 target gene. Notably, elevated IL-7 receptor levels were sufficient to trigger increased activation of the STAT5 and PI3K/AKT pathways. Moreover, derepression of directly targeted cell cycle regulators such as Ccne1, Chek1 and Wee1 further facilitates G-to-S transition. Thus, by deregulating a target gene network of cell cycle and signaling mediators, loss of the miR-15 family establishes a pro-proliferative milieu manifesting in an enlarged pro-B cell pool.

immunology↗

bioRxiv2020

A Genome-wide Library of MADM Mice for Single-Cell Genetic Mosaic Analysis

Mosaic Analysis with Double Markers (MADM) offers a unique approach to visualize and concomitantly manipulate genetically-defined cells in mice with single-cell resolution. MADM applications include the analysis of lineage; single-cell morphology and physiology; genomic imprinting phenotypes; and dissection of cell-autonomous gene functions in vivo in health and disease. Yet, MADM could only be applied to <25% of all mouse genes on select chromosomes thus far. To overcome this limitation, we generated transgenic mice with knocked-in MADM cassettes near the centromeres of all 19 autosomes and validated their use across organs. With this resource, >96% of the entire mouse genome can now be subjected to single-cell genetic mosaic analysis. Beyond proof-of-principle, we applied our MADM library to systematically trace sister chromatid segregation in distinct mitotic cell lineages. We found striking chromosome-specific biases in segregation patterns, reflecting a putative mechanism for the asymmetric segregation of genetic determinants in somatic stem cell division.

developmental biology↗