Biology subjects

Kaestel-Hansen, J.

Publications and source records attributed to Kaestel-Hansen, J..

3 recordsLinked to original sources

bioRxiv2023

Assessing the performance of protein regression models

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.

bioinformatics↗

bioRxiv2023

SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis.

The morphology of protein assemblies impacts their behavior and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data; time-resolved insulin aggregates and imaging of nuclear pore complexes. SEMORE extracts and quantifies all protein assemblies enabling classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being the first time-awar e framework can also support the rise of 4D super-resolution data.

biophysics↗

bioRxiv2023

Dimensional Reduction for Single Molecule Imaging of DNA and Nucleosome Condensation by Polyamines, HP1α and Ki-67

Macromolecules organize themselves into discrete membrane-less compartments. Mounting evidence has suggested that nucleosomes as well as DNA itself can undergo clustering or condensation to regulate genomic activity. Current in vitro condensation studies provide insight into the physical properties of condensates, such as surface tension and diffusion. However, such studies lack the resolution needed for complex kinetic studies of multicomponent condensation. Here, we use a supported lipid bilayer platform in tandem with total internal reflection microscopy to observe the 2-dimensional movement of DNA and nucleosomes at the single-molecule resolution. This dimensional reduction from 3-dimensional studies allows us to observe the initial condensation events and dissolution of these early condensates in the presence of physiological condensing agents. Using polyamines, we observed that the initial condensation happens on a timescale of minutes while dissolution occurs within seconds upon charge inversion. Polyamine valency, DNA length and GC content affect threshold polyamine concentration for condensation. Protein-based nucleosome condensing agents, HP1 and Ki-67, have much lower threshold concentration for condensation than charge-based condensing agents, with Ki-67 being the most effective as low as 100 pM for nucleosome condensation. In addition, we did not observe condensate dissolution even at the highest concentrations of HP1 and Ki-67 tested. We also introduce a two-color imaging scheme where nucleosomes of high density labeled in one color is used to demarcate condensate boundaries and identical nucleosomes of another color at low density can be tracked relative to the boundaries after Ki-67 mediated condensation. Our platform should enable the ultimate resolution of single molecules in condensation dynamics studies of chromatin components under defined physicochemical conditions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/522433v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1ed0cc9org.highwire.dtl.DTLVardef@1e26b5dorg.highwire.dtl.DTLVardef@1f6e73corg.highwire.dtl.DTLVardef@c7227b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗