Biology subjects

Gaalswyk, K.

Publications and source records attributed to Gaalswyk, K..

5 recordsLinked to original sources

bioRxiv2026

Highly diverged non-vertebrate Ki-67 proteins with conserved biophysical and functional properties

The Ki-67 protein is a widely used marker of mammalian cell proliferation that plays critical roles in heterochromatin organisation and mitotic perichromosomal layer formation. Thus, its apparent restriction to vertebrates is puzzling. However, sequences with limited homology to subdomains of Ki-67 are present in genomes from some non-vertebrate species. Here, using structural modelling, biophysical and functional analysis, we identify Ki-67-like proteins across diverse non-vertebrate eukaryotes, including tunicates, sponges, molluscs, insects and fungi. Despite extremely low sequence homology, the disordered domains of Ki-67 homologues have generally conserved biophysical and molecular properties. This is further reflected by comparison of cellular localisation, dynamics and effects on chromatin organisation of human, Ciona and Drosophila Ki-67 homologues. These results reinforce the emerging paradigm that functions may be encoded by general biophysical features in highly diverged intrinsically disordered domains. Our approach provides a blueprint for comparative biology of disordered proteins and a foundation for future studies exploring functions of Ki-67 homologues across eukaryotes.

Cell Biology↗

bioRxiv2024

MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics

Integrative structural biology synergizes experimental data with computational methods to elucidate the structures and interactions within biomolecules, a task that becomes critical in the absence of high-resolution structural data. A critical step in integrating the data, is knowing the expected accuracy or belief in the dataset. We previously showed that the Modeling Employing Limited Data (MELD) approach succeeds at predicting structures and finding the best interpretation of the data when the initial belief is equal or slightly lower than the real value. But, this initial belief is not known for many data sources. Here we introduce MELD-Adapt, designed to dynamically evaluate and infer the reliability of input data, thereby enhancing the accuracy of structure predictions. We demonstrate the utility of this method across different systems, particularly emphasizing its capability to correct initial assumptions and identify the correct fraction of data to produce reliable structural models. The approach is tested with two benchmark sets: the folding of twelve proteins with coarse physical insights and the binding of peptides with varying affinities to the Extrateterminal (ET) domain. Our findings also show the importance of how data is distributed in the system (locally clustered or globally distributed) and the subtle balance between data, beliefs, and force fields.

biophysics↗

bioRxiv2018

High accuracy protein structures from minimal sparse paramagnetic solid-state NMR restraints

There is a pressing need for new computational tools to integrate data from diverse experimental approaches in structural biology. We present a strategy that combines sparse paramagnetic solid-state NMR restraints with physics-based atomistic simulations. Our approach explicitly accounts for uncertainty in the interpretation of experimental data through the use of a semi-quantitative mapping between the data and the restraint energy that is calibrated by extensive simulations. We apply our approach to solid-state NMR data for the model protein GB1 labeled with Cu2+-EDTA at six different sites. We are able to determine the structure to ca. 1 [A] accuracy within a single day of computation on a modest GPU cluster. We further show that in 4 of 6 cases, the data from only a single paramagnetic tag are sufficient to fold the protein to high accuracy.

biochemistry↗

bioRxiv2017

The emerging role of physical modeling in the future of structure determination

Biomolecular structure determination has long relied on heuristics based on physical insight; however, recent efforts to model conformational ensembles and to make sense of sparse, ambiguous, and noisy data have revealed the value of detailed, quantitative physical models in structure determination. We review these two key challenges, describe different approaches to physical modeling in structure determination, and illustrate several successes and emerging technologies enabled by physical modeling.\n\nHighlightsO_LIQuantitative physical modeling is emerging as a key tool in structure determination\nC_LIO_LIThere are different approaches to incorporate physical modeling into structure determination\nC_LIO_LIModeling conformational ensembles and making sense of sparse, noisy, and ambiguous data are two challenges where physical modeling can play a prominent role\nC_LI

biochemistry↗

bioRxiv2017

On-line Optimization of Hamiltonian Replica Exchange Simulations

Replica exchange is a widely used sampling strategy in molecular simulation. While a variety of methods exist for optimizing temperature replica exchange, less is known about how to optimize more general Hamiltonian replica exchange simulations. We present an algorithm for the on-line optimization of both temperature and Hamiltonian replica exchange simulations that draws on techniques from the optimization of deep neural networks in machine learning. We optimize a heuristic-based objective function capturing the efficiency of replica exchange. Our approach is general, and has several desirable properties, including: (1) it makes few assumptions about the system of interest; (2) optimization occurs on-line wihout the requirement of pre-simulation; and (3) it readily generalizes to systems where there are multiple control parameters per replica. We explore some general properties of the algorithm on a simple harmonic oscillator system, and demonstrate its effectiveness on a more complex data-guided protein folding simulation.

biophysics↗