Biology subjects

Kooistra, A. J.

Publications and source records attributed to Kooistra, A. J..

4 recordsLinked to original sources

bioRxiv2026

PrimeKG-Plus: a literature-derived expansion of a multimodal precision medicine knowledge graph

BackgroundDisease-centered knowledge graphs (KGs) support drug repurposing and precision medicine research, yet many remain static after release while primary databases and literature continue to expand. PrimeKG (Precision Medicine Knowledge Graph) is a widely used multimodal KG providing a holistic view of diseases. However, its public release reflects a June 2021 data cutoff, omitting several years of subsequent data growth. This lag is especially consequential for rare diseases, where mechanistic and therapeutic evidence often remains scattered across publications rather than structured resources. FindingWe present PrimeKG-Plus, a refreshed, rare-disease-enriched release of PrimeKG, rebuilt from all twenty original data resources updated to their December 2025 releases and three additional resources: OpenTargets, RepurposeDrugs, and nSIDES. Beyond synchronizing biomedical databases, PrimeKG-Plus captures approximately five years of previously unavailable rare-disease knowledge from the biomedical literature, curated from 637 PubMed abstracts and PubMed Central full-text articles using a language-model-assisted workflow focused on four rare neurological disorders: Canavan disease, Niemann-Pick disease type C, Tay-Sachs disease, and Batten disease. Extracted relations were refined through entity normalization, UMLS synonym mapping, embedding-based similarity ranking, and human expert review. Network topology analysis showed improved indirect drug-disease connectivity across three to six hops and added 447,288 drug-protein-disease paths linking previously unreachable drug-disease pairs. Temporal validation using drug approval records identified 55 molecular entities approved after the original PrimeKG June 2021 cutoff, 46 of which were absent from the original graph. ConclusionPrimeKG-Plus restores the temporal relevance of PrimeKG, providing an updated resource for drug repurposing, rare-disease research, and downstream machine-learning applications.

bioinformatics↗

bioRxiv2025

Unlocking Scalable Ligand Residence Time Predictions with Koffee Unbinding Kinetics Simulations

A great number of drug discovery programs fail due to poor in vivo efficacy and ADMET liabilities. On- and off-target ligand residence times can act as important drivers of these problems. While modern experimental techniques have made measuring compound kinetics data more routine, there is a lack of accurate, high-throughput simulation techniques to guide compound prioritization by residence time. In this work, we introduce Koffee Unbinding Kinetics as a solution to the hitherto unanswered problem of scalable ligand-protein residence time prediction. By bypassing conventional approaches based on molecular dynamics simulations, Koffee Unbinding Kinetics performs physics-based residence time screening at the atomistic level in {approx} 1 GPU minute per complex using inexpensive hardware, a speed-up of at least 3 - 5 orders of magnitude compared to current state-of-the-art simulation approaches. Koffee Unbinding Kinetics can enhance compound selection to mitigate costly future program failures by adding fast, predictive residence time simulations to early-stage computational drug discovery pipelines. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/686759v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@120d5dcorg.highwire.dtl.DTLVardef@b86b67org.highwire.dtl.DTLVardef@193554forg.highwire.dtl.DTLVardef@1c5c6c1_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2020

Differential GLP-1R binding and activation by peptide and non-peptide agonists

Peptide drugs targeting class B1 GPCRs can treat multiple diseases, however there remains substantial interest in the development of orally delivered non-peptide drugs. Here we reveal unexpected overlap between signalling and regulation of the glucagon-like peptide-1 (GLP-1) receptor by the non-peptide agonist, PF 06882961, and GLP-1 that was not observed for another compound, OWL-833. Both compounds are currently in clinical trials for treatment of type 2 diabetes. High resolution cryo-EM structures reveal the binding sites for PF-06882961 and GLP-1 substantially overlap, whereas OWL-833 adopts a unique binding mode with a more open receptor conformation at the extracellular face. Structural differences involving extensive water-mediated hydrogen bond networks could be correlated to functional data to understand how PF 06882961, but not OWL-833, can closely mimic the pharmacological properties of GLP-1. These findings will facilitate rational structure-based discovery of non-peptide agonists targeting class B GPCRs.

pharmacology and toxicology↗

bioRxiv2019

Uncovering patterns of atomic interactions in static and dynamic structures of proteins

The number of structures and molecular dynamics simulations of proteins is exploding owing to dramatic advances in cryo-electron microscopy, crystallography, and computing. One of the most powerful ways to analyze structural information involves comparisons of interatomic interactions across different structures or simulations of the same protein or related proteins from the same family (e.g. different GPCRs). Such comparative analyses are of interest to a wide range of researchers but currently prove challenging for all but a few. To facilitate comparative structural analyses, we have developed tools for (i) rapidly computing and comparing interatomic interactions and (ii) interactively visualizing interactions to enable structure-based interpretations. Using these tools, we have developed the Contact Comparison Atlas, a web-based resource for the comparative analysis of interactions in structures and simulations of proteins. Using the Contact Comparison Atlas and our tools, we have identified patterns of interactions with functional implications in structures of G-protein-coupled receptors, G proteins and kinases and in the dynamics of muscarinic receptors. The Contact Comparison Atlas can be used to enable structure modeling, drug discovery, protein engineering, and the prediction of disease-associated mutations. Contact Comparison Atlas website: https://getcontacts.github.io/atlas/

biophysics↗