Biology subjects

Tomczak, J. M.

Publications and source records attributed to Tomczak, J. M..

5 recordsLinked to original sources

bioRxiv2026

Orthohantavirus-related Proteases as Therapeutic Targets: Opportunities for Antiviral Drug Development

Orthohantaviruses cause severe human diseases including hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS), with case fatality rates up to 40%. No FDA-approved therapeutics are currently available, highlighting urgent need for drug development following recent outbreak events. We systematically examined host protease dependencies in hantavirus replication, focusing on Signal Peptidase (SP) and Signal Peptide Peptidase (SPP) essential for viral glycoprotein maturation. Through comprehensive database mining and molecular docking analysis, we identified six potential protease inhibitors, with Compound E achieving the highest binding confidence score (-0.28) against SPP. Our analysis reveals that targeting host ER proteases represents a viable antiviral strategy, providing a systematic framework for protease-targeted antihantavirus drug development and identifying specific lead compounds for experimental validation.

microbiology↗

bioRxiv2025

rbio1-training scientific reasoning LLMs with biological world models as soft verifiers

Reasoning models are typically trained against verification mechanisms in formally specified systems such as code or symbolic math. In open domains like biology, however, we lack exact rules to enable large-scale formal verification and instead often rely on lab experiments to test predictions. Such experiments are slow, costly, and cannot scale with computation. In this work, we show that world models of biology or other prior knowledge can serve as approximate oracles for soft verification, allowing reasoning systems to be trained without additional experimental data. We present two paradigms of training models with approximate verifiers: RLEMF: reinforcement learning with experimental model feedback and RLPK: reinforcement learning from prior knowledge. Using these paradigms, we introduce rbio1, a reasoning model for biology post-trained from a pretrained LLM with reinforcement learning, using learned biological models for verification during training. We demonstrate that soft verification can distill biological world models into rbio1, enabling it to achieve state-of-the-art performance on perturbation prediction in the PerturbQA benchmark. We further show that composing multiple AI-verifiers improves performance and that models trained with soft biological rewards transfer zero-shot to cross-domain tasks such as disease-state prediction. We present rbio1 as a proof of concept that predictions from biological models can train powerful reasoning systems using simulations rather than experimental data, offering a new paradigm for model training.

bioinformatics↗

bioRxiv2020

GEM-based metabolic profiling for Human Bone Osteosarcoma under different glucose and glutamine availability

Cancer cell metabolism is dependent on cell-intrinsic factors like genetics, and cell-extrinsic factors like nutrient availability. In this context, understanding how these two aspects interact and how diet influences cellular metabolism is important for developing personalized treatment. In order to achieve this goal, genome-scale metabolic models (GEMs) are used, however, genetics and nutrient availability are rarely considered together. Here, we propose an integrated metabolic profiling, a framework that allows to enrich GEMs with metabolic gene expression data and information about nutrients. First, the RNA-seq is converted into Reaction Activity Score (RAS) to further scale reaction bounds. Second, nutrient availability is converted to Maximal Uptake Rate (MUR) to modify exchange reactions in a GEM. We applied our framework to the human osteosarcoma cell line (U2OS). Osteosarcoma is a common and primary malignant form of bone cancer with poor prognosis, and, as indicated in our study, a glutamine-dependent type of cancer.

systems biology↗

bioRxiv2020

Discovery of potent inhibitors of PLproCoV2 by screening libraries of selen-containing compounds

A collection of twelve organoselenium compounds, structural analogues of antioxidant drug ebselen were screened for inhibition of the papain-like protease (PLpro) from the acute respiratory syndrome coronavirus 2 (SARS-CoV-2, CoV2). This cysteine protease, being responsible for the hydrolysis of peptide bonds between specific amino acids, plays a critical role in CoV2 replication and in assembly of new viral particles within human cells. The activity of the PLpro CoV2 is essential for the progression of coronavirus disease 2019 (COVID-19) and it constitutes a key target for the development of anti-COVID-19 drugs. Here, we identified four strong inhibitors that bind favorably to the PLpro CoV2 with the IC50 in the nanomolar range.

biochemistry↗

bioRxiv2020

Ebselen as a highly active inhibitor of PLProCoV2

Since December 2019 a novel a coronavirus identified as SARS-CoV-2 or COV2 has been spreading around the world. On the 16th of May around 4.5 million people got infected and over 300,000 died due to the infection of COV2. The effective treatment remains a challenge. Targeted therapeutics are still under investigation. The papain-like protease (PLPro) from the human SARS-CoV-2 coronavirus is a cysteine protease that plays a critical role in virus replication. Its activity is required to process the viral polyprotein into functional, mature subunits. Moreover, COV2 uses this enzyme to modulate the hosts immune system to its own benefit. Therefore, it represents a highly promising target for the development of antiviral drugs. In this work, we discovered that ebselen, a synthetic organoselenium drug molecule with anti-inflammatory, anti-oxidant and cytoprotective activity in mammalian cells and cytotoxicity in lower organisms, is a highly active inhibitor of PLProCoV2. We proved that ebselen is a covalent, fast-binding inhibitor of PLProCoV2 exhibiting a low micromolar potency. Furthermore, we identified a difference between PLPro from SARS-CoV-1 (the corona virus which caused the 2002-2004 outbreak, SARS) and SARS-CoV-2 that allows to explain the difference in dynamics of the replication, and, thus, the disease progression. Namely, we present that they show differences in the binding affinity of substrates that we observed through kinetics and molecular docking studies. Using a novel Approximate Bayesian Computation method we were able to find kinetic constants for both enzymes. Molecular modeling study on the structure of the active site and binding mode of the ebselen with SARS and COV2 showed also significant differences that could explain our observation that ebselen is less active and slower bounding with SARS than COV2. In conclusion, we show that ebselen inhibits the activity of the essential viral enzyme papain-like protease (PLpro) from SARS-COV-2 in low micromolar range.

biochemistry↗