Biology subjects

Czodrowski, P.

Publications and source records attributed to Czodrowski, P..

4 recordsLinked to original sources

bioRxiv2024

Protonation effects in protein-ligand complexes - a case study of endothiapepsin and pepstatin A with computational and experimental methods

1Protonation states serve as an essential molecular recognition motif for biological processes. Their correct consideration is key to successful drug design campaigns, since chemoinformatic tools usually deal with default protonation states of ligands and proteins and miss atypical protonation states. The protonation pattern for the Endothiapepsin/PepstatinA (EP/pepA) complex is investigated using different dry lab and wet lab techniques. ITC experiments revealed an uptake of more than one mole of protons upon pepA binding to EP. Since these experiments were performed at physiological conditions (and not at pH=4 at which a large variety of crystal structures is available), a novel crystal structure at pH=7.6 was determined. This crystal structure showed that only modest structural changes occur upon increasing the pH value. This lead to computational studies to reveal the exact location of the protonation event. Both computational studies could reveal a significant pKa shift resulting in non-default protonation state and that the catalytic dyad is responsible for the uptake of protons. This study shows that assessing protonation states for two separate systems (protein and ligand) might result in the incorrect assignment of protonation states and hence incorrect calculation of binding energy.

molecular biology↗

bioRxiv2024

Identification of SARS-CoV-2 Mpro inhibitors through deep reinforcement learning for de novo drug design and computational chemistry approaches

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic of coronavirus disease (COVID-19) since its emergence in December 2019. As of January 2024, there has been over 774 million reported cases and 7 million deaths worldwide.[1] While vaccination efforts have been successful in reducing the severity of the disease and decreasing the transmission rate, the development of effective therapeutics against SARS-CoV-2 remains a critical need.[2] The main protease (Mpro) of SARS-CoV-2 is an essential enzyme required for viral replication and has been identified as a promising target for drug development. In this study, we report the identification of novel Mpro inhibitors, using a combination of deep reinforcement learning for de novo drug design with 3D pharmacophore/shape-based alignment and privileged fragment match count scoring components followed by hit expansions and molecular docking approaches. Our experimentally validated results show that 3 novel series exhibit potent inhibitory activity against SARS-CoV-2 Mpro, with IC50 values ranging from 1.3 uM to 2.3 uM and a high degree of selectivity. These findings represent promising starting points for the development of new antiviral therapies against COVID-19.

molecular biology↗

bioRxiv2023

FSL-CP: A Benchmark for Small Molecule Activity Few-Shot Prediction using Cell Microscopy Images

Predicting small molecule activities using information from high-throughput microscopy images has been shown to tremendously increase hit rates and chemical diversity of the hits in previous drug discovery projects. However, due to high cost of acquiring data or ethical reasons, data sparsity remains a big challenge in drug discovery. This opens up the opportunity for few-shot prediction: fine-tuning a model on a low-data assay of interest after pretraining on other more populated assays. Previous efforts have been made to establish a benchmark for few-shot learning of molecules based on molecular structure. With cell images as a molecular representation, methods in the computer vision domain are also applicable for activity prediction. In this paper, we make two contributions: a) A public data set for few-shot learning with cell microscopy images for the scientific community, b) A range of baseline models encompassing different existing single-task, multi-task and meta-learning approaches.

pharmacology and toxicology↗

bioRxiv2022

Targeting oncogenic KRasG13C with nucleotide-based covalent inhibitors

Mutations within Ras proteins represent major drivers in human cancer. In this study, we report the structure-based design, synthesis, as well as biochemical and cellular evaluation of nucleotide-based covalent inhibitors for KRasG13C, an important oncogenic mutant of Ras that has not been successfully addressed in the past. Mass spectrometry experiments and kinetic studies reveal promising molecular properties of these covalent inhibitors, and X-ray crystallographic analysis has yielded the first reported crystal structures of KRasG13C covalently locked with these GDP analogues. Importantly, KRasG13C covalently modified with these inhibitors can no longer undergo SOS-catalysed nucleotide exchange. As a final proof-of-concept, we show that in contrast to KRasG13C, the covalently locked protein is unable to induce oncogenic signalling in cells, further highlighting the possibility of using nucleotide-based inhibitors with covalent warheads in KRasG13C-driven cancer.

cancer biology↗