Biology subjects

Wu, M.-H.

Publications and source records attributed to Wu, M.-H..

4 recordsLinked to original sources

bioRxiv2024

Protein-ligand binding affinity prediction: Is 3D binding pose needed?

Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands in scenarios where crystallized protein-ligand binding conformations are unavailable. Now, with breakthroughs in deep learning AI-based protein folding and binding conformation prediction, can we improve binding affinity prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines protein-ligand binding conformations, and predicts binding affinities from three-dimensional protein-ligand binding structures. Our experimental results indicate that FDA performs comparably to state-of-the-art docking-free methods. We anticipate that our proposed framework serves as a starting point for integrating binding structures for more accurate binding affinity prediction.

bioinformatics↗

bioRxiv2023

Can motional dynamics account for the cytotoxicity of beta amyloid oligomers?

The underlying biophysical principle governing the cytotoxicity of the oligomeric aggregates of {beta}-amyloid (A{beta}) peptides has long been an enigma. Here we show that the size of A{beta}40 oligomers can be actively controlled by incubating the peptides in reverse micelles. Our approach allowed for the first time a detailed comparison of the structures and dynamics of two A{beta}40 oligomers of different size, viz., 10 and 23 nm, by solid-state NMR. From the chemical shift data, we infer that the conformation of the residues from K16 to K28 are different between the 10-nm and 23-nm oligomers. We find that the 10-nm oligomers are more cytotoxic, and the molecular motions of their charged residues are more dynamic. Interestingly, the residue A21 exhibits an unusually high structural rigidity. Our data raise the interesting possibility that the cytotoxicity of A{beta}40 oligomers could also be correlated to the motional dynamics of the charged residues.

biophysics↗

bioRxiv2022

Drosophila Mechanical Nociceptors Preferentially Sense Localized Poking

Mechanical nociception is an evolutionarily conserved sensory process required for the survival of living organisms. Previous studies have revealed much about the neural circuits and key sensory molecules in mechanical nociception, but the cellular mechanisms adopted by nociceptors in force detection remain elusive. To address this issue, we study the mechanosensation of a fly larval nociceptor (class IV da neurons, c4da) using a customized mechanical device. We find that c4da are sensitive to mN-scale forces and make uniform responses to the forces applied at different dendritic regions. Moreover, c4da showed a greater sensitivity to more localized forces, consistent with them being able to sense the poking of sharp objects, such as wasp ovipositor. Further analysis reveals that high morphological complexity, mechanosensitivity to lateral tension and active signal propagation in the dendrites altogether facilitate the mechanosensitivity and sensory features of c4da. In particular, we discover that Piezo and Ppk1/Ppk26, two key mechanosensory molecules, make differential but additive contributions to the mechanosensation of c4da. In all, our results provide updates into understanding how c4da process mechanical signals at the cellular level and reveal the contributions of key molecules.

neuroscience↗

bioRxiv2020

Rapidly predicting vancomycin resistance of Enterococcus faecium through MALDI-TOF MS spectra obtained in real-world clinical microbiology laboratory

Enterococcus faecium is one of the leading pathogens in the world. In this study, we proposed a strategy to rapidly and accurately distinguish vancomycin-resistant Enterococcus faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) to help doctors correctly determine the use of vancomycin by a machine learning (ML)-based algorithm. A predictive model was developed and validated to distinguish VREfm and VSEfm by analyzing MALDI-TOF MS spectra of unique E. faecium isolates from different specimen types. Firstly, 5717 mass spectra, including 2795 VREfm and 2922 VSEfm, were used to develop the algorithm. And 2280 mass spectra of isolates, namely 1222 VREfm and 1058 VSEfm, were used to externally validate the algorithm. The random forest-based algorithm demonstrated good classification performances for overall specimens, whose mean AUROC in 5-fold cross validation, time-wise validation, and external validation was all greater than 0.84. For the detection of VREfm in blood, sterile body fluid, urinary tract, and wound, the AUROC in external validation was also greater than 0.84. The predictions with algorithms were significantly more accurate than empirical antibiotic use. The accuracy of antibiotics administration could be improved by 30%. And the algorithm could provide rapid antibiotic susceptibility results at least 24 hours ahead of routine laboratory tests. The turn-around-time of antibiotic susceptibility could be reduced by 50%. In conclusion, a ML algorithm using MALDI-TOF MS spectra obtained in routine workflow accurately differentiated VREfm from VSEfm, especially in blood and sterile body fluid, which can be applied to facilitate the clinical testing process due to its accuracy, generalizability, and rapidness.

microbiology↗