HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures

Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are freely available at http://zhang.bioinformatics.ku.edu/HAAD

c-Met activation leads to the establishment of a TGFβ-receptor regulatory network in bladder cancer progression

Treatment of muscle-invasive bladder cancer remains a major clinical challenge. Aberrant HGF/c-MET upregulation and activation is frequently observed in bladder cancer correlating with cancer progression and invasion. However, the mechanisms underlying HGF/c-MET-mediated invasion in bladder cancer remains unknown. As part of a negative feedback loop SMAD7 binds to SMURF2 targeting the TGFβ receptor for degradation. Under these conditions, SMAD7 acts as a SMURF2 agonist by disrupting the intramolecular interactions within SMURF2. We demonstrate that HGF stimulates TGFβ signalling through c-SRC-mediated phosphorylation of SMURF2 resulting in loss of SMAD7 binding and enhanced SMURF2 C2-HECT interaction, inhibiting SMURF2 and enhancing TGFβ receptor stabilisation. This upregulation of the TGFβ pathway by HGF leads to TGFβ-mediated EMT and invasion. In vivo we show that TGFβ receptor inhibition prevents bladder cancer invasion. Furthermore, we make a rationale for the use of combinatorial TGFβ and MEK inhibitors for treatment of high-grade non-muscle-invasive bladder cancers

Nanoparticle-induced neuronal toxicity across placental barriers is mediated by autophagy and dependent on astrocytes

The potential for maternal nanoparticle (NP) exposures to cause developmental toxicity in the fetus without the direct passage of NPs has previously been shown, but the mechanism remained elusive. We now demonstrate that exposure of cobalt and chromium NPs to BeWo cell barriers, an in vitro model of the human placenta, triggers impairment of the autophagic flux and release of interleukin-6. This contributes to the altered differentiation of human neural progenitor cells and DNA damage in the derived neurons and astrocytes. Crucially, neuronal DNA damage is mediated by astrocytes. Inhibiting the autophagic degradation in the BeWo barrier by overexpression of the dominant-negative human ATG4BC74A significantly reduces the levels of DNA damage in astrocytes. In vivo, indirect NP toxicity in mice results in neurodevelopmental abnormalities with reactive astrogliosis and increased DNA damage in the fetal hippocampus. Our results demonstrate the potential importance of autophagy to elicit NP toxicity and the risk of indirect developmental neurotoxicity after maternal NP exposure