Biology subjects

Han, K.-S.

Publications and source records attributed to Han, K.-S..

4 recordsLinked to original sources

bioRxiv2026

PredIDR3: A new output-encoding scheme and abundant negative source provide more information for deep learning-based protein intrinsic disorder prediction

Many computational methods to predict intrinsic disordered regions (IDRs) in proteins have been developed and their performances are blindly evaluated in community-driven assessment, Critical Assessment of protein Intrinsic Disorder (CAID). In this study, we developed PredIDR3 series, an updated version of PredIDR2 tested in CAID3 to accurately predict IDRs from protein sequences. It includes two methods depending on ensemble way. The performances of PredIDR3 series (AUC_ROC=0.953) are remarkably better than our previous PredIDR2 (AUC_ROC=0.936) on Disorder-PDB dataset of CAID3, which is thought to be mainly attributed to the use of more information for intrinsic disorder prediction based on deep convolutional neural network. In details, we introduced a new output-encoding scheme permitting a large sliding window (size=91) for the first time and extracted negative samples of the training set from non-IDRs of both PDB and DisProt databases, allowing to use more information for prediction of intrinsic disorder. PredIDR3 achieved comparable performance to the top-ranking methods of CAID3 in all criteria measured. PredIDR3 series can be freely available through the CAID Prediction Portal at https://caid.idpcentral.org/portal or downloaded as a Singularity container from https://biocomputingup.it/shared/caid-predictors/.

bioinformatics↗

bioRxiv2024

PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network

The involvement of protein intrinsic disorder in essential biological processes, it is well known in structural biology. However, experimental methods for detecting intrinsic structural disorder and directly measuring highly dynamic behavior of protein structure are limited. To address this issue, several computational methods to predict intrinsic disorder from protein sequences were developed and their performance is evaluated by the Critical Assessment of protein Intrinsic Disorder (CAID). In this paper, we describe a new computational method, PredIDR, which provides accurate prediction of intrinsically disordered regions in proteins, mimicking experimental X-ray missing residues. Indeed, missing residues in Protein Data Bank (PDB) were used as positive examples to train a deep convolutional neural network which produces two types of output for short and long regions. PredIDR took part in the second round of CAID and was as accurate as the top state-of-the-art IDR prediction methods. PredIDR can be freely used through the CAID Prediction Portal available at https://caid.idpcentral.org/portal or downloaded as a Singularity container from https://biocomputingup.it/shared/caid-predictors/.

bioinformatics↗

bioRxiv2021

Visualizing reactive astrogliosis extends survival in glioblastoma patients

Glioblastoma multiforme (GBM) is a devastating brain tumor with dismal prognosis of only 15-month survival regardless of surgical resection. Here, we report an advanced neuroimaging technique combining 11C-acetate PET and MRI (AcePET), visualizing the boundary beyond the MRI-defined tumor. Targeted biopsy of the regions with increased 11C-acetate uptake revealed the presence of reactive astrocytes with enhanced acetate-transporter MCT1, along with cancer stem cells. Reactive astrogliosis and MCT1-dependent 11C-acetate-uptake were recapitulated in U87MG-orthotopic models. Mechanistically, glycolytic tumor cells release excessive acetate causing reactive astrogliosis, leading to the release of aberrant astrocytic GABA and H2O2, which further down-regulate the neuronal glucose uptake through GLUT3. Clincally, AcePET-guided surgery allows complete tumor resection of infiltrating cancer stem cells and extends the overall survival of patients by 5.25 months compared to conventional MRI-guided surgery. We established a new concept of the metabolic interactions between GBM cells and neighboring neurons through reactive astrocytes and developed AcePET-guided surgery to fight against GBM.

cancer biology↗

bioRxiv2019

Climbing fiber synapses rapidly inhibit neighboring Purkinje cells via ephaptic coupling

Climbing fibers (CFs) from the inferior olive (IO) provide strong excitatory inputs onto the dendrites of cerebellar Purkinje cells (PC), and trigger distinctive responses known as complex spikes (CSs). We find that in awake, behaving mice, a CS in one PC suppresses conventional simple spikes (SSs) in neighboring PCs for several milliseconds. This involves a novel form of ephaptic coupling, in which an excitatory synapse nonsynaptically inhibits neighboring cells by generating large negative extracellular signals near their dendrites. The distance dependence of CS-SS ephaptic signaling, combined with the known divergence of CF synapses made by IO neurons, allows a single IO neuron to influence the output of the cerebellum by synchronously suppressing the firing of potentially over one hundred PCs. Optogenetic studies in vivo and dynamic clamp studies in slice indicate that such brief PC suppression can effectively promote firing in neurons in the deep cerebellar nuclei and motor thalamus.

neuroscience↗