Biology subjects

Hou, J.

Publications and source records attributed to Hou, J..

At least 109 records · Page 6Linked to original sources

bioRxiv2021

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system in the three main aspects: (1) a new deep-learning based protein inter-residue distance predictor (DeepDist) to improve template-free (ab initio) tertiary structure prediction, (2) an enhanced template-based tertiary structure prediction method, and (3) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked 7th out of 146 predictors in protein tertiary structure prediction and ranked 3rd out of 136 predictors in inter-domain structure prediction. The results of MULTICOM demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. The performance of template-free tertiary structure prediction largely depends on the accuracy of distance predictions that is closely related to the quality of multiple sequence alignments. The structural model quality assessment works reasonably well on targets for which a sufficient number of good models can be predicted, but may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed.

bioinformatics↗

bioRxiv2021

Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). During the 2020 CASP14 experiment, we developed and tested several EMA predictors that used deep learning with the new features based on inter-residue distance/contact predictions as well as the existing model quality features. The average global distance test (GDT-TS) score loss of ranking CASP14 structural models by three multi-model MULTICOM EMA predictors (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) is 0.073, 0.079, and 0.081, respectively, which are ranked first, second, and third places out of 68 CASP14 EMA predictors. The single-model EMA predictor (MULTICOM-DEEP) is ranked 10th place among all the single-model EMA methods in terms of GDT_TS score loss. The results show that deep learning and contact/distance predictions are useful in ranking and selecting protein structural models.

bioinformatics↗

bioRxiv2021

SynthQA - Hierarchical Machine Learning-based Protein Quality Assessment

MotivationIt has been a challenge for biologists to determine 3D shapes of proteins from a linear chain of amino acids and understand how proteins carry out lifes tasks. Experimental techniques, such as X-ray crystallography or Nuclear Magnetic Resonance, are time-consuming. This highlights the importance of computational methods for protein structure predictions. In the field of protein structure prediction, ranking the predicted protein decoys and selecting the one closest to the native structure is known as protein model quality assessment (QA), or accuracy estimation problem. Traditional QA methods dont consider different types of features from the protein decoy, lack various features for training machine learning models, and dont consider the relationship between features. In this research, we used multi-scale features from energy score to topology of the protein structure, and proposed a hierarchical architecture for training machine learning models to tackle the QA problem. ResultsWe introduce a new single-model QA method that incorporates multi-scale features from protein structures, utilizes the hierarchical architecture of training machine learning models, and predicts the quality of any protein decoy. Based on our experiment, the new hierarchical architecture is more accurate compared to traditional machine learning-based methods. It also considers the relationship between features and generates additional features so machine learning models can be trained more accurately. We trained our new tool, SynthQA, on the CASP dataset (CASP10 to CASP12), and validated our method on 33 targets from the latest CASP 14 dataset. The result shows that our method is comparable to other state-of-the-art single-model QA methods, and consistently outperforms each of the 14 used features. Availabilityhttps://github.com/Cao-Labs/SynthQA.git Contactcaora@plu.edu

bioinformatics↗

bioRxiv2020

Negative durotaxis: cell movement toward softer environments

Durotaxis - the ability of cells to sense and migrate along stiffness gradients - is important for embryonic development and has been implicated in pathologies including fibrosis and cancer. Although cellular processes can sometimes turn toward softer environments, durotaxis at the level of cells has thus far been observed exclusively as migration from soft to stiff regions. The molecular basis of durotaxis, especially the factors that contribute to different durotactic behaviors in various cell types, are still inadequately understood. With the recent discovery of optimal stiffness, where cells generate maximal traction forces on substrates in an intermediate stiffness range, we hypothesized that some migratory cells may be capable of moving away from stiff environments and toward matrix on which they can generate more traction. Combining hydrogel-based stiffness gradients, live-cell imaging, genetic manipulations, and computational modeling, we found that cells move preferentially toward their stiffness optimum for maximal force transmission. Importantly, we directly observed biased migration toward softer environments, i.e. negative durotaxis, in human glioblastoma cells. This directional migration did not coincide with changes in FAK, ERK or YAP signaling, or with altered actomyosin contractility. Instead, integrin-mediated adhesion and motor-clutch dynamics alone are sufficient to generate asymmetric traction to drive both positive and negative durotaxis. We verified this mechanistically by applying a motor-clutch-based model to explain negative durotaxis in the glioblastoma cells and in neurites, and experimentally by switching breast cancer cells from positive to negative durotaxis via talin downregulation. Our results identify the likely molecular mechanisms of durotaxis, with a cells contractile and adhesive machinery dictating its capacity to exert traction on mechanically distinct substrates, directing cell migration.

biophysics↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗

bioRxiv2020

Small Molecule Channels Harness Membrane Potential to Concentrate Potassium in trk1Δtrk2Δ Yeast

Many protein ion channels harness membrane potential to move ions in opposition to their chemical gradient. Deficiencies of such proteins cause several human diseases, including cystic fibrosis, Bartter Syndrome Type II, and proximal renal tubular acidosis. Using yeast as a readily manipulated eukaryotic model system, we asked whether, in the context of a deficiency of such protein ion channel function in vivo, small molecule channels could similarly harness membrane potential to concentrate ions. In yeast, Trk potassium transporters use membrane potential to move potassium ions from a compartment of relatively low concentration outside cells ([~]15mM) to one of >10 times higher concentration inside (150-500mM). trk1{Delta}trk2{Delta} yeast are missing these potassium transporters and thus cannot concentrate potassium or grow in standard media. Here we show that potassium permeable, but not potassium selective, small molecule ion channels formed by the natural product amphotericin B can harness membrane potential to concentrate potassium in trk1{Delta}trk2{Delta} cells and thereby restore growth. This finding expands the list of potential human channelopathies that might be addressed by a molecular prosthetics approach.

physiology↗

bioRxiv2020

DeepDist: real-value inter-residue distance prediction with deep residual network

MotivationDriven by deep learning techniques, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently all the distance prediction methods classify inter-residue distances into multiple distance intervals (i.e. a multi-classification problem) instead of directly predicting real-value distances (i.e. a regression problem). The output of the former has to be converted into real-value distances in order to be used in tertiary structure prediction. ResultsTo explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. We demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone, indicating their complementarity. On 43 CASP13 hard domains, the average mean square error (MSE) of DeepDists real-value distance predictions is 0.896 [A] when filtering out the predicted distance >=16 [A], which is lower than 1.003 [A] of DeepDists multi-class distance predictions. When the predicted real-value distances are converted to binary contact predictions at 8[A] threshold, the precisions of top L/5 and L/2 contact predictions are 78.6% and 64.5%, respectively, higher than the best results reported in the CASP13 experiment. These results demonstrate that the real-value distance prediction can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE.

bioinformatics↗

bioRxiv2020

Two antagonistic effect genes mediate separation of sexes in a fully dioecious plant

Plant sex determining systems and sex chromosomes are often evolutionarily young. Here, we present the early stage of sex chromosome in a fully dioecious plant, P. deltoides, by determining separate sequences of the physically small X- and Y-linked regions. Intriguingly, two Y genes are absent from the X counterpart. One gene represses female structures by producing siRNAs that block expression of a gene necessary for development of female structures, via RNA-directed DNA methylation and siRNA-guided mRNA cleavage. The other gene generates long non-coding RNA transcripts that, in males, soak up miRNAs that specifically inhibit androecium development. Transformation experiments in Arabidopsis thaliana show that the two genes affect gynoecium and androecium development independently and antagonistically. Sex determination in the poplar therefore has the properties proposed for the first steps in the evolution of dioecy in flowering plants, with two genes whose joint effects favor close linkage, as is observed in poplar.

plant biology↗

bioRxiv2020

PD-L1 upregulation by IFN-α/γ-mediated Stat1 suppresses anti-HBV T cell response

Programmed death ligand 1 (PD-L1) has been recently shown to be a major obstacle to antiviral immunity by binding to its receptor programmed death 1 (PD-1) on specific IFN-{gamma} producing T cells in chronic hepatitis B. Currently, IFN- is widely used to treat hepatitis B virus(HBV) infection, but its antiviral effect vary greatly and the mechanism is not totally clear. We found that IFN-/{gamma} induced a marked increase of PD-L1 expression in hepatocytes. Signal and activators of transcription (Stat1) was then identified as a major transcription factor involved in IFN-/{gamma}-mediated PD-L1 elevation both in vitro and in mice. Blockage of the PD-L1/PD-1 interaction by a specific mAb greatly enhanced HBV-specific T cell activity by the gp96 adjuvanted therapeutic vaccine, and promoted HBV clearance in HBV transgenic mice. Our results demonstrate the IFN-/{gamma}-Stat1-PD-L1 axis plays an important role in mediating T cell hyporesponsiveness and inactivating liver-infiltrating T cells in the hepatic microenvironment. These data raise further potential interest in enhancing the anti-HBV efficacy of IFN- and therapeutic vaccines.

molecular biology↗

bioRxiv2019

Kinetic Analysis of Lipid Metabolism in Live Breast Cancer Cells via Nonlinear Optical Microscopy

Investigating the behavior of breast cancer cells via reaction kinetics may help unravel the mechanisms that underlie metabolic changes in tumors. However, obtaining human in vivo kinetic data is challenging due to difficulties associated with measuring these parameters. Non-destructive methods of measuring lipid content in live cells, provide a novel approach to quantitatively model lipid synthesis and consumption. In this study, two-photon excited fluorescence (TPEF) was used to determine metabolic rates via the cells optical redox ratio (ORR) as reported by fluorescence intensity ratios of metabolic coenzymes, nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD+). Concurrently, coherent Raman scattering (CRS) microscopy was used to probe de novo intracellular lipid content. Combining non-linear optical microscopy and Michaelis-Menten-kinetics based simulations, we isolated fatty acid synthesis/consumption rates and elucidated effects of altered lipid metabolism in T47D breast cancer cells. When treated with 17{beta}-Estradiol (E2), cancer cells showed a 3-fold increase in beta-oxidation rate as well as a 50% increase in cell proliferation rate. Similarly, the rate of de novo lipid synthesis in cancer cells treated with E2 was increased by 60%. Furthermore, we treated T47D cells with etomoxir (ETO) and observed that cancer cells treated with ETO exhibited a [~]70% reduction in {beta}-oxidation. These results show the ability to probe lipid alterations in live cells with minimum interruption, to characterize both glucose and lipid metabolism in breast cancer cells via quantitative kinetic models and parameters.\n\nStatement of SignificanceCombining non-linear optical microscopy (NLOM) and deuterium labeling provides insight into lipid metabolism in live cancer cells during cancer development and progression. The dynamic metabolic data is modelled with Michaelis-Menten-kinetics to independently quantify the lipid synthesis and utilization in cancer cells. Changes in lipid levels are found to originate from de novo lipid synthesis using glucose as a source, lipid consumption from {beta}-oxidation and lipid consumption from cell proliferation, processes that can separately analyzed with the Michaelis-Menten model. In this work, we isolate fatty acid synthesis/consumption rates and elucidated effects of altered lipid metabolism in T47D breast cancer cells in response to estradiol stimulation and etomoxir treatment, dynamic processes that cannot be easily observed without the application of appropriate models.

bioengineering↗

bioRxiv2019

A novel antiviral lncRNA EDAL shields a T309 O-GlcNAcylation site to promote EZH2 degradation

The central nervous system (CNS) is vulnerable for viral infection, yet few host factors in the CNS are known to defend invasion by neurotropic viruses. We report here that multiple neurotropic viruses, including rabies virus (RABV), vesicular stomatitis virus (VSV), Semliki Forest virus (SFV) and herpes simplex virus 1 (HSV-1), elicit the neuronal expression of a host-encoded lncRNA EDAL. EDAL inhibits the replication of these neurotropic viruses in neuronal cells and RABV infection in mouse brains. EDAL binds to the conserved histone methyltransferase enhancer of zest homolog 2 (EZH2) and specifically causes EZH2 degradation via lysosomes, reducing the cellular H3K27me3 level. The antiviral function of EDAL resides in a 56-nt antiviral substructure through which its 18-nt helix-loop intimately contacts multiple EZH2 sites surrounding T309, a known O-GlcNAcylation site. EDAL positively regulate the transcription of Pcp4l1 encoding a 10 kDa peptide, which inhibits the replication of mutiple neurotropic viruses. Our findings proposed a model in which a neuronal lncRNA can exert an effective antiviral function via blocking a specific O-GlcNAcylation that determines EZH2 lysosomal degradation.

microbiology↗

bioRxiv2019

γ-Secretase controls the specification of astrocytes from oligodendrocyte precursor cells via Stat3

Oligodendrocytes (OLs) and astrocytes play critical roles in a variety of brain functions. OL precursor cells (OPCs) are known to give rise to OLs as well as astrocytes. However, little is known about the mechanism by which OPCs determine their specification choice for OLs versus astrocytes in the central nervous system (CNS). Here we show that genetic inhibition of {gamma}-secretase in OPCs reduces OL differentiation but enhances astrocyte specification. Mechanistic analysis reveals that inhibition of {gamma}-secretase results in decreased levels of Hes1, and that Hes1 down-regulates the expression of signal transducer and activator of transcription3 (Stat3) via binding to specific regions of its promoter. We demonstrate that conditional inactivation of Stat3 in OL lineages restores the number of astrocytes in {gamma}-secretase mutant mice. In summary, this study identifies a key mechanism which controls OPCs specification choice for OL versus astrocyte during postnatal development. This {gamma}-secretase-dependent machinery may be essential for the CNS to maintain the population balance between OLs and astrocytes.

neuroscience↗

bioRxiv2019

Pharyngeal motor cortex grey matter abnormalities and retinal photoreceptor layer dysfunction in macaques exposed to Zika virus in utero

Congenital Zika virus (ZIKV) exposure results in a spectrum of disease ranging from severe birth defects to delayed onset neurodevelopmental deficits. ZIKV-related neuropathogenesis, predictors of birth defects, and neurodevelopmental deficits are not well defined in people. Here we assess the methodological and statistical feasibility of a congenital ZIKV exposure macaque model for identifying infant neurobehavior and brain abnormalities that may underlie neurodevelopmental deficits. We inoculated five pregnant macaques with ZIKV and mock-inoculated one macaque in the first trimester. Following birth, growth, ocular structure/function, brain structure, hearing, histopathology, and neurobehavior were quantitatively assessed during the first week of life. We identified the typical pregnancy outcomes of congenital ZIKV infection, with fetal demise and placental abnormalities. We estimated sample sizes needed to define differences between groups and demonstrated that future studies quantifying brain region volumes, retinal structure, hearing, and visual pathway function require a sample size of 14 animals per group (14 ZIKV, 14 control) to detect statistically significant differences in at least half of the infant exam parameters. Establishing the parameters for future studies of neurodevelopmental outcomes following congenital ZIKV exposure in macaques is essential for robust and rigorous experimental design.

microbiology↗

bioRxiv2019

Intracellular XBP1-IL-24 axis dismantles cytotoxic unfolded protein response in the liver

Endoplasmic reticulum (ER) stress-associated cell death is prevalent in various liver diseases. However, the determinant mechanism how hepatocytes survive unresolved stress was still unclear. Interleukin-24 (IL-24) was previously found to promote ER stress-mediated cell death, and yet its expression and function in the liver remained elusive. Here we identified an anti-apoptotic role of IL-24, which transiently accumulated within ER-stressed hepatocytes in a X-box binding protein 1 (XBP1)-dependent manner. Disruption of IL-24 increased cell death in the CCL4- or APAP-challenged mouse liver or Tm-treated hepatocytes. In contrast, pharmaceutical blockade of eukaryotic initiation factor 2 (eIF2) or genetical ablation of C/EBP homologous protein (CHOP) restored hepatocyte function in the absence of IL-24. In a clinical setting, patients with acute liver failure manifested a profound decrease of hepatic IL-24 expression, which was associated with disease progression. In conclusion, intrinsic hepatocyte IL-24 maintains ER homeostasis by restricting the eIF2-CHOP pathway-mediated stress signal, which might be exploited as a bio-index for prognosis or therapeutic intervention in patients with liver injury.

cell biology↗

bioRxiv2019

Interpretable attention model in transcription factor binding site prediction with deep neural networks

Due to the complexity of the biological factors that may influence the binding of transcription factors to DNA sequences, prediction of the potential binding sites remains a difficult task in computational biology. The attention mechanism in deep learning has shown its capability to learn from input features with long-range dependencies. Until now, no study has applied this mechanism in deep neural network models with input data from massively parallel sequencing. In this study, we aim to build a model for binding site prediction with the combination of attention mechanism and traditional deep learning techniques, including convolutional neural networks and recurrent neural networks. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets.\n\nThe benchmark shows that our implementation with attention mechanism (called DeepGRN) improves the performance of the deep learning models. Our model achieves better performance in at least 9 of 13 targets than any of the methods participated in the DREAM challenge. Visualization of the attention weights extracted from the trained models reveals how those weights shift when binding signal peaks move along the genomic sequence, which can interpret how the predictions are made. Case studies show that the attention mechanism helps to extract useful features by focusing on regions that are critical to successful prediction while ignoring the irrelevant signals from the input.

bioinformatics↗

bioRxiv2019

DNSS2: improved ab initio protein secondary structure prediction using advanced deep learning architectures

MotivationAccurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction.\n\nResultsThe major improvements over the DNSS1 method include (i) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict secondary structure, and (ii) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on two independent test datasets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the 82 protein targets of 2018 CASP13 experiment and achieved the best Q3 score of 83.74% and SOV score of 72.46%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.

bioinformatics↗

bioRxiv2019

Extensive impact of low-frequency variants on the phenotypic landscape at population-scale

Genome-wide association studies (GWAS) allows to dissect the genetic basis of complex traits at the population level1. However, despite the extensive number of trait-associated loci found, they often fail to explain a large part of the observed phenotypic variance2-4. One potential source of this discrepancy could be the preponderance of undetected low-frequency genetic variants in natural populations5,6. To increase the allele frequency of those variants and assess their phenotypic effects at the population level, we generated a diallel panel consisting of 3,025 hybrids, derived from pairwise crosses between a subset of natural isolates from a completely sequenced 1,011 Saccharomyces cerevisiae population. We examined each hybrid across a large number of growth traits, resulting in a total of 148,225 cross/trait combinations. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a significant proportion (29%) is governed by non-additive effects. This is confirmed by the fact that a majority of complete dominance is observed in 25% of the traits. By performing GWAS on the diallel panel, we detected 1,723 significantly associated genetic variants, with 16.3% of them being low-frequency variants in the initial population. These variants, which would not be detected using classical GWAS, explain 21% of the phenotypic variance on average. Altogether, our results demonstrate that low-frequency variants should be accounted for as they contribute to a large part of the phenotypic variation observed in a population.

genomics↗

bioRxiv2019

Deep convolutional neural networks for predicting the quality of single protein structural models

Predicting the global quality and local (residual-specific) quality of a single protein structural model is important for protein structure prediction and application. In this work, we developed a deep one-dimensional convolutional neural network (1DCNN) that predicts the absolute local quality of a single protein model as well as two 1DCNNs to predict both local and global quality simultaneously through a novel multi-task learning framework. The networks accept sequential and structural features (i.e. amino acid sequence, agreement of secondary structure and solvent accessibilities, residual disorder properties and Rosetta energies) of a protein model of any size as input to predict its quality, which is different from existing methods using a fixed number of hand-crafted features as input. Our three methods (InteractQA-net, JointQA-net and LocalQA-net) were trained on the structural models of the single-domain protein targets of CASP8, 9, 10 and evaluated on the models of CASP11 and CASP12 targets. The results show that the performance of our deep learning methods is comparable to the state-of-the-art quality assessment methods. Our study also demonstrates that combining local and global quality predictions together improves the global quality prediction accuracy. The source code and executable of our methods are available at: https://github.com/multicom-toolbox/DeepCovQA

bioinformatics↗