Biology subjects

Wei, T.

Publications and source records attributed to Wei, T..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

Scaffold-Lab: Critical Evaluation and Ranking of Protein Backbone Generation Methods in A Unified Framework

De novo protein design has undergone a rapid development in recent years, especially for backbone generation, which stands out as more challenging yet valuable, offering the ability to design novel protein folds with fewer constraints. However, a comprehensive delineation of its potential for practical application in protein engineering remains lacking, as does a standardized evaluation framework to accurately assess the diverse methodologies within this field. Here, we proposed Scaffold-Lab benchmark focusing on evaluating unconditional generation across metrics like designability, novelty, diversity, efficiency and structural properties. We also extrapolated our benchmark to include the motif-scaffolding problem, demonstrating the utility of these conditional generation models. Our findings reveal that FrameFlow and RFdiffusion in unconditional generation along with Rfdiffusion and GPDL in conditional generation showcased the most outstanding performances. Furthermore, we described a systematic study to investigate conditional generation and applied it to the motif-scaffolding task, offering a novel perspective for the analysis and development of conditional protein design methods. All data and scripts will be available at https://github.com/Immortals-33/Scaffold-Lab.

bioinformatics↗

bioRxiv2023

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-grained Molecular Dynamics Approach to Understanding Cofilin's Regulation on Actin Filaments

Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experimental work suggests that cofilin also forms functionally distinct oligomers through thiol post-translational modification (PTM) that encourages actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. We also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol post-translational modification of cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.

biophysics↗

bioRxiv2023

Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method

Proteins perform essential roles in numerous biological processes, largely driven by the three-dimensional structure of several key motif residues. Recently, a variety of energy-based and machine learning backbone generation methods have been developed to solve the motif-scaffolding task. However, it is still challenging to generate diverse and accurate scaffold structures around motifs for models either fine-tuned pre-trained multiple sequence alignment-based (MSA-based) structure prediction models or trained from scratch. Here, we introduced Generative Protein Design by Language model (GPDL) for effectively replacing traditional MSA-based pretraining. Using our scalable design strategy, GPDL successfully solved 22 out of 24 benchmark problems and outperformed other methods by generating 33.5% more unique designable clusters than RFdiffusion. This demonstrates that our approach can generate accurate and physically plausible structures across diverse protein design scenarios. GPDL also showed strong robustness in orphan proteins that have low sequence similarity with the training set. Our approach underscores the promise of protein language models in protein design and has the potential to accelerate the discovery of novel functional proteins for a wide range of biological and therapeutic applications.

bioinformatics↗

bioRxiv2023

M1 Macrophage-Related Genes Model for NSCLC Immunotherapy Response Prediction

2.Patients diagnosed with non-small cell lung cancer have a limited lifespan and exhibit poor immunotherapy outcomes. M1 macrophages have been found to be essential for anti-tumor immunity. This study aimed to develop an immunotherapy response evaluation model for NSCLC patients based on transcriptional expression. RNA sequencing profiles of 254 advanced-stage NSCLC patients treated with immunotherapy were downloaded from POPLAR and OAK projects. Immune cell infiltration in NSCLC patients has been examined, and thereafter different co-expressed genes were identified. Following that, the impact of M1 macrophage related genes on the prognosis of NSCLC patients was investigated. Six M1 macrophage co-expression genes, namely NKX2-1, CD8A, SFTA3, IL2RB, IDO1, and CXCL9, exhibited a strong association with the prognosis of NSCLC and served as effective predictors for immunotherapy response. A response model was constructed using Cox regression model and Lasso Cox regression analysis. The M1 genes were validated on our previous TD- FOREKNOW NSCLC clinical trial by RT-qPCR. The response model showed excellent immunotherapy response predicting and prognosis evaluating value in advanced stage of NSCLC. The model can effectively predict advanced NSCLC prognosis and aid in identifying patients who could benefit from customized immunotherapy as well as sensitive drugs.

immunology↗

bioRxiv2023

Pathogenic mitochondrial DNA mutations inhibit melanoma metastasis

Mitochondrial DNA (mtDNA) mutations are frequently observed in cancer, but their contribution to tumor progression is controversial. To evaluate the impact of mtDNA variants on tumor growth and metastasis, we created human melanoma cytoplasmic hybrid (cybrid) cell lines transplanted with wildtype mtDNA or pathogenic mtDNA encoding variants that partially or completely inhibit oxidative phosphorylation. Homoplasmic pathogenic mtDNA cybrids reliably established tumors despite dysfunctional oxidative phosphorylation. However, pathogenic mtDNA variants disrupted spontaneous metastasis of subcutaneous tumors and decreased the abundance of circulating melanoma cells in the blood. Pathogenic mtDNA did not induce anoikis or inhibit organ colonization of melanoma cells following intravenous injections. Instead, migration and invasion were reduced, indicating that limited circulation entry functions as a metastatic bottleneck amidst mtDNA dysfunction. Furthermore, analysis of selective pressure exerted on the mitochondrial genomes of heteroplasmic cybrid lines revealed a suppression of pathogenic mtDNA allelic frequency during melanoma growth. Collectively, these findings demonstrate that functional mtDNA is favored during melanoma growth and enables metastatic entry into the blood.

cancer biology↗

bioRxiv2023

HEARTSVG: a fast and accurate method for spatially variable gene identification in large-scale spatial transcriptomic data

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F1 scores (average F1 score=0.903), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average recall=0.985, average AUC=0.788) than other comparative methods without prespecifing spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

bioinformatics↗

bioRxiv2023

De novo Protein Sequence Design Based on Deep Learning and Validation on CalB Hydrolase

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological function, such as improving the catalytic efficiency of enzymes. As one of the key tasks of protein design, fixed-backbone protein sequence design aims to design novel sequence that would fold into a given protein backbone structure. However, current sequence design methods have limitations in terms of low sequence diversity and experimental validation of designed protein function, which cannot meet the needs of functional protein design. We firstly constructed Graphormer-based Protein Design (GPD) model that directly applies Transformer to graph-based representation of 3D protein structure, and added Gaussian noise and sequence random mask to node features to improve the sequence recovery and diversity. Additionally, functional filtering based on the structure folding, solubility, and function were performed to improve the success rate in experiments. The process of "sequence design-functional filtering -functional experiment" was carried out for CalB hydrolase. The experimental results showed that the specify activity of designed protein improved 1.7 times than CalB wild type. This design and filtering platform will be a valuable tool for generating industrial enzymes and protein drugs with specific functions.

biochemistry↗

bioRxiv2023

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural needs significantly limit its clinical application, highlighting the need for more accessible alternatives. To bridge this gap, we introduce the Integrated Graph and Image Deep Learning (IGI-DL) model. This innovation, a fusion of Convolutional Neural Networks and Graph Neural Networks, is designed to predict gene spatial expression using HE images. The IGI-DL model outperforms its predecessors in analyzing colorectal cancer (CRC), breast cancer, and cutaneous squamous cell carcinoma (cSCC) by leveraging both pixel intensity and structural features in images. Significantly, across all cancer types, the IGI-DL model enhances the mean correlation of the top five genes by an average of 0.125 in internal and external test sets, rising from 0.306 to 0.431, surpassing existing state-of-the-art (SOTA) models. We further present a novel risk score derived from a super-patch graph, where gene expression predicted by IGI-DL serves as node features. Demonstrating superior prognostic accuracy, this risk score, with a C-index of 0.713 and 0.741 for CRC and breast cancer, supersedes traditional HE-based risk scores. In summary, the approach augments our understanding of the TME from the aspect of histological images, portending a transformation in cancer prognostics and treatment planning and ushering in a new era of personalized and precision oncology.

bioinformatics↗

bioRxiv2023

iPSC-Astrocyte morphology reflects patient clinical markers

Human iPSCs provide powerful cellular models of Alzheimers disease (AD) and offer many advantages over non-human models, including the potential to reflect variation in individual-specific pathophysiology and clinical symptoms Previous studies have demonstrated that iPSC-neurons from individuals with Alzheimers disease (AD) reflect clinical markers, including {beta}-amyloid (A{beta}) levels and synaptic vulnerability. However, despite neuronal loss being a key hallmark of AD pathology, many risk genes are predominantly expressed in glia, highlighting them as potential therapeutic targets. In this work iPSC-derived astrocytes were generated from a cohort of individuals with high versus low levels of the inflammatory marker YKL-40, in their cerebrospinal fluid (CSF). iPSC-derived astrocytes were treated with exogenous A{beta} oligomers and high content imaging demonstrated a correlation between astrocytes that underwent the greatest morphology change from patients with low levels of CSF-YKL-40 and more protective APOE genotypes. This finding was subsequently verified using similarity learning as an unbiased approach. This study shows that iPSC-derived astrocytes from AD patients reflect key aspects of the pathophysiological phenotype of those same patients, thereby offering a novel means of modelling AD, stratifying AD patients and conducting therapeutic screens. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/548687v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1df3486org.highwire.dtl.DTLVardef@f184d8org.highwire.dtl.DTLVardef@48a33dorg.highwire.dtl.DTLVardef@d53a90_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

A flexible, efficient, and scalable platform to produce circular RNAs as new therapeutics

Messenger RNA (mRNA) has recently emerged as a new drug modality with great therapeutic potential. However, linear mRNAs are relatively unstable and require base modification to reduce their immunogenicity, imposing a limitation to the broad application. The circular RNA (circRNA) presents a better alternative for prolonged expression of the proteins. However the in vitro circularization of RNA at industrial scale is technically challenging, and the direct comparison of efficacy between the circRNA and linear mRNA drugs is lacking. Here we developed a new self-catalyzed system to efficiently produce circRNAs in a co-transcriptional fashion. By rational sequence design, we can efficiently produce scarless circRNAs that do not contain foreign sequences. The resulting circRNAs are very stable and have low immunogenicity, enabling prolonged protein translation in different cells without cellular toxicity. The circRNAs encapsulated in lipid nanoparticles can be efficiently delivered into mice to direct robust protein expression with improved duration and efficiency. Finally, the circRNAs encoding RBD of SARS-CoV-2 S protein induced strong antibody productions, with neutralization antibody titers higher than the preclinical data from the linear mRNAs. Collectively, this study provided a general platform for efficient production of circRNAs with robust activity in directing protein production, demonstrating the potential of circRNAs as the new generation of mRNA therapy.

bioengineering↗

bioRxiv2021

Neurons derived from individual early Alzheimer's disease patients reflect clinical vulnerability

Establishing preclinical models of Alzheimers disease that predict clinical outcomes remains a critically important, yet to date not fully realised, goal. Models derived from human cells offer considerable advantages over non-human models, including the potential to reflect some of the inter-individual differences that are apparent in patients. Here we report an approach using induced pluripotent stem cell-derived cortical neurons from people with early symptomatic Alzheimers disease where we sought a match between individual disease characteristics in cells with analogous characteristics in the people from whom they were derived. We show that the response to amyloid-{beta} burden in life, as measured by cognitive decline and brain activity levels, varies between individuals and this vulnerability rating correlates with the individual cellular vulnerability to extrinsic amyloid-{beta} in vitro as measured by synapse loss and function. Our findings indicate that patient induced pluripotent stem cell-derived cortical neurons not only present key aspects of Alzheimers disease pathology, but also reflect key aspects of the clinical phenotypes of the same patients. Cellular models that reflect an individuals in-life clinical vulnerability thus represent a tractable method of Alzheimers disease modelling using clinical data in combination with cellular phenotypes.

neuroscience↗

bioRxiv2021

A Potential Novel COVID-19 Vaccine With RBD-HR1/HR2 Hexamer Structure

The COVID-19 pandemic and the continued spreading of the SARS-CoV-2 variants have brought a grave public health consequence and severely devastated the global economy with recessions. Vaccination is considered as one of the most promising and efficient methods to end the COVID-19 pandemic and mitigate the disease conditions if infected. Although a few vaccines have been developed with an unprecedented speed, scientists around the world are continuing pursuing the best possible vaccines with innovations. Comparing to the expensive mRNA vaccines and attenuated/inactivated SARS-CoV-2 vaccines, recombinant protein vaccines have certain advantages, including their safety (non-virus components), potential stronger immunogenicity, broader protection, ease of scaling-up production, reduced cost, etc. In this study, we reported a novel COVID-19 vaccine generated with RBD-HR1/HR2 hexamer that was creatively fused with the RBD domain and heptad repeat 1 (HR1) or heptad repeat 2 (HR2) to form a dumbbell-shaped hexamer to target the spike S1 subunit. The novel hexamer COVID-19 vaccine induced high titers of neutralizing antibody in mouse studies (>100,000), and further experiments also showed that the vaccine also induced an alternative antibody to the HR1 region, which probably alleviated the drop of immunogenicity from the frequent mutations of SARS-CoV-2.

biochemistry↗

bioRxiv2021

Trivial Excitation Energy Transfer to Carotenoids is an Unlikely Mechanism for Non-Photochemical Quenching in LHCII

Higher plants defend themselves from bursts of intense light via the mechanism of Non-Photochemical Quenching (NPQ). It involves the Photosystem II (PSII) antenna protein (LHCII) adopting a conformation that favours excitation quenching. In recent years several structural models have suggested that quenching proceeds via energy transfer to the optically forbidden and short-lived S1 states of a carotenoid. It was proposed that this pathway was controlled by subtle changes in the relative orientation of a small number of pigments. However, quantum chemical calculations of S1 properties are not trivial and therefore its energy, oscillator strength and lifetime are treated as rather loose parameters. Moreover, the models were based either on a single LHCII crystal structure or Molecular Dynamics (MD trajectories) about a single minimum. Here we try and address these limitations by parameterizing the vibronic structure and relaxation dynamics of lutein in terms of observable quantities, namely linear absorption (LA) transient absorption (TA) and two-photon excitation (TPE) spectra. We also analyze a number of minima taken from an exhaustive meta-dynamical search of the LHCII potential energy surface. We show that trivial, Coulomb-mediated energy transfer to S1 is an unlikely quenching mechanism. Pigment movements are insufficient to switch the system between quenched and unquenched states. Modulation of S1 energy level as a quenching switch is similarly unlikely. Moreover, the quenching predicted by previous models is likely an artefact of quantum chemical over-estimation of S1 oscillator strength and the real mechanism likely involves non-trivial inter-molecular states.

biophysics↗

bioRxiv2021

Salicylaldehyde ester-mediated protein semi-synthesis enables studies on the tetra-acetylation of HMGB1

HMGB1 (high-mobility group box 1) protein is a nonhistone chromatin-associated protein that has been widely reported to be a representative damage-associated molecular pattern (DAMP) and to play a pivotal role in proinflammatory process once it is in an extracellular location. Accumulating evidence has shown HMGB1 undergoes extensive PTMs that remarkably regulated its conformation, localization, and intermolecular interaction. However, the PTMrelated study has been dramatically hindered by the difficulty to access to homogenous proteins with site-specific PTMs of interest. Here, we introduce a protein semi-synthesis strategy via salicylaldehyde ester-mediated chemical ligations (Ser/Thr ligation and Cys/Pen ligation, STL/CPL). This methodology has enabled us to generate N-terminal acetylated HMGB1 proteins in high purity. Further studies revealed that the acetylation on N-terminus regulates its interaction with heparin and modulates its stability, representing a regulatory switch to control the HMGB1s activity.

biochemistry↗

bioRxiv2021

Programmable RNA base editing with a single gRNA-free enzyme

Programmable RNA editing enables rewriting gene expression without changing genome sequences. Current tools for specific RNA editing dependent on the assembly of guide RNA into an RNA/protein complex, causing delivery barrier and low editing efficiency. We report a new gRNA-free system, RNA editing with individual RNA-binding enzyme (REWIRE), to perform precise base editing with a single engineered protein. This artificial enzyme contains a human-originated programmable PUF domain to specifically recognize RNAs and different deaminase domains to achieve efficient A-to-I or C-to-U editing, which achieved 60-80% editing rate in human cells, with a few non-specific editing sites in the targeted region and a low level off-target effect globally. The RNA-binding domain in REWIREs was further optimized to improve editing efficiency and minimize off-target effects. We applied the REWIREs to correct disease-associated mutations and achieve both types of base editing in mice. As a single-component system originated from human proteins, REWIRE presents a precise and efficient RNA editing platform with broad applicability.

bioengineering↗

bioRxiv2021

The Mus musculus papillomavirus type 1 E7 protein binds to the retinoblastoma tumor suppressor - implications for viral pathogenesis

The species specificity of papillomaviruses has been a significant roadblock for performing in vivo pathogenesis studies in common model organisms. The Mus musculus papillomavirus type 1 (MmuPV1) causes cutaneous papillomas that can progress to squamous cell carcinomas in laboratory mice. The papillomavirus E6 and E7 genes encode proteins that establish and maintain a cellular milieu that allows for viral genome synthesis and viral progeny synthesis in growth-arrested, terminally differentiated keratinocytes. The E6 and E7 proteins provide this activity by binding to and functionally reprogramming key cellular regulatory proteins. The MmuPV1 E7 protein lacks the canonical LXCXE motif that mediates the binding of multiple viral oncoproteins to the cellular retinoblastoma tumor suppressor protein, RB1. Our proteomic experiments, however, revealed that MmuPV1 E7 still interacts specifically with RB1. We show that MmuPV1 E7 interacts through its C-terminus with the C-terminal domain of RB1. Binding of MmuPV1 E7 to RB1 did not cause significant activation of E2F-regulated cellular genes. MmuPV1 E7 expression was shown to be essential for papilloma formation. Experimental infection of mice with MmuPV1 virus expressing an E7 mutant that is defective for binding to RB1 caused delayed onset, lower incidence, and smaller sizes of papillomas. Our results demonstrate that the MmuPV1 E7 gene is essential and that targeting non-canonical activities of RB1, which are independent of RB1s ability to modulate the expression of E2F-regulated genes, contribute to papillomavirus-mediated pathogenesis. ImportancePapillomavirus infections cause a variety of epithelial hyperplastic lesions, warts. While most warts are benign, some papillomaviruses cause lesions that can progress to squamous cell carcinomas and approximately 5% of all human cancers are caused by human papillomavirus (HPV) infections. The papillomavirus E6 and E7 proteins are thought to function to reprogram host epithelial cells to enable viral genome replication in terminally differentiated, normally growth-arrested cells. E6 and E7 lack enzymatic activities and function by interacting and functionally altering host cell regulatory proteins. Many cellular proteins that can interact with E6 and E7 have been identified, but the biological relevance of these interactions for viral pathogenesis has not been determined. This is because papillomaviruses are species-specific and do not infect heterologous hosts. Here we use a recently established mouse papillomavirus (MmuPV1) model to investigate the role of the E7 protein in viral pathogenesis. We show that MmuPV1 E7 is necessary for papilloma formation. The retinoblastoma tumor suppressor protein (RB1) is targeted by many papillomaviral E7 proteins, including cancer-associated HPVs. We show that MmuPV1 E7 can bind RB1 and that infection with a mutant MmuPV1 virus that expresses an RB1 binding defective E7 mutant caused smaller and fewer papillomas that arise with delayed kinetics.

molecular biology↗

bioRxiv2021

Computational mechanisms for neural representation of words

A critical way for humans to acquire, represent and communicate information is through language, yet the underlying computation mechanisms through which language contributes to our word meaning representations are poorly understood. We compared three major types of word computation mechanisms from large language corpus (simple co-occurrence, graph-space relations and neural-network-vector-embedding relations) in terms of the association of words brain activity patterns, measured by two functional magnetic resonance imaging (fMRI) experiments. Word relations derived from a graph-space representation, and not neural-network-vector-embedding, had unique explanatory power for the neural activity patterns in brain regions that have been shown to be particularly sensitive to language processes, including the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were robust across different window sizes and graph sizes and were relatively specific to language inputs. These findings highlight the role of cumulative language inputs in organizing word meaning neural representations and provide a mathematical model to explain how different brain regions capture different types of language-derived information.

neuroscience↗

bioRxiv2021

The First High-Quality Reference Genome of Sika Deer Provides Insights for High-Tannin Adaptation

Sika deer are known to prefer oak leaves, which are rich in tannins and toxic to most mammals; however, the genetic mechanisms underlying their unique ability to adapt to living in the jungle are still unclear. In identifying the mechanism responsible for the tolerance of a highly toxic diet, we have made a major advancement in the elucidation of the genomics of sika deer. We generated the first high-quality, chromosome-level genome assembly of sika deer and measured the correlation between tannin intake and RNA expression in 15 tissues through 180 experiments. Comparative genome analyses showed that the UGT and CYP gene families are functionally involved in the adaptation of sika deer to high-tannin food, especially the expansion of UGT genes in a subfamily. The first chromosome-level assembly and genetic characterization of the tolerance toa highly toxic diet suggest that the sika deer genome will serve as an essential resource for understanding evolutionary events and tannin adaptation. Our study provides a paradigm of comparative expressive genomics that can be applied to the study of unique biological features in non-model animals.

genomics↗