Development of a radiolabeled site-specific single-domain antibody positron emission tomography probe for monitoring PD-L1 expression in cancer

Despite advances in immunotherapy for the treatment of cancers,not all patients can benefit from programmed cell death ligand 1(PD-L1)immune checkpoint blockade therapy.Anti-PD-L1 therapeutic effects reportedly correlate with the PD-L1 expression level;hence,accurate detection of PD-L1 expression can guide immunotherapy to achieve better therapeutic effects.Therefore,based on the high affinity antibody Nb109,a new site-specifically radiolabeled tracer,^(68)Ga-NODA-cysteine,aspartic acid,and valine(CDV)-Nb109,was designed and synthesized to accurately monitor PD-L1 expression.The tracer ^(68)Ga-NODA-CDV-Nb109 was obtained using a site-specific conjugation strategy with a radiochemical yield of about 95%and radiochemical purity of 97%.It showed high affinity for PD-L1 with a dissociation constant of 12.34±1.65 nM.Both the cell uptake assay and positron emission tomography(PET)imaging revealed higher tracer uptake in PD-L1-positive A375-hPD-L1 and U87 tumor cells than in PD-L1-negative A375 tumor cells.Meanwhile,dynamic PET imaging of a NCI-H1299 xenograft indicated that doxorubicin could upregulate PD-L1 expression,allowing timely interventional immunotherapy.In conclusion,this tracer could sensitively and dynamically monitor changes in PD-L1 expression levels in different cancers and help screen patients who can benefit from anti-PD-L1 immunotherapy.

Mapping conformational changes on bispecific antigen-binding biotherapeutic by covalent labeling and mass spectrometry

Biotherapeutic's higher order structure(HOS)is a critical determinant of its functional properties and conformational relevance.Here,we evaluated two covalent labeling methods:diethylpyrocarbonate(DEPC)-labeling and fast photooxidation of proteins(FPOP),in conjunction with mass spectrometry(MS),to investigate structural modifications for the new class of immuno-oncological therapy known as bispecific antigen-binding biotherapeutics(BABB).The evaluated techniques unveiled subtle structural changes occurring at the amino acid residue level within the antigen-binding domain under both native and thermal stress conditions,which cannot be detected by conventional biophysical techniques,e.g.,near-ultraviolet circular dichroism(NUV-CD).The determined variations in labeling uptake under native and stress conditions,corroborated by binding assays,shed light on the binding effect,and highlighted the potential of covalent-labeling methods to effectively monitor conformational changes that ultimately influence the product quality.Our study provides a foundation for implementing the developed techniques in elucidating the inherent structural characteristics of novel therapeutics and their conformational stability.

TurboID Proximity Labeling of a Protocadherin Protein to Characterize Interacting Protein Complex

The study of the neuron has always been a fundamental aspect when it came to studying mental illnesses such as autism and depression. The protein protocadherin-9 (PCDH9) is an important transmembrane protein in the development of the neuron synapse. Hence, research on its protein interactome is key to understanding its functionality and specific properties. A newly discovered biotin ligase, TurboID, is a proximity labeler that is designed to be able to label and observe transmembrane proteins, something that previous methods struggled with. The TurboID method is verified in HEK293T cells and primary cultured mouse cortical neurons. Results have proven the validity of the TurboID method in observing PCDH9-interacting proteins.

LABELING OF PROTEIN WITH A NEW EUROPIUM CHELATE OF 5-CHLOROSULFOYL-2-THENOYLTRIFLUOROACETONE(CTTA)AND APPLICATION TO IMMUNOASSAY

We describe for the first time the synthesis and the optimal conditions for protein labeling with a new fluorescent probe,5-chlorosulfoyl-2-thenoyltrifluoroacetone(CTTA),whicb forms a highly fluorescent conplex with Eu^(3+) when conjugated to protein.The labeled proteins were characterized by absorbance and fluorescence measurements and the effect of labeling on the biological activity of sone proteins was also studied.It is shown that the new label is suitable for applications in time-resolved fluoroimmunoassay.

Application of N-succinimidyl 4-[^18F](fluoromethyl) benzoate to protein labeling

N-succinimidyl 4-[18F](fluoromethyl) benzoate for protein labeling was prepared (57%, EOB) in about 30min. Reaction conditions of S18FMB with IgG including pH of solutions, protein concentration, reaction temperature and time were studied. The optimal labeling conditions were: 0.2mg/mL IgG, pH = 7.8-8.5, 25℃, and reaction time 5min.Under these conditions the yield was about 80%. The 18F-labeled protein was purified by size exclusion chromatography.

Chemical tags and beyond:Live-cell protein labeling technologies for modern optical imaging

Imaging proteins with high resolution is crucial for studying cellular physiology and pathology.Fluorescence imaging is a privileged method to visualize proteins with subcellular precision in live cells.In recent years,there has been a tremendous advance in the field of fluorescent dyes that are optically more sophisticated than genetically-encodable fluorescent proteins.In this review,we aim to discuss modern bioconjugation methods to specifically incorporate these dyes into protein-of-interests.We focus on advances in live-cell labeling strategies and fluorescent probes,especially the HaloTag,SNAP-tag,TMP-tag,and unnatural amino acid systems and their applications.These protein labeling methods,along with cutting-edge dyes and novel microscopy methods,have become the infrastructure for biological research in the era of super-resolution imaging.

Altered expression of nuclear matrix proteins in etoposide induced apoptosis in HL-60 cells

The events of cell death and the expression of nuclear matrix protein (NMP) have been investigated in a promyelocytic leukemic cell line HL-60 induced with etoposide. By means of TUNEL assay, the nuclei displayed a characteristic morphology change, and the amount of apoptotic cells increased early and reached maximun about 39% after treatment with etoposide for 2 h. Nucleosomal DNA fragmentation was observed after treatment for 4 h. The morphological change of HL-60 cells, thus, occurred earlier than the appearance of DNA ladder. Total nuclear matrix proteins were analyzed by 2-dimensional gel electrophoresis. Differential expression of 59 nuclear matrix proteins was found in 4 h etoposide treated cells. Western blotting was then performed on three nuclear matrix acssociated proteins, PML, HSC70 and NuMA. The expression of the suppressor PML protein and heat shock protein HSC70 were significantly upregulated after etoposide treatment, while NuMA, a nuclear mitotic apparatus protein, was down regulated. These results demonstrate that significant biochemical alterations in nuclear matrix proteins take place during the apoptotic process.

pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning

Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning

Recently, the life of worldwide human beings has been endangering by the spreading of pneu- monia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is prerequisite. In 2019, a predictor called “pLoc_bal-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mEuk was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporating the “deep- learning” technique and developed a new predictor called “pLoc_Deep-mEuk”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web- server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/, by which the majority of experimental scientists can easily get their desired data.

pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/.

pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of plant protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based plant protein subcellular localization predictor called “pLoc_Deep-mPlant” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 95% and its local accuracy is about 90%?-?100%. Both have substantially exceeded the?other existing state-of-the-art predictors. To maximize the convenience for most?experimental scientists, a user-friendly web-server for the new predictor has been established?at?http://www.jci-bioinfo.cn/pLoc_Deep-mPlant/, by which the majority of experimental?scientists can easily obtain their desired data without the need to go through the?mathematical details.

pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins

Current coronavirus pandemic has endangered mankind life. The reported cases are increasing exponentially. Information of animal protein subcellular localization can provide useful clues to develop antiviral drugs. To cope with such a catastrophe, a CNN based animal protein subcellular localization predictor called “pLoc_Deep-mAnimal” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 92% and its local accuracy is over 95%. Both have substantially exceeded the other existing state-of-the-art predictors. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mAnimal/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram negative bacterial protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGnet” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 98% and its local accuracy is around 94% - 100%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGneg/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

Chemotactic Activity of Site-Specific Multivalent Conjugates of Stromal Cell-Derived Factor 1<i>α</i>on Branched Nanoparticles

Stromal cell-derived factor 1α (SDF1α) is a potent chemokine for the recruitment of stem cells. A challenge is to maintain its activity and control its release. In this study, we engineered a recombinant cysteine-SDF1α (cysSDF1α) protein, and performed multivalent conjugation of cysSDF1 through the maleimide functional group to two forms of branched nanoparticles: multi-arm poly (ethylene glycol) (MA-PEG) and hyaluronic acid (HA). We characterized the chemotactic activity of the conjugates, and determined how the molecular weight (MW) of MA-PEG and HA affected the chemotactic activity. CysSDF1α had similar efficiency to wild-type SDF1α in cell recruitment. Multivalent conjugation of cysSDF1α to low MW MA-PEG (~18 nm) did not significantly affect the chemotactic activity, while the conjugation of cysSDF1α to high MW MA-PEG (~72 nm) lowered the efficiency, possibly due to the larger spacing between conjugated SDF1α molecules. HA has a linear backbone and a high density of multivalent binding sites;however, the chemotactic activity of HA-linked cys-SDF1α was much lower, which further decreased with the increase of HA MW from 200 kDa (~0.78 μm) to 700 kDa (~2.7 μm). Digestion of HA into smaller fragments using hyaluronidase partially recovered the chemotactic activity of cysSDF1α, suggesting that high MW HA might exert steric hindrance for SDF1α binding to its receptors on cell surface and that HA could be used as a depot for SDF1α storage and release. These results demonstrate that multivalent conjugates of SDF1α to nanoparticles may be used to engineer SDF1α delivery for cell recruitment and tissue regeneration.

pLoc_Deep-mGpos: Predict Subcellular Localization of Gram Positive Bacteria Proteins by Deep Learning

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of Gram positive bacteria protein subcellular localization is vitally important. In view of this, a CNN based protein subcellular localization predictor called “pLoc_Deep-mGpos” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 99% and its local accuracy is around 92% - 99%. Both are transcending other existing state-of-the-art predictors significantly. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_Deep-mGpos/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.

Construction of bicistronic green fluorescent protein labeled pSELECT GFPzeo human bone morphogenetic protein 2 eukaryotic expression vector

Objective To construct green fluorescent protein (GFP)-labeled pSELECT-GFP zeohBMP2 eukaryotic expression vector.Methods The encoding fragment of hBMP2 gene was obtained from a recombinant plasmid pcDNA3.1/CT-hBMP2 by using polymerase

Semantic Similarity over Gene Ontology for Multi-Label Protein Subcellular Localization

As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.

Site-specific recombination in Escherichia coli mediated by actinomyces phage R4 integrase

The purpose of this study was to demonstrate that actinomyces phage R4 integrase Sre protein efficiently mediate site-specific recombination in Escherichia coll. An intramolecular recombination assay system in E. coli was constructed. The plasmid pBZP contains attB and attP sites in direct orientation flanking a lacZ gene. When pBZP was introduced into E. coli cells, in which the plasmid pSREA containing sre gene was resident, Sre protein catalyzed integration of attP into attB site, resulting in excision of the lacZ gene. This integration changed bacteria colonies from blue to white on agar plates containing X-Gal, which showed that the lacZ was removed. The integrant DNAs were identified by enzyme digestion, PCR and DNA sequencing. The minimal sizes of attB and attP were 50 bp and 47 bp for 100% recombination efficiency. The phage recombinase Sre efficiently integrated attP into attB site to create attR and attL in E. coli host environment without Streptomyces specific cofactors. This intrmolecular assay system is a simple and efficient system for Sre-mediated recombination in E. coll.

基于label-free定量蛋白质组学方法筛选沉默CHAF1B基因后心肌细胞差异表达蛋白及调控网络分析

目的分析沉默染色质装配因子1亚基B(chromatin assembly factor 1 subunit B,CHAF1B)基因后心肌细胞中差异表达蛋白,预测CHAF1B基因调控网络,为寻找促进心肌细胞修复的潜在治疗靶点提供参考。方法采用转染和蛋白质印迹法筛选沉默CHAF1B基因的有效小干扰RNA(small interfering RNA,siRNA)。应用有效siRNA沉默人源心肌AC16细胞CHAF1B基因后,采用细胞活力检测方法检测细胞活力;提取总蛋白质进行定量、还原、烷基化和胰蛋白酶裂解成肽段,利用高效液相串联质谱法鉴定肽段;搜索UniProt蛋白库筛选差异表达的蛋白质进行基因本体(Gene Ontology,GO)富集分析、京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路富集和蛋白质互作网络(protein-protein interaction networks,PPI)分析。结果siRNA有效沉默CHAF1B基因后,心肌细胞存活明显受到抑制;label-free定量蛋白质组学方法鉴定结果显示,共有69个差异表达蛋白质,其中50个表达显著上调(差异倍数≥2,P<0.05),19个表达显著下调(差异倍数≤0.5,P<0.05)。GO分析显示,差异表达蛋白质主要参与大分子复合亚基体、细胞组分生物合成和组装等生物学过程,分布在细胞质和囊泡等区域,发挥蛋白质结合等分子功能。KEGG通路富集和PPI分析显示,差异表达蛋白质参与的信号通路包括蛋白酶体、氨酰tRNA生物合成、胞吞、嘧啶代谢和氨基酸生物合成等10条信号途径;表达显著上调的蛋白质如蛋白酶体亚单位A2和B7、26 S蛋白酶体调节亚单位6B和10B参与蛋白酶体途径,丝氨酸、甘氨酸、谷氨酰胺和赖氨酸tRNA合成酶介导氨酰tRNA生物合成;表达显著下调的蛋白质包括骨架相关蛋白2/3复合体亚单位3和热休克70蛋白1样参与胞吞作用,核糖核苷-二磷酸还原酶大亚基介导嘧啶代谢等通路。实时荧光定量聚合酶链式反应结果证实,转染CHAF1B siRNA后心肌细胞中合成骨架相关蛋白2/3复合体亚单位3的基因ARPC3和氨酰tRNA生物合成关键基因QARS1的mRNA水平均显著降低。结论CHAF1B为心肌细胞存活的关键蛋白质,参与调控心肌细胞的胞吞和氨基酸生物合成等多种生物学过程,参考其调控网络可帮助寻找促进心肌细胞修复的干预环节。

Electron-withdrawing inductive effects enhanced strategy for protein thiol sensing and blocking agent design

It is a great challenge to discover novel chemical reactions suitable for biological analysis in a living system.The development of novel protein thiol blocking agents is a crucial need for exploring protein thiol functions in protein refolding,signal transduction,and redox regulation.We are always keen on seeking novel chemical reactions applied to endogenous biological macromolecules or protein thiol sensing,blocking,and labeling.In the present work,we have successfully developed a novel agent to block protein thiol by enhanced electron-withdrawing inductive effects.This sensing and blocking process was detailedly monitored by UV-vis,fluorescent spectra,and SDS-Page gel separation.The spectral studies demonstrated that the agent could react ultrafastly with thiol within seconds atμM level.Furthermore,fluorescent imaging in cells and in vivo was further used for the validation of its ability to sensing and blocking thiol,providing evidence of downregulated protein thiols in Parkinson's disease.The enhanced electronwithdrawing inductive effect strategy in this work may provide a general guideline for designing protein thiol agent.