Key residues of the receptor binding motif in the spike protein of SARS-CoV-2 that interact with ACE2 and neutralizing antibodies

Coronavirus disease 2019(COVID-19),caused by the novel human coronavirus SARS-CoV-2,is currently a major threat to public health worldwide.The viral spike protein binds the host receptor angiotensin-converting enzyme 2(ACE2)via the receptor-binding domain(RBD),and thus is believed to be a major target to block viral entry.Both SARS-CoV-2 and SARS-CoV share this mechanism.Here we functionally analyzed the key amino acid residues located within receptor binding motif of RBD that may interact with human ACE2 and available neutralizing antibodies.The in vivo experiments showed that immunization with either the SARS-CoV RBD or SARS-CoV-2 RBD was able to induce strong clade-specific neutralizing antibodies in mice;however,the cross-neutralizing activity was much weaker,indicating that there are distinct antigenic features in the RBDs of the two viruses.This finding was confirmed with the available neutralizing monoclonal antibodies against SARS-CoV or SARS-CoV-2.It is worth noting that a newly developed SARS-CoV-2 human antibody,HA001,was able to neutralize SARS-CoV-2,but failed to recognize SARS-CoV.Moreover,the potential epitope residues of HA001 were identified as A475 and F486 in the SARS-CoV-2 RBD,representing new binding sites for neutralizing antibodies.Overall,our study has revealed the presence of different key epitopes between SARS-CoV and SARSCoV-2,which indicates the necessity to develop new prophylactic vaccine and antibody drugs for specific control of the COVID-19 pandemic although the available agents obtained from the SARS-CoV study are unneglectable.

Model-based Comparative Prediction of Transcription-Factor Binding Motifs in Anabolic Responses in Bone

Understanding the regulatory mechanism that controls the alteration of global gene expression patterns continues to be a challenging task in computational biology. We previously developed an ant algorithm, a biologically-inspired computational technique for microarray data, and predicted putative transcription-factor binding motifs （TFBMs） through mimicking interactive behaviors of natural ants. Here we extended the algorithm into a set of web-based software, Ant Modeler, and applied it to investigate the transcriptional mechanism underlying bone formation. Mechanical loading and administration of bone morphogenic proteins （BMPs） are two known treatments to strengthen bone. We addressed a question： Is there any TFBM that stimulates both ＂anabolic responses of mechanical loading＂ and ＂BMP-mediated osteogenic signaling＂？ Although there is no significant overlap among genes in the two responses, a comparative model-based analysis suggests that the two independent osteogenic processes employ common TFBMs, such as a stress responsive element and a motif for peroxisome proliferator-activated receptor （PPAR）. The post-modeling in vitro analysis using mouse osteoblast cells supported involvements of the predicted TFBMs such as PPAR, Ikaros 3, and LMO2 in response to mechanical loading. Taken together, the results would be useful to derive a set of testable hypotheses and examine the role of specific regulators in complex transcriptional control of bone formation.

Defining A Global Map of Functional Group-based 3D Ligand-binding Motifs

Uncovering conserved 3D protein–ligand binding patterns on the basis of functional groups(FGs)shared by a variety of small molecules can greatly expand our knowledge of protein–ligand interactions.Despite that conserved binding patterns for a few commonly used FGs have been reported in the literature,large-scale identification and evaluation of FG-based 3D binding motifs are still lacking.Here,we propose a computational method,Automatic FG-based Three-dimensional Motif Extractor(AFTME),for automatic mapping of 3D motifs to different FGs of a specific ligand.Applying our method to 233 naturally-occurring ligands,we define 481 FG-binding motifs that are highly conserved across different ligand-binding pockets.Systematic analysis further reveals four main classes of binding motifs corresponding to distinct sets of FGs.Combinations of FG-binding motifs facilitate the binding of proteins to a wide spectrum of ligands with various binding affinities.Finally,we show that our FG–motif map can be used to nominate FGs that potentially bind to specific drug targets,thus providing useful insights and guidance for rational design of small-molecule drugs.

ZNF554 Inhibits Endometrial Cancer Progression via Regulating RBM5 and Inactivating WNT/β-Catenin Signaling Pathway

Objective:Uterine corpus endometrial carcinoma(UCEC),a kind of gynecologic malignancy,poses a significant risk to women’s health.The precise mechanism underlying the development of UCEC remains elusive.Zinc finger protein 554(ZNF554),a member of the Krüppel-associated box domain zinc finger protein superfamily,was reported to be dysregulated in various illnesses,including malignant tumors.This study aimed to examine the involvement of ZNF554 in the development of UCEC.Methods:The expression of ZNF554 in UCEC tissues and cell lines were examined by qRT-PCR and Western blot assay.Cells with stably overexpressed or knocked-down ZNF554 were established through lentivirus infection.CCK-8,wound healing,and Transwell invasion assays were employed to assess cell proliferation,migration,and invasion.Propidium iodide(PI)staining combined with fluorescence-activated cell sorting(FACS)flow cytometer was utilized to detect cell cycle distribution.qRT-PCR and Western blotting were conducted to examine relative mRNA and protein levels.Chromatin immunoprecipitation assay and luciferase reporter assay were used to explore the regulatory role of ZNF554 in RNA binding motif 5(RBM5).Results:The expression of ZNF554 was found to be reduced in both UCEC samples and cell lines.Decreased expression of ZNF554 was associated with higher tumor stage,decreased overall survival,and reduced disease-free survival in UCEC.ZNF554 overexpression suppressed cell proliferation,migration,and invasion,while also inducing cell cycle arrest.In contrast,a decrease in ZNF554 expression resulted in the opposite effect.Mechanistically,ZNF554 transcriptionally regulated RBM5,leading to the deactivation of the Wingless(WNT)/β-catenin signaling pathway.Moreover,the findings from rescue studies demonstrated that the inhibition of RBM5 negated the impact of ZNF554 overexpression onβ-catenin and p-glycogen synthase kinase-3β(p-GSK-3β).Similarly,the deliberate activation of RBM5 reduced the increase inβ-catenin and p-GSK-3βcaused by the suppression of ZNF554.In vitro experiments showed that ZNF554 overexpression-induced decreases in cell proliferation and migration were counteracted by RBM5 knockdown.Additionally,when RBM5 was overexpressed,it hindered the improvements in cell proliferation and migration caused by reducing the ZNF554 levels.Conclusion:ZNF554 functions as a tumor suppressor in UCEC.Furthermore,ZNF554 regulates UCEC progression through the RBM5/WNT/β-catenin signaling pathway.ZNF554 shows a promise as both a prognostic biomarker and a therapeutic target for UCEC.

A Cdc2-related protein kinase hPFTAIRE1 from human brain interacting with 14-3-3 proteins

hPFTAIRE1 （PFTK1）, a Cdc2-related protein kinase, is highly expressed in human brain. It exhibits cytoplasmic distribution in Hela cells, although it contains two nuclear localization signals （NLSs） in its N-terminus. To search for its substrates and regulatory components, we screened a two-hybrid library by using the full-length hPFTAIRE1 as a bait. Four 14-3-3 isoforms （β,ε,η,τ） were identified interacting with the hPFTAIRE1. We found a putative 14-3-3 binding consensus motif（RHSSPSS） in the hPFTAIRE 1, which overlapped with its second NLS. Deletion of the RHSSPSS motif or substitution of Ser^119 gwithAla in the conserved binding motif abolished the specific interaction between the hPFTAIRE 1 and the 14-3 -3 proteins. The mutant S 120A hPFTAIRE1 also showed a weak interaction to the 14-3-3 proteins. The results suggested that the Ser^119 is crucial for the interaction between hPFTAIREI and the 14-3-3 proteins. All the hPFTAIRE1 mutants distributed in cytoplasm of Hela cells and human neuroblastoma cells （SH-SY5Y） when fused to the C-terminus of a green fluorescent protein （GFP）, indicating that binding with the 14-3-3 proteins does not contribute to the subcellular localization of the hPFTAIRE1, although the binding may be involved in its signaling regulation.

Integration of Known Transcription Factor Binding Site Information and Gene Expression Data to Advance from Co-Expression to Co-Regulation

The common approach to find co-regulated genes is to cluster genes based on gene expression. However, due to the limited information present in any dataset, genes in the same cluster might be co-expressed but not necessarily co-regulated. In this paper, we propose to integrate known transcription factor binding site information and gene expression data into a single clustering scheme. This scheme will find clusters of co-regulated genes that are not only expressed similarly under the measured conditions, but also share a regulatory structure that may explain their common regulation. We demonstrate the utility of this approach on a microarray dataset of yeast grown under different nutrient and oxygen limitations. Our integrated clustering method not only unravels many regulatory modules that are consistent with current biological knowledge, but also provides a more profound understanding of the underlying process. The added value of our approach, compared with the clustering solely based on gene expression, is its ability to uncover clusters of genes that are involved in more specific biological processes and are evidently regulated by a set of transcription factors.

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation,cell differentiation,and disease development.High-throughput experimental approaches,which contain successfully reported enhancers in typical cell lines,are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines.Existing computational methods,capable of predicting regulatory elements purely relying on DNA sequences,lack the power of cell line-specific screening.Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation,and thus may provide useful information in identifying regulatory elements.Motivated by the aforementioned understanding,we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner.We proposed Deep CAPE,a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data.Benefitting from the well-designed feature extraction mechanism and skip connection strategy,our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences,but also has the ability to self-adapt to different sizes of datasets.Besides,with the adoption of autoencoder,our model is capable of making cross-cell line predictions.We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs.We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate diseaserelated enhancers.The source code and detailed tutorial of Deep CAPE are freely available at https://github.com/Shengquan Chen/DeepCAPE.

Evolutionary direction of processed pseudogenes

While some pseudogenes have been reported to play important roles in gene regulation, little is known about the possible relationship between pseudogene functions and evolutionary process of pseudogenes, or about the forces responsible for the pseudogene evolution. In this study, we characterized human processed pseudogenes in terms of evolutionary dynamics. Our results show that pseudogenes tend to evolve toward: lower GC content, strong dinucleotide bias, reduced abundance of transcription factor binding motifs and short palindromes, and decreased ability to form nucleosomes. We explored possible evolutionary forces that shaped the evolution pattern of pseudogenes, and concluded that mutations in pseudogenes are likely determined, at least partially, by neighbor-dependent mutational bias and recombination-associated selection.