Article
Biochemistry & Molecular Biology
Joan Frigola, Radhakrishnan Sabarinathan, Abel Gonzalez-Perez, Nuria Lopez-Bigas
Summary: An abnormally high rate of UV-light related mutations is observed at transcription factor binding sites (TFBS) across melanomas, with certain TFs impairing the repair of UV-induced lesions and increasing the rate of lesion generation at their binding sites. Through nucleotide-resolution data, it is found that mutation rate increase in TFBS is mainly due to decreased repair efficiency, rather than the rate of lesion formation.
NUCLEIC ACIDS RESEARCH
(2021)
Article
Multidisciplinary Sciences
Yuko Hasegawa, Kevin Struhl
Summary: The transcription factor SP1 exhibits varying binding dynamics at different target sites in the human genome, potentially influenced by factors such as location and cobinding factors.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Biochemistry & Molecular Biology
Lei Deng, Hui Wu, Xuejun Liu, Hui Liu
Summary: The paper introduces a hybrid deep learning framework called DeepD2V for predicting transcription factor binding sites. By using a pre-trained k-mer word distributed representation model, deep convolutional neural network, and bidirectional LSTM, DeepD2V demonstrates superior performance compared to other methods in predicting protein-DNA binding sites.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Review
Biochemistry & Molecular Biology
Kangwen Xu, Long Lin, Danyu Shen, Shan-Ho Chou, Guoliang Qian
Summary: The review summarizes the mechanisms through which Clp, a CRP-like protein, initiates a mobile-attack strategy in Lysobacter enzymogenes against fungal pathogens, including binding to DNA in a unique pattern, interacting directly with small molecules or responding to them, and specific interactions with proteins adopting distinct structures.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Article
Cell Biology
Luqian Zheng, Jingjing Liu, Lijie Niu, Mohammad Kamran, Ally W. H. Yang, Arttu Jolma, Qi Dai, Timothy R. Hughes, Dinshaw J. Patel, Long Zhang, Supriya G. Prasanth, Yang Yu, Aiming Ren, Eric C. Lai
Summary: In this study, the researchers investigated how BEN factors identify their targets in humans by characterizing several mammalian BEN domain factors. They provided structural insights into sequence-specific DNA binding by these BEN proteins. The findings expand the understanding of BEN factors' DNA recognition activities and shed light on the mechanism of sequence-specific DNA binding by mammalian BEN proteins.
GENES & DEVELOPMENT
(2022)
Article
Biochemistry & Molecular Biology
Migle Tomkuvien, Markus Meier, Diana Ikasalaite, Julia Wildenauer, Visvaldas Kairys, Saulius Klimasauskas, Laura Manelyt
Summary: Methylation of cytosine is an important epigenetic mark that can alter DNA and chromatin structure. This study investigates how larger chemical variations in DNA affect chromatin structure and nucleosome formation.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Oncology
Linan Cao, Pei Liu, Jialong Chen, Lei Deng
Summary: In this study, we developed an accurate and interpretable attention-based hybrid approach called DeepARC, which combines CNN and RNN to predict TFBS. DeepARC utilizes a positional embedding method to extract hidden embeddings from DNA sequences and uses a CNN-BiLSTM-Attention framework to search for motifs. Our results demonstrate that DeepARC achieves promising performances on multiple cell lines and provides interpretability through attention weight graphs.
FRONTIERS IN ONCOLOGY
(2022)
Article
Biochemical Research Methods
Wenkai Yan, Zutan Li, Cong Pian, Yufeng Wu
Summary: In this study, a method called PlantBind for integrated prediction and interpretation of plant transcription factor binding sites (TFBSs) based on DNA sequences and DNA shape profiles was proposed. PlantBind not only predicts the potential binding sites of multiple TFs simultaneously, but also identifies the motifs bound by transcription factors. The study demonstrated the effectiveness of the model through cross-species prediction performance. It provides an effective solution for identifying plant TFBSs and enhances the understanding of transcriptional regulatory mechanisms in plants.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Plant Sciences
Lucia Strader, Dolf Weijers, Doris Wagner
Summary: This article reviews new findings on the function of plant transcription factors and their role in shaping transcription in the context of chromatin.
CURRENT OPINION IN PLANT BIOLOGY
(2022)
Article
Biochemical Research Methods
Yongqing Zhang, Zixuan Wang, Yuanqi Zeng, Yuhang Liu, Shuwen Xiong, Maocheng Wang, Jiliu Zhou, Quan Zou
Summary: The paper presents a novel Deep Convolution Attention network combining Sequence and Shape (D-SSCA) for predicting putative transcription factor binding sites (TFBSs). The experiments show that D-SSCA outperforms other state-of-the-art methods in TFBSs prediction and that shape features contribute to the predictive power for transcription factors-DNA binding. Additionally, D-SSCA enables cross-cell line prediction of TFBSs.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
P. C. Kunthavai, Muthu Kannan, Preethi Ragunathan
Summary: Alternate sigma factors, such as ComX in Streptococcus pyogenes, play a crucial role in gene expression under stress conditions. This study characterized ComX and its interactions with RNA polymerase subunits, as well as analyzed the promoter melting mechanism. The findings suggest that ComX follows a distinctive promoter flip out mechanism, different from other known sigma factors.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2022)
Article
Microbiology
Yizhao Luan, Zehua Tang, Yao He, Zhi Xie
Summary: In this study, evidence was provided showing that coevolving residues in TF domains contribute to DNA binding specificity. It was demonstrated that the coevolving residues are more likely to coevolve with other TF subclass-determining sites and mutation of these coevolving residues could significantly reduce the stability of the TF-DNA complex. Overall, this study expands our understanding of the interaction among coevolving residues in TFs and their importance in transcriptional regulation.
MICROBIOLOGY SPECTRUM
(2023)
Article
Biochemistry & Molecular Biology
Tiebin Wang, Nathan Tague, Stephen A. Whelan, Mary J. Dunlop
Summary: Transcription factor decoys can effectively regulate gene expression, with tunability through changes in copy number or modifications to the DNA decoy site sequence. Introducing the decoy system can significantly increase arginine production in metabolic flux steering, without affecting growth compared to wild type strains.
NUCLEIC ACIDS RESEARCH
(2021)
Review
Biotechnology & Applied Microbiology
Yue Zhang, Wenzheng Bao, Yi Cao, Hanhan Cong, Baitong Chen, Yuehui Chen
Summary: This article provides an overview of the computational and experimental methods used in the field of protein-DNA-binding site prediction. The methods based on traditional machine learning and deep learning are discussed, helping researchers better understand this field.
BRIEFINGS IN FUNCTIONAL GENOMICS
(2022)
Article
Biochemistry & Molecular Biology
Eugeniya I. Bondar, Maxim E. Troukhan, Konstantin V. Krutovsky, Tatiana V. Tatarinova
Summary: This study utilized computational approaches to predict genome-wide TSS in four conifer species, laying the groundwork for future research on gene regulatory regions.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Virology
Hongwei Chen, Haoyang Zhang, Simin Wen, Xuehao Xiu, Danming You, Huiying Zhao, Dayan Wang, Yuedong Yang, Yuelong Shu
Summary: Currently, there is a lack of systematic exploration on the clinical factors influencing immune responses to influenza vaccines. The mechanism of low responsiveness to influenza vaccination (LRIV) is complex and not well understood. In this study, we combined our in-house genome-wide association studies (GWAS) analysis of LRIV with the GWAS summary of 10 blood-based biomarkers to investigate the genetics shared between LRIV and blood-based biomarkers using Mendelian randomization (MR). The results suggest a potential causal relationship between genetically instrumented LRIV and decreased eosinophil count.
JOURNAL OF MEDICAL VIROLOGY
(2023)
Article
Biochemical Research Methods
Yuansong Zeng, Rui Yin, Mai Luo, Jianing Chen, Zixiang Pan, Yutong Lu, Weijiang Yu, Yuedong Yang
Summary: Recent advances in spatial transcriptomics have allowed for gene expression measurement at cell/spot resolution, while retaining spatial information and histology images of the tissues. Accurately identifying the spatial domains of spots is crucial for downstream tasks in spatial transcriptomics analysis. In this study, a novel method called ConGI is proposed, which utilizes contrastive learning to accurately exploit spatial domains by combining gene expression with histopathological images. The method outperforms existing methods and the learned representations are useful for various downstream tasks.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Yidong Song, Qianmu Yuan, Sheng Chen, Ken Chen, Yaoqi Zhou, Yuedong Yang
Summary: Determining intrinsically disordered regions of proteins is crucial for understanding protein biological functions and associated diseases. This study proposes a fast and accurate protein disorder predictor, LMDisorder, which utilizes embedding generated by unsupervised pretrained language models as features. LMDisorder outperforms other single-sequence-based methods and compares favorably to another language-model-based technique in independent test sets. Additionally, LMDisorder shows equivalent or better performance than the state-of-the-art profile-based technique SPOT-Disorder2. The high computation efficiency of LMDisorder allows for proteome-scale analysis, revealing associations between proteins with high predicted disorder content and specific biological functions. The datasets, source codes, and trained model are available at https://github.com/biomed-AI/LMDisorder.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Qianmu Yuan, Junjie Xie, Jiancong Xie, Huiying Zhao, Yuedong Yang
Summary: Protein function prediction is crucial in bioinformatics and has implications for disease mechanism elucidation and drug target discovery. However, accurately predicting protein functions solely from sequences remains challenging. This study introduces SPROF-GO, a sequence-based alignment-free predictor that utilizes a pretrained language model to extract informative sequence embeddings and implements self-attention pooling to focus on important residues. SPROF-GO outperforms state-of-the-art approaches in precision-recall curves and demonstrates generalization capabilities.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Hegang Chen, Yuyin Lu, Yuedong Yang, Yanghui Rao
Summary: Combination therapy plays an important role in treating complex diseases, but the large number of possible combinations limits our ability to identify effective ones. This study introduces a new computational pipeline, DCMGCN, which integrates diverse drug-related information to predict novel drug combinations. The tests show that DCMGCN outperforms existing methods and may help to clarify the understanding of drug mechanisms.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Cheng Tian, Liyuan Li, Qingfei Pan, Beisi Xu, Yizhen Li, Li Fan, Anthony Brown, Michelle Morrison, Kaushik Dey, Jun J. Yang, Jiyang Yu, Evan S. Glazer, Liqin Zhu
Summary: Intrahepatic cholangiocarcinoma (iCCA) is characterized by highly desmoplastic stroma. Contact between tumor cells and peritumoral myofibroblasts (pMFs) initially suppresses tumor cell growth but promotes invasion and dissemination in the long term. Vascular cell adhesion molecule-1 (Vcam1) plays a significant role in this process by regulating epithelial-to-mesenchymal transition. Overall, this study reveals the spatiotemporal regulation of iCCA growth and dissemination by pMFs in a Vcam1-dependent manner.
Article
Multidisciplinary Sciences
Jennifer L. Kamens, Stephanie Nance, Cary Koss, Beisi Xu, Anitria Cotton, Jeannie W. Lam, Elizabeth A. R. Garfinkle, Pratima Nallagatla, Amelia M. R. Smith, Sharnise Mitchell, Jing Ma, Duane Currier, William C. Wright, Kanisha Kavdia, Vishwajeeth R. Pagala, Wonil Kim, LaShanale M. Wallace, Ji-Hoon Cho, Yiping Fan, Aman Seth, Nathaniel Twarog, John K. Choi, Esther A. Obeng, Mark E. Hatley, Monika L. Metzger, Hiroto Inaba, Sima Jeha, Jeffrey E. Rubnitz, Junmin Peng, Taosheng Chen, Anang A. Shelat, R. Kiplin Guy, Tanja A. Gruber
Summary: Proteasome inhibition is found to be effective in KMT2Ar infant acute lymphoblastic leukemia, leading to the depletion of histone modifications and downregulation of KMT2A gene expression signature. A cohort of relapsed/refractory KMT2Ar patients treated with this approach showed a high overall response rate. This innovative treatment approach is now being evaluated in a multi-institutional upfront trial for infants with newly diagnosed ALL.
NATURE COMMUNICATIONS
(2023)
Article
Engineering, Biomedical
Fudan Zheng, Luhao Wang, Yuxian Pang, Zhiguang Chen, Yutong Lu, Yuedong Yang, Jianfeng Wu
Summary: Septic shock has become the leading cause of morbidity and mortality in the ICU. However, currently there is no model to predict the mortality of septic shock patients. We aim to develop such a model.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Neurosciences
Siying Lin, Haoyang Zhang, Mengling Qi, David N. Cooper, Yuedong Yang, Yuanhao Yang, Huiying Zhao
Summary: Observational studies consistently show that brain imaging-derived phenotypes (IDPs) are critical markers for the early diagnosis of brain disorders and cardiovascular diseases. However, the shared genetic landscape between brain IDPs and the risk of these diseases remains unclear, limiting the application of potential diagnostic techniques using brain IDPs.
Article
Biochemistry & Molecular Biology
Cong Fan, Xin Wang, Tianze Ling, Yuedong Yang, Huiying Zhao
Summary: Recent studies suggest that RNAs have potential as drug targets, but progress in detecting RNA-ligand interactions is limited. To guide the discovery of RNA-binding ligands, it is necessary to comprehensively characterize them in terms of binding specificity, binding affinity, and drug-like properties. We established the RNALID database, which contains 358 validated RNA-ligand interactions. Comparisons with other databases show that the majority of ligands in RNALID are novel, and the analysis of ligand structure, binding affinity, and cheminformatic parameters reveals insights into the characteristics of different ligand types. Additionally, comparing RNALID ligands to FDA-approved drugs and ligands without bioactivity sheds light on their differences in chemical properties and drug-likeness.
Article
Multidisciplinary Sciences
Yurika Matsui, Mohamed Nadhir Djekidel, Katherine Lindsay, Parimal Samir, Nina Connolly, Gang Wu, Xiaoyang Yang, Yiping Fan, Beisi Xu, Jamy C. Peng
Summary: The study shows that SNIP1 is critical for the survival and differentiation of stem cells in the developing brain. It regulates PRC2 activities downstream of TGFb and NFkB, influencing cell fates. Understanding the role of SNIP1 in brain development can provide insights into cell survival and death during development.
NATURE COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Shuangjia Zheng, Sijie Mai, Ya Sun, Haifeng Hu, Yuedong Yang
Summary: Link prediction for knowledge graphs aims to predict missing connections between entities. Prevailing methods are limited to a transductive setting and hard to process unseen entities. The recently proposed subgraph-based models provide alternatives to predict links from the subgraph structure surrounding a candidate triplet.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Biochemical Research Methods
Yuansong Zeng, Zhuoyi Wei, Qianmu Yuan, Sheng Chen, Weijiang Yu, Yutong Lu, Jianzhao Gao, Yuedong Yang
Summary: Drawing on the breakthrough of AlphaFold2 in protein structure prediction, we propose a novel graph-based model, GraphBepi, for accurate B-cell epitope prediction. By utilizing the predicted structure from AlphaFold2, GraphBepi constructs the protein graph and captures both sequence and spatial information through edge-enhanced deep graph neural networks (EGNN) and bidirectional long short-term memory neural networks (BiLSTM). The combined representations are input into a multilayer perceptron to predict B-cell epitopes. Comprehensive tests demonstrate that GraphBepi outperforms state-of-the-art methods in terms of AUC and AUPR.
Article
Biochemistry & Molecular Biology
Chuwei Liu, Arabella H. Wan, Heng Liang, Lei Sun, Jiarui Li, Ranran Yang, Qinghai Li, Ruibo Wu, Kunhua Hu, Yuedong Yang, Shirong Cai, Guohui Wan, Weiling He
Summary: Tumor mutation burden (TMB) is an important biomarker for assessing the efficacy of cancer immunotherapy, but its correlation with immune checkpoint inhibitors (ICIs) responsiveness varies among different cancer types. This study explores the relationship between TMB and multi-omics data in various cancer types and develops the PGLCN model to improve the interpretability and prediction accuracy of TMB. By integrating multi-omics data, the PGLCN model outperforms traditional machine learning methods in predicting TMB status and identifies potential combined biomarkers for TMB in gastric cancer.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hai-Qing Liu, Si-Ying Lin, Yi-Dong Song, Si-Yao Mai, Yue-Dong Yang, Kai Chen, Zhuo Wu, Hui-Ying Zhao
Summary: This study developed a machine learning model based on MRI to predict molecular subtype alterations in breast cancer after neoadjuvant therapy. The model showed favorable predictive efficacy in identifying molecular subtype alteration and could be a useful tool in clinical practice.
EUROPEAN RADIOLOGY
(2023)
