Genomics, Proteomics & Bioinformatics最新文献

{"title":"AB-Gen: Antibody Library Design with Generative Pre-trained Transformer and Deep Reinforcement Learning","authors":"Xiaopeng Xu ,&nbsp;Tiantian Xu ,&nbsp;Juexiao Zhou ,&nbsp;Xingyu Liao ,&nbsp;Ruochi Zhang ,&nbsp;Yu Wang ,&nbsp;Lu Zhang ,&nbsp;Xin Gao","doi":"10.1016/j.gpb.2023.03.004","DOIUrl":"10.1016/j.gpb.2023.03.004","url":null,"abstract":"<div><div>Antibody leads must fulfill multiple desirable properties to be clinical candidates. Primarily due to the low throughput in the experimental procedure, the need for such multi-property optimization causes the bottleneck in preclinical antibody discovery and development, because addressing one issue usually causes another. We developed a <strong>reinforcement learning</strong> (RL) method, named AB-Gen, for antibody library design using a generative pre-trained <strong>transformer</strong> (GPT) as the policy network of the RL agent. We showed that this model can learn the antibody space of heavy chain complementarity determining region 3 (CDRH3) and generate sequences with similar property distributions. Besides, when using human epidermal growth factor receptor-2 (HER2) as the target, the agent model of AB-Gen was able to generate novel CDRH3 sequences that fulfill multi-property constraints. Totally, 509 generated sequences were able to pass all property filters, and three highly conserved residues were identified. The importance of these residues was further demonstrated by molecular dynamics simulations, consolidating that the agent model was capable of grasping important information in this complex optimization task. Overall, the AB-Gen method is able to design novel antibody sequences with an improved success rate than the traditional propose-then-filter approach. It has the potential to be used in practical antibody design, thus empowering the antibody discovery and development process. The source code of AB-Gen is freely available at Zenodo (<span><span>https://doi.org/10.5281/zenodo.7657016</span><svg><path></path></svg></span>) and BioCode (<span><span>https://ngdc.cncb.ac.cn/biocode/tools/BT007341</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 1043-1053"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10045398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From BIG Data Center to China National Center for Bioinformation","authors":"Yiming Bao ,&nbsp;Yongbiao Xue","doi":"10.1016/j.gpb.2023.10.001","DOIUrl":"10.1016/j.gpb.2023.10.001","url":null,"abstract":"","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 900-903"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41223972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms","authors":"Bin Huang ,&nbsp;Lupeng Kong ,&nbsp;Chao Wang ,&nbsp;Fusong Ju ,&nbsp;Qi Zhang ,&nbsp;Jianwei Zhu ,&nbsp;Tiansu Gong ,&nbsp;Haicang Zhang ,&nbsp;Chungong Yu ,&nbsp;Wei-Mou Zheng ,&nbsp;Dongbo Bu","doi":"10.1016/j.gpb.2022.11.014","DOIUrl":"10.1016/j.gpb.2022.11.014","url":null,"abstract":"<div><div><strong>Protein structure prediction</strong> is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing <strong>protein folding</strong>; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem — finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of <strong>deep learning</strong> in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 913-925"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9593946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding Human Biology and Disease Using Single-cell Omics Technologies","authors":"Qiang Shi ,&nbsp;Xueyan Chen ,&nbsp;Zemin Zhang","doi":"10.1016/j.gpb.2023.06.003","DOIUrl":"10.1016/j.gpb.2023.06.003","url":null,"abstract":"<div><div>Over the past decade, advances in <strong>single-cell omics</strong> (SCO) technologies have enabled the investigation of <strong>cellular heterogeneity</strong> at an unprecedented resolution and scale, opening a new avenue for understanding human biology and <strong>disease</strong>. In this review, we summarize the developments of sequencing-based SCO technologies and <strong>computational methods</strong>, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on <strong>cancer research</strong>. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 926-949"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RCoV19: A One-stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-warning","authors":"Cuiping Li ,&nbsp;Lina Ma ,&nbsp;Dong Zou ,&nbsp;Rongqin Zhang ,&nbsp;Xue Bai ,&nbsp;Lun Li ,&nbsp;Gangao Wu ,&nbsp;Tianhao Huang ,&nbsp;Wei Zhao ,&nbsp;Enhui Jin ,&nbsp;Yiming Bao ,&nbsp;Shuhui Song","doi":"10.1016/j.gpb.2023.10.004","DOIUrl":"10.1016/j.gpb.2023.10.004","url":null,"abstract":"<div><div>The Resource for Coronavirus 2019 (RCoV19) is an open-access information resource dedicated to providing valuable data on the genomes, <strong>mutations</strong>, and <strong>variants</strong> of the severe acute respiratory syndrome coronavirus 2 (<strong>SARS-CoV-2</strong>). In this updated implementation of RCoV19, we have made significant improvements and advancements over the previous version. Firstly, we have implemented a highly refined genome data curation model. This model now features an automated integration pipeline and optimized curation rules, enabling efficient daily updates of data in RCoV19. Secondly, we have developed a global and regional lineage evolution monitoring platform, alongside an outbreak risk <strong>pre-warning</strong> system. These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns, enabling better preparedness and response strategies. Thirdly, we have developed a powerful interactive mutation spectrum comparison module. This module allows users to compare and analyze mutation patterns, assisting in the detection of potential new lineages. Furthermore, we have incorporated a comprehensive knowledgebase on mutation effects. This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations. In summary, RCoV19 serves as a vital scientific resource, providing access to valuable data, relevant information, and technical support in the global fight against COVID-19. The complete contents of RCoV19 are available to the public at <span><span>https://ngdc.cncb.ac.cn/ncov/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 1066-1079"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66784787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Database Commons: A Catalog of Worldwide Biological Databases","authors":"Lina Ma ,&nbsp;Dong Zou ,&nbsp;Lin Liu ,&nbsp;Huma Shireen ,&nbsp;Amir A. Abbasi ,&nbsp;Alex Bateman ,&nbsp;Jingfa Xiao ,&nbsp;Wenming Zhao ,&nbsp;Yiming Bao ,&nbsp;Zhang Zhang","doi":"10.1016/j.gpb.2022.12.004","DOIUrl":"10.1016/j.gpb.2022.12.004","url":null,"abstract":"<div><div><strong>Biological databases</strong> serve as a global fundamental infrastructure for the worldwide scientific community, which dramatically aid the transformation of big data into knowledge discovery and drive significant innovations in a wide range of research fields. Given the rapid data production, biological databases continue to increase in size and importance. To build a <strong>catalog</strong> of worldwide biological databases, we curate a total of 5825 biological databases from 8931 publications, which are geographically distributed in 72 countries/regions and developed by 1975 institutions (as of September 20, 2022). We further devise a <em><strong>z</strong></em><strong>-index</strong>, a novel index to characterize the scientific impact of a database, and rank all these biological databases as well as their hosting institutions and countries in terms of <strong>citation</strong> and <em>z</em>-index. Consequently, we present a series of statistics and trends of worldwide biological databases, yielding a global perspective to better understand their status and impact for life and health sciences. An up-to-date catalog of worldwide biological databases, as well as their curated meta-information and derived statistics, is publicly available at Database Commons (<span><span>https://ngdc.cncb.ac.cn/databasecommons/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 1054-1058"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10787370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential Transcriptomic Landscapes of SARS-CoV-2 Variants in Multiple Organs from Infected Rhesus Macaques","authors":"Tingfu Du ,&nbsp;Chunchun Gao ,&nbsp;Shuaiyao Lu ,&nbsp;Qianlan Liu ,&nbsp;Yun Yang ,&nbsp;Wenhai Yu ,&nbsp;Wenjie Li ,&nbsp;Yong Qiao Sun ,&nbsp;Cong Tang ,&nbsp;Junbin Wang ,&nbsp;Jiahong Gao ,&nbsp;Yong Zhang ,&nbsp;Fangyu Luo ,&nbsp;Ying Yang ,&nbsp;Yun-Gui Yang ,&nbsp;Xiaozhong Peng","doi":"10.1016/j.gpb.2023.06.002","DOIUrl":"10.1016/j.gpb.2023.06.002","url":null,"abstract":"<div><div>Severe acute respiratory syndrome coronavirus 2 (<strong>SARS-CoV-2</strong>) caused the persistent coronavirus disease 2019 (COVID-19) pandemic, which has resulted in millions of deaths worldwide and brought an enormous public health and global economic burden. The recurring global wave of infections has been exacerbated by growing variants of SARS-CoV-2. In this study, the virological characteristics of the original SARS-CoV-2 strain and its <strong>variants of concern</strong> (VOCs; including Alpha, Beta, and Delta) <em>in vitro</em>, as well as differential transcriptomic landscapes in multiple organs (lung, right ventricle, blood, cerebral cortex, and cerebellum) from the infected <strong>rhesus macaques</strong>, were elucidated. The original strain of SARS-CoV-2 caused a stronger innate immune response in host cells, and its VOCs markedly increased the levels of <strong>subgenomic RNA</strong><strong>s</strong>, such as <em>N</em>, <em>Orf9b</em>, <em>Orf6</em>, and <em>Orf7ab</em>, which are known as the innate immune antagonists and the inhibitors of antiviral factors. Intriguingly, the original SARS-CoV-2 strain and Alpha variant induced larger alteration of RNA abundance in tissues of rhesus monkeys than Beta and Delta variants did. Moreover, a hyperinflammatory state and active immune response were shown in the right ventricles of rhesus monkeys by the up-regulation of inflammation- and immune-related RNAs. Furthermore, peripheral blood may mediate signaling transmission among tissues to coordinate the molecular changes in the infected individuals. Collectively, these data provide insights into the pathogenesis of COVID-19 at the early stage of infection by the original SARS-CoV-2 strain and its VOCs.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 1014-1029"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10154985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-small Cell Lung Cancer Epigenomes Exhibit Altered DNA Methylation in Smokers and Never-smokers","authors":"Jennifer A. Karlow ,&nbsp;Erica C. Pehrsson ,&nbsp;Xiaoyun Xing ,&nbsp;Mark Watson ,&nbsp;Siddhartha Devarakonda ,&nbsp;Ramaswamy Govindan ,&nbsp;Ting Wang","doi":"10.1016/j.gpb.2023.03.006","DOIUrl":"10.1016/j.gpb.2023.03.006","url":null,"abstract":"<div><div>Epigenetic alterations are widespread in cancer and can complement genetic alterations to influence cancer progression and treatment outcome. To determine the potential contribution of <strong>DNA</strong> <strong>methylation</strong> alterations to tumor phenotype in <strong>non-small cell lung cancer</strong> (NSCLC) in both smoker and never-smoker patients, we performed genome-wide profiling of DNA methylation in 17 primary NSCLC tumors and 10 matched normal lung samples using the complementary assays, methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation sensitive restriction enzyme sequencing (MRE-seq). We reported recurrent methylation changes in the promoters of several genes, many previously implicated in cancer, including <em>FAM83A</em> and <em>SEPT9</em> (hypomethylation), as well as <em>PCDH7</em>, <em>NKX2-1</em>, and <em>SOX17</em> (hypermethylation). Although many methylation changes between tumors and their paired normal samples were shared across patients, several were specific to a particular smoking status. For example, never-smokers displayed a greater proportion of hypomethylated differentially methylated regions (hypoDMRs) and a greater number of recurrently hypomethylated promoters, including those of <em>ASPSCR1</em>, <em>TOP2A</em>, <em>DPP9</em>, and <em>USP39</em>, all previously linked to cancer. Changes outside of promoters were also widespread and often recurrent, particularly methylation loss over repetitive elements, highly enriched for ERV1 subfamilies. Recurrent hypoDMRs were enriched for several transcription factor binding motifs, often for genes involved in signaling and cell proliferation. For example, 71% of recurrent promoter hypoDMRs contained a motif for NKX2-1. Finally, the majority of DMRs were located within an active chromatin state in tissues profiled using the Roadmap Epigenomics data, suggesting that methylation changes may contribute to altered regulatory programs through the adaptation of cell type-specific expression programs.</div></div>","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 991-1013"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Inclusiveness and Thoroughness: A Paradigm Shift from More-ever-omics to Holovivology","authors":"Jun Yu","doi":"10.1016/j.gpb.2023.10.003","DOIUrl":"10.1016/j.gpb.2023.10.003","url":null,"abstract":"","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 895-896"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54232890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Historic Retrospective on the Early Bioinformatics Research in China","authors":"Runsheng Chen","doi":"10.1016/j.gpb.2023.10.006","DOIUrl":"10.1016/j.gpb.2023.10.006","url":null,"abstract":"","PeriodicalId":12528,"journal":{"name":"Genomics, Proteomics & Bioinformatics","volume":"21 5","pages":"Pages 897-899"},"PeriodicalIF":11.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71490437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}