Computational and structural biotechnology journal最新文献

{"title":"YabXnization platform: A monoclonal antibody heterologization server based on rational design and artificial intelligence-assisted computation","authors":"Xiaohu Hao, Dongping Liu, Long Fan","doi":"10.1016/j.csbj.2024.08.013","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.013","url":null,"abstract":"The application of antibody therapeutics is promising in the field of immunotherapy. While, heterologization should be done in most cases before applying the therapeutic antibodies into bodies, e.g., humanization, caninization and felinization for human beings, canine and feline, respectively. Here we report YabXnization, the platform which realizes antibody heterologization on the basis of rational design and artificial intelligence (AI)-assisted computation. YabXnization provides two ways for heterologization: traditional CDR-grafting and backmutation-based rational design; and AI-assisted fusion computational design. Taking humanization as example, both of the two ways first find the proper template for heavy and light chains with CDR-grafting followed. For rational design, bioinformatics analysis-based backmutation is then conducted. For AI-assisted computational design, the backmutation and humanness evaluation are implemented through evolutionary computation framework with DeepForest-based humanness evaluation model and the distance to the previously found human template as objective functions. Finally, the top heterologized antibodies can be provided by YabXnization platform. We examined the platform with 18 antibodies to be heterologized, in which 10 for humanization, 6 for caninization and 2 for felinization, respectively. The heterologized antibodies were measured by indirect ELISA and BLI(Octet)/SPR(Biacore) binding affinity measurement methods. Test results show a 90% success rate with the binding affinity loss of heterologized antibodies within an order of magnitude compared to the corresponding chimeric antibodies. It even shows an increase in the binding affinity on some of the heterologized antibodies. The platform can be reached through .","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling axolotl transcriptome for tissue regeneration with high-resolution annotation via long-read sequencing","authors":"Tian Qin, Jie Han, Chunmei Fan, Heng Sun, Naveed Rauf, Tingzhang Wang, Zi Yin, Xiao Chen","doi":"10.1016/j.csbj.2024.08.014","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.014","url":null,"abstract":"Axolotls are known for their remarkable regeneration ability. Exploring their transcriptome provides insight into regenerative mechanisms. However, the current annotation of the axolotl transcriptome is limited, leaving the role of unannotated transcripts in regeneration unknown. To discourse this challenge, we exploited long-read sequencing technology, which enables direct observation of full-length RNA transcripts, greatly enhancing the coverage and accuracy of axolotl transcriptome annotation. By utilizing this method, we identified 222 novel gene loci and 4775 novel transcripts, which were quantified using short-read sequencing data. Through the inclusive analysis, we discovered novel homologs, potential functional proteins, noncoding RNAs, and alternative splicing events in key regeneration pathways. In particular, we identified novel transcripts with high protein-coding potential implicated in cell cycle regulation and musculoskeletal development, and regeneration were identified. Interestingly, alternative splice variants were also detected across diverse pathways critical to regeneration. This specifies that these novel transcripts potentially play vital roles underpinning the robust regenerative capacities of axolotls. Single-cell transcriptomic analysis further revealed these isoforms to predominantly exist in axolotl limb chondrocytes and mature tissue cell populations. Overall, the findings significantly advanced consideration of the axolotl transcriptome and provided a new perspective for understanding the mechanisms of regenerative abilities of axolotls.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery","authors":"Theodosios Theodosiou, Konstantinos Vrettos, Ismini Baltsavia, Fotis Baltoumas, Nikolas Papanikolaou, Andreas Ν. Antonakis, Dimitrios Mossialos, Christos A. Ouzounis, Vasilis J. Promponas, Makrina Karaglani, Ekaterini Chatzaki, Sven Brandau, Georgios A. Pavlopoulos, Evangelos Andreakos, Ioannis Iliopoulos","doi":"10.1016/j.csbj.2024.08.016","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.016","url":null,"abstract":"The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methylation and transcriptomic profiling reveals short term and long term regulatory responses in polarized macrophages","authors":"","doi":"10.1016/j.csbj.2024.08.018","DOIUrl":"10.1016/j.csbj.2024.08.018","url":null,"abstract":"<div><p>Macrophage plasticity allows the adoption of distinct functional states in response to environmental cues. While unique transcriptomic profiles define these states, focusing solely on transcription neglects potential long-term effects. The investigation of epigenetic changes can be used to understand how temporary stimuli can result in lasting effects. Epigenetic alterations play an important role in the pathophysiology of macrophages, including their trained innate immunity, enabling faster and more efficient inflammatory responses upon subsequent encounters to the same pathogen or insult. In this study, we used a multi-omics approach to elucidate the interplay between gene expression and DNA-methylation, to explore the potential long-term effects of diverse polarizing environments on macrophage activity. We identified a common core set of genes that are differentially methylated regardless of exposure type, indicating a potential common fundamental mechanism for adaptation to various stimuli. Functional analysis revealed that processes requiring rapid responses displayed transcriptomic regulation, whereas functions critical for long-term adaptations exhibited co-regulation at both transcriptomic and epigenetic levels. Our study uncovers a novel set of genes linked to the long-term effects of macrophage polarization. This discovery underscores the potential of epigenetics in elucidating how macrophages establish long-term memory and influence health outcomes.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002769/pdfft?md5=e2b76882a78145c414f3236fa1d52b03&pid=1-s2.0-S2001037024002769-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044620","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":"OligoM-Cancer: A multidimensional information platform for deep phenotyping of heterogenous oligometastatic cancer","authors":"","doi":"10.1016/j.csbj.2024.08.015","DOIUrl":"10.1016/j.csbj.2024.08.015","url":null,"abstract":"<div><p>Patients with oligometastatic cancer (OMC) exhibit better response to local therapeutic interventions and a more treatable tendency than those with polymetastatic cancers. However, studies on OMC are limited and lack effective integration for systematic comparison and personalized application, and the diagnosis and precise treatment of OMC remain controversial. The application of large language models in medicine remains challenging because of the requirement of high-quality medical data. Moreover, these models must be enhanced using precise domain-specific knowledge. Therefore, we developed the OligoM-Cancer platform (<span><span>http://oligo.sysbio.org.cn</span><svg><path></path></svg></span>), pioneering knowledge curation that depicts various aspects of oligometastases spectrum, including markers, diagnosis, prognosis, and therapy choices. A user-friendly website was developed using HTML, FLASK, MySQL, Bootstrap, Echarts, and JavaScript. This platform encompasses comprehensive knowledge and evidence of phenotypes and their associated factors. With 4059 items of literature retrieved, OligoM-Cancer includes 1345 valid publications and 393 OMC-associated factors. Additionally, the included clinical assistance tools enhance the interpretability and credibility of clinical translational practice. OligoM-Cancer facilitates knowledge-guided modeling for deep phenotyping of OMC and potentially assists large language models in supporting specialised oligometastasis applications, thereby enhancing their generalization and reliability.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002733/pdfft?md5=9b1a390e76398a94bfa893bb782c7cfd&pid=1-s2.0-S2001037024002733-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044409","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":"Entropy-extreme model for predicting the development of cyber epidemics at early stages","authors":"","doi":"10.1016/j.csbj.2024.08.017","DOIUrl":"10.1016/j.csbj.2024.08.017","url":null,"abstract":"<div><p>The approaches used in biomedicine to analyze epidemics take into account features such as exponential growth in the early stages, slowdown in dynamics upon saturation, time delays in spread, segmented spread, evolutionary adaptations of the pathogen, and preventive measures based on universal communication protocols. All these characteristics are also present in modern cyber epidemics. Therefore, adapting effective biomedical approaches to epidemic analysis for the investigation of the development of cyber epidemics is a promising scientific research task. The article is dedicated to researching the problem of predicting the development of cyber epidemics at early stages. In such conditions, the available data is scarce, incomplete, and distorted. This situation makes it impossible to use artificial intelligence models for prediction. Therefore, the authors propose an entropy-extreme model, defined within the machine learning paradigm, to address this problem. The model is based on estimating the probability distributions of its controllable parameters from input data, taking into account the variability characteristic of the last ones. The entropy-extreme instance, identified from a set of such distributions, indicates the most uncertain (most negative) trajectory of the investigated process. Numerical methods are used to analyze the generated set of investigated process development trajectories based on the assessments of probability distributions of the controllable parameters and the variability characteristic. The result of the analysis includes characteristic predictive trajectories such as the average and median trajectories from the set, as well as the trajectory corresponding to the standard deviation area of the parameters’ values. Experiments with real data on the infection of Windows-operated devices by various categories of malware showed that the proposed model outperforms the classical competitor (least squares method) in predicting the development of cyber epidemics near the extremum of the time series representing the deployment of such a process over time. Moreover, the proposed model can be applied without any prior hypotheses regarding the probabilistic properties of the available data.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002770/pdfft?md5=93adf58e38f4237644ddd0c1ca45aefd&pid=1-s2.0-S2001037024002770-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148887","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":"Towards accurate and efficient diagnoses in nephropathology: An AI-based approach for assessing kidney transplant rejection","authors":"","doi":"10.1016/j.csbj.2024.08.011","DOIUrl":"10.1016/j.csbj.2024.08.011","url":null,"abstract":"<div><p>The Banff classification is useful for diagnosing renal transplant rejection. However, it has limitations due to subjectivity and varying concordance in physicians' assessments. Artificial intelligence (AI) can help standardize research, increase objectivity and accurately quantify morphological characteristics, improving reproducibility in clinical practice. This study aims to develop an AI-based solutions for diagnosing acute kidney transplant rejection by introducing automated evaluation of prognostic morphological patterns. The proposed approach aims to help accurately distinguish borderline changes from rejection. We trained a deep-learning model utilizing a fine-tuned Mask R-CNN architecture which achieved a mean Average Precision value of 0.74 for the segmentation of renal tissue structures. A strong positive nonlinear correlation was found between the measured infiltration areas and fibrosis, indicating the model's potential for assessing these parameters in kidney biopsies. The ROC analysis showed a high predictive ability for distinguishing between ci and i scores based on infiltration area and fibrosis area measurements. The AI model demonstrated high precision in predicting clinical scores which makes it a promising AI assisting tool for pathologists. The application of AI in nephropathology has a potential for advancements, including automated morphometric evaluation, 3D histological models and faster processing to enhance diagnostic accuracy and efficiency.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002691/pdfft?md5=0c5e6d6c9e074579fb1684e1d90ca60a&pid=1-s2.0-S2001037024002691-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047966","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":"Structural insight into the function of human peptidyl arginine deiminase 6","authors":"Jack P.C. Williams, Stephane Mouilleron, Rolando Hernandez Trapero, M. Teresa Bertran, Joseph A. Marsh, Louise J. Walport","doi":"10.1016/j.csbj.2024.08.019","DOIUrl":"https://doi.org/10.1016/j.csbj.2024.08.019","url":null,"abstract":"Peptidyl arginine deiminase 6 (PADI6 or PAD6) is vital for early embryonic development in mice and humans, yet its function remains elusive. PADI6 is less conserved than other PADIs and it is currently unknown whether it has a catalytic function. Here we show that human PADI6 dimerises like hPADIs 2–4, however, does not bind Ca and is inactive in assays against standard PADI substrates. By determining the crystal structure of hPADI6, we show that hPADI6 is structured in the absence of Ca where hPADI2 and hPADI4 are not, and the Ca-binding sites are not conserved. Moreover, we show that whilst the key catalytic aspartic acid and histidine residues are structurally conserved, the cysteine is displaced far from the active site centre and the hPADI6 active site pocket appears closed through a unique evolved mechanism in hPADI6, not present in the other PADIs. Taken together, these findings provide insight into how the function of hPADI6 may differ from the other PADIs based on its structure and provides a resource for characterising the damaging effect of clinically significant variants.","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico vaccine design: Targeting highly epitopic regions of Clostridium perfringens type D epsilon toxin and Clostridium novyi type B alpha toxin for optimal immunogenicity","authors":"","doi":"10.1016/j.csbj.2024.08.009","DOIUrl":"10.1016/j.csbj.2024.08.009","url":null,"abstract":"<div><p>Livestock infections caused by highly toxic bacteria, such as <em>Clostridium perfringens</em> type D and <em>Clostridium novyi</em> type B, present significant challenges in veterinary medicine. Such infections often require complex and elusive treatment regimens. Developing effective vaccines tailored to combat these specific pathogens remains a pressing need within the field. These bacteria are notorious for their extreme toxicity and the difficulty in culturing them for vaccine production. To address this challenge, we engineered a new potential vaccine candidate capable of neutralizing the virulence of both bacterial strains. Leveraging computational techniques, we identified epitopic regions within <em>C. perfringens</em> Epsilon Toxin (ETX) and <em>C. novyi</em> Alpha Toxin (ATX). Through fusion gene design, we integrated these epitopic regions alongside the PADRE-peptide sequence. The PADRE-peptide serves as a universal adjuvant to induce an immune response. The culmination of our efforts materialized in a Recombinant Fusion Protein D (rFPD), a novel vaccine construct designed to elicit robust and specific immune defenses against both bacterial species. By combining in-silico design and molecular engineering, our study represents a promising stride toward combating the impact of these pathogenic bacteria in livestock.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002666/pdfft?md5=8d6c9561f4cbdbd041b8e1b498768c9a&pid=1-s2.0-S2001037024002666-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049917","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":"Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis","authors":"","doi":"10.1016/j.csbj.2024.08.005","DOIUrl":"10.1016/j.csbj.2024.08.005","url":null,"abstract":"<div><p>This systematic literature review examines state-of-the-art Explainable Artificial Intelligence (XAI) methods applied to medical image analysis, discussing current challenges and future research directions, and exploring evaluation metrics used to assess XAI approaches. With the growing efficiency of Machine Learning (ML) and Deep Learning (DL) in medical applications, there's a critical need for adoption in healthcare. However, their “black-box” nature, where decisions are made without clear explanations, hinders acceptance in clinical settings where decisions have significant medicolegal consequences. Our review highlights the advanced XAI methods, identifying how they address the need for transparency and trust in ML/DL decisions. We also outline the challenges faced by these methods and propose future research directions to improve XAI in healthcare.</p><p>This paper aims to bridge the gap between cutting-edge computational techniques and their practical application in healthcare, nurturing a more transparent, trustworthy, and effective use of AI in medical settings. The insights guide both research and industry, promoting innovation and standardisation in XAI implementation in healthcare.</p></div>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2001037024002642/pdfft?md5=155b5aaa7bd4d84654a57d988e54c094&pid=1-s2.0-S2001037024002642-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141984616","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}