Methods最新文献

{"title":"Deepstack-ACE: A deep stacking-based ensemble learning framework for the accelerated discovery of ACE inhibitory peptides.","authors":"Phasit Charoenkwan, Pramote Chumnanpuen, Nalini Schaduangrat, Watshara Shoombuatong","doi":"10.1016/j.ymeth.2024.12.005","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.12.005","url":null,"abstract":"<p><p>Identifying angiotensin-I-converting enzyme (ACE) inhibitory peptides accurately is crucial for understanding the primary factor that regulates the renin-angiotensin system and for providing guidance in developing new potential drugs. Given the inherent experimental complexities, using computational methods for in silico peptide identification could be indispensable for facilitating the high-throughput characterization of ACE inhibitory peptides. In this paper, we propose a novel deep stacking-based ensemble learning framework, termed Deepstack-ACE, to precisely identify ACE inhibitory peptides. In Deepstack-ACE, the input peptide sequences are fed into the word2vec embedding technique to generate sequence representations. Then, these representations were employed to train five powerful deep learning methods, including long short-term memory, convolutional neural network, multi-layer perceptron, gated recurrent unit network, and recurrent neural network, for the construction of base-classifiers. Finally, the optimized stacked model was constructed based on the best combination of selected base-classifiers. Benchmarking experiments showed that Deepstack-ACE attained a more accurate and robust identification of ACE inhibitory peptides compared to its base-classifiers and several conventional machine learning classifiers. Remarkably, in the independent test, our proposed model significantly outperformed the current state-of-the-art methods, with a balanced accuracy of 0.916, sensitivity of 0.911, and Matthews correlation coefficient scores of 0.826. Moreover, we developed a user-friendly web server for Deepstack-ACE, which is freely available at https://pmlabqsar.pythonanywhere.com/Deepstack-ACE. We anticipate that our proposed Deepstack-ACE model can provide a faster and reasonably accurate identification of ACE inhibitory peptides.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting cyclins based on key features and machine learning methods.","authors":"Cheng-Yan Wu, Zhi-Xue Xu, Nan Li, Dan-Yang Qi, Hong-Ye Wu, Hui Ding, Yan-Ting Jin","doi":"10.1016/j.ymeth.2024.12.009","DOIUrl":"10.1016/j.ymeth.2024.12.009","url":null,"abstract":"<p><p>Cyclins are a group of proteins that regulate the cell cycle process by modulating various stages of cell division to ensure correct cell proliferation, differentiation, and apoptosis. Research on cyclins is crucial for understanding the biological functions and pathological states of cells. However, current research on cyclin identification based on machine learning only focuses on accuracy ignoring the interpretability of features. Therefore, in this study, we pay more attention to the interpretation and analysis of key features associated with cyclins. Firstly, we developed an SVM-based model for identifying cyclins with an accuracy of 92.8% through 5-fold. Then we analyzed the physicochemical properties of the 14 key features used in the model construction and identified the G and charged C1 features that are critical for distinguishing cyclins from non-cyclins. Furthermore, we constructed an SVM-based model using only these two features with an accuracy of 81.3% through the leave-one-out cross-validation. Our study shows that cyclins differ from non-cyclins in their physicochemical properties and that using only two features can achieve good prediction accuracy.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":"112-119"},"PeriodicalIF":4.2,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BCDB: A Dual-Branch Network Based on Transformer for Predicting Transcription Factor Binding Sites.","authors":"Jia He, Yupeng Zhang, Yuhang Liu, Zhigan Zhou, Tianhao Li, Yongqing Zhang, Boqia Xie","doi":"10.1016/j.ymeth.2024.12.006","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.12.006","url":null,"abstract":"<p><p>Transcription factor binding sites (TFBSs) are critical in regulating gene expression. Precisely locating TFBSs can reveal the mechanisms of action of different transcription factors in gene transcription. Various deep learning methods have been proposed to predict TFBS; however, these models often need help demonstrating ideal performance under limited data conditions. Furthermore, these models typically have complex structures, which makes their decision-making processes difficult to transparentize. Addressing these issues, we have developed a framework named BCDB. This framework integrates multi-scale DNA information and employs a dual-branch output strategy. Integrating DNABERT, convolutional neural networks(CNN), and multi-head attention mechanisms enhances the feature extraction capabilities, significantly improving the accuracy of predictions. This innovative method aims to balance the extraction of global and local information, enhancing predictive performance while utilizing attention mechanisms to provide an intuitive way to explain the model's predictions, thus strengthening the overall interpretability of the model. Prediction results on 165 ChIP-seq datasets show that BCDB significantly outperforms other existing deep learning methods in terms of performance. Additionally, since the BCDB model utilizes transfer learning methods, it can transfer knowledge learned from many unlabeled data to specific cell line prediction tasks, allowing our model to achieve cross-cell line TFBS prediction. The source code for BCDB is available on https://github.com/ZhangLab312/BCDB.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nanotechnological approaches to improve corticosteroids ocular therapy.","authors":"María Lina Formica, Juan Matías Pernochi Scerbo, Hamoudi Ghassan Awde Alfonso, Pablo Tomás Palmieri, Julieta Ribotta, Santiago Daniel Palma","doi":"10.1016/j.ymeth.2024.12.008","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.12.008","url":null,"abstract":"<p><p>The administration of corticosteroids is the first-line treatment of the clinical conditions with ocular inflammation. Nonetheless, ocular physiological mechanisms, anatomical barriers and corticosteroid properties prevent it from reaching the target site. Thus, frequent topical administered doses or ocular injections are required, leading to a higher risk of adverse events and poor patient compliance. Designing novel drug delivery systems based on nanotechnological tools is a useful approach to overcome disadvantages associated with the ocular delivery of corticosteroids. Nanoparticle-based drug delivery systems represent an alternative to the current dosage forms for the ocular administration of corticosteroids, since due to their particle size and the properties of their materials, they can increase their solubility, improve ocular permeability, control their release and increase bioavailability after their ocular administration. In this way, lipid and polymer-based nanoparticles have been the main strategies developed, giving rise to novel patent applications to protect these innovative drug delivery systems as a product, its preparation or administration method. Additionally, it should be noted that at least 10 clinical trials are being carried out to evaluate the ocular application of different pharmaceutical formulations based on corticosteroid-loaded nanoparticles. Through a comprehensive and extensive analysis, this review highlights the impact of nanotechnology applications in ocular inflammation therapy with corticosteroids.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142826531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CHiTA: A scarless High-Throughput pipeline for characterization of ribozymes.","authors":"Lauren N McKinley, Philip C Bevilacqua","doi":"10.1016/j.ymeth.2024.12.007","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.12.007","url":null,"abstract":"<p><p>Small self-cleaving ribozymes are catalytic RNAs that cleave their phosphodiester backbone rapidly and site-specifically without the assistance of proteins. Their catalytic properties make them ideal targets for applications in RNA pharmaceuticals and bioengineering. Consequently, computational pipelines that predict or design thousands of self-cleaving ribozyme candidates have been developed. Traditional experimental techniques for verifying the activity of these putative ribozymes, however, are low-throughput and time intensive. High-throughput (HT) pipelines that employ next-generation sequencing (NGS) analyze the activity of these thousands of ribozymes simultaneously. Until recently, the application of these HT pipelines has been limited to studying all single and double mutants of a select representative ribozyme. Unfortunately, this prevents the exploration of candidates having different lengths, circular permutations, and auxiliary stem-loops. Moreover, pipelines that analyze ribozymes en masse often include transcription of non-native flanking sequences that preclude accurate assessment of the intrinsic rate of ribozyme self-cleavage. To overcome these limitations, we developed a HT pipeline, \"Cleavage High-Throughput Assay (CHiTA)\", which employs NGS and massively parallel oligonucleotide synthesis (MPOS) to characterize ribozyme activity for thousands of candidates in a scarless fashion. Herein, we describe detailed strategies and protocols to implement CHiTA to measure the activity of putative ribozymes from a wide range of ribozyme classes.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142811777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of plasmonic-based biosensors for rapid and in depth characterization of monoclonal antibodies directed against rabbit haemorrhagic and foot-and-mouth disease viruses in biological samples.","authors":"Chiara Urbinati, Giulia Pezzoni, Patrizia Cavadini, Vittoria Di Giovanni, Lorenzo Capucci, Marco Rusnati","doi":"10.1016/j.ymeth.2024.12.003","DOIUrl":"10.1016/j.ymeth.2024.12.003","url":null,"abstract":"<p><p>ELISA and RT-PCR represent the standard tools for the sensitive identification of viruses in biological samples, but they lack the capacity to finely characterize the binding of viruses or viral antigens to monoclonal antibodies (MAbs). Biosensing technologies are gaining increasing importance as powerful MAb characterization tools in the field of virology. Surface plasmon resonance (SPR) is an optical biosensing technology already used for the in depth characterization of MAbs of diagnostic and therapeutic value. Rabbit haemorrhagic disease virus (RHDV) and foot-and-mouth disease virus (FMDV) are top veterinary issues for which the development of novel methods aimed at the characterization of antiviral MAbs represents a priority with important livestock healthcare and economic implications. With these premises in mind, here we prepared a series of SPR biosensors by immobilizing RHDV2 or its 6S subunit by different strategies that were then used to characterize the binding capacity of a panel of anti-RHDV2 MAbs. From the comparison of the results obtained, the biosensor composed of intact RHDV2 captured with catcher-MAb covalently immobilized to the surface showed the best analytical performances. To evaluate the versatility of the biosensor, the same strategy was then adopted using FMVD in cell extracts. The results obtained are discussed in view of the exploitation of SPR in the rapid and resilient fine characterization of antiviral MAbs for diagnostic or therapeutic purposes in the field of animal virology.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":"85-92"},"PeriodicalIF":4.2,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cleaving the way for heterologous peptide production: An overview of cleavage strategies.","authors":"Karen Ofuji Osiro, Harry Morales Duque, Kamila Botelho Sampaio de Oliveira, Nadielle Tamires Moreira Melo, Letícia Ferreira Lima, Hugo Costa Paes, Octavio Luiz Franco","doi":"10.1016/j.ymeth.2024.12.002","DOIUrl":"10.1016/j.ymeth.2024.12.002","url":null,"abstract":"<p><p>One of the main bottlenecks for recombinant peptide production is choosing the proper cleavage method to remove fusion protein tags from target peptides. While these tags are crucial for inhibiting the activity of the target peptide during heterologous expression, incorporating a cleavage site is essential for their later removal, ensuring the pure sequencing of the peptide. This review evaluates different cleavage methods, including protease-mediated, self-cleavable protein, and chemical-mediated sites, regarding their advantages and limitations. For instance, intein, N^pro EDDIE, enterokinase, factor Xa, SUMO, and CNBr are options for residue-free cleavage. Although protease-mediated cleavage is widely used, it can be expensive, due to its own cost added to the whole process. As an alternative, self-cleavable sites eliminate the requirement for proteinases. Another crucial step in defining the proper cleavage method is cost consideration, which relates to the purpose of peptide production. Here, we explore a range of cleavage approaches, meeting the needs of both cost-constrained applications and a more flexible budget. Overall, selecting the most suitable cleavage method should be based on careful consideration of toxicity, cost, accuracy, and specific application requirements to ensure a state-of-the-art approach.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":"36-44"},"PeriodicalIF":4.2,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Terahertz-based biosensors for biomedical applications: A review.","authors":"Meraline Selvaraj, Sreeja B S, Mohamed Aly Saad Aly","doi":"10.1016/j.ymeth.2024.12.001","DOIUrl":"10.1016/j.ymeth.2024.12.001","url":null,"abstract":"<p><p>Biosensors have many life sciences-related applications, particularly in the healthcare sector. They are employed in a wide range of fields, including drug development, food quality management, early diagnosis of diseases, and environmental monitoring. Terahertz-based biosensing has shown great promise as a label-free, non-invasive, and non-contact method of detecting biological substances. THz Spectroscopy has achieved a remarkable advancement in biomolecule recognition providing a rapid, highly sensitive, and non-destructive approach for various biomedical applications. The significance of THz-based biosensors and the broad spectrum of biomolecules that can be detected and analyzed with biosensors are reviewed in this work. Additionally, this work summarizes several techniques that were previously reported to improve the sensitivity and selectivity of these biosensors. Furthermore, an in-depth comparison between previously developed biosensors with an emphasis on their performance is presented and highlighted in the current review. Lastly, the challenges, the potential, and the future prospects of THz-based biosensing technology are critically addressed.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":"54-66"},"PeriodicalIF":4.2,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low friction hydrogel with diclofenac eluting ability for dry eye therapeutic contact lenses.","authors":"Diana C Silva, Margarida Oliveira, Carolina Marto-Costa, João Teixeira, Madalena Salema Oom, Carlos A Pinto, Jorge A Saraiva, Ana Clara Marques, Laurence Fitzhenry, Ana Paula Serro","doi":"10.1016/j.ymeth.2024.11.015","DOIUrl":"10.1016/j.ymeth.2024.11.015","url":null,"abstract":"<p><p>When placed in the eye, contact lenses (CLs) disturb the tear fluid and affect the natural tribological behaviour of the eye. The disruption in the contact mechanics between the ocular tissues can increase frictional shear stress and ocular dryness, causing discomfort. Ultimately, continuous CLs wear can trigger inflammation which is particularly critical for people suffering from dry eye. In this work, a double strategy was followed to obtain therapeutic daily disposable CLs for dry eye: a hydroxyethyl methacrylate (HEMA) based hydrogel was coated with two natural polysaccharides, chitosan (CHI) and hyaluronic acid (HA) and posteriorly loaded with an anti-inflammatory drug (diclofenac, DCF). Material sterilisation was carried out by high hydrostatic pressure (HHP) combined with moderate temperature. The friction coefficient (μ) was determined in the presence of different tear biomolecules (cholesterol, lysozyme and albumin) using a nanotribometer. Drug release experiments were performed in static and in hydrodynamic conditions. The material was extensively characterised, regarding surface morphology/topography, optical properties, water content and swelling behaviour, wettability, ionic and oxygen permeability and mechanical properties. It was found that the coating did not impair the physico-chemical properties relevant for the material's application in CLs. Besides, it also ensured a sustained release of DCF for 24 h in tests performed in hydrodynamic conditions that simulate those found in the eye, increasing significantly the amount of drug released. It reduced friction, improving the lubrication ability of the hydrogel, and presented antibacterial properties against S. aureus, P. aeruginosa and B. Cereus. The coated samples did not reveal any signs of cytotoxicity or potential eye irritation. Overall, the coating of the hydrogel may be useful to produce daily CLs able to alleviate dry eye symptoms and the discomfort of CLs wearers.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":"67-84"},"PeriodicalIF":4.2,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142765111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Massively parallel flow-cytometry-based screening of hematopoietic lineage cell populations from up to 25 donors simultaneously.","authors":"Jan Devan, Michaela Sandalova, Pamela Bitterli, Nick Herger, Tamara Mengis, Kenta Brender, Irina Heggli, Oliver Distler, Stefan Dudli","doi":"10.1016/j.ymeth.2024.11.014","DOIUrl":"10.1016/j.ymeth.2024.11.014","url":null,"abstract":"<p><p>This study aimed to develop a method allowing high-dimensional and technically uniform screening of surface markers on cells of hematopoietic origin. High-dimensional screening of cell phenotypes is primarily the domain of single-cell RNA sequencing (RNAseq), which allows simultaneous analysis of the expression of thousands of genes in several thousands of cells. However, rare cell populations can often substantially impact tissue homeostasis or disease pathogenesis, and dysregulation of rare populations can easily be missed when only a few thousand cells are analyzed. With the presented methodological approach, it is possible to screen hundreds of markers on millions of cells in a technically uniform manner and thus identify and characterize changes in rare populations. We utilize the highly expressed markers CD45 on immune cells and CD71 on erythroid progenitors to create unique fluorescent barcodes on each of the 25 samples. Double-barcoded samples are co-stained with a broad immunophenotyping panel. The panel is designed in such a way that allows the addition of PE-labelled antibody, which was used for screening purposes. Multiplexed samples are divided into hundreds of aliquots and co-stained, each aliquot with a different PE-labelled antibody. Utilizing a broad immunophenotyping panel and machine-learning algorithms, we can predict the co-expression of hundreds of screened markers with a high degree of precision. This technique is suitable for screening immune cells in bone marrow from different locations, blood specimens, or any tissue with a substantial presence of immune cells, such as tumors or inflamed tissue areas in autoimmune conditions. It represents an approach that can significantly improve our ability to recognize dysregulated immune cell populations and, if needed, precisely target subsequent experiments covering lower cell counts such as RNAseq.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":"45-53"},"PeriodicalIF":4.2,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142749496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}