Pub Date : 2025-06-28DOI: 10.1016/j.ab.2025.115929
Qing Zhang , You-Hang Hu , Yu Zhou , Jun Hu , Xiao-Gen Zhou , Biao Zhang
Protein-protein interactions (PPIs) play a pivotal role in numerous biological processes. Accurate identification of the amino acid residues involved in these interactions is essential for understanding the functional mechanisms of proteins. To effectively integrate both structure and sequence information, we propose a new interaction site predictor, TargetPPI, which leverages bidirectional long short-term memory networks (Bi-LSTM), convolutional neural networks (CNN), and Edge Aggregation through Graph Attention layers with Node Similarity (EGR-NS) neural networks. In TargetPPI, CNN and Bi-LSTM are first employed to extract the global and local feature information, respectively. The combination of global and local features is then used as node embeddings in the graph derived from the protein structure. We have also extracted six discriminative structural features as edge features in the graph. Additionally, a mean ensemble strategy is used to integrate multiple prediction models with diverse model parameters into the final model, resulting in more accurate PPIs prediction performance. Benchmarked results on seven independent testing datasets demonstrate that, compared to most of the state-of-the-art methods, TargetPPI achieves higher accuracy, precision, and Matthews Correlation Coefficient (MCC) values on average, specifically, 84.3 %, 57.6 %, and 0.383, respectively. The source code of TargetPPI is freely available at https://github.com/bukkeshuo/TargetPPI.
{"title":"Improving protein-protein interaction site prediction using graph neural network and structure profiles","authors":"Qing Zhang , You-Hang Hu , Yu Zhou , Jun Hu , Xiao-Gen Zhou , Biao Zhang","doi":"10.1016/j.ab.2025.115929","DOIUrl":"10.1016/j.ab.2025.115929","url":null,"abstract":"<div><div>Protein-protein interactions (PPIs) play a pivotal role in numerous biological processes. Accurate identification of the amino acid residues involved in these interactions is essential for understanding the functional mechanisms of proteins. To effectively integrate both structure and sequence information, we propose a new interaction site predictor, TargetPPI, which leverages bidirectional long short-term memory networks (Bi-LSTM), convolutional neural networks (CNN), and Edge Aggregation through Graph Attention layers with Node Similarity (EGR-NS) neural networks. In TargetPPI, CNN and Bi-LSTM are first employed to extract the global and local feature information, respectively. The combination of global and local features is then used as node embeddings in the graph derived from the protein structure. We have also extracted six discriminative structural features as edge features in the graph. Additionally, a mean ensemble strategy is used to integrate multiple prediction models with diverse model parameters into the final model, resulting in more accurate PPIs prediction performance. Benchmarked results on seven independent testing datasets demonstrate that, compared to most of the state-of-the-art methods, TargetPPI achieves higher accuracy, precision, and Matthews Correlation Coefficient (MCC) values on average, specifically, 84.3 %, 57.6 %, and 0.383, respectively. The source code of TargetPPI is freely available at <span><span>https://github.com/bukkeshuo/TargetPPI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"705 ","pages":"Article 115929"},"PeriodicalIF":2.6,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-27DOI: 10.1016/j.ab.2025.115928
Dimitrios Tsikas
In quantitative analyses, thousands of compounds are extracted from a biological matrix in addition to the analytes of interest and can affect their quantification by many different effects. They are widely known as matrix effects (ME). A frequently used approach in LC-MS/MS to quantify ME is performing two series of analyses, that is 1) in the biological sample, and 2) in analyte solutions in water, organic solvents and/or in mixtures of them, and by comparing the slope values of the two standard curves. This article suggests a new approach for the quantification of ME in GC-MS using isotopologs, namely their specific peak area. The approach is exemplified for amino acids, representing an important group of physiological substances, for human serum and urine, two capital matrices in biological analysis.
{"title":"Assessment of matrix effects in quantitative GC-MS by using isotopologs","authors":"Dimitrios Tsikas","doi":"10.1016/j.ab.2025.115928","DOIUrl":"10.1016/j.ab.2025.115928","url":null,"abstract":"<div><div>In quantitative analyses, thousands of compounds are extracted from a biological matrix in addition to the analytes of interest and can affect their quantification by many different effects. They are widely known as matrix effects (ME). A frequently used approach in LC-MS/MS to quantify ME is performing two series of analyses, that is 1) in the biological sample, and 2) in analyte solutions in water, organic solvents and/or in mixtures of them, and by comparing the slope values of the two standard curves. This article suggests a new approach for the quantification of ME in GC-MS using isotopologs, namely their specific peak area. The approach is exemplified for amino acids, representing an important group of physiological substances, for human serum and urine, two capital matrices in biological analysis.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"705 ","pages":"Article 115928"},"PeriodicalIF":2.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-21DOI: 10.1016/j.ab.2025.115927
Rania A. Hussien
The determination of iron in fortified biological and foods samples is mandatory by many global regulatory agencies. A polymer inclusion membrane (PIM) composed of poly (vinyl chloride) (PVC) as the polymer matrix, dioctyl phthalate (DOP) serving as the plasticizer, and methyltrioctyl ammonium chloride (Aliquat 336) acting as the carrier, has proven effective for the preconcentration and analysis of iron. This method utilizes 2-(2-amino-3-hydroxypyridine-4-ylazo) benzoic acid (AHPAB) as a complexing agent, enabling a straightforward colorimetric detection process. Adjusting the chemical and physical factors influencing membrane efficiency expanded its practical applications. The Fe3+ transport remained consistent under the optimum conditions used in fabricating the PIM. Additionally, employing an acetate buffer at pH 3.66 as the stripping phase facilitated efficient Fe3+ transfer, even in the presence of significant competing anions within the analyzed samples. Total iron content was measured after an on-line oxidation process where Fe2+ was converted to Fe3+ using a hydrogen peroxide stream. The Fe2+ concentration was determined by subtracting the Fe3+ value from the total iron concentration. Mass calibration was achieved within the range compatible with ICP-AES. The detection limit, defined as 3σsβ/S, was 1.75 ng/mL. Repeatability, expressed as the relative standard deviation (RSD) from nine measurements at 60 ng/mL, was 1.75 %, while the inter-sensor repeatability across five chelating sensors was 2.5 %. Furthermore, highlight the potential advantages of incorporating AHPAB into PIMs, such as improved stability, reusability, and enhanced selectivity for Fe ions was achieved. The validated PIM-based method was effectively applied to analyze food, biological, and environmental samples containing naturally occurring Fe2+ ions.
{"title":"Development of a green polymer-based sensor for enhanced iron detection in diverse biological, food and environmental matrices","authors":"Rania A. Hussien","doi":"10.1016/j.ab.2025.115927","DOIUrl":"10.1016/j.ab.2025.115927","url":null,"abstract":"<div><div>The determination of iron in fortified biological and foods samples is mandatory by many global regulatory agencies. A polymer inclusion membrane (PIM) composed of poly (vinyl chloride) (PVC) as the polymer matrix, dioctyl phthalate (DOP) serving as the plasticizer, and methyltrioctyl ammonium chloride (Aliquat 336) acting as the carrier, has proven effective for the preconcentration and analysis of iron. This method utilizes 2-(2-amino-3-hydroxypyridine-4-ylazo) benzoic acid (AHPAB) as a complexing agent, enabling a straightforward colorimetric detection process. Adjusting the chemical and physical factors influencing membrane efficiency expanded its practical applications. The Fe<sup>3+</sup> transport remained consistent under the optimum conditions used in fabricating the PIM. Additionally, employing an acetate buffer at pH 3.66 as the stripping phase facilitated efficient Fe<sup>3+</sup> transfer, even in the presence of significant competing anions within the analyzed samples. Total iron content was measured after an on-line oxidation process where Fe<sup>2+</sup> was converted to Fe<sup>3+</sup> using a hydrogen peroxide stream. The Fe<sup>2+</sup> concentration was determined by subtracting the Fe<sup>3+</sup> value from the total iron concentration. Mass calibration was achieved within the range compatible with ICP-AES. The detection limit, defined as 3σ<sub>sβ</sub>/S, was 1.75 ng/mL. Repeatability, expressed as the relative standard deviation (RSD) from nine measurements at 60 ng/mL, was 1.75 %, while the inter-sensor repeatability across five chelating sensors was 2.5 %. Furthermore, highlight the potential advantages of incorporating AHPAB into PIMs, such as improved stability, reusability, and enhanced selectivity for Fe ions was achieved. The validated PIM-based method was effectively applied to analyze food, biological, and environmental samples containing naturally occurring Fe<sup>2+</sup> ions.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"705 ","pages":"Article 115927"},"PeriodicalIF":2.6,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-19DOI: 10.1016/j.ab.2025.115926
John T. Pinto , Thambi Dorai , Thomas M. Jeitner , Travis T. Denton
In this issue of Analytical Biochemistry, we honor the life and legacy of Arthur Joseph Lawrence Cooper (1946–2024). Born in London, Arthur made pioneering contributions to medical science, particularly in understanding the role of glutamine in the brain and cancer cell metabolism. His research revealed how glutamine supports neurotransmitter synthesis, energy production, and ammonia detoxification, as well as its critical role in cancer cell growth. His work has greatly advanced both neuroscience and cancer biology, offering insights that could lead to new therapeutic strategies targeting glutamine metabolism.
{"title":"The long run: A tribute to Arthur Joseph Lawrence Cooper","authors":"John T. Pinto , Thambi Dorai , Thomas M. Jeitner , Travis T. Denton","doi":"10.1016/j.ab.2025.115926","DOIUrl":"10.1016/j.ab.2025.115926","url":null,"abstract":"<div><div>In this issue of <em>Analytical Biochemistry</em>, we honor the life and legacy of Arthur Joseph Lawrence Cooper (1946–2024). Born in London, Arthur made pioneering contributions to medical science, particularly in understanding the role of glutamine in the brain and cancer cell metabolism. His research revealed how glutamine supports neurotransmitter synthesis, energy production, and ammonia detoxification, as well as its critical role in cancer cell growth. His work has greatly advanced both neuroscience and cancer biology, offering insights that could lead to new therapeutic strategies targeting glutamine metabolism.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"705 ","pages":"Article 115926"},"PeriodicalIF":2.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-16DOI: 10.1016/j.ab.2025.115925
Mohamed Abdur Rahman , Muthusamy Karthikeyan , Iruthayasamy Johnson , Kalimuthu Raja , Chinnathambi Sekar , Xavier Anitha Mary , Jaffer Shajith Basha
The increasing global population and rising demands for food production require innovative approaches for managing crop losses caused by plant diseases. Conventional diagnostic methods are often limited by time-consuming protocols, lack of real-time monitoring, and the need for specialized laboratory infrastructure. Meanwhile, sensor technology has emerged as a promising tool for early detection and diagnosis of plant diseases. Sensor technology offers rapid, real-time, high sensitivity, and specificity in diagnosing plant diseases. This review comprehensively presents various biosensors based on biorecognition elements and transducer types. It emphasizes the pivotal role of nanotechnology in enhancing biosensor performance through improved conductivity, surface reactivity, and miniaturization, particularly for plant disease detection. Additionally, electronic nose (E-nose) sensors detecting pathogen-induced volatile organic compounds (VOCs) are highlighted for their potential in non-invasive, early-stage diagnosis. The review also discusses the application of nanobiosensors in agriculture for detecting pesticide residues, toxins, and agrochemicals. Metal oxide nanoparticles (MONPs) are recognized for their multifunctional roles in agriculture and environmental remediation, owing to their unique structural and electronic properties. Furthermore, recent advances in photoelectrocatalysis (PEC), which combines light and applied voltage to degrade toxic pollutants via reactive oxygen species (ROS), are examined. Finally, the ultrasensitive Rolling Circle Amplification-Enabled Point-of-Care Test (RCA-POCT) for rapid detection of aflatoxin B1 in food and environmental samples is presented, utilizing biotin-streptavidin interactions coupled with nucleic acid amplification. Alon with challenges and future prospects, underscoring the transformative potential of these technologies in precision agriculture through rapid, in-field detection benefiting farmers, researchers, and scientists.
{"title":"Ensuring food security through rapid and in-field detection of diseases in food crops using real time and portable sensors","authors":"Mohamed Abdur Rahman , Muthusamy Karthikeyan , Iruthayasamy Johnson , Kalimuthu Raja , Chinnathambi Sekar , Xavier Anitha Mary , Jaffer Shajith Basha","doi":"10.1016/j.ab.2025.115925","DOIUrl":"10.1016/j.ab.2025.115925","url":null,"abstract":"<div><div>The increasing global population and rising demands for food production require innovative approaches for managing crop losses caused by plant diseases. Conventional diagnostic methods are often limited by time-consuming protocols, lack of real-time monitoring, and the need for specialized laboratory infrastructure. Meanwhile, sensor technology has emerged as a promising tool for early detection and diagnosis of plant diseases. Sensor technology offers rapid, real-time, high sensitivity, and specificity in diagnosing plant diseases. This review comprehensively presents various biosensors based on biorecognition elements and transducer types. It emphasizes the pivotal role of nanotechnology in enhancing biosensor performance through improved conductivity, surface reactivity, and miniaturization, particularly for plant disease detection. Additionally, electronic nose (E-nose) sensors detecting pathogen-induced volatile organic compounds (VOCs) are highlighted for their potential in non-invasive, early-stage diagnosis. The review also discusses the application of nanobiosensors in agriculture for detecting pesticide residues, toxins, and agrochemicals. Metal oxide nanoparticles (MONPs) are recognized for their multifunctional roles in agriculture and environmental remediation, owing to their unique structural and electronic properties. Furthermore, recent advances in photoelectrocatalysis (PEC), which combines light and applied voltage to degrade toxic pollutants via reactive oxygen species (ROS), are examined. Finally, the ultrasensitive Rolling Circle Amplification-Enabled Point-of-Care Test (RCA-POCT) for rapid detection of aflatoxin B1 in food and environmental samples is presented, utilizing biotin-streptavidin interactions coupled with nucleic acid amplification. Alon with challenges and future prospects, underscoring the transformative potential of these technologies in precision agriculture through rapid, in-field detection benefiting farmers, researchers, and scientists.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"705 ","pages":"Article 115925"},"PeriodicalIF":2.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144324315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-14DOI: 10.1016/j.ab.2025.115924
Nuri Ceran , Rafig Gurbanov
This study presents a novel diagnostic platform for the rapid and non-invasive detection of neonatal calf diarrhea using ATR-FTIR spectroscopy combined with predictive analytics. Neonatal calf diarrhea is a leading cause of economic losses and animal welfare issues in the cattle industry, and current diagnostic methods are often time-consuming and require invasive sampling. Our approach leverages the unique biochemical fingerprints of serum obtained from healthy, diseased, and recovered calves. The spectral data were preprocessed and analyzed using Principal Component Analysis to extract key molecular features, which were subsequently classified using Linear Discriminant Analysis and Support Vector Machines. These predictive models demonstrated high accuracy in distinguishing the physiological states of the calves, underscoring the potential of this platform as a reliable diagnostic tool. Another significant innovation of this work is the development of the 1080 cm−1/3300 cm−1 spectrochemical index, a single, interpretable parameter derived from the ratio of the PO2− symmetric stretching band to the Amide A band. This quantitative index correlates with molecular-level changes associated with disease progression and recovery, further enhancing diagnostic precision and enabling timely intervention. The integration of spectral data into an easily interpretable metric contributes to improved animal welfare and sustainable livestock management practices.
{"title":"A proof-of-concept diagnostic platform for neonatal calf diarrhea using serum infrared spectroscopy and predictive analytics","authors":"Nuri Ceran , Rafig Gurbanov","doi":"10.1016/j.ab.2025.115924","DOIUrl":"10.1016/j.ab.2025.115924","url":null,"abstract":"<div><div>This study presents a novel diagnostic platform for the rapid and non-invasive detection of neonatal calf diarrhea using ATR-FTIR spectroscopy combined with predictive analytics. Neonatal calf diarrhea is a leading cause of economic losses and animal welfare issues in the cattle industry, and current diagnostic methods are often time-consuming and require invasive sampling. Our approach leverages the unique biochemical fingerprints of serum obtained from healthy, diseased, and recovered calves. The spectral data were preprocessed and analyzed using Principal Component Analysis to extract key molecular features, which were subsequently classified using Linear Discriminant Analysis and Support Vector Machines. These predictive models demonstrated high accuracy in distinguishing the physiological states of the calves, underscoring the potential of this platform as a reliable diagnostic tool. Another significant innovation of this work is the development of the 1080 cm<sup>−1/</sup>3300 cm<sup>−1</sup> spectrochemical index, a single, interpretable parameter derived from the ratio of the PO<sub>2</sub><sup>−</sup> symmetric stretching band to the Amide A band. This quantitative index correlates with molecular-level changes associated with disease progression and recovery, further enhancing diagnostic precision and enabling timely intervention. The integration of spectral data into an easily interpretable metric contributes to improved animal welfare and sustainable livestock management practices.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"705 ","pages":"Article 115924"},"PeriodicalIF":2.6,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-12DOI: 10.1016/j.ab.2025.115922
Dimitrios Tsikas
Analytical methods based on GC-MS and GC-MS/MS are widely used for the qualitative and quantitative analysis of physiological and non-physiological organic compounds in biological samples. They include chemical derivatization, GC separation of gasous analytes, their inline ionization in the ion-source, separation of ions by MS or MS/MS, their conversion to electrons, and their final multiplication and registration. Negative-ion chemical ionization (NICI) is usually performed by using methane as the reagent gas. For MS/MS analysis, argon is a common collision gas. In many GC–NICI–MS and GC–NICI–MS/MS mass spectra, the negative charge cannot always be assigned to particular atoms. In this article, rules are proposed for the interpretation of GC–NICI–MS and GC–NICI–MS/MS mass spectra. The NICI nitrogen rules (NICI-NR) were derived from GC–NICI–MS and GC–NICI–MS/MS mass spectra reported in the literature, and their utility is demonstrated exemplarily for N-containing analytes. The NICI-NR say that ions with even m/z values and odd number of N atoms, and ions with odd m/z values and even number of N atoms have a definitely assignable negative charge. In all other cases, including analyte derivatives that contain stable isotopes including 15N and are radicals, the negative charge is hidden. In those cases, the negative charge is associated with a reduction of a particular C atom, such as that in carbonyl groups, by the uptake of one methane-derived secondary electron. In NICI, carbonyl functionalities are introduced into the analytes by means of perfluorinated derivatization reagents such as pentafluoropropionic anhydride and pentafluorobenzoyl chloride that target N atoms of analytes such as amino acids, bioamines and drugs. The relative importance of carbonylic and F atoms in NICI is discussed.
{"title":"Proposal and application of nitrogen rules in interpreting gas chromatography negative-ion chemical ionization mass spectrometry spectra","authors":"Dimitrios Tsikas","doi":"10.1016/j.ab.2025.115922","DOIUrl":"10.1016/j.ab.2025.115922","url":null,"abstract":"<div><div>Analytical methods based on GC-MS and GC-MS/MS are widely used for the qualitative and quantitative analysis of physiological and non-physiological organic compounds in biological samples. They include chemical derivatization, GC separation of gasous analytes, their inline ionization in the ion-source, separation of ions by MS or MS/MS, their conversion to electrons, and their final multiplication and registration. Negative-ion chemical ionization (NICI) is usually performed by using methane as the reagent gas. For MS/MS analysis, argon is a common collision gas. In many GC–NICI–MS and GC–NICI–MS/MS mass spectra, the negative charge cannot always be assigned to particular atoms. In this article, rules are proposed for the interpretation of GC–NICI–MS and GC–NICI–MS/MS mass spectra. The NICI nitrogen rules (NICI-NR) were derived from GC–NICI–MS and GC–NICI–MS/MS mass spectra reported in the literature, and their utility is demonstrated exemplarily for N-containing analytes. The NICI-NR say that ions with even <em>m/z</em> values and odd number of N atoms, and ions with odd <em>m/z</em> values and even number of N atoms have a definitely assignable negative charge. In all other cases, including analyte derivatives that contain stable isotopes including <sup>15</sup>N and are radicals, the negative charge is hidden. In those cases, the negative charge is associated with a reduction of a particular C atom, such as that in carbonyl groups, by the uptake of one methane-derived secondary electron. In NICI, carbonyl functionalities are introduced into the analytes by means of perfluorinated derivatization reagents such as pentafluoropropionic anhydride and pentafluorobenzoyl chloride that target N atoms of analytes such as amino acids, bioamines and drugs. The relative importance of carbonylic and F atoms in NICI is discussed.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"705 ","pages":"Article 115922"},"PeriodicalIF":2.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144293224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-06DOI: 10.1016/j.ab.2025.115923
Biao Luo , Fang Wang , Qiming Wang , Wanchang Li , Yan Wang , Chaoyang Lin , Wei Wu , Ruiqiu Fang , Liqun Rao , Xianwen Zhang
Glyphosate-resistant crops, developed using the EPSPS gene, are widely cultivated globally. Previous studies indicate that the TIPS-OsEPSPS gene provides glyphosate resistance in rice. In this research, the OsmEPSPS gene was utilized to develop glyphosate-resistant, maize, and soybean, and a rapid immunochromatographic strip (ICS) test was designed for specific detection of the OsmEPSPS protein. The ICS test employs a double-antibody sandwich format with two monoclonal antibodies: mAb #9, conjugated to colloidal gold for capture, and mAb #15, for detection at the test line. The ICS exhibited a limit of detection (LOD) of 0.031 μg/mL within 10 min. Additionally, the ICS could detect a concentration of 0.817 μg/mL within 1 min in both Eleusine indica and OsmEPSPS-overexpressing plants. The ICS is highly specific, sensitive, and stable, with an R2 value of 0.982. Notably, the ICS is capable of detecting EPSPS proteins with high homology to OsmEPSPS from Poaceae and other plants. In summary, this study developed a sensitive and specific test strip for OsmEPSPS detection, facilitating semiquantitative analysis on the basis of colorimetric response time and intensity. This ICS is not only suitable for the rapid identification of OsmEPSPS-overexpressing plants but also for phylogenetic analysis of EPSPS gene homology.
{"title":"Development of an immunochromatographic strip for rapid detection of recombinant rice EPSPS in transgenic crops","authors":"Biao Luo , Fang Wang , Qiming Wang , Wanchang Li , Yan Wang , Chaoyang Lin , Wei Wu , Ruiqiu Fang , Liqun Rao , Xianwen Zhang","doi":"10.1016/j.ab.2025.115923","DOIUrl":"10.1016/j.ab.2025.115923","url":null,"abstract":"<div><div>Glyphosate-resistant crops, developed using the EPSPS gene, are widely cultivated globally. Previous studies indicate that the <em>TIPS-OsEPSPS</em> gene provides glyphosate resistance in rice. In this research, the <em>OsmEPSPS</em> gene was utilized to develop glyphosate-resistant, maize, and soybean, and a rapid immunochromatographic strip (ICS) test was designed for specific detection of the OsmEPSPS protein. The ICS test employs a double-antibody sandwich format with two monoclonal antibodies: mAb #9, conjugated to colloidal gold for capture, and mAb #15, for detection at the test line. The ICS exhibited a limit of detection (LOD) of 0.031 μg/mL within 10 min. Additionally, the ICS could detect a concentration of 0.817 μg/mL within 1 min in both <em>Eleusine indica</em> and <em>OsmEPSPS</em>-overexpressing plants. The ICS is highly specific, sensitive, and stable, with an R<sup>2</sup> value of 0.982. Notably, the ICS is capable of detecting EPSPS proteins with high homology to OsmEPSPS from Poaceae and other plants. In summary, this study developed a sensitive and specific test strip for OsmEPSPS detection, facilitating semiquantitative analysis on the basis of colorimetric response time and intensity. This ICS is not only suitable for the rapid identification of <em>OsmEPSPS</em>-overexpressing plants but also for phylogenetic analysis of EPSPS gene homology.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"705 ","pages":"Article 115923"},"PeriodicalIF":2.6,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144245955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-02DOI: 10.1016/j.ab.2025.115920
Jian Liu , Aoyun Geng , Feifei Cui , Junlin Xu , Yajie Meng , Leyi Wei , Qingchen Zhang , Quan Zou , Zilong Zhang
Neuropeptides (NPs), a unique class of neuronal signaling molecules, involved in neurotransmission, endocrine regulation, immune response, mood, and appetite control. The identification of neuropeptides provides critical scientific insights for early diagnosis, targeted therapy, and personalized medicine of related diseases. Previous models struggle to capture complex relationships among features and inter-sample connections. In this study, we introduce NeuroCL, a deep learning model harnessing contrastive learning and a cross-attention mechanism to efficiently identify NPs through multifaceted attribute representation. Experimental outcomes demonstrate that NeuroCL effectively captures data nuances, achieving an impressive accuracy of 93.8 % and a Matthews correlation coefficient (MCC) of 87.8 % on an independent test set. Contrastive learning enhances class distinction and coherence, while cross-attention mechanisms integrate pre-trained large models with manually encoded features, synergistically boosting their capabilities and strengthening feature connections. Our model surpasses current state-of-the-art predictors in NPs identification. Visualization via uniform manifold approximation and projection (UMAP) reveals that NeuroCL distinctly segregates positive NPs from negative ones. To facilitate the accessibility and application of our model, we have established a web-based platform available at http://www.bioai-lab.com/NeuroCL.
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