Pub Date : 2025-12-19DOI: 10.1016/j.ab.2025.116035
Jessica K. Bilyj, Christina M. Gregg, Trevor D. Rapson
Accurate determination of metallation states in metalloenzymes is essential for correlating metal content with enzymatic function. Inductively coupled plasma mass spectrometry (ICP-MS) and optical emission spectroscopy (ICP-OES) are among the most sensitive and practical techniques for this purpose. However, complete digestion of protein samples is critical for accurate results, and the use of acid often leads to protein precipitation before the metals are fully released into solution.
Here, we present a case study using the nitrogenase protein NifDK, which contains both Fe and Mo, to demonstrate that trypsin digestion can prevent acid-induced precipitation. A 1:1 (w/w) ratio of protein to trypsin in Tris buffer at 37 °C overnight results in complete digestion of NifDK. Upon acid addition, a homogeneous solution is obtained without precipitation. This approach yields highly reproducible ICP-MS data.
Further improvements include performing protein quantification on the exact vial used for ICP-MS analysis, applying a drift correction to the ICP-MS data, and using nitric acid with 0.1 % (v/v) HF for accurate molybdenum quantification.
{"title":"Trypsin digestion to prevent acid induced precipitation of metalloproteins for accurate ICP-MS analysis: Nitrogenase case study","authors":"Jessica K. Bilyj, Christina M. Gregg, Trevor D. Rapson","doi":"10.1016/j.ab.2025.116035","DOIUrl":"10.1016/j.ab.2025.116035","url":null,"abstract":"<div><div>Accurate determination of metallation states in metalloenzymes is essential for correlating metal content with enzymatic function. Inductively coupled plasma mass spectrometry (ICP-MS) and optical emission spectroscopy (ICP-OES) are among the most sensitive and practical techniques for this purpose. However, complete digestion of protein samples is critical for accurate results, and the use of acid often leads to protein precipitation before the metals are fully released into solution.</div><div>Here, we present a case study using the nitrogenase protein NifDK, which contains both Fe and Mo, to demonstrate that trypsin digestion can prevent acid-induced precipitation. A 1:1 (w/w) ratio of protein to trypsin in Tris buffer at 37 °C overnight results in complete digestion of NifDK. Upon acid addition, a homogeneous solution is obtained without precipitation. This approach yields highly reproducible ICP-MS data.</div><div>Further improvements include performing protein quantification on the exact vial used for ICP-MS analysis, applying a drift correction to the ICP-MS data, and using nitric acid with 0.1 % (v/v) HF for accurate molybdenum quantification.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"710 ","pages":"Article 116035"},"PeriodicalIF":2.5,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145802967","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 : 2025-12-18DOI: 10.1016/j.ab.2025.116034
Moses Mayonu , Saeedeh Babaee , Julie Pollak , Lin Jiang , Bo Wang
Nuclear magnetic resonance (NMR), which is famous for its nondestructive nature and high reliability, is one of the principal analytical platforms in metabolomics. NMR metabolomics has been widely used in human and environmental health studies in the past few decades. However, NMR metabolomics data processing remains challenging due to data complexity. Although automated approaches have been explored, their reliability and accuracy are still limited. One of the limitations is the lack of cross-evaluation of the same peak in all samples during raw data processing. In this study, we developed a new approach that applies machine learning models to evaluate the peak quality for all the samples in an NMR metabolomics study. Our new approach combines the automatically selected potential peaks from all samples into a new spectrum for each peak (potential metabolite), which provides an overview of all the samples to ensure the overall data quality for the downstream statistical analysis. The results indicated that two machine learning approaches, Support Vector Machine Discriminant Analysis (SVMDA) and Extreme Gradient Boosting Discriminant Analysis (XGBDA), demonstrated high prediction rates in identifying high-quality peaks. In addition, the raw data conversion resolution was tested to optimize the performance of each machine learning approach, and XGBDA showed better tolerance to data resolution. The results indicated that machine learning approaches, such as SVMDA and XGBDA, can be used to identify high-quality peaks generated through automated peak picking, ensuring data quality for metabolomics studies. Our study paves the way for automated data processing in future NMR metabolomics research.
{"title":"The application of machine learning in nuclear magnetic resonance (NMR) peak selection for metabolomics studies","authors":"Moses Mayonu , Saeedeh Babaee , Julie Pollak , Lin Jiang , Bo Wang","doi":"10.1016/j.ab.2025.116034","DOIUrl":"10.1016/j.ab.2025.116034","url":null,"abstract":"<div><div>Nuclear magnetic resonance (NMR), which is famous for its nondestructive nature and high reliability, is one of the principal analytical platforms in metabolomics<strong>.</strong> NMR metabolomics has been widely used in human and environmental health studies in the past few decades. However, NMR metabolomics data processing remains challenging due to data complexity. Although automated approaches have been explored, their reliability and accuracy are still limited. One of the limitations is the lack of cross-evaluation of the same peak in all samples during raw data processing. In this study, we developed a new approach that applies machine learning models to evaluate the peak quality for all the samples in an NMR metabolomics study. Our new approach combines the automatically selected potential peaks from all samples into a new spectrum for each peak (potential metabolite), which provides an overview of all the samples to ensure the overall data quality for the downstream statistical analysis. The results indicated that two machine learning approaches, Support Vector Machine Discriminant Analysis (SVMDA) and Extreme Gradient Boosting Discriminant Analysis (XGBDA), demonstrated high prediction rates in identifying high-quality peaks. In addition, the raw data conversion resolution was tested to optimize the performance of each machine learning approach, and XGBDA showed better tolerance to data resolution. The results indicated that machine learning approaches, such as SVMDA and XGBDA, can be used to identify high-quality peaks generated through automated peak picking, ensuring data quality for metabolomics studies. Our study paves the way for automated data processing in future NMR metabolomics research.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"710 ","pages":"Article 116034"},"PeriodicalIF":2.5,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145800380","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 : 2025-12-15DOI: 10.1016/j.ab.2025.116033
Jack Brydon , Radovan Krejcir , Mariana Grima , Filip Zavadil-Kokas , Zuzana Kuncova , Sousan Sousan , Robin L. Pflughaupt , Jennifer R. Thomson , Paul Davies , Ella Senior , Geoffrey A. Wood , Kathryn L. Ball , David Saliba , David J. Argyle , Borivoj Vojtesek , Ted Hupp , Maciej Parys
The TIM3 receptor acts as an immune checkpoint protein. Canine cancers exhibit higher pan-cancer penetrance of TIM3 compared to PD1, highlighting the potential of TIM3 as a compelling target in comparative immuno-oncology. We have used a highly diverse naïve canine scFv phage library to isolate antibodies to canine TIM3. Alternating rounds of biopanning were performed using either Fc or GST tagged canine TIM3 synthesized in mammalian cells. scFv sequences were identified using colony screening and next generation deep sequencing (NGS). The NGS protocol identified lower abundant frequency clones, demonstrating the enhanced depth of repertoire discovery possible using sequence-tag based tracing. Three representative scFv were expressed as mouse-canine chimeric scFv-Fc fusions or as full-length chimeric IgG. These antibodies all bound to the TIM3 receptor using either ELISA or immunoblotting. All the antibodies displayed sensitivity to reducing agents, which indicates the existence of disulfide-stabilized conformational epitopes. Epitope mapping using pepscan libraries suggested that the antibodies recognize a shared structural motif within the IgV domain of TIM3, within β-sheets fixed by disulfide bonds which would form the conformational epitope. Such conformational epitopes might be functional because they overlap with ligand-binding interfaces. Consistent with this, the antibodies attenuated TIM3 binding to Galectin-9. These data affirm that naïve canine scFv antibody libraries can yield self-antigen reactive antibodies to immune blockade receptor antigens. The data also emphasize the value in using native, folded, mammalian expressed receptor antigens to increase the probability of acquiring conformationally sensitive antibodies with potential therapeutic applications in veterinary and human medicine.
{"title":"Towards canine immunotherapy models: Monoclonal antibodies with redox regulated epitopes targeting TIM3 attenuate Galectin-9 binding","authors":"Jack Brydon , Radovan Krejcir , Mariana Grima , Filip Zavadil-Kokas , Zuzana Kuncova , Sousan Sousan , Robin L. Pflughaupt , Jennifer R. Thomson , Paul Davies , Ella Senior , Geoffrey A. Wood , Kathryn L. Ball , David Saliba , David J. Argyle , Borivoj Vojtesek , Ted Hupp , Maciej Parys","doi":"10.1016/j.ab.2025.116033","DOIUrl":"10.1016/j.ab.2025.116033","url":null,"abstract":"<div><div>The TIM3 receptor acts as an immune checkpoint protein. Canine cancers exhibit higher pan-cancer penetrance of TIM3 compared to PD1, highlighting the potential of TIM3 as a compelling target in comparative immuno-oncology. We have used a highly diverse naïve canine scFv phage library to isolate antibodies to canine TIM3. Alternating rounds of biopanning were performed using either Fc or GST tagged canine TIM3 synthesized in mammalian cells. scFv sequences were identified using colony screening and next generation deep sequencing (NGS). The NGS protocol identified lower abundant frequency clones, demonstrating the enhanced depth of repertoire discovery possible using sequence-tag based tracing. Three representative scFv were expressed as mouse-canine chimeric scFv-Fc fusions or as full-length chimeric IgG. These antibodies all bound to the TIM3 receptor using either ELISA or immunoblotting. All the antibodies displayed sensitivity to reducing agents, which indicates the existence of disulfide-stabilized conformational epitopes. Epitope mapping using pepscan libraries suggested that the antibodies recognize a shared structural motif within the IgV domain of TIM3, within β-sheets fixed by disulfide bonds which would form the conformational epitope. Such conformational epitopes might be functional because they overlap with ligand-binding interfaces. Consistent with this, the antibodies attenuated TIM3 binding to Galectin-9. These data affirm that naïve canine scFv antibody libraries can yield self-antigen reactive antibodies to immune blockade receptor antigens. The data also emphasize the value in using native, folded, mammalian expressed receptor antigens to increase the probability of acquiring conformationally sensitive antibodies with potential therapeutic applications in veterinary and human medicine.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"711 ","pages":"Article 116033"},"PeriodicalIF":2.5,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772941","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 : 2025-12-13DOI: 10.1016/j.ab.2025.116031
Josephine Esposto , Naomi L. Stock , Robert J. Huber , Sanela Martic
Copper (Cu) and zinc (Zn) metal ions play important roles in the proper functioning and localization of neurological proteins, such as transactive response DNA-binding protein 43 (TDP-43), which is linked to amyotrophic lateral sclerosis (ALS). Previous experimental and computational studies have identified putative Zn-binding regions within the RNA recognition motif 1 (RRM1) of TDP-43. However, Cu-binding interactions have been less explored despite their redox activity in regulating thiol (C173/175) conversion to disulfide within the RRM1 domain, influencing protein structure and function. Herein, the structural characterization and fragmentation pattern analysis of a TDP-43 decapeptide (166-HMIDGRWCDC-175), within RRM1, coordinated to Cu(II) and Zn(II) ions using electrospray ionization tandem mass spectrometry (ESI-MS/MS) was conducted under non-denaturing conditions. Higher-energy collision dissociation (HCD) fragmentation analysis identified that Cu(II) prefers His/Met residues, while Zn(II) was weakly coordinated to various binding sites in the peptide, specifically His, Met, Glu, Cys, Trp and Asp residues. Computational modeling using a metal ion binding server (MIB2) confirmed the binding sites and coordination sphere of metal-peptide complexes. No significant coordination to C173 and C175 was observed with Cu or Zn, as identified by using a double Cys mutant peptide. A complete thiol-to-disulfide conversion was observed in the presence of Cu(II)/(I) only, which was confirmed by the comparison of a preformed intramolecular disulfide peptide. Overall, unique differential coordination environments were observed for each metal ion with the peptide. The study provides new insights into metal ion interactions with TDP-43 RRM1 peptide, leading to a greater understanding of metal homeostasis in TDP-43 protein biochemistry and neurodegeneration.
{"title":"Differential binding of copper and zinc to a TDP-43 RNA recognition motif decapeptide and disulfide formation at residues C173/5 revealed by ESI-MS/MS","authors":"Josephine Esposto , Naomi L. Stock , Robert J. Huber , Sanela Martic","doi":"10.1016/j.ab.2025.116031","DOIUrl":"10.1016/j.ab.2025.116031","url":null,"abstract":"<div><div>Copper (Cu) and zinc (Zn) metal ions play important roles in the proper functioning and localization of neurological proteins, such as transactive response DNA-binding protein 43 (TDP-43), which is linked to amyotrophic lateral sclerosis (ALS). Previous experimental and computational studies have identified putative Zn-binding regions within the RNA recognition motif 1 (RRM1) of TDP-43. However, Cu-binding interactions have been less explored despite their redox activity in regulating thiol (C173/175) conversion to disulfide within the RRM1 domain, influencing protein structure and function. Herein, the structural characterization and fragmentation pattern analysis of a TDP-43 decapeptide (166-HMIDGRWCDC-175), within RRM1, coordinated to Cu(II) and Zn(II) ions using electrospray ionization tandem mass spectrometry (ESI-MS/MS) was conducted under non-denaturing conditions. Higher-energy collision dissociation (HCD) fragmentation analysis identified that Cu(II) prefers His/Met residues, while Zn(II) was weakly coordinated to various binding sites in the peptide, specifically His, Met, Glu, Cys, Trp and Asp residues. Computational modeling using a metal ion binding server (MIB2) confirmed the binding sites and coordination sphere of metal-peptide complexes. No significant coordination to C173 and C175 was observed with Cu or Zn, as identified by using a double Cys mutant peptide. A complete thiol-to-disulfide conversion was observed in the presence of Cu(II)/(I) only, which was confirmed by the comparison of a preformed intramolecular disulfide peptide. Overall, unique differential coordination environments were observed for each metal ion with the peptide. The study provides new insights into metal ion interactions with TDP-43 RRM1 peptide, leading to a greater understanding of metal homeostasis in TDP-43 protein biochemistry and neurodegeneration.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"710 ","pages":"Article 116031"},"PeriodicalIF":2.5,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145762025","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 : 2025-12-11DOI: 10.1016/j.ab.2025.116032
Emile Van Schaftingen , Alessio Peracchi , Maria Veiga-da-Cunha
Nit1 and Nit2 were initially identified in the context of cancer research, as proteins encoded by putative (anti)oncogenes. However, the presence of homologous proteins in bacteria suggested that they might be enzymes with a fundamental metabolic function. Our group, while interacting with Arthur Cooper and his collaborators, contributed to uncovering these roles: Nit2 was identified in 2009 as an ω-amidase, the enzyme that hydrolyses the ‘ω’ amide of α-ketoglutaramate and α-ketosuccinamate, the ‘deaminated’ derivatives of glutamine and asparagine produced in some irreversible transamination reactions. Later, in 2017, we showed that Nit1 functions as a metabolite-repair enzyme. Specifically, Nit1 efficiently hydrolyzes deaminated gluthathione (dGSH), a non-functional byproduct generated by a side activity of various classical transaminases. This repair function prevents the accumulation of the useless metabolite dGSH. The physiological significance of Nit1 is underscored by recent discoveries linking its deficiency in humans to a neurological disorder.
{"title":"Identification of the function of the metabolite repair enzyme Nit1: the story of a collaboration with Arthur Cooper","authors":"Emile Van Schaftingen , Alessio Peracchi , Maria Veiga-da-Cunha","doi":"10.1016/j.ab.2025.116032","DOIUrl":"10.1016/j.ab.2025.116032","url":null,"abstract":"<div><div>Nit1 and Nit2 were initially identified in the context of cancer research, as proteins encoded by putative (anti)oncogenes. However, the presence of homologous proteins in bacteria suggested that they might be enzymes with a fundamental metabolic function. Our group, while interacting with Arthur Cooper and his collaborators, contributed to uncovering these roles: Nit2 was identified in 2009 as an ω-amidase, the enzyme that hydrolyses the ‘ω’ amide of α-ketoglutaramate and α-ketosuccinamate, the ‘deaminated’ derivatives of glutamine and asparagine produced in some irreversible transamination reactions. Later, in 2017, we showed that Nit1 functions as a metabolite-repair enzyme. Specifically, Nit1 efficiently hydrolyzes deaminated gluthathione (dGSH), a non-functional byproduct generated by a side activity of various classical transaminases. This repair function prevents the accumulation of the useless metabolite dGSH. The physiological significance of Nit1 is underscored by recent discoveries linking its deficiency in humans to a neurological disorder.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"710 ","pages":"Article 116032"},"PeriodicalIF":2.5,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145751493","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}
The combination of Rhei Radix et Rhizoma (RR) and Magnoliae Officinalis Cortex (MO) has been used to treat constipation for thousands of years. However, the synergistic mechanism of two herbs to exert the laxative effects is still unclear.
Materials and methods
In this study, the constipation rat model was successfully induced by loperamide hydrochloride, which was applied to systematically and comprehensively evaluate the effect of RR and MO in ameliorating the constipation. Additionally, network pharmacology, molecular docking and UPLC-Q-TOF/MS-based metabolomics were integrated to uncover the potential mechanism of the compatibility of RR and MO. And MetaboAnalyst and MetaScape jointly analyze the metabolic pathways involved in network pharmacology targets and metabolomics metabolites, selecting key metabolic pathways.
Results
In terms of physiological and biochemical level, the laxative effect of RR and MO was significantly better than that of single herb. The results of network pharmacology and molecular docking showed that RR and MO could improve the constipation through multi-components, multi-targets and multi-pathways. Moreover, metabolomics indicated that the disrupted metabolic profile in plasma of model rats was markedly reversed by the combination treatment of RR and MO. Additionally, after a comprehensive analysis of 9 metabolites and 124 targets, arachidonic acid was selected as the most critical pathway. The results showed that the herb pair could relieve constipation through arachidonic acid pathway and inhibited the levels of PTGS2 and PLA2G2A protein in this pathway.
Conclusion
This study revealed the synergistic effect of the combination of RR and MO in improving the constipation from the perspective of bioinformatics and metabolomics. Furthermore, the integrated method of multi-omics could contribute to exploring the potential compatibility mechanism of traditional Chinese medicines.
{"title":"Multi-disciplinary combination to explore the laxation mechanism of Rhei Radix et Rhizoma and Magnoliae Officinalis Cortex","authors":"Meiyu Wan, Yu Wang, Meijuan Liu, Wenwen Zhou, Xiaoxiao Zhang, Mingyang Wang, Shu Jiang, Erxin Shang, Jinao Duan","doi":"10.1016/j.ab.2025.116030","DOIUrl":"10.1016/j.ab.2025.116030","url":null,"abstract":"<div><h3>Background</h3><div>The combination of Rhei Radix et Rhizoma (RR) and Magnoliae Officinalis Cortex (MO) has been used to treat constipation for thousands of years. However, the synergistic mechanism of two herbs to exert the laxative effects is still unclear.</div></div><div><h3>Materials and methods</h3><div>In this study, the constipation rat model was successfully induced by loperamide hydrochloride, which was applied to systematically and comprehensively evaluate the effect of RR and MO in ameliorating the constipation. Additionally, network pharmacology, molecular docking and UPLC-Q-TOF/MS-based metabolomics were integrated to uncover the potential mechanism of the compatibility of RR and MO. And MetaboAnalyst and MetaScape jointly analyze the metabolic pathways involved in network pharmacology targets and metabolomics metabolites, selecting key metabolic pathways.</div></div><div><h3>Results</h3><div>In terms of physiological and biochemical level, the laxative effect of RR and MO was significantly better than that of single herb. The results of network pharmacology and molecular docking showed that RR and MO could improve the constipation through multi-components, multi-targets and multi-pathways. Moreover, metabolomics indicated that the disrupted metabolic profile in plasma of model rats was markedly reversed by the combination treatment of RR and MO. Additionally, after a comprehensive analysis of 9 metabolites and 124 targets, arachidonic acid was selected as the most critical pathway. The results showed that the herb pair could relieve constipation through arachidonic acid pathway and inhibited the levels of PTGS2 and PLA2G2A protein in this pathway.</div></div><div><h3>Conclusion</h3><div>This study revealed the synergistic effect of the combination of RR and MO in improving the constipation from the perspective of bioinformatics and metabolomics. Furthermore, the integrated method of multi-omics could contribute to exploring the potential compatibility mechanism of traditional Chinese medicines.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"710 ","pages":"Article 116030"},"PeriodicalIF":2.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145735328","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 : 2025-12-04DOI: 10.1016/j.ab.2025.116029
Yicun Lin, Yuanfeng Li, Wei Sun, Jian Wang
Predicting drug-target interactions is essential for virtual drug screening. While many models predict the binding affinity between small molecules and proteins, they often overemphasize molecular features while overlooking important protein characteristics, leading to biased predictions. Traditional deep learning models, such as transformerCPI, show limited performance in tasks like label inversion on G protein-coupled receptor (GPCR) datasets. To address this, this study proposes an enhanced transformer-based model that integrates both molecular and protein information. By leveraging Molr and ProtTrans networks for feature extraction, and incorporating a transposed attention mechanism with a triple-loss self-supervised learning approach, the model improves prediction accuracy. Experimental results show that the proposed model achieved observed Area Under the Curve (AUC) of 0.81 on the GPCR label-inversion dataset and 0.92 on a human target dataset, which are numerically higher than those reported for TransformerCPI and several baseline methods in our experiments. These observations indicate improved performance in our experimental setting, offering promising prospects for advancing virtual drug screening and drug discovery.
预测药物-靶标相互作用对虚拟药物筛选至关重要。虽然许多模型预测了小分子与蛋白质之间的结合亲和力，但它们往往过分强调分子特征，而忽略了重要的蛋白质特征，导致预测有偏差。传统的深度学习模型，如transformerCPI，在G蛋白偶联受体（GPCR）数据集的标签反演等任务中表现有限。为了解决这个问题，本研究提出了一种基于转换器的增强模型，该模型集成了分子和蛋白质信息。通过利用Molr和ProtTrans网络进行特征提取，并将转置注意机制与三损失自监督学习方法相结合，该模型提高了预测精度。实验结果表明，该模型在GPCR标记反演数据集上实现了0.81的曲线下面积（Area Under the Curve， AUC），在人类目标数据集上实现了0.92的曲线下面积（Area Under the Curve， AUC），在数值上高于TransformerCPI和几种基线方法。这些观察结果表明，在我们的实验环境中，性能有所提高，为推进虚拟药物筛选和药物发现提供了广阔的前景。
{"title":"Classification prediction of drug target binding affinity based on the MolrProtTrans model","authors":"Yicun Lin, Yuanfeng Li, Wei Sun, Jian Wang","doi":"10.1016/j.ab.2025.116029","DOIUrl":"10.1016/j.ab.2025.116029","url":null,"abstract":"<div><div>Predicting drug-target interactions is essential for virtual drug screening. While many models predict the binding affinity between small molecules and proteins, they often overemphasize molecular features while overlooking important protein characteristics, leading to biased predictions. Traditional deep learning models, such as transformerCPI, show limited performance in tasks like label inversion on G protein-coupled receptor (GPCR) datasets. To address this, this study proposes an enhanced transformer-based model that integrates both molecular and protein information. By leveraging Molr and ProtTrans networks for feature extraction, and incorporating a transposed attention mechanism with a triple-loss self-supervised learning approach, the model improves prediction accuracy. Experimental results show that the proposed model achieved observed Area Under the Curve (AUC) of 0.81 on the GPCR label-inversion dataset and 0.92 on a human target dataset, which are numerically higher than those reported for TransformerCPI and several baseline methods in our experiments. These observations indicate improved performance in our experimental setting, offering promising prospects for advancing virtual drug screening and drug discovery.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"710 ","pages":"Article 116029"},"PeriodicalIF":2.5,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145695935","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 : 2025-12-03DOI: 10.1016/j.ab.2025.116028
Sameera Sh. Mohammed Ameen , Fiasal K. Algethami , Khalid M. Omer , Idrees B. Qader , Hemn A. Qader
In fluorescence-based sensing, self-referencing ratiometric analysis offers a significant advantage over external referencing by integrating both the probe and reference signals within a single material, rather than relying on two separate components. This intrinsic approach eliminates the need for additional reference dyes or materials, which can introduce inconsistencies due to variations in concentration, uneven dispersion, or environmental instability. Self-referencing materials provide a built-in correction mechanism, enhancing detection accuracy, reliability, and reproducibility while minimizing background interference. Despite their advantages, the design and synthesis of dual-emitting metal-organic frameworks (MOFs) with self-referencing capabilities remain rare and challenging. In this study, we introduce a novel Eu-based MOF with intrinsic dual-state, dual-emission properties, exhibiting distinct blue and red fluorescence in both liquid and solid states. The blue emission arises from the coordination-induced emission of the free, non-emissive ligand within the MOF structure. Interestingly, the red emission is selectively quenched by amoxicillin (AMX), while the blue fluorescence remains unaffected. This unique dual-emission feature enables a ratiometric sensing platform without requiring an external reference, ensuring greater stability, accuracy, and ease of use. With a linear detection range of 8.0–218 μM and a limit of detection of 0.354 μM, this Eu-MOF offers a robust and selective AMX sensing strategy. Additionally, a smartphone-assisted visual detection method using RGB analysis via the Color Grab App was developed, enabling portable and on-site quantification. This self-referencing Eu-MOF is inherently stable, recyclable, providing consistent signals and making it highly effective for pharmaceutical applications.
{"title":"Single-Entity Dual-Emissive MOF Platform for Reliable Ratiometric Point-of-Care Detection of Amoxicillin Residues","authors":"Sameera Sh. Mohammed Ameen , Fiasal K. Algethami , Khalid M. Omer , Idrees B. Qader , Hemn A. Qader","doi":"10.1016/j.ab.2025.116028","DOIUrl":"10.1016/j.ab.2025.116028","url":null,"abstract":"<div><div>In fluorescence-based sensing, self-referencing ratiometric analysis offers a significant advantage over external referencing by integrating both the probe and reference signals within a single material, rather than relying on two separate components. This intrinsic approach eliminates the need for additional reference dyes or materials, which can introduce inconsistencies due to variations in concentration, uneven dispersion, or environmental instability. Self-referencing materials provide a built-in correction mechanism, enhancing detection accuracy, reliability, and reproducibility while minimizing background interference. Despite their advantages, the design and synthesis of dual-emitting metal-organic frameworks (MOFs) with self-referencing capabilities remain rare and challenging. In this study, we introduce a novel Eu-based MOF with intrinsic dual-state, dual-emission properties, exhibiting distinct blue and red fluorescence in both liquid and solid states. The blue emission arises from the coordination-induced emission of the free, non-emissive ligand within the MOF structure. Interestingly, the red emission is selectively quenched by amoxicillin (AMX), while the blue fluorescence remains unaffected. This unique dual-emission feature enables a ratiometric sensing platform without requiring an external reference, ensuring greater stability, accuracy, and ease of use. With a linear detection range of 8.0–218 μM and a limit of detection of 0.354 μM, this Eu-MOF offers a robust and selective AMX sensing strategy. Additionally, a smartphone-assisted visual detection method using RGB analysis via the Color Grab App was developed, enabling portable and on-site quantification. This self-referencing Eu-MOF is inherently stable, recyclable, providing consistent signals and making it highly effective for pharmaceutical applications.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"710 ","pages":"Article 116028"},"PeriodicalIF":2.5,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684009","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 : 2025-12-02DOI: 10.1016/j.ab.2025.116017
Pernille Nedergaard Madsen, Pernille Baden Jørgensen, Mille Varisbøl Clausen, Charlotte Rohde Knudsen
The eukaryotic elongation factor 1A (eEF1A) is crucial for translation, delivering aminoacyl-tRNAs to the ribosomal A site. While its bacterial counterpart, elongation factor thermo unstable (EF-Tu), has been extensively studied, the canonical and non-canonical roles of eEF1A remain less understood, partly due to the lack of an efficient purification method. This study optimized an affinity chromatography-based protocol for Saccharomyces cerevisiae eEF1A, evaluating the effects of N- and C-terminal His-tagging on stability, functionality, purification, and yield. While N-terminal deletion of four residues impaired ternary complex formation in vitro, His-tagging at the same position did not. However, nano differential scanning fluorimetry (NanoDSF) revealed that an N-terminal His-tag destabilizes eEF1A, whereas a C-terminal His-tag preserves its integrity and enhances yield. Additionally, reducing glycerol concentration from 25 % to 10 % expedited purification without compromising stability or tRNA binding. This optimized C-terminal His-tag protocol provides a streamlined approach for studying the functional and dynamic properties of eEF1A.
{"title":"Optimized purification workflow for advanced eEF1A studies: Impact of His-tagging on stability, functionality, and yield","authors":"Pernille Nedergaard Madsen, Pernille Baden Jørgensen, Mille Varisbøl Clausen, Charlotte Rohde Knudsen","doi":"10.1016/j.ab.2025.116017","DOIUrl":"10.1016/j.ab.2025.116017","url":null,"abstract":"<div><div>The eukaryotic elongation factor 1A (eEF1A) is crucial for translation, delivering aminoacyl-tRNAs to the ribosomal A site. While its bacterial counterpart, elongation factor thermo unstable (EF-Tu), has been extensively studied, the canonical and non-canonical roles of eEF1A remain less understood, partly due to the lack of an efficient purification method. This study optimized an affinity chromatography-based protocol for <em>Saccharomyces cerevisiae</em> eEF1A, evaluating the effects of N- and C-terminal His-tagging on stability, functionality, purification, and yield. While N-terminal deletion of four residues impaired ternary complex formation <em>in vitro</em>, His-tagging at the same position did not. However, nano differential scanning fluorimetry (NanoDSF) revealed that an N-terminal His-tag destabilizes eEF1A, whereas a C-terminal His-tag preserves its integrity and enhances yield. Additionally, reducing glycerol concentration from 25 % to 10 % expedited purification without compromising stability or tRNA binding. This optimized C-terminal His-tag protocol provides a streamlined approach for studying the functional and dynamic properties of eEF1A.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"710 ","pages":"Article 116017"},"PeriodicalIF":2.5,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145675946","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 : 2025-11-27DOI: 10.1016/j.ab.2025.116018
Zijuan Miao , Bowen Kan , Pan Liu , Yongming Guo
Azodicarbonamide (ADA) is widely used as a foaming and oxidizing agent in the food and plastic industries. However, long-term exposure to it could cause serious health problems. Its metabolic product, semicarbazide (SEM), has raised health concerns due to findings of potential carcinogenicity and genotoxicity in some studies, though the evidence is context-dependent and sometimes conflicting. Therefore, the development of efficient, sensitive, and rapid detection methods for ADA and SEM is of great significance for food safety assurance. This review systematically summarizes recent advances in detection techniques for ADA and SEM, including high-performance liquid chromatography, capillary electrophoresis, Raman spectroscopy, immunoassays, electrochemical methods, near-infrared spectroscopy, colorimetry, fluorescence, chemiluminescence, photoacoustic, and photothermal detection. Subsequently, the sensitivity, selectivity, applicability, and limitations of these methods are compared and analyzed. Lastly, the challenges and future research trends are discussed. This review will pave the way for the development of advanced sensing strategies for ADA and SEM.
{"title":"Advances in the detection of azodicarbonamide and the metabolic product semicarbazide","authors":"Zijuan Miao , Bowen Kan , Pan Liu , Yongming Guo","doi":"10.1016/j.ab.2025.116018","DOIUrl":"10.1016/j.ab.2025.116018","url":null,"abstract":"<div><div>Azodicarbonamide (ADA) is widely used as a foaming and oxidizing agent in the food and plastic industries. However, long-term exposure to it could cause serious health problems. Its metabolic product, semicarbazide (SEM), has raised health concerns due to findings of potential carcinogenicity and genotoxicity in some studies, though the evidence is context-dependent and sometimes conflicting. Therefore, the development of efficient, sensitive, and rapid detection methods for ADA and SEM is of great significance for food safety assurance. This review systematically summarizes recent advances in detection techniques for ADA and SEM, including high-performance liquid chromatography, capillary electrophoresis, Raman spectroscopy, immunoassays, electrochemical methods, near-infrared spectroscopy, colorimetry, fluorescence, chemiluminescence, photoacoustic, and photothermal detection. Subsequently, the sensitivity, selectivity, applicability, and limitations of these methods are compared and analyzed. Lastly, the challenges and future research trends are discussed. This review will pave the way for the development of advanced sensing strategies for ADA and SEM.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"709 ","pages":"Article 116018"},"PeriodicalIF":2.5,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145627607","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}
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