Human epidermal growth factor receptors (HER)—also known as EGFR or ErbB receptors—are a subfamily of receptor tyrosine kinases (RTKs) that play crucial roles in cell growth, division, and differentiation. HER4 (ErbB4) is the least studied member of this family, partly because its expression is lower in later stages of development. Recent work has suggested that HER4 can play a role in metastasis by regulating cell migration and invasiveness; however, unlike EGFR and HER2, the precise role that HER4 plays in tumorigenesis is still unresolved. Early work on HER family proteins suggested that there are direct interactions between the four members, but to date, there has been no single study of all four receptors in the same cell line with the same biophysical method. Here, we quantitatively measure the degree of association between HER4 and the other HER family proteins in live cells with a time‐resolved fluorescence technique called pulsed interleaved excitation fluorescence cross‐correlation spectroscopy (PIE‐FCCS). PIE‐FCCS is sensitive to the oligomerization state of membrane proteins in live cells, while simultaneously measuring single‐cell protein expression levels and diffusion coefficients. Our PIE‐FCCS results demonstrate that HER4 interacts directly with all HER family members in the cell plasma membrane. The interaction between HER4 and other HER family members intensified in the presence of a HER4‐specific ligand. Our work suggests that HER4 is a preferred dimerization partner for all HER family proteins, even in the absence of ligands.
人类表皮生长因子受体（HER）--又称表皮生长因子受体或 ErbB 受体--是受体酪氨酸激酶（RTKs）的一个亚家族，在细胞生长、分裂和分化过程中起着至关重要的作用。HER4（ErbB4）是这个家族中研究最少的成员，部分原因是它在发育后期的表达较低。最近的研究表明，HER4 可通过调节细胞迁移和侵袭性而在转移中发挥作用；然而，与表皮生长因子受体和 HER2 不同，HER4 在肿瘤发生中的确切作用仍未得到解决。关于 HER 家族蛋白的早期研究表明，这四种成员之间存在直接的相互作用，但迄今为止，还没有用同一种生物物理方法对同一细胞系中的所有四种受体进行过研究。在这里，我们利用一种名为脉冲交错激发荧光交叉相关光谱（PIE-FCCS）的时间分辨荧光技术，定量测量了活细胞中 HER4 与其他 HER 家族蛋白之间的关联程度。PIE-FCCS 对活细胞中膜蛋白的寡聚状态非常敏感，同时还能测量单细胞蛋白质的表达水平和扩散系数。我们的 PIE-FCCS 结果表明，HER4 直接与细胞质膜中的所有 HER 家族成员相互作用。在 HER4 特异性配体存在的情况下，HER4 与其他 HER 家族成员之间的相互作用会加强。我们的研究表明，即使在没有配体的情况下，HER4 也是所有 HER 家族蛋白的首选二聚化伙伴。
{"title":"HER4 is a high‐affinity dimerization partner for all EGFR/HER/ErbB family proteins","authors":"Pradeep Kumar Singh, Soyeon Kim, Adam W. Smith","doi":"10.1002/pro.5171","DOIUrl":"https://doi.org/10.1002/pro.5171","url":null,"abstract":"Human epidermal growth factor receptors (HER)—also known as EGFR or ErbB receptors—are a subfamily of receptor tyrosine kinases (RTKs) that play crucial roles in cell growth, division, and differentiation. HER4 (ErbB4) is the least studied member of this family, partly because its expression is lower in later stages of development. Recent work has suggested that HER4 can play a role in metastasis by regulating cell migration and invasiveness; however, unlike EGFR and HER2, the precise role that HER4 plays in tumorigenesis is still unresolved. Early work on HER family proteins suggested that there are direct interactions between the four members, but to date, there has been no single study of all four receptors in the same cell line with the same biophysical method. Here, we quantitatively measure the degree of association between HER4 and the other HER family proteins in live cells with a time‐resolved fluorescence technique called pulsed interleaved excitation fluorescence cross‐correlation spectroscopy (PIE‐FCCS). PIE‐FCCS is sensitive to the oligomerization state of membrane proteins in live cells, while simultaneously measuring single‐cell protein expression levels and diffusion coefficients. Our PIE‐FCCS results demonstrate that HER4 interacts directly with all HER family members in the cell plasma membrane. The interaction between HER4 and other HER family members intensified in the presence of a HER4‐specific ligand. Our work suggests that HER4 is a preferred dimerization partner for all HER family proteins, even in the absence of ligands.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"211 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253088","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}
Chemical protein synthesis (CPS), in which custom peptide segments of ~20–60 aa are produced by solid‐phase peptide synthesis and then stitched together through sequential ligation reactions, is an increasingly popular technique. The workflow of CPS is often depicted with a “bracket” style diagram detailing the starting segments and the order of all ligation, desulfurization, and/or deprotection steps to obtain the product protein. Brackets are invaluable tools for comparing multiple possible synthetic approaches and serve as blueprints throughout a synthesis. Drawing CPS brackets by hand or in standard graphics software, however, is a painstaking and error‐prone process. Furthermore, the CPS field lacks a standard bracket format, making side‐by‐side comparisons difficult. To address these problems, we developed BracketMaker, an open‐source Python program with built‐in graphic user interface (GUI) for the rapid creation and analysis of CPS brackets. BracketMaker contains a custom graphics engine which converts a text string (a protein sequence annotated with reaction steps, introduced herein as a standardized format for brackets) into a high‐quality vector or PNG image. To aid with new syntheses, BracketMaker's “AutoBracket” tool automatically performs retrosynthetic analysis on a set of segments to draft and rank all possible ligation orders using standard native chemical ligation, protection, and desulfurization techniques. AutoBracket, in conjunction with an improved version of our previously reported Automated Ligator (Aligator) program, provides a pipeline to rapidly develop synthesis plans for a given protein sequence. We demonstrate the application of both programs to develop a blueprint for 65 proteins of the minimal Escherichia coli ribosome.
{"title":"BracketMaker: Visualization and optimization of chemical protein synthesis","authors":"Judah L. Evangelista, Michael S. Kay","doi":"10.1002/pro.5174","DOIUrl":"https://doi.org/10.1002/pro.5174","url":null,"abstract":"Chemical protein synthesis (CPS), in which custom peptide segments of ~20–60 aa are produced by solid‐phase peptide synthesis and then stitched together through sequential ligation reactions, is an increasingly popular technique. The workflow of CPS is often depicted with a “bracket” style diagram detailing the starting segments and the order of all ligation, desulfurization, and/or deprotection steps to obtain the product protein. Brackets are invaluable tools for comparing multiple possible synthetic approaches and serve as blueprints throughout a synthesis. Drawing CPS brackets by hand or in standard graphics software, however, is a painstaking and error‐prone process. Furthermore, the CPS field lacks a standard bracket format, making side‐by‐side comparisons difficult. To address these problems, we developed BracketMaker, an open‐source Python program with built‐in graphic user interface (GUI) for the rapid creation and analysis of CPS brackets. BracketMaker contains a custom graphics engine which converts a text string (a protein sequence annotated with reaction steps, introduced herein as a standardized format for brackets) into a high‐quality vector or PNG image. To aid with new syntheses, BracketMaker's “AutoBracket” tool automatically performs retrosynthetic analysis on a set of segments to draft and rank all possible ligation orders using standard native chemical ligation, protection, and desulfurization techniques. AutoBracket, in conjunction with an improved version of our previously reported Automated Ligator (Aligator) program, provides a pipeline to rapidly develop synthesis plans for a given protein sequence. We demonstrate the application of both programs to develop a blueprint for 65 proteins of the minimal <jats:italic>Escherichia coli</jats:italic> ribosome.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"19 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253068","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}
Millions of years of evolution have optimized many biosynthetic pathways by use of multi‐step catalysis. In addition, multi‐step metabolic pathways are commonly found in and on membrane‐bound organelles in eukaryotic biochemistry. The fundamental mechanisms that facilitate these reaction processes provide strategies to bioengineer metabolic pathways in synthetic chemistry. Using Brownian dynamics simulations, here we modeled intermediate substrate transportation of colocalized yeast–ester biosynthesis enzymes on the membrane. The substrate acetate ion traveled from the pocket of aldehyde dehydrogenase to its target enzyme acetyl‐CoA synthetase, then the substrate acetyl CoA diffused from Acs1 to the active site of the next enzyme, alcohol‐O‐acetyltransferase. Arranging two enzymes with the smallest inter‐enzyme distance of 60 Å had the fastest average substrate association time as compared with anchoring enzymes with larger inter‐enzyme distances. When the off‐target side reactions were turned on, most substrates were lost, which suggests that native localization is necessary for efficient final product synthesis. We also evaluated the effects of intermolecular interactions, local substrate concentrations, and membrane environment to bring mechanistic insights into the colocalization pathways. The computation work demonstrates that creating spatially organized multi‐enzymes on membranes can be an effective strategy to increase final product synthesis in bioengineering systems.
{"title":"Molecular mechanics studies of factors affecting overall rate in cascade reactions: Multi‐enzyme colocalization and environment","authors":"Shivansh Kaushik, Ta I Hung, Chia‐en A. Chang","doi":"10.1002/pro.5175","DOIUrl":"https://doi.org/10.1002/pro.5175","url":null,"abstract":"Millions of years of evolution have optimized many biosynthetic pathways by use of multi‐step catalysis. In addition, multi‐step metabolic pathways are commonly found in and on membrane‐bound organelles in eukaryotic biochemistry. The fundamental mechanisms that facilitate these reaction processes provide strategies to bioengineer metabolic pathways in synthetic chemistry. Using Brownian dynamics simulations, here we modeled intermediate substrate transportation of colocalized yeast–ester biosynthesis enzymes on the membrane. The substrate acetate ion traveled from the pocket of aldehyde dehydrogenase to its target enzyme acetyl‐CoA synthetase, then the substrate acetyl CoA diffused from Acs1 to the active site of the next enzyme, alcohol‐O‐acetyltransferase. Arranging two enzymes with the smallest inter‐enzyme distance of 60 Å had the fastest average substrate association time as compared with anchoring enzymes with larger inter‐enzyme distances. When the off‐target side reactions were turned on, most substrates were lost, which suggests that native localization is necessary for efficient final product synthesis. We also evaluated the effects of intermolecular interactions, local substrate concentrations, and membrane environment to bring mechanistic insights into the colocalization pathways. The computation work demonstrates that creating spatially organized multi‐enzymes on membranes can be an effective strategy to increase final product synthesis in bioengineering systems.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"2 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253069","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}
Claiborne W. Tydings, Bhuminder Singh, Adam W. Smith, Kaitlyn V. Ledwitch, Benjamin P. Brown, Christine M. Lovly, Allison S. Walker, Jens Meiler
The epidermal growth factor (EGF) receptor (EGFR) is activated by the binding of one of seven EGF‐like ligands to its ectodomain. Ligand binding results in EGFR dimerization and stabilization of the active receptor conformation subsequently leading to activation of downstream signaling. Aberrant activation of EGFR contributes to cancer progression through EGFR overexpression/amplification, modulation of its positive and negative regulators, and/or activating mutations within EGFR. EGFR targeted therapeutic antibodies prevent dimerization and interaction with endogenous ligands by binding the ectodomain of EGFR. However, these antibodies have had limited success in the clinic, partially due to EGFR ectodomain resistance mutations, and are only applicable to a subset of patients with EGFR‐driven cancers. These limitations suggest that alternative EGFR targeted biologics need to be explored for EGFR‐driven cancer therapy. To this end, we analyze the EGFR interfaces of known inhibitory biologics with determined structures in the context of endogenous ligands, using the Rosetta macromolecular modeling software to highlight the most important interactions on a per‐residue basis. We use this analysis to identify the structural determinants of EGFR targeted biologics. We suggest that commonly observed binding motifs serve as the basis for rational design of new EGFR targeted biologics, such as peptides, antibodies, and nanobodies.
{"title":"Analysis of EGFR binding hotspots for design of new EGFR inhibitory biologics","authors":"Claiborne W. Tydings, Bhuminder Singh, Adam W. Smith, Kaitlyn V. Ledwitch, Benjamin P. Brown, Christine M. Lovly, Allison S. Walker, Jens Meiler","doi":"10.1002/pro.5141","DOIUrl":"https://doi.org/10.1002/pro.5141","url":null,"abstract":"The epidermal growth factor (EGF) receptor (EGFR) is activated by the binding of one of seven EGF‐like ligands to its ectodomain. Ligand binding results in EGFR dimerization and stabilization of the active receptor conformation subsequently leading to activation of downstream signaling. Aberrant activation of EGFR contributes to cancer progression through EGFR overexpression/amplification, modulation of its positive and negative regulators, and/or activating mutations within EGFR. EGFR targeted therapeutic antibodies prevent dimerization and interaction with endogenous ligands by binding the ectodomain of EGFR. However, these antibodies have had limited success in the clinic, partially due to EGFR ectodomain resistance mutations, and are only applicable to a subset of patients with EGFR‐driven cancers. These limitations suggest that alternative EGFR targeted biologics need to be explored for EGFR‐driven cancer therapy. To this end, we analyze the EGFR interfaces of known inhibitory biologics with determined structures in the context of endogenous ligands, using the Rosetta macromolecular modeling software to highlight the most important interactions on a per‐residue basis. We use this analysis to identify the structural determinants of EGFR targeted biologics. We suggest that commonly observed binding motifs serve as the basis for rational design of new EGFR targeted biologics, such as peptides, antibodies, and nanobodies.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"211 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253065","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}
Dilip Narayanan, Anne Sofie G. Larsen, Stine Juul Gauger, Ruth Adafia, Rikke Bartschick Hammershøi, Louise Hamborg, Jesper Bruus‐Jensen, Nane Griem‐Krey, Christine L. Gee, Bente Frølund, Margaret M. Stratton, John Kuriyan, Jette Sandholm Kastrup, Annette E. Langkilde, Petrine Wellendorph, Sara M. Ø. Solbak
γ‐Hydroxybutyric acid (GHB) analogs are small molecules that bind competitively to a specific cavity in the oligomeric CaMKIIα hub domain. Binding affects conformation and stability of the hub domain, which may explain the neuroprotective action of some of these compounds. Here, we describe molecular details of interaction of the larger‐type GHB analog 2‐(6‐(4‐chlorophenyl)imidazo[1,2‐b]pyridazine‐2‐yl)acetic acid (PIPA). Like smaller‐type analogs, PIPA binding to the CaMKIIα hub domain promoted thermal stability. PIPA additionally modulated CaMKIIα activity under sub‐maximal CaM concentrations and ultimately led to reduced substrate phosphorylation. A high‐resolution X‐ray crystal structure of a stabilized CaMKIIα (6x mutant) hub construct revealed details of the binding mode of PIPA, which involved outward placement of tryptophan 403 (Trp403), a central residue in a flexible loop close to the upper hub cavity. Small‐angle X‐ray scattering (SAXS) solution structures and mass photometry of the CaMKIIα wild‐type hub domain in the presence of PIPA revealed a high degree of ordered self‐association (stacks of CaMKIIα hub domains). This stacking neither occurred with the smaller compound 3‐hydroxycyclopent‐1‐enecarboxylic acid (HOCPCA), nor when Trp403 was replaced with leucine (W403L). Additionally, CaMKIIα W403L hub was stabilized to a larger extent by PIPA compared to CaMKIIα hub wild type, indicating that loop flexibility is important for holoenzyme stability. Thus, we propose that ligand‐induced outward placement of Trp403 by PIPA, which promotes an unforeseen mechanism of hub domain stacking, may be involved in the observed reduction in CaMKIIα kinase activity. Altogether, this sheds new light on allosteric regulation of CaMKIIα activity via the hub domain.
{"title":"Ligand‐induced CaMKIIα hub Trp403 flip, hub domain stacking, and modulation of kinase activity","authors":"Dilip Narayanan, Anne Sofie G. Larsen, Stine Juul Gauger, Ruth Adafia, Rikke Bartschick Hammershøi, Louise Hamborg, Jesper Bruus‐Jensen, Nane Griem‐Krey, Christine L. Gee, Bente Frølund, Margaret M. Stratton, John Kuriyan, Jette Sandholm Kastrup, Annette E. Langkilde, Petrine Wellendorph, Sara M. Ø. Solbak","doi":"10.1002/pro.5152","DOIUrl":"https://doi.org/10.1002/pro.5152","url":null,"abstract":"γ‐Hydroxybutyric acid (GHB) analogs are small molecules that bind competitively to a specific cavity in the oligomeric CaMKIIα hub domain. Binding affects conformation and stability of the hub domain, which may explain the neuroprotective action of some of these compounds. Here, we describe molecular details of interaction of the larger‐type GHB analog 2‐(6‐(4‐chlorophenyl)imidazo[1,2‐b]pyridazine‐2‐yl)acetic acid (PIPA). Like smaller‐type analogs, PIPA binding to the CaMKIIα hub domain promoted thermal stability. PIPA additionally modulated CaMKIIα activity under sub‐maximal CaM concentrations and ultimately led to reduced substrate phosphorylation. A high‐resolution X‐ray crystal structure of a stabilized CaMKIIα (6x mutant) hub construct revealed details of the binding mode of PIPA, which involved outward placement of tryptophan 403 (Trp403), a central residue in a flexible loop close to the upper hub cavity. Small‐angle X‐ray scattering (SAXS) solution structures and mass photometry of the CaMKIIα wild‐type hub domain in the presence of PIPA revealed a high degree of ordered self‐association (stacks of CaMKIIα hub domains). This stacking neither occurred with the smaller compound 3‐hydroxycyclopent‐1‐enecarboxylic acid (HOCPCA), nor when Trp403 was replaced with leucine (W403L). Additionally, CaMKIIα W403L hub was stabilized to a larger extent by PIPA compared to CaMKIIα hub wild type, indicating that loop flexibility is important for holoenzyme stability. Thus, we propose that ligand‐induced outward placement of Trp403 by PIPA, which promotes an unforeseen mechanism of hub domain stacking, may be involved in the observed reduction in CaMKIIα kinase activity. Altogether, this sheds new light on allosteric regulation of CaMKIIα activity via the hub domain.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"39 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253090","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}
The integration of proteomics data with constraint‐based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome‐level phenomena and functional adaptations. Integrating a generic genome‐scale model with information on proteins enables generation of a context‐specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome‐scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade‐off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.
{"title":"Integration of proteomic data with genome‐scale metabolic models: A methodological overview","authors":"Farid Zare, Ronan M. T. Fleming","doi":"10.1002/pro.5150","DOIUrl":"https://doi.org/10.1002/pro.5150","url":null,"abstract":"The integration of proteomics data with constraint‐based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome‐level phenomena and functional adaptations. Integrating a generic genome‐scale model with information on proteins enables generation of a context‐specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome‐scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade‐off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"22 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253064","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}
Bispecific antibodies (BsAbs) have emerged as a major class of antibody therapeutics owing to their substantial potential in disease treatment. While several BsAbs have been successfully approved in recent years, ongoing development efforts continue to focus on optimizing various BsAbs tailored to particular antigens and action mechanisms, aiming to achieve favorable physicochemical properties. BsAbs generally encounter challenges due to their unfavorable physicochemical characteristics and poor manufacturing efficiencies, highlighting the need for optimization to achieve reliable productivity and developability. Herein, we describe the development of a novel symmetric BsAb, REGULGENT™ (N‐term/C‐term), comprising two Fab domains, using a common light chain. The heavy chain fragment encoded two antigen‐binding determinants in one chain. The design and production of REGULGENT™ (N‐term/C‐term) are simple owing to the use of the same light chain, which does not induce heavy and light chain mispairing, frequently observed with the asymmetric BsAb format. REGULGENT™ (N‐term/C‐term) exhibited high expression and low aggregation characteristics during cell culture and stress treatment under low pH conditions. Differential scanning calorimetric data indicated that REGULGENT™ molecules had high conformational stability, similar to that of stabilized monoclonal antibodies. Surface plasmon resonance data showed that REGULGENT™ (N‐term/C‐term) could bind to two antigens simultaneously and exhibited a high affinity for two antigens. In summary, the symmetric BsAb format of REGULGENT™ confers its desirable IgG‐like physicochemical properties, thus making it an excellent candidate for commercial development. The findings demonstrate a novel BsAb with substantial development potential for clinical applications.
{"title":"Engineering and physicochemical characterization of a novel, stable, symmetric bispecific antibody with dual target‐binding using a common light chain","authors":"Seiji Saito, Makoto Nakayama, Kaori Yamazaki, Yuya Miyamoto, Keiko Hiraishi, Daisuke Tomioka, Sayaka Takagi‐Maeda, Katsuaki Usami, Nobuaki Takahashi, Shinji Nara, Eiichiro Imai","doi":"10.1002/pro.5121","DOIUrl":"https://doi.org/10.1002/pro.5121","url":null,"abstract":"Bispecific antibodies (BsAbs) have emerged as a major class of antibody therapeutics owing to their substantial potential in disease treatment. While several BsAbs have been successfully approved in recent years, ongoing development efforts continue to focus on optimizing various BsAbs tailored to particular antigens and action mechanisms, aiming to achieve favorable physicochemical properties. BsAbs generally encounter challenges due to their unfavorable physicochemical characteristics and poor manufacturing efficiencies, highlighting the need for optimization to achieve reliable productivity and developability. Herein, we describe the development of a novel symmetric BsAb, REGULGENT™ (N‐term/C‐term), comprising two Fab domains, using a common light chain. The heavy chain fragment encoded two antigen‐binding determinants in one chain. The design and production of REGULGENT™ (N‐term/C‐term) are simple owing to the use of the same light chain, which does not induce heavy and light chain mispairing, frequently observed with the asymmetric BsAb format. REGULGENT™ (N‐term/C‐term) exhibited high expression and low aggregation characteristics during cell culture and stress treatment under low pH conditions. Differential scanning calorimetric data indicated that REGULGENT™ molecules had high conformational stability, similar to that of stabilized monoclonal antibodies. Surface plasmon resonance data showed that REGULGENT™ (N‐term/C‐term) could bind to two antigens simultaneously and exhibited a high affinity for two antigens. In summary, the symmetric BsAb format of REGULGENT™ confers its desirable IgG‐like physicochemical properties, thus making it an excellent candidate for commercial development. The findings demonstrate a novel BsAb with substantial development potential for clinical applications.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"14 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253071","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}
L‐cysteine is an essential component in pharmaceutical and agricultural industries, and synthetic biology has made strides in developing new metabolic pathways for its production, particularly in archaea with unique O‐phosphoserine sulfhydrylases (OPSS) as key enzymes. In this study, we employed database mining to identify a highly catalytic activity OPSS from Acetobacterium sp. (AsOPSS). However, it was observed that the enzymatic activity of AsOPSS suffered significant feedback inhibition from the product L‐cysteine, exhibiting an IC50 value of merely 1.2 mM. A semi‐rational design combined with tunnel analysis strategy was conducted to engineer AsOPSS. The best variant, AsOPSSA218R was achieved, totally eliminating product inhibition without sacrificing catalytic efficiency. Molecular docking and molecular dynamic simulations indicated that the binding conformation of AsOPSSA218R with L‐cys was altered, leading to a reduced affinity between L‐cysteine and the active pocket. Tunnel analysis revealed that the AsOPSSA218R variant reshaped the landscape of the tunnel, resulting in the construction of a new tunnel. Furthermore, random acceleration molecular dynamics simulation and umbrella sampling simulation demonstrated that the novel tunnel improved the suitability for product release and effectively separated the interference between the product release and substrate binding processes. Finally, more than 45 mM of L‐cysteine was produced in vitro within 2 h using the AsOPSSA218R variant. Our findings emphasize the potential for relieving feedback inhibition by artificially generating new product release channels, while also laying an enzymatic foundation for efficient L‐cysteine production.
{"title":"Mining unique cysteine synthetases and computational study on thoroughly eliminating feedback inhibition through tunnel engineering","authors":"Shuai Xu, Zong‐Lin Li, Zhi‐Min Li, Hong‐Lai Liu","doi":"10.1002/pro.5160","DOIUrl":"https://doi.org/10.1002/pro.5160","url":null,"abstract":"L‐cysteine is an essential component in pharmaceutical and agricultural industries, and synthetic biology has made strides in developing new metabolic pathways for its production, particularly in archaea with unique O‐phosphoserine sulfhydrylases (OPSS) as key enzymes. In this study, we employed database mining to identify a highly catalytic activity OPSS from <jats:italic>Acetobacterium</jats:italic> sp. (AsOPSS). However, it was observed that the enzymatic activity of AsOPSS suffered significant feedback inhibition from the product L‐cysteine, exhibiting an IC<jats:sub>50</jats:sub> value of merely 1.2 mM. A semi‐rational design combined with tunnel analysis strategy was conducted to engineer AsOPSS. The best variant, AsOPSS<jats:sup>A218R</jats:sup> was achieved, totally eliminating product inhibition without sacrificing catalytic efficiency. Molecular docking and molecular dynamic simulations indicated that the binding conformation of AsOPSS<jats:sup>A218R</jats:sup> with L‐cys was altered, leading to a reduced affinity between L‐cysteine and the active pocket. Tunnel analysis revealed that the AsOPSS<jats:sup>A218R</jats:sup> variant reshaped the landscape of the tunnel, resulting in the construction of a new tunnel. Furthermore, random acceleration molecular dynamics simulation and umbrella sampling simulation demonstrated that the novel tunnel improved the suitability for product release and effectively separated the interference between the product release and substrate binding processes. Finally, more than 45 mM of L‐cysteine was produced in vitro within 2 h using the AsOPSS<jats:sup>A218R</jats:sup> variant. Our findings emphasize the potential for relieving feedback inhibition by artificially generating new product release channels, while also laying an enzymatic foundation for efficient L‐cysteine production.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"82 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253089","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}
Predicting the binding of ligands to the human proteome via reverse‐docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off‐target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet‐SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off‐target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.
{"title":"Benchmarking reverse docking through AlphaFold2 human proteome","authors":"Qing Luo, Sheng Wang, Hoi Yeung Li, Liangzhen Zheng, Yuguang Mu, Jingjing Guo","doi":"10.1002/pro.5167","DOIUrl":"https://doi.org/10.1002/pro.5167","url":null,"abstract":"Predicting the binding of ligands to the human proteome via reverse‐docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off‐target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet‐SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off‐target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"5 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253094","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}
Qisheng Pan, Georgina Becerra Parra, Yoochan Myung, Stephanie Portelli, Thanh Binh Nguyen, David B. Ascher
Alzheimer's disease (AD) is one of the most common forms of dementia and neurodegenerative diseases, characterized by the formation of neuritic plaques and neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, and missense mutations can alter the folding and functions of these proteins, significantly increasing the risk of AD. However, many methods to identify AD‐causing variants did not consider the effect of mutations from the perspective of a protein three‐dimensional environment. Here, we present a machine learning‐based analysis to classify the AD‐causing mutations from their benign counterparts in 21 AD‐related proteins leveraging both sequence‐ and structure‐based features. Using computational tools to estimate the effect of mutations on protein stability, we first observed a bias of the pathogenic mutations with significant destabilizing effects on family AD‐related proteins. Combining this insight, we built a generic predictive model, and improved the performance by tuning the sample weights in the training process. Our final model achieved the performance on area under the receiver operating characteristic curve up to 0.95 in the blind test and 0.70 in an independent clinical validation, outperforming all the state‐of‐the‐art methods. Feature interpretation indicated that the hydrophobic environment and polar interaction contacts were crucial to the decision on pathogenic phenotypes of missense mutations. Finally, we presented a user‐friendly web server, AlzDiscovery, for researchers to browse the predicted phenotypes of all possible missense mutations on these 21 AD‐related proteins. Our study will be a valuable resource for AD screening and the development of personalized treatment.
阿尔茨海默病（AD）是最常见的痴呆症和神经退行性疾病之一，其特征是神经嵴斑块和神经纤维缠结的形成。许多不同的蛋白质都参与了这一复杂的致病机制，而错义突变会改变这些蛋白质的折叠和功能，从而大大增加患痴呆症的风险。然而，许多识别导致注意力缺失症变异的方法都没有从蛋白质三维环境的角度考虑突变的影响。在这里，我们提出了一种基于机器学习的分析方法，利用序列和结构特征对21种AD相关蛋白中的致AD变异和良性变异进行分类。利用计算工具估算突变对蛋白质稳定性的影响，我们首先观察到致病突变对AD相关蛋白家族具有显著的不稳定性影响。结合这一发现，我们建立了一个通用预测模型，并在训练过程中通过调整样本权重提高了模型的性能。我们的最终模型在盲测中的接收者操作特征曲线下面积达到了 0.95，在独立临床验证中达到了 0.70，优于所有最先进的方法。特征解释表明，疏水环境和极性相互作用接触对决定错义突变的致病表型至关重要。最后，我们介绍了一个用户友好型网络服务器 AlzDiscovery，供研究人员浏览这 21 种 AD 相关蛋白上所有可能的错义突变的预测表型。我们的研究将成为AD筛查和开发个性化治疗的宝贵资源。
{"title":"AlzDiscovery: A computational tool to identify Alzheimer's disease‐causing missense mutations using protein structure information","authors":"Qisheng Pan, Georgina Becerra Parra, Yoochan Myung, Stephanie Portelli, Thanh Binh Nguyen, David B. Ascher","doi":"10.1002/pro.5147","DOIUrl":"https://doi.org/10.1002/pro.5147","url":null,"abstract":"Alzheimer's disease (AD) is one of the most common forms of dementia and neurodegenerative diseases, characterized by the formation of neuritic plaques and neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, and missense mutations can alter the folding and functions of these proteins, significantly increasing the risk of AD. However, many methods to identify AD‐causing variants did not consider the effect of mutations from the perspective of a protein three‐dimensional environment. Here, we present a machine learning‐based analysis to classify the AD‐causing mutations from their benign counterparts in 21 AD‐related proteins leveraging both sequence‐ and structure‐based features. Using computational tools to estimate the effect of mutations on protein stability, we first observed a bias of the pathogenic mutations with significant destabilizing effects on family AD‐related proteins. Combining this insight, we built a generic predictive model, and improved the performance by tuning the sample weights in the training process. Our final model achieved the performance on area under the receiver operating characteristic curve up to 0.95 in the blind test and 0.70 in an independent clinical validation, outperforming all the state‐of‐the‐art methods. Feature interpretation indicated that the hydrophobic environment and polar interaction contacts were crucial to the decision on pathogenic phenotypes of missense mutations. Finally, we presented a user‐friendly web server, AlzDiscovery, for researchers to browse the predicted phenotypes of all possible missense mutations on these 21 AD‐related proteins. Our study will be a valuable resource for AD screening and the development of personalized treatment.","PeriodicalId":20761,"journal":{"name":"Protein Science","volume":"22 1","pages":""},"PeriodicalIF":8.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253091","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}
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