Jacob Kronenberg, Dustin Britton, Leif Halvorsen, Stanley Chu, Maria Jinu Kulapurathazhe, Jason Chen, Ashwitha Lakshmi, P Douglas Renfrew, Richard Bonneau, Jin Kim Montclare
Phosphotriesterases (PTEs) represent a class of enzymes capable of efficient neutralization of organophosphates (OPs), a dangerous class of neurotoxic chemicals. PTEs suffer from low catalytic activity, particularly at higher temperatures, due to low thermostability and low solubility. Supercharging, a protein engineering approach via selective mutation of surface residues to charged residues, has been successfully employed to generate proteins with increased solubility and thermostability by promoting charge–charge repulsion between proteins. We set out to overcome the challenges in improving PTE activity against OPs by employing a computational protein supercharging algorithm in Rosetta. Here, we discover two supercharged PTE variants, one negatively supercharged (with −14 net charge) and one positively supercharged (with +12 net charge) and characterize them for their thermostability and catalytic activity. We find that positively supercharged PTE possesses slight but significant losses in thermostability, which correlates to losses in catalytic efficiency at all temperatures, whereas negatively supercharged PTE possesses increased catalytic activity across 25°C – 55°C while offering similar thermostability characteristic to the parent PTE. The impact of supercharging on catalytic efficiency will inform the design of shelf-stable PTE and criteria for enzyme engineering.
{"title":"Supercharged Phosphotriesterase for improved Paraoxon activity","authors":"Jacob Kronenberg, Dustin Britton, Leif Halvorsen, Stanley Chu, Maria Jinu Kulapurathazhe, Jason Chen, Ashwitha Lakshmi, P Douglas Renfrew, Richard Bonneau, Jin Kim Montclare","doi":"10.1093/protein/gzae015","DOIUrl":"https://doi.org/10.1093/protein/gzae015","url":null,"abstract":"Phosphotriesterases (PTEs) represent a class of enzymes capable of efficient neutralization of organophosphates (OPs), a dangerous class of neurotoxic chemicals. PTEs suffer from low catalytic activity, particularly at higher temperatures, due to low thermostability and low solubility. Supercharging, a protein engineering approach via selective mutation of surface residues to charged residues, has been successfully employed to generate proteins with increased solubility and thermostability by promoting charge–charge repulsion between proteins. We set out to overcome the challenges in improving PTE activity against OPs by employing a computational protein supercharging algorithm in Rosetta. Here, we discover two supercharged PTE variants, one negatively supercharged (with −14 net charge) and one positively supercharged (with +12 net charge) and characterize them for their thermostability and catalytic activity. We find that positively supercharged PTE possesses slight but significant losses in thermostability, which correlates to losses in catalytic efficiency at all temperatures, whereas negatively supercharged PTE possesses increased catalytic activity across 25°C – 55°C while offering similar thermostability characteristic to the parent PTE. The impact of supercharging on catalytic efficiency will inform the design of shelf-stable PTE and criteria for enzyme engineering.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Srinivas S Thota, Grace L Allen, Ashley K Grahn, Brian K Kay
Antibodies play a crucial role in monitoring post-translational modifications, like phosphorylation, which regulates protein activity and location; however, commercial polyclonal and monoclonal antibodies have limitations in renewability and engineering compared to recombinant affinity reagents. A scaffold based on the Forkhead-associated domain (FHA) has potential as a selective affinity reagent for this post-translational modification. Engineered FHA domains, termed phosphothreonine-binding domains (pTBDs), with limited cross-reactivity were isolated from an M13 bacteriophage display library by affinity selection with phosphopeptides corresponding to human mTOR, Chk2, 53BP1, and Akt1 proteins. To determine the specificity of the representative pTBDs, we focused on binders to the pT543 phosphopeptide (536-IDEDGENpTQIEDTEP-551) of the DNA repair protein 53BP1. ELISA and western blot experiments have demonstrated the pTBDs are specific to phosphothreonine, demonstrating the potential utility of pTBDs for monitoring the phosphorylation of specific threonine residues in clinically relevant human proteins.
{"title":"Engineered FHA domains can bind to a variety of Phosphothreonine-containing peptides","authors":"Srinivas S Thota, Grace L Allen, Ashley K Grahn, Brian K Kay","doi":"10.1093/protein/gzae014","DOIUrl":"https://doi.org/10.1093/protein/gzae014","url":null,"abstract":"Antibodies play a crucial role in monitoring post-translational modifications, like phosphorylation, which regulates protein activity and location; however, commercial polyclonal and monoclonal antibodies have limitations in renewability and engineering compared to recombinant affinity reagents. A scaffold based on the Forkhead-associated domain (FHA) has potential as a selective affinity reagent for this post-translational modification. Engineered FHA domains, termed phosphothreonine-binding domains (pTBDs), with limited cross-reactivity were isolated from an M13 bacteriophage display library by affinity selection with phosphopeptides corresponding to human mTOR, Chk2, 53BP1, and Akt1 proteins. To determine the specificity of the representative pTBDs, we focused on binders to the pT543 phosphopeptide (536-IDEDGENpTQIEDTEP-551) of the DNA repair protein 53BP1. ELISA and western blot experiments have demonstrated the pTBDs are specific to phosphothreonine, demonstrating the potential utility of pTBDs for monitoring the phosphorylation of specific threonine residues in clinically relevant human proteins.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"203 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Samantha G Martinusen, Ethan W Slaton, Sage E Nelson, Marian A Pulgar, Julia T Besu, Cassidy F Simas, Carl A Denard
The yeast endoplasmic reticulum sequestration and screening (YESS) system is a generalizable platform that has become highly useful to investigate post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CLpro), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.
{"title":"Modular and integrative activity reporters enhance biochemical studies in the yeast ER","authors":"Samantha G Martinusen, Ethan W Slaton, Sage E Nelson, Marian A Pulgar, Julia T Besu, Cassidy F Simas, Carl A Denard","doi":"10.1093/protein/gzae008","DOIUrl":"https://doi.org/10.1093/protein/gzae008","url":null,"abstract":"The yeast endoplasmic reticulum sequestration and screening (YESS) system is a generalizable platform that has become highly useful to investigate post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CLpro), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need to be considered properly. These couplings are treated explicitly in iterative methods or autoregressive methods. Non-autoregressive models treating these couplings implicitly are computationally more efficient, but still await tests by wet experiment. Currently, sequence design methods are evaluated mainly using native sequence recovery rate and native sequence perplexity. These metrics can be complemented by sequence-structure compatibility metrics obtained from energy calculation or structure prediction. However, existing computational metrics have important limitations that may render the generalization of computational test results to performance in real applications unwarranted. Validation of design methods by wet experiments should be encouraged.
{"title":"Protein sequence design on given backbones with deep learning","authors":"Yufeng Liu, Haiyan Liu","doi":"10.1093/protein/gzad024","DOIUrl":"https://doi.org/10.1093/protein/gzad024","url":null,"abstract":"Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need to be considered properly. These couplings are treated explicitly in iterative methods or autoregressive methods. Non-autoregressive models treating these couplings implicitly are computationally more efficient, but still await tests by wet experiment. Currently, sequence design methods are evaluated mainly using native sequence recovery rate and native sequence perplexity. These metrics can be complemented by sequence-structure compatibility metrics obtained from energy calculation or structure prediction. However, existing computational metrics have important limitations that may render the generalization of computational test results to performance in real applications unwarranted. Validation of design methods by wet experiments should be encouraged.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139071439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julia R Rogers, Gergö Nikolényi, Mohammed AlQuraishi
Numerous cellular functions rely on protein–protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically-informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.
{"title":"Growing ecosystem of deep learning methods for modeling protein–protein interactions","authors":"Julia R Rogers, Gergö Nikolényi, Mohammed AlQuraishi","doi":"10.1093/protein/gzad023","DOIUrl":"https://doi.org/10.1093/protein/gzad023","url":null,"abstract":"Numerous cellular functions rely on protein–protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically-informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138690954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Wollacott, Chonghua Xue, Qiuyuan Qin, June Hua, T. Bohnuud, Karthik Viswanathan, V. Kolachalama
Abstract Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.
{"title":"Quantifying the nativeness of antibody sequences using long short-term memory networks","authors":"A. Wollacott, Chonghua Xue, Qiuyuan Qin, June Hua, T. Bohnuud, Karthik Viswanathan, V. Kolachalama","doi":"10.1093/protein/gzz031","DOIUrl":"https://doi.org/10.1093/protein/gzz031","url":null,"abstract":"Abstract Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"1 1","pages":"347 - 354"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77015250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Intrinsically disordered proteins (IDPs) contribute to a multitude of functions. De novo design of IDPs should open the door to modulating functions and phenotypes controlled by these systems. Recent design efforts have focused on compositional biases and specific sequence patterns as the design features. Analysis of the impact of these designs on sequence-function relationships indicates that individual sequence/compositional parameters are insufficient for describing sequence-function relationships in IDPs. To remedy this problem, we have developed information theoretic measures for sequence–ensemble relationships (SERs) of IDPs. These measures rely on prior availability of statistically robust conformational ensembles derived from all atom simulations. We show that the measures we have developed are useful for comparing sequence-ensemble relationships even when sequence is poorly conserved. Based on our results, we propose that de novo designs of IDPs, guided by knowledge of their SERs, should provide improved insights into their sequence–ensemble–function relationships.
{"title":"Information theoretic measures for quantifying sequence–ensemble relationships of intrinsically disordered proteins","authors":"Megan C. Cohan, Kiersten M. Ruff, R. Pappu","doi":"10.1093/protein/gzz014","DOIUrl":"https://doi.org/10.1093/protein/gzz014","url":null,"abstract":"Abstract Intrinsically disordered proteins (IDPs) contribute to a multitude of functions. De novo design of IDPs should open the door to modulating functions and phenotypes controlled by these systems. Recent design efforts have focused on compositional biases and specific sequence patterns as the design features. Analysis of the impact of these designs on sequence-function relationships indicates that individual sequence/compositional parameters are insufficient for describing sequence-function relationships in IDPs. To remedy this problem, we have developed information theoretic measures for sequence–ensemble relationships (SERs) of IDPs. These measures rely on prior availability of statistically robust conformational ensembles derived from all atom simulations. We show that the measures we have developed are useful for comparing sequence-ensemble relationships even when sequence is poorly conserved. Based on our results, we propose that de novo designs of IDPs, guided by knowledge of their SERs, should provide improved insights into their sequence–ensemble–function relationships.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"9 5 1","pages":"191 - 202"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83782388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. van der Kant, Joschka Bauer, Anne R. Karow-Zwick, Sebastian Kube, P. Garidel, M. Blech, F. Rousseau, J. Schymkowitz
Abstract Monoclonal antibodies bind with high specificity to a wide range of diverse antigens, primarily mediated by their hypervariable complementarity determining regions (CDRs). The defined antigen binding loops are supported by the structurally conserved β-sandwich framework of the light chain (LC) and heavy chain (HC) variable regions. The LC genes are encoded by two separate loci, subdividing the entity of antibodies into kappa (LCκ) and lambda (LCλ) isotypes that exhibit distinct sequence and conformational preferences. In this work, a diverse set of techniques were employed including machine learning, force field analysis, statistical coupling analysis and mutual information analysis of a non-redundant antibody structure collection. Thereby, it was revealed how subtle changes between the structures of LCκ and LCλ isotypes increase the diversity of antibodies, extending the predetermined restrictions of the general antibody fold and expanding the diversity of antigen binding. Interestingly, it was found that the characteristic framework scaffolds of κ and λ are stabilized by diverse amino acid clusters that determine the interplay between the respective fold and the embedded CDR loops. In conclusion, this work reveals how antibodies use the remarkable plasticity of the beta-sandwich Ig fold to incorporate a large diversity of CDR loops.
{"title":"Adaption of human antibody λ and κ light chain architectures to CDR repertoires","authors":"R. van der Kant, Joschka Bauer, Anne R. Karow-Zwick, Sebastian Kube, P. Garidel, M. Blech, F. Rousseau, J. Schymkowitz","doi":"10.1093/protein/gzz012","DOIUrl":"https://doi.org/10.1093/protein/gzz012","url":null,"abstract":"Abstract Monoclonal antibodies bind with high specificity to a wide range of diverse antigens, primarily mediated by their hypervariable complementarity determining regions (CDRs). The defined antigen binding loops are supported by the structurally conserved β-sandwich framework of the light chain (LC) and heavy chain (HC) variable regions. The LC genes are encoded by two separate loci, subdividing the entity of antibodies into kappa (LCκ) and lambda (LCλ) isotypes that exhibit distinct sequence and conformational preferences. In this work, a diverse set of techniques were employed including machine learning, force field analysis, statistical coupling analysis and mutual information analysis of a non-redundant antibody structure collection. Thereby, it was revealed how subtle changes between the structures of LCκ and LCλ isotypes increase the diversity of antibodies, extending the predetermined restrictions of the general antibody fold and expanding the diversity of antigen binding. Interestingly, it was found that the characteristic framework scaffolds of κ and λ are stabilized by diverse amino acid clusters that determine the interplay between the respective fold and the embedded CDR loops. In conclusion, this work reveals how antibodies use the remarkable plasticity of the beta-sandwich Ig fold to incorporate a large diversity of CDR loops.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"78 1","pages":"109 - 127"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79995913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There have been many reports exploring the engineering of the cofactor specificity of aldo-keto reductases (AKRs), as this class of proteins is ubiquitous and exhibits many useful activities. A common approach is the mutagenesis of amino acids involved in interactions with the 2'-phosphate group of NADP(H) in the cofactor binding pocket. We recently performed a 'loop-grafting' approach to engineer the substrate specificity of the thermostable alcohol dehydrogenase D (AdhD) from Pyrococcus furiosus and we found that a loop insertion after residue 211, which is on the back side of the cofactor binding pocket, could also alter cofactor specificity. Here, we further explore this approach by introducing single point mutations and single amino acid insertions at the loop insertion site. Six different mutants of AdhD were created by either converting glycine 211 to cysteine or serine or by inserting alanine, serine, glycine or cysteine between the 211 and 212 residues. Several mutants gained activity with NADP+ above the wild-type enzyme. And remarkably, it was found that all of the mutants investigated resulted in some degree of reversal of cofactor specificity in the oxidative direction. These changes were generally a result of changes in conformations of the ternary enzyme/cofactor/substrate complexes as opposed to changes in affinities or binding energies of the cofactors. This study highlights the role that amino acids which are distal to the cofactor binding pocket but are involved in substrate interactions can influence cofactor specificity in AdhD, and this strategy should translate to other AKR family members.
{"title":"Engineering the cofactor specificity of an alcohol dehydrogenase via single mutations or insertions distal to the 2′-phosphate group of NADP(H)","authors":"K. Solanki, Walaa Abdallah, S. Banta","doi":"10.1093/protein/gzx009","DOIUrl":"https://doi.org/10.1093/protein/gzx009","url":null,"abstract":"There have been many reports exploring the engineering of the cofactor specificity of aldo-keto reductases (AKRs), as this class of proteins is ubiquitous and exhibits many useful activities. A common approach is the mutagenesis of amino acids involved in interactions with the 2'-phosphate group of NADP(H) in the cofactor binding pocket. We recently performed a 'loop-grafting' approach to engineer the substrate specificity of the thermostable alcohol dehydrogenase D (AdhD) from Pyrococcus furiosus and we found that a loop insertion after residue 211, which is on the back side of the cofactor binding pocket, could also alter cofactor specificity. Here, we further explore this approach by introducing single point mutations and single amino acid insertions at the loop insertion site. Six different mutants of AdhD were created by either converting glycine 211 to cysteine or serine or by inserting alanine, serine, glycine or cysteine between the 211 and 212 residues. Several mutants gained activity with NADP+ above the wild-type enzyme. And remarkably, it was found that all of the mutants investigated resulted in some degree of reversal of cofactor specificity in the oxidative direction. These changes were generally a result of changes in conformations of the ternary enzyme/cofactor/substrate complexes as opposed to changes in affinities or binding energies of the cofactors. This study highlights the role that amino acids which are distal to the cofactor binding pocket but are involved in substrate interactions can influence cofactor specificity in AdhD, and this strategy should translate to other AKR family members.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"126 1","pages":"373–380"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87826838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Developmentally important proteins that are crucial for fertilization and embryogenesis are synthesized through highly regulated translation of maternal mRNA. The Zygote arrest proteins, Zar1 and Zar2, are crucial for embryogenesis and have been implicated in binding mRNA and repressing mRNA translation. To investigate Zar1 and Zar2, the full-length proteins had been fused to glutathione-S-transferase (GST) or MS2 protein tags with minimal inter-domain linkers derived from multiple cloning sites; however, these fusion proteins expressed poorly and/or lacked robust function. Here, we tested the effect of inserting additional linkers between the fusion domains. Three linkers were tested, each 17 amino acids long with different physical and chemical properties: flexible hydrophilic, rigid extended or rigid helical. In the presence of any of the three linkers, GST-Zar1 and GST-Zar2 had fewer breakdown products. Moreover, in the presence of any of the linkers, MS2-Zar1 was expressed to higher levels, and in dual luciferase tethered assays, both MS2-Zar1 and MS2-Zar2 repressed luciferase translation to a greater extent. These data suggest that for Zar fusion proteins, increasing the length of linkers, regardless of their physical or chemical properties, improves stability, expression and bioactivity.
对受精和胚胎发生至关重要的发育重要蛋白是通过母体mRNA的高度调控翻译合成的。合子阻滞蛋白Zar1和Zar2对胚胎发生至关重要，与结合mRNA和抑制mRNA翻译有关。为了研究Zar1和Zar2，将全长蛋白融合到谷胱甘肽- s -转移酶(GST)或MS2蛋白标签上，并使用来自多个克隆位点的最小结构域间连接物;然而，这些融合蛋白表达差和/或缺乏强大的功能。在这里，我们测试了在融合域之间插入额外的连接器的效果。测试了三种连接剂，每种连接剂长17个氨基酸，具有不同的物理和化学性质:柔性亲水，刚性延伸或刚性螺旋。在三种连接物中的任何一种存在下，GST-Zar1和GST-Zar2的分解产物较少。此外，在任何一种连接物存在的情况下，MS2-Zar1的表达水平更高，并且在双荧光素酶捆绑试验中，MS2-Zar1和MS2-Zar2都在更大程度上抑制了荧光素酶的翻译。这些数据表明，对于Zar融合蛋白，增加连接体的长度，无论其物理或化学性质如何，都可以提高其稳定性、表达和生物活性。
{"title":"Insertion of inter-domain linkers improves expression and bioactivity of Zygote arrest (Zar) fusion proteins","authors":"J. Cook, A. Charlesworth","doi":"10.1093/protein/gzx002","DOIUrl":"https://doi.org/10.1093/protein/gzx002","url":null,"abstract":"Developmentally important proteins that are crucial for fertilization and embryogenesis are synthesized through highly regulated translation of maternal mRNA. The Zygote arrest proteins, Zar1 and Zar2, are crucial for embryogenesis and have been implicated in binding mRNA and repressing mRNA translation. To investigate Zar1 and Zar2, the full-length proteins had been fused to glutathione-S-transferase (GST) or MS2 protein tags with minimal inter-domain linkers derived from multiple cloning sites; however, these fusion proteins expressed poorly and/or lacked robust function. Here, we tested the effect of inserting additional linkers between the fusion domains. Three linkers were tested, each 17 amino acids long with different physical and chemical properties: flexible hydrophilic, rigid extended or rigid helical. In the presence of any of the three linkers, GST-Zar1 and GST-Zar2 had fewer breakdown products. Moreover, in the presence of any of the linkers, MS2-Zar1 was expressed to higher levels, and in dual luciferase tethered assays, both MS2-Zar1 and MS2-Zar2 repressed luciferase translation to a greater extent. These data suggest that for Zar fusion proteins, increasing the length of linkers, regardless of their physical or chemical properties, improves stability, expression and bioactivity.","PeriodicalId":20681,"journal":{"name":"Protein Engineering, Design and Selection","volume":"31 1","pages":"313–319"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89179325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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