Three‐dimensional structures of I86A and C295A mutant secondary alcohol dehydrogenase (SADH) from Thermoanaerobacter pseudoethanolicus were determined by x‐ray crystallography. The tetrameric structure of C295A‐SADH soaked with NADP+ and dimethyl sulfoxide (DMSO) was determined to 1.85 Å with an Rfree of 0.225. DMSO is bound to the tetrahedral zinc in each subunit, with ligands from SG of Cys‐37, NE2 of His‐59, and OD2 of Asp‐150. The nicotinamide ring of NADP is hydrogen‐bonded to the N of Ala‐295 and the O of Val‐265 and Gly‐293. The O of DMSO is connected to a network of hydrogen bonds with OG of Ser‐39, the 3′‐OH of NADP, and ND1 of His‐42. The structure of I86A‐SADH soaked with 2‐pentanol and NADP+ contains (R)‐2‐pentanol bound in each subunit, ligated to the tetrahedral zinc, and connected to the proton relay network. The structure of I86A‐SADH soaked with 3‐methylcyclohexanol and NADP+ has alcohol bound in three subunits. Two of the sites have the alcohol ligated to the zinc in an axial position, with OE2 of Glu‐60 in the other axial position of a trigonal bipyramidal complex. One site has 3‐methylcyclohexanol bound noncovalently, with the zinc in an inverted tetrahedral geometry with Glu‐60. The fourth site also has the zinc in a trigonal bipyramidal complex with axial Glu‐60 and water ligands. These structures demonstrate that ligand exchange of SADH involves pentacoordinate and inverted zinc complexes with Glu‐60. Furthermore, we see a network of hydrogen bonds connecting the substrate oxygen to the external solvent that is likely to play a role in the mechanism of SADH.
{"title":"Crystallographic snapshots of ternary complexes of thermophilic secondary alcohol dehydrogenase from Thermoanaerobacter pseudoethanolicus reveal the dynamics of ligand exchange and the proton relay network","authors":"T. Dinh, K. Rahn, R. Phillips","doi":"10.1002/prot.26339","DOIUrl":"https://doi.org/10.1002/prot.26339","url":null,"abstract":"Three‐dimensional structures of I86A and C295A mutant secondary alcohol dehydrogenase (SADH) from Thermoanaerobacter pseudoethanolicus were determined by x‐ray crystallography. The tetrameric structure of C295A‐SADH soaked with NADP+ and dimethyl sulfoxide (DMSO) was determined to 1.85 Å with an Rfree of 0.225. DMSO is bound to the tetrahedral zinc in each subunit, with ligands from SG of Cys‐37, NE2 of His‐59, and OD2 of Asp‐150. The nicotinamide ring of NADP is hydrogen‐bonded to the N of Ala‐295 and the O of Val‐265 and Gly‐293. The O of DMSO is connected to a network of hydrogen bonds with OG of Ser‐39, the 3′‐OH of NADP, and ND1 of His‐42. The structure of I86A‐SADH soaked with 2‐pentanol and NADP+ contains (R)‐2‐pentanol bound in each subunit, ligated to the tetrahedral zinc, and connected to the proton relay network. The structure of I86A‐SADH soaked with 3‐methylcyclohexanol and NADP+ has alcohol bound in three subunits. Two of the sites have the alcohol ligated to the zinc in an axial position, with OE2 of Glu‐60 in the other axial position of a trigonal bipyramidal complex. One site has 3‐methylcyclohexanol bound noncovalently, with the zinc in an inverted tetrahedral geometry with Glu‐60. The fourth site also has the zinc in a trigonal bipyramidal complex with axial Glu‐60 and water ligands. These structures demonstrate that ligand exchange of SADH involves pentacoordinate and inverted zinc complexes with Glu‐60. Furthermore, we see a network of hydrogen bonds connecting the substrate oxygen to the external solvent that is likely to play a role in the mechanism of SADH.","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79552057","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}
Membrane transport proteins, which include transporters and channels, are delicate protein machineries that mediate the exchange of a variety of substances across biomembranes. Accumulated structural and functional knowledge allows for the de novo design of transport proteins with new structures that do not exist in nature. Analysis based on these novel proteins provides new insights into the principles that govern protein assembly, conformational change, and substrate recognition. Here, we review the advances in the de novo design of transporters and channels over recent years and highlight the challenges and opportunities in this field.
{"title":"De novo design of membrane transport proteins","authors":"Chen Zhou, P. Lu","doi":"10.1002/prot.26336","DOIUrl":"https://doi.org/10.1002/prot.26336","url":null,"abstract":"Membrane transport proteins, which include transporters and channels, are delicate protein machineries that mediate the exchange of a variety of substances across biomembranes. Accumulated structural and functional knowledge allows for the de novo design of transport proteins with new structures that do not exist in nature. Analysis based on these novel proteins provides new insights into the principles that govern protein assembly, conformational change, and substrate recognition. Here, we review the advances in the de novo design of transporters and channels over recent years and highlight the challenges and opportunities in this field.","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86706273","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}
Glutathione (GSH) is synthesized in two ATP‐dependent reactions by glutamate‐cysteine ligase (Gcl) and glutathione synthetase (Gs). Myxococcus xanthus, a gram‐negative bacterium belonging to δ‐proteobacteria, possesses mxGcl and mxGs, which have high sequence identity with the enzymes from plants and bacteria, respectively. MxGcl2 was activated by Mn2+, but not by Mg2+, and stabilized in the presence of 5 mM Mn2+ or Mg2+. Sequence comparison of mxGcl2 and Brassica juncea Gcl indicated that they have the same active site residues, except for Tyr330, which interacts with Cys and which in mxGcl2 is represented by Leu267. The substitution of Leu267 with Tyr resulted in the loss of mxGcl2 activity, but that with Met (found in cyanobacterial Gcls) increased the mxGcl2 affinity for Cys. GSH and its oxidized form GSSG equally inhibited the activity of mxGcl2; the inhibition was augmented by ATP at concentrations >3 mM. Buthionine sulfoximine inactivated mxGcl2 with Ki = 2.1 μM, which was lower than those for Gcls from other organisms. The mxGcl2 activity was also suppressed by pyrophosphate and polyphosphates. MxGs was a dimer, and its activity was induced by Mg2+ but strongly inhibited by Mn2+ even in the presence of 10 mM Mg2+. MxGs was inhibited by GSSG at Ki = 3.6 mM. Approximately 1 mM GSH was generated with 3 units of mxGcl2 and 6 units of mxGs from 5 mM Glu, Cys, and Gly, and 10 mM ATP. Our results suggest that GSH production in M. xanthus mostly depends on mxGcl2 activity.
谷胱甘肽(GSH)是由谷氨酸-半胱氨酸连接酶(Gcl)和谷胱甘肽合成酶(Gs)两种ATP依赖性反应合成的。黄粘球菌(Myxococcus xanthus)是一种革兰氏阴性细菌，属于δ -变形菌门，具有mxGcl和mxGs，它们分别与植物和细菌中的酶具有高度的序列一致性。MxGcl2被Mn2+激活，而不被Mg2+激活，在5 mM Mn2+或Mg2+存在下稳定。mxGcl2与芥菜Gcl的序列比较表明，除了与Cys相互作用的Tyr330和在mxGcl2中以Leu267为代表的Tyr330外，它们具有相同的活性位点残基。用Tyr取代Leu267导致mxGcl2活性的丧失，而用Met(在蓝藻Gcls中发现)取代则增加了mxGcl2对Cys的亲和力。GSH及其氧化形式GSSG对mxGcl2活性的抑制作用相同;浓度为bbb30 mM的ATP增强了抑制作用。丁硫氨酸亚砜对mxGcl2的灭活作用Ki = 2.1 μM，低于其他生物的灭活作用。mxGcl2活性也受到焦磷酸盐和多磷酸盐的抑制。MxGs是一种二聚体，其活性受Mg2+的诱导，但在Mg2+浓度为10 mM时，其活性仍被Mn2+强烈抑制。在Ki = 3.6 mM时，GSSG对MxGs有抑制作用。5 mM Glu、Cys和Gly产生3单位mxGcl2和6单位mxGs，以及10 mM ATP，产生约1 mM GSH。我们的研究结果表明，黄原草中GSH的产生主要取决于mxGcl2的活性。
{"title":"Characterization of glutamate‐cysteine ligase and glutathione synthetase from the δ‐proteobacterium Myxococcus xanthus","authors":"Misaki Okada, Y. Kimura","doi":"10.1002/prot.26333","DOIUrl":"https://doi.org/10.1002/prot.26333","url":null,"abstract":"Glutathione (GSH) is synthesized in two ATP‐dependent reactions by glutamate‐cysteine ligase (Gcl) and glutathione synthetase (Gs). Myxococcus xanthus, a gram‐negative bacterium belonging to δ‐proteobacteria, possesses mxGcl and mxGs, which have high sequence identity with the enzymes from plants and bacteria, respectively. MxGcl2 was activated by Mn2+, but not by Mg2+, and stabilized in the presence of 5 mM Mn2+ or Mg2+. Sequence comparison of mxGcl2 and Brassica juncea Gcl indicated that they have the same active site residues, except for Tyr330, which interacts with Cys and which in mxGcl2 is represented by Leu267. The substitution of Leu267 with Tyr resulted in the loss of mxGcl2 activity, but that with Met (found in cyanobacterial Gcls) increased the mxGcl2 affinity for Cys. GSH and its oxidized form GSSG equally inhibited the activity of mxGcl2; the inhibition was augmented by ATP at concentrations >3 mM. Buthionine sulfoximine inactivated mxGcl2 with Ki = 2.1 μM, which was lower than those for Gcls from other organisms. The mxGcl2 activity was also suppressed by pyrophosphate and polyphosphates. MxGs was a dimer, and its activity was induced by Mg2+ but strongly inhibited by Mn2+ even in the presence of 10 mM Mg2+. MxGs was inhibited by GSSG at Ki = 3.6 mM. Approximately 1 mM GSH was generated with 3 units of mxGcl2 and 6 units of mxGs from 5 mM Glu, Cys, and Gly, and 10 mM ATP. Our results suggest that GSH production in M. xanthus mostly depends on mxGcl2 activity.","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74734451","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}
Stephanie Nguyen, Blagojce Jovcevski, J. Truong, T. Pukala, J. Bruning
The metabolic enzyme, enolase, plays a crucial role in the cytoplasm where it maintains cellular energy production within the process of glycolysis. The main role of enolase in glycolysis is to convert 2‐phosphoglycerate to phosphoenolpyruvate; however, enolase can fulfill roles that deviate from this function. In pathogenic bacteria and fungi, enolase is also located on the cell surface where it functions as a virulence factor. Surface‐expressed enolase is a receptor for human plasma proteins, including plasminogen, and this interaction facilitates nutrient acquisition and tissue invasion. A novel approach to developing antifungal drugs is to inhibit the formation of this complex. To better understand the structure of enolase and the interactions that may govern complex formation, we have solved the first X‐ray crystal structure of enolase from Aspergillus fumigatus (2.0 Å) and have shown that it preferentially adopts a dimeric quaternary structure using native mass spectrometry. Two additional X‐ray crystal structures of A. fumigatus enolase bound to the endogenous substrate 2‐phosphoglycerate and product phosphoenolpyruvate were determined and kinetic characterization was carried out to better understand the details of its canonical function. From these data, we have produced a model of the A. fumigatus enolase and human plasminogen complex to provide structural insights into the mechanisms of virulence and aid future development of small molecules or peptidomimetics for antifungal drug design.
{"title":"A structural model of the human plasminogen and Aspergillus fumigatus enolase complex","authors":"Stephanie Nguyen, Blagojce Jovcevski, J. Truong, T. Pukala, J. Bruning","doi":"10.1002/prot.26331","DOIUrl":"https://doi.org/10.1002/prot.26331","url":null,"abstract":"The metabolic enzyme, enolase, plays a crucial role in the cytoplasm where it maintains cellular energy production within the process of glycolysis. The main role of enolase in glycolysis is to convert 2‐phosphoglycerate to phosphoenolpyruvate; however, enolase can fulfill roles that deviate from this function. In pathogenic bacteria and fungi, enolase is also located on the cell surface where it functions as a virulence factor. Surface‐expressed enolase is a receptor for human plasma proteins, including plasminogen, and this interaction facilitates nutrient acquisition and tissue invasion. A novel approach to developing antifungal drugs is to inhibit the formation of this complex. To better understand the structure of enolase and the interactions that may govern complex formation, we have solved the first X‐ray crystal structure of enolase from Aspergillus fumigatus (2.0 Å) and have shown that it preferentially adopts a dimeric quaternary structure using native mass spectrometry. Two additional X‐ray crystal structures of A. fumigatus enolase bound to the endogenous substrate 2‐phosphoglycerate and product phosphoenolpyruvate were determined and kinetic characterization was carried out to better understand the details of its canonical function. From these data, we have produced a model of the A. fumigatus enolase and human plasminogen complex to provide structural insights into the mechanisms of virulence and aid future development of small molecules or peptidomimetics for antifungal drug design.","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88556212","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}
Trinh-trung-duong Nguyen, Syun Chen, Quang-Thai Ho, Yu-Yen Ou
Protein multiple sequence alignment information has long been important features to know about functions of proteins inferred from related sequences with known functions. It is therefore one of the underlying ideas of Alpha fold 2, a breakthrough study and model for the prediction of three‐dimensional structures of proteins from their primary sequence. Our study used protein multiple sequence alignment information in the form of position‐specific scoring matrices as input. We also refined the use of a convolutional neural network, a well‐known deep‐learning architecture with impressive achievement on image and image‐like data. Specifically, we revisited the study of prediction of adenosine triphosphate (ATP)‐binding sites with more efficient convolutional neural networks. We applied multiple convolutional window scanning filters of a convolutional neural network on position‐specific scoring matrices for as much as useful information as possible. Furthermore, only the most specific motifs are retained at each feature map output through the one‐max pooling layer before going to the next layer. We assumed that this way could help us retain the most conserved motifs which are discriminative information for prediction. Our experiment results show that a convolutional neural network with not too many convolutional layers can be enough to extract the conserved information of proteins, which leads to higher performance. Our best prediction models were obtained after examining them with different hyper‐parameters. Our experiment results showed that our models were superior to traditional use of convolutional neural networks on the same datasets as well as other machine‐learning classification algorithms.
{"title":"Using multiple convolutional window scanning of convolutional neural network for an efficient prediction of ATP‐binding sites in transport proteins","authors":"Trinh-trung-duong Nguyen, Syun Chen, Quang-Thai Ho, Yu-Yen Ou","doi":"10.1002/prot.26329","DOIUrl":"https://doi.org/10.1002/prot.26329","url":null,"abstract":"Protein multiple sequence alignment information has long been important features to know about functions of proteins inferred from related sequences with known functions. It is therefore one of the underlying ideas of Alpha fold 2, a breakthrough study and model for the prediction of three‐dimensional structures of proteins from their primary sequence. Our study used protein multiple sequence alignment information in the form of position‐specific scoring matrices as input. We also refined the use of a convolutional neural network, a well‐known deep‐learning architecture with impressive achievement on image and image‐like data. Specifically, we revisited the study of prediction of adenosine triphosphate (ATP)‐binding sites with more efficient convolutional neural networks. We applied multiple convolutional window scanning filters of a convolutional neural network on position‐specific scoring matrices for as much as useful information as possible. Furthermore, only the most specific motifs are retained at each feature map output through the one‐max pooling layer before going to the next layer. We assumed that this way could help us retain the most conserved motifs which are discriminative information for prediction. Our experiment results show that a convolutional neural network with not too many convolutional layers can be enough to extract the conserved information of proteins, which leads to higher performance. Our best prediction models were obtained after examining them with different hyper‐parameters. Our experiment results showed that our models were superior to traditional use of convolutional neural networks on the same datasets as well as other machine‐learning classification algorithms.","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74754039","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}
{"title":"Issue Information ‐ Forthcoming","authors":"P. V. Balaji, Rahul Banerjee","doi":"10.1002/prot.26103","DOIUrl":"https://doi.org/10.1002/prot.26103","url":null,"abstract":"","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84535161","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}
{"title":"Issue Information ‐ Table of Content","authors":"","doi":"10.1002/prot.26102","DOIUrl":"https://doi.org/10.1002/prot.26102","url":null,"abstract":"","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89914628","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}
Jiaan Yang, Wenxiang Cheng, Xiaoyan Zhao, Gang Wu, Shi Tong Sheng, Qiyue Hu, Hu Ge, Qianshan Qin, Xinshen Jin, Lianshan Zhang, Peng Zhang
The revelation of protein folding is a challenging subject in both discovery and description. Except for acquirement of accurate 3D structure in protein stable state, another big hurdle is how to discover structural flexibility for protein innate character. Even if a huge number of flexible conformations are known, difficulty is how to represent these conformations. A novel approach, protein structure fingerprint, has been developed to expose the comprehensive local folding variations, and then construct folding conformations for entire protein. The backbone of five amino acid residues was identified as a universal folden, and then a set of Protein Folding Shape Code (PFSC) was derived for completely covering folding space in alphabetic description. Sequentially, a database was created to collect all possible folding shapes of local folding variations for all permutation of five amino acids. Successively, Protein Folding Variation Matrix (PFVM) assembled all possible local folding variations along sequence for a protein, which possesses several prominent features. First, it showed the fluctuation with certain folding patterns along sequence which revealed how the protein folding was related the order of amino acids in sequence. Second, all folding variations for an entire protein can be simultaneously apprehended at a glance within PFVM. Third, all conformations can be determined by local folding variations from PFVM, so total number of conformations is no longer ambiguous for any protein. Finally, the most possible folding conformation and its 3D structure can be acquired according PFVM for protein structure prediction. Therefore, the protein structure fingerprint approach provides a significant means for investigation of protein folding problem.
{"title":"Comprehensive folding variations for protein folding","authors":"Jiaan Yang, Wenxiang Cheng, Xiaoyan Zhao, Gang Wu, Shi Tong Sheng, Qiyue Hu, Hu Ge, Qianshan Qin, Xinshen Jin, Lianshan Zhang, Peng Zhang","doi":"10.1002/prot.26381","DOIUrl":"https://doi.org/10.1002/prot.26381","url":null,"abstract":"The revelation of protein folding is a challenging subject in both discovery and description. Except for acquirement of accurate 3D structure in protein stable state, another big hurdle is how to discover structural flexibility for protein innate character. Even if a huge number of flexible conformations are known, difficulty is how to represent these conformations. A novel approach, protein structure fingerprint, has been developed to expose the comprehensive local folding variations, and then construct folding conformations for entire protein. The backbone of five amino acid residues was identified as a universal folden, and then a set of Protein Folding Shape Code (PFSC) was derived for completely covering folding space in alphabetic description. Sequentially, a database was created to collect all possible folding shapes of local folding variations for all permutation of five amino acids. Successively, Protein Folding Variation Matrix (PFVM) assembled all possible local folding variations along sequence for a protein, which possesses several prominent features. First, it showed the fluctuation with certain folding patterns along sequence which revealed how the protein folding was related the order of amino acids in sequence. Second, all folding variations for an entire protein can be simultaneously apprehended at a glance within PFVM. Third, all conformations can be determined by local folding variations from PFVM, so total number of conformations is no longer ambiguous for any protein. Finally, the most possible folding conformation and its 3D structure can be acquired according PFVM for protein structure prediction. Therefore, the protein structure fingerprint approach provides a significant means for investigation of protein folding problem.","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84893075","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}
{"title":"Issue Information ‐ Table of Content","authors":"","doi":"10.1002/prot.26098","DOIUrl":"https://doi.org/10.1002/prot.26098","url":null,"abstract":"","PeriodicalId":20789,"journal":{"name":"Proteins: Structure","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74932812","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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