Protein Engineering. Applications in Science, Medicine, and Industry.Masayori Inouye , Raghupathy Sarma

1988 ◽  
Vol 63 (1) ◽  
pp. 72-74
Author(s):  
Johann Ott
2014 ◽  
Author(s):  
Dimitris Nikoloudis ◽  
Jim E. Pitts ◽  
José W. Saldanha

The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence-rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak.


2014 ◽  
Author(s):  
Dimitris Nikoloudis ◽  
Jim E. Pitts ◽  
José W. Saldanha

The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence-rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak.


2014 ◽  
Author(s):  
Dimitris Nikoloudis ◽  
Jim E. Pitts ◽  
José W. Saldanha

The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence-rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak.


2014 ◽  
Author(s):  
Dimitris Nikoloudis ◽  
Jim E. Pitts ◽  
José W. Saldanha

The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence-rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak.


2019 ◽  
Vol 476 (4) ◽  
pp. 665-682 ◽  
Author(s):  
Salvatore Di Girolamo ◽  
Chasper Puorger ◽  
Mara Castiglione ◽  
Maren Vogel ◽  
Rémy Gébleux ◽  
...  

Abstract Sortase enzymes play an important role in Gram-positive bacteria. They are responsible for the covalent attachment of proteins to the surface of the bacteria and perform this task via a highly sequence-specific transpeptidation reaction. Since these immobilized proteins are often involved in pathogenicity of Gram-positive bacteria, characterization of this type of enzyme is also of medical relevance. Different classes of sortases (A–F) have been found, which recognize characteristic recognition sequences present in substrate proteins. Up to date, sortase A from Staphylococcus aureus, a housekeeping class A sortase, is the most thoroughly studied representative of the sortase family of enzymes. Here we report the in-depth characterization of the class F sortase from Propionibacterium acnes, a class of sortases that has not been investigated before. As Sortase F is the only transpeptidase found in the P. acnes genome, it is the housekeeping sortase of this organism. Sortase F from P. acnes shows a behavior similar to sortases from class A in terms of pH dependence, recognition sequence and catalytic activity; furthermore, its activity is independent of bivalent ions, which contrasts to sortase A from S. aureus. We demonstrate that sortase F is useful for protein engineering applications, by producing a site-specifically conjugated homogenous antibody–drug conjugate with a potency similar to that of a conjugate prepared with sortase A. Thus, the detailed characterization presented here will not only enable the development of anti-virulence agents targeting P. acnes but also provides a powerful alternative to sortase A for protein engineering applications.


Sign in / Sign up

Export Citation Format

Share Document