scholarly journals Protein Engineering Applications on Industrially Important Enzymes: Candida methylica FDH as a Case Study

10.5772/26675 ◽  
2012 ◽  
Author(s):  
Emel Ordu ◽  
Nevin Gul
Keyword(s):  
Protein Engineering ◽  
Engineering Applications

scholarly journals Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence

2014 ◽  
Author(s):  
Dimitris Nikoloudis ◽  
Jim E. Pitts ◽  
José W. Saldanha
Keyword(s):  
Protein Engineering ◽  
Prediction Accuracy ◽  
Variable Region ◽  
New Method ◽  
Engineering Applications ◽  
Variable Regions ◽  
Blind Prediction ◽  
Novel Method ◽  
Complementarity Determining Regions ◽  
Sequence Rules

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.

Using Computational Intelligence for Improved Teleoperation over Wireless Network

2012 ◽  
pp. 102-123 ◽  
Author(s):  
Salman H. Khan ◽  
Arsalan H. Khan ◽  
Zeashan H. Khan
Keyword(s):  
Applied Sciences ◽  
Real Time ◽  
Wireless Network ◽  
Computational Intelligence ◽  
Networked Control Systems ◽  
Engineering Applications ◽  
Intelligent Decision ◽  
Application Requirements

The role of computational intelligence techniques in applied sciences and engineering is becoming popular today. It is essential because the autonomous engineering applications require intelligent decision in real time in order to achieve the desired goal. This chapter discusses some of the approaches to demonstrate various applications of computational intelligence in dependable networked control systems and a case study of teleoperation over wireless network. The results have shown that computational intelligence algorithms can be successfully implemented on an embedded application to offer an improved online performance. The different approaches have been compared and could be chosen as per application requirements.

Physics-based seismic input for engineering applications: a case study in the Aterno river valley, Central Italy

2017 ◽  
Vol 15 (7) ◽  
pp. 2645-2671 ◽  
Author(s):  
Lorenza Evangelista ◽  
Sergio del Gaudio ◽  
Chiara Smerzini ◽  
Anna d’Onofrio ◽  
Gaetano Festa ◽  
...  
Keyword(s):  
Central Italy ◽  
River Valley ◽  
Engineering Applications ◽  
Seismic Input

Poly(Lactic Acid)-Based Blends With Tailored Physicochemical Properties for Tissue Engineering Applications: A Case Study

2014 ◽  
Vol 64 (2) ◽  
pp. 90-98 ◽  
Author(s):  
Irene Carmagnola ◽  
Tiziana Nardo ◽  
Piergiorgio Gentile ◽  
Chiara Tonda-Turo ◽  
Clara Mattu ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Lactic Acid ◽  
Physicochemical Properties ◽  
Poly Lactic Acid ◽  
Engineering Applications

scholarly journals Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence.

2014 ◽  
Author(s):  
Dimitris Nikoloudis ◽  
Jim E. Pitts ◽  
José W. Saldanha
Keyword(s):  
Protein Engineering ◽  
Prediction Accuracy ◽  
Variable Region ◽  
New Method ◽  
Engineering Applications ◽  
Variable Regions ◽  
Blind Prediction ◽  
Novel Method ◽  
Complementarity Determining Regions ◽  
Sequence Rules

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.

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