Toward “Wet” Implementation of Genetic Algorithm for Protein Engineering

2005 ◽  
pp. 308-318 ◽  
Author(s):  
Kensaku Sakamoto ◽  
Masayuki Yamamura ◽  
Hiroshi Someya
Keyword(s):  
Genetic Algorithm ◽  
Protein Engineering

Insights into the biochemical and functional characterization of sortase E transpeptidase of Corynebacterium glutamicum

2019 ◽  
Vol 476 (24) ◽  
pp. 3835-3847 ◽  
Author(s):  
Aliyath Susmitha ◽  
Kesavan Madhavan Nampoothiri ◽  
Harsha Bajaj
Keyword(s):  
Protein Engineering ◽  
Cell Attachment ◽  
Functional Characterization ◽  
Protein Structures ◽  
Structural Similarity ◽  
Surface Proteins ◽  
Sortase A ◽  
Peptide Cleavage ◽  
New Findings

Most Gram-positive bacteria contain a membrane-bound transpeptidase known as sortase which covalently incorporates the surface proteins on to the cell wall. The sortase-displayed protein structures are involved in cell attachment, nutrient uptake and aerial hyphae formation. Among the six classes of sortase (A–F), sortase A of S. aureus is the well-characterized housekeeping enzyme considered as an ideal drug target and a valuable biochemical reagent for protein engineering. Similar to SrtA, class E sortase in GC rich bacteria plays a housekeeping role which is not studied extensively. However, C. glutamicum ATCC 13032, an industrially important organism known for amino acid production, carries a single putative sortase (NCgl2838) gene but neither in vitro peptide cleavage activity nor biochemical characterizations have been investigated. Here, we identified that the gene is having a sortase activity and analyzed its structural similarity with Cd-SrtF. The purified enzyme showed a greater affinity toward LAXTG substrate with a calculated KM of 12 ± 1 µM, one of the highest affinities reported for this class of enzyme. Moreover, site-directed mutation studies were carried to ascertain the structure functional relationship of Cg-SrtE and all these are new findings which will enable us to perceive exciting protein engineering applications with this class of enzyme from a non-pathogenic microbe.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

Vertical-Axis Wind Turbine Blade-Shape Optimization Using a Genetic Algorithm and Direct-Forcing Immersed Boundary Method

2021 ◽  
Vol 147 (2) ◽  
pp. 04020091
Author(s):  
Ming-Jyh Chern ◽  
Desta Goytom Tewolde ◽  
Chao-Ching Kao ◽  
Nima Vaziri
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Immersed Boundary Method ◽  
Vertical Axis ◽  
Immersed Boundary ◽  
Vertical Axis Wind Turbine ◽  
Blade Shape ◽  
Direct Forcing

Construction of Urban Planning Supporting System Using Genetic Algorithm Responding to Urban Decentralization: Case of Kanazawa City

2020 ◽  
Vol 146 (4) ◽  
pp. 04020044
Author(s):  
Yajing Zhang ◽  
Zhenjiang Shen ◽  
Bin Cui ◽  
Yuming Lin
Keyword(s):  
Genetic Algorithm ◽  
Urban Planning ◽  
Supporting System

Multiscale Island Injection Genetic Algorithm for Ground Water Remediation

2004 ◽  
Author(s):  
Eva Sinha ◽  
Barbara Minsker ◽  
Meghna Babbar
Keyword(s):  
Genetic Algorithm ◽  
Ground Water ◽  
Water Remediation

Protein Engineering of Penicillin Acylase

Acta Naturae ◽  
2010 ◽  
Vol 2 (3) ◽  
pp. 47-61 ◽  
Author(s):  
V I Tishkov ◽  
S S Savin ◽  
A S Yasnaya
Keyword(s):  
Protein Engineering ◽  
Penicillin Acylase

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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