INNOVATION IN PROTEIN ENGINEERING: A REVIEW
Abstract
The most recent decade has seen an exponential increment of protein structures understood by X-beam crystallography, NMR and cryo-electron microscopy. The current data on the protein precious stone structure and different computational plan tool stash are outfitting protein building more precisely than any time in recent memory. Structure-based protein building includes the utilization of auxiliary learning and programming instruments to adjust protein structures and capacities. Much work has been centered around chemical structure examination by computational devices to distinguish key buildups in charge of particular properties. We watch that structure-based building procedures are potential and good methodologies that incredibly streamline the way toward enhancing certain properties of compounds. Today, attributable to the advancement in recombinant DNA innovation and high-throughput screening strategies, protein designing techniques and applications are turning out to be progressively critical and across the board. In this survey, an ordered audit of protein building techniques and applications is given.Keywords: Introduction, Epitope prediction, Antibody engineering, Protein engineering strategies
References
Hirokawa K, Ichiyanagi A, Kajiyama N; Enhancement of thermostability of fungal deglycating enzymes by directed evolution. Applied microbiology and biotechnology, 2008; 78(5): 775-781.
Bessler C, Schmitt J, Maurer KH, Schmid RD; Directed evolution of a bacterial alphaamylase: toward enhanced pH-performance and higher specific activity. Protein science : a publication of the Protein Society, 2003; 12(10): 2141-2149.
Rubingh DN; Protein engineering from a bioindustrial point of view. Current opinion in biotechnology, 1997,8, 417-422.
Ulmer KM; Protein engineering. Science (New York, N.Y.), 1983; 219(4585): 666-671.
Brannigan JA, Wilkinson AJ; Protein engineering 20 years on. Nature reviews. Molecular cell biology, 2002; 3(12): 964-970.
Kuchner O, Arnold FH; Directed evolution of enzyme catalysts. Trends in biotechnology, 1997; 15(12): 523-530.
Guo LT, Helgadottir S, Soll D, Ling J; Rational design and directed evolution of a bacterial-type glutaminyl-tRNA synthetase precursor. Nucleic acids research, 2012; 40(16): 7967-7974.
Nixon AE, Firestine SM; Rational and "irrational" design of proteins and their use in biotechnology. IUBMB life, 2000; 49(3): 181- 187.
Eriksen DT, Lian J, Zhao H; Protein design for pathway engineering. Journal of structural biology, 2014; 185(2): 234-342.
Yang H, Liu L, Wang M, Li J, Wang NS, Du G, Chen J; Structure-based engineering of methionine residues in the catalytic cores of alkaline amylase from Alkalimonas amylolytica for improved oxidative stability. Applied and environmental microbiology, 2012; 78(21): 7519-7526.
Lim KH, Huang H, Pralle A, Park S; Stable, high-affinity streptavidin monomer for protein labeling and monovalent biotin detection. Biotechnology and bioengineering, 2013; 10(1): 57-67.
Demonte D, Drake EJ, Lim KH, Gulick AM, Park S; Structure-based engineering of streptavidin monomer with a reduced biotin dissociation rate. Proteins, 2013; 81(9): 1621- 1633.
Procko E, Hedman R, Hamilton K, Seetharaman J, Fleishman SJ, Su M, Aramini J, Kornhaber G, Hunt JF, Tong L, Montelione GT, Baker D; Computational design of a protein-based enzyme inhibitor. Journal of molecular biology, 2013, 425(18): 3563-3575.
Vajda S, Guarnieri F; Characterization of protein-ligand interaction sites using experimental and computational methods. Current opinion in drug discovery & development, 2006; 9(3): 354-362.
Owono LC, Keita M, Megnassan E, Frecer V, Miertus S; Design of Thymidine Analogues Targeting Thymidilate Kinase of Mycobacterium tuberculosis. Tuberculosis research and treatment, 2013; 670836.
Segura Mora J, Assi SA, FernandezFuentes N; Presaging critical residues in protein interfaces-web server (PCRPi-W): a web server to chart hot spots in protein interfaces. PloS one, 2010; 5(8): 12352.
Segura J, Fernandez-Fuentes N; PCRPi-DB: a database of computationally annotated hot spots in protein interfaces. Nucleic acids research, 2011; 39(Database issue): 755-760.
Agrawal NJ, Helk B, Trout BL; A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein. FEBS letters, 2014; 588(2): 326-333.
Tuncbag N, Gursoy A, Keskin O; Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics (Oxford, England), 2009; 25(12): 1513-1520.
Tuncbag N, Kar G, Keskin O, Gursoy A, Nussinov R; A survey of available tools and web servers for analysis of protein-protein interactions and interfaces. Briefings in bioinformatics, 2009; 10(3): 217-232.
Ali MH, Taylor CM, Grigoryan G, Allen KN, Imperiali B, Keating AE; Design of a heterospecific, tetrameric, 21-residue miniprotein with mixed alpha/beta structure. Structure (London, England : 1993), 2005; 13(2): 225-234.
Ashworth J, Havranek JJ, Duarte CM, Sussman D, Monnat RJJr, Stoddard BL, Baker D; Computational redesign of endonuclease DNA binding and cleavage specificity. Nature, 2006; 441(7093): 656-659.
Schneider M, Fu X, Keating AE; X-ray vs. NMR structures as templates for computational protein design. Proteins, 2009; 77(1): 97-110.
Wijma HJ, Janssen DB; Computational design gains momentum in enzyme catalysis engineering. Tstabilization. Protein engineering, design & selection : PEDS, 2014; 27(2): 49-58.
Damborsky J, Brezovsky J; Computational tools for designing and engineering enzymes. Current opinion in chemical biology, 2014; 19: 8-16.
Suplatov D, Kirilin E, Takhaveev V, Švedas V; Zebra: a web server for bioinformatic analysis of diverse protein families. Journal of Biomolecular Structure and Dynamics, 2013, 1-7.
Arnold K, Bordoli L, Kopp J, Schwede T; The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics (Oxford, England), 2006; 22(2): 195-201.
Addington TA, Mertz RW, Siegel JB, Thompson JM, Fisher AJ, Filkov V, Fleischman NM, Suen AA, Zhang C, Toney MD; Janus: prediction and ranking of mutations required for functional interconversion of enzymes. Journal of molecular biology, 2013; 425(8): 1378-1389.
Feng X, Sanchis J, Reetz MT, Rabitz H; Enhancing the efficiency of directed evolution in focused enzyme libraries by the adaptive substituent reordering algorithm. Chemistry (Weinheim an der Bergstrasse, Germany), 2012; 18(18): 5646-5654.
Kuipers RK, Joosten HJ, van Berkel WJ, Leferink NG, Rooijen E, Ittmann E, van Zimmeren F, Jochens H, Bornscheuer U, Vriend G, dos Santos VA, Schaap PJ; 3DM: systematic analysis of heterogeneous superfamily data to discover protein functionalities. Proteins, 2010; 78 (9): 2101- 2113.
