Protein design by binary patterning of polar and nonpolar amino acids

Science ◽  
1993 ◽  
Vol 262 (5140) ◽  
pp. 1680-1685 ◽  
Author(s):  
S Kamtekar ◽  
J. Schiffer ◽  
H Xiong ◽  
J. Babik ◽  
M. Hecht
Keyword(s):  
Amino Acids ◽  
Protein Design ◽  
Binary Patterning

Protein Design by Binary Patterning of Polar and Nonpolar Amino Acids

2006 ◽  
pp. 155-166 ◽  
Author(s):  
Luke H. Bradley ◽  
Yinan Wei ◽  
Peter Thumfort ◽  
Christine Wurth ◽  
Michael H. Hecht
Keyword(s):  
Amino Acids ◽  
Protein Design ◽  
Binary Patterning

ChemInform Abstract: Protein Design by Binary Patterning of Polar and Nonpolar Amino Acids.

ChemInform ◽  
2010 ◽  
Vol 25 (10) ◽  
pp. no-no
Author(s):  
S. KAMTEKAR ◽  
J. M. SCHIFFER ◽  
H. XIONG ◽  
J. M. BABIK ◽  
M. H. HECHT
Keyword(s):  
Amino Acids ◽  
Protein Design ◽  
Binary Patterning

scholarly journals Making Sense of “Nonsense” and More: Challenges and Opportunities in the Genetic Code Expansion, in the World of tRNA Modifications

2022 ◽  
Vol 23 (2) ◽  
pp. 938
Author(s):  
Olubodun Michael Lateef ◽  
Michael Olawale Akintubosun ◽  
Olamide Tosin Olaoba ◽  
Sunday Ocholi Samson ◽  
Malgorzata Adamczyk
Keyword(s):  
Amino Acids ◽  
Genetic Code ◽  
Protein Design ◽  
Side Chains ◽  
Vast Number ◽  
Trna Modifications ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Code Extension ◽  
Genetic Code Expansion

The evolutional development of the RNA translation process that leads to protein synthesis based on naturally occurring amino acids has its continuation via synthetic biology, the so-called rational bioengineering. Genetic code expansion (GCE) explores beyond the natural translational processes to further enhance the structural properties and augment the functionality of a wide range of proteins. Prokaryotic and eukaryotic ribosomal machinery have been proven to accept engineered tRNAs from orthogonal organisms to efficiently incorporate noncanonical amino acids (ncAAs) with rationally designed side chains. These side chains can be reactive or functional groups, which can be extensively utilized in biochemical, biophysical, and cellular studies. Genetic code extension offers the contingency of introducing more than one ncAA into protein through frameshift suppression, multi-site-specific incorporation of ncAAs, thereby increasing the vast number of possible applications. However, different mediating factors reduce the yield and efficiency of ncAA incorporation into synthetic proteins. In this review, we comment on the recent advancements in genetic code expansion to signify the relevance of systems biology in improving ncAA incorporation efficiency. We discuss the emerging impact of tRNA modifications and metabolism in protein design. We also provide examples of the latest successful accomplishments in synthetic protein therapeutics and show how codon expansion has been employed in various scientific and biotechnological applications.

scholarly journals DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins

2021 ◽  
Author(s):  
Mikita Misiura ◽  
Raghav Shroff ◽  
Ross Thyer ◽  
Anatoly Kolomeisky
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Protein Design ◽  
Structure Prediction ◽  
Deep Neural Networks ◽  
Side Chain ◽  
Amino Acid Side Chain ◽  
Image Transformation ◽  
Hydrophobic Amino Acids ◽  
Chain Conformations

Prediction of side chain conformations of amino acids in proteins (also termed 'packing') is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this work we evaluate the potential of Deep Neural Networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr and Trp being up to 50% smaller.

scholarly journals Protein design under competing conditions for the availability of amino acids

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Francesca Nerattini ◽  
Luca Tubiana ◽  
Chiara Cardelli ◽  
Valentino Bianco ◽  
Christoph Dellago ◽  
...  
Keyword(s):  
Amino Acids ◽  
Protein Design
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