scholarly journals Simplified protein design biased for prebiotic amino acids yields a foldable, halophilic protein

2013 ◽  
Vol 110 (6) ◽  
pp. 2135-2139 ◽  
Author(s):  
L. M. Longo ◽  
J. Lee ◽  
M. Blaber
2022 ◽  
Vol 23 (2) ◽  
pp. 938
Author(s):  
Olubodun Michael Lateef ◽  
Michael Olawale Akintubosun ◽  
Olamide Tosin Olaoba ◽  
Sunday Ocholi Samson ◽  
Malgorzata Adamczyk

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.


Science ◽  
1971 ◽  
Vol 174 (4013) ◽  
pp. 1039-1040
Author(s):  
Y. Wolman ◽  
Stanley L. Miller ◽  
J. Ibanez ◽  
J. Oró

2019 ◽  
Vol 116 (35) ◽  
pp. 17239-17244 ◽  
Author(s):  
Caitlin E. Cornell ◽  
Roy A. Black ◽  
Mengjun Xue ◽  
Helen E. Litz ◽  
Andrew Ramsay ◽  
...  

The membranes of the first protocells on the early Earth were likely self-assembled from fatty acids. A major challenge in understanding how protocells could have arisen and withstood changes in their environment is that fatty acid membranes are unstable in solutions containing high concentrations of salt (such as would have been prevalent in early oceans) or divalent cations (which would have been required for RNA catalysis). To test whether the inclusion of amino acids addresses this problem, we coupled direct techniques of cryoelectron microscopy and fluorescence microscopy with techniques of NMR spectroscopy, centrifuge filtration assays, and turbidity measurements. We find that a set of unmodified, prebiotic amino acids binds to prebiotic fatty acid membranes and that a subset stabilizes membranes in the presence of salt and Mg2+. Furthermore, we find that final concentrations of the amino acids need not be high to cause these effects; membrane stabilization persists after dilution as would have occurred during the rehydration of dried or partially dried pools. In addition to providing a means to stabilize protocell membranes, our results address the challenge of explaining how proteins could have become colocalized with membranes. Amino acids are the building blocks of proteins, and our results are consistent with a positive feedback loop in which amino acids bound to self-assembled fatty acid membranes, resulting in membrane stabilization and leading to more binding in turn. High local concentrations of molecular building blocks at the surface of fatty acid membranes may have aided the eventual formation of proteins.


Science ◽  
2004 ◽  
Vol 303 (5661) ◽  
pp. 1151-1151 ◽  
Author(s):  
S. Pizzarello

Author(s):  
Jianxun Shen ◽  
Pauline M. Schwartz ◽  
Carl Barratt

On the primitive Earth, both L- and D-amino acids would have been present. However, only L-amino acids are essential blocks to construct proteins in modern life. To study the relative stability of homochiral and heterochiral peptides, a variety of computational methods were employed. 10 prebiotic amino acids (Gly, Ala, Asp, Glu, Ile, Leu, Pro, Ser, Thr, and Val) were previously determined by multiple previous meteorite, spark discharge, and hydrothermal vent studies. We focused on what had been reported as primary early Earth polypeptide analogs: 1ARK, 1PPT, 1ZFI, and 2LZE. Tripeptide composed of only Asp, Ser, and Val exemplified that different positions (i.e., N-terminus, C-terminus, and middle) made a difference in minimal folding energy of peptides, while the classification of amino acid (hydrophobic, acidic, or hydroxylic) did not show significant difference. Hierarchical cluster analysis for dipeptides with all possible combinations of the proposed 10 prebiotic amino acids and their D-amino acid substituted derivatives generated five clusters. Prebiotic polypeptides were built up to test the significance of molecular fluctuations, secondary structure occupancies, and folding energy differences based on these clusters. Most interestingly, among 129 residues, mutation sensitivity profiles presented that the ratio of more stable to less stable to equally stable D-amino acids was about 1:1:1. In conclusion, some combinations of a mixture of L- and D-amino acids can act as essential building blocks of life. Peptides with α-helices, long β-sheets, and long loops are usually less sensitive to D-amino acid replacements in comparison to short β-sheets.


2021 ◽  
Author(s):  
Mikita Misiura ◽  
Raghav Shroff ◽  
Ross Thyer ◽  
Anatoly Kolomeisky

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.


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