Component vicinal coupling constants for calculating side-chain conformations in amino acids

2009 ◽  
Vol 20 (2) ◽  
pp. 120-125 ◽  
Author(s):  
F.A.A.M. LEEUW ◽  
C. ALTONA
1985 ◽  
Vol 63 (5) ◽  
pp. 1143-1149 ◽  
Author(s):  
Helmut Beierbeck ◽  
George Kotovych ◽  
Makiko Sugiura

The conformations of prostacyclin, PGI2, and three of its analogues, 6R- and 6S-PGI1 and carbacyclin, were studied by high field 1H nmr spectroscopy. The cis-bicyclo- and cis-oxabicyclo[3.3.0]octane ring conformations were completely assigned. The minima for the pseudorotational conformations are observed at 7E/12E for PGI2, [Formula: see text] for 6R-PGI1, and 6E/11E for 6S-PGI1, and carbacyclin. The data indicate that each molecule adopts a narrow pseudolibrational range, if not a single conformation. The α- and ω-side chain conformations were characterized, but not unambiguously. Vicinal coupling constants and nuclear Overhauser enhancements proved to be the most useful spectroscopic parameters in this study.


1980 ◽  
Vol 45 (2) ◽  
pp. 482-490 ◽  
Author(s):  
Jaroslav Vičar ◽  
François Piriou ◽  
Pierre Fromageot ◽  
Karel Bláha ◽  
Serge Fermandjian

The diastereoisomeric pairs of cyclodipeptides cis- and trans-cyclo(Ala-Ala), cyclo(Ala-Phe), cyclo(Val-Val) and cyclo(Leu-Leu) containing 85% 13C enriched amino-acid residues were synthesized and their 13C-13C coupling constants were measured. The combination of 13C-13C and 1H-1H coupling constants enabled to estimate unequivocally the side chain conformation of the valine and leucine residues.


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.


2016 ◽  
Vol 72 (7) ◽  
pp. 536-543 ◽  
Author(s):  
Carl Henrik Görbitz ◽  
David S. Wragg ◽  
Ingrid Marie Bergh Bakke ◽  
Christian Fleischer ◽  
Gaute Grønnevik ◽  
...  

Racemates of hydrophobic amino acids with linear side chains are known to undergo a unique series of solid-state phase transitions that involve sliding of molecular bilayers upon heating or cooling. Recently, this behaviour was shown to extend also to quasiracemates of two different amino acids with opposite handedness [Görbitz & Karen (2015).J. Phys. Chem. B,119, 4975–4984]. Previous investigations are here extended to an L-2-aminobutyric acid–D-methionine (1/1) co-crystal, C4H9NO2·C5H11NO2S. The significant difference in size between the –CH2CH3and –CH2CH2SCH3side chains leads to extensive disorder at room temperature, which is essentially resolved after a phase transition at 229 K to an unprecedented triclinic form where all four D-methionine molecules in the asymmetric unit have different side-chain conformations and all three side-chain rotamers are used for the four partner L-2-aminobutyric acid molecules.


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