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.

A phase transition from monoclinicC2 withZ′ = 1 to triclinicP1 withZ′ = 4 for the quasiracemateL-2-aminobutyric acid–D-methionine (1/1)

2016 ◽  
Vol 72 (7) ◽  
pp. 536-543 ◽  
Author(s):  
Carl Henrik Görbitz ◽  
David S. Wragg ◽  
Ingrid Marie Bergh Bakke ◽  
Christian Fleischer ◽  
Gaute Grønnevik ◽  
...  
Keyword(s):  
Phase Transition ◽  
Amino Acids ◽  
Room Temperature ◽  
Aminobutyric Acid ◽  
Side Chain ◽  
Side Chains ◽  
Significant Difference ◽  
Hydrophobic Amino Acids ◽  
Chain Conformations ◽  
Triclinic Form

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.

scholarly journals Poly(ester amide)s based on (L)-lactic acid oligomers and α-amino acids: influence of the α-amino acid side chain in the poly(ester amide)s properties

2013 ◽  
Vol 24 (12) ◽  
pp. 1391-1409 ◽  
Author(s):  
Ana C. Fonseca ◽  
Jorge F.J. Coelho ◽  
Joana F.A. Valente ◽  
Tiago R. Correia ◽  
Ilídio J. Correia ◽  
...  
Keyword(s):  
Amino Acids ◽  
Lactic Acid ◽  
Amino Acid ◽  
Side Chain ◽  
Amino Acid Side Chain

scholarly journals Amino acid side chain contribution to protein FTIR spectra: impact on secondary structure evaluation

2021 ◽  
Author(s):  
Joëlle De Meutter ◽  
Erik Goormaghtigh
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Spectral Region ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Ftir Spectra ◽  
Side Chain ◽  
Amino Acid Side Chain ◽  
Amino Acid Side Chains ◽  
Β Sheet

AbstractPrediction of protein secondary structure from FTIR spectra usually relies on the absorbance in the amide I–amide II region of the spectrum. It assumes that the absorbance in this spectral region, i.e., roughly 1700–1500 cm−1 is solely arising from amide contributions. Yet, it is accepted that, on the average, about 20% of the absorbance is due to amino acid side chains. The present paper evaluates the contribution of amino acid side chains in this spectral region and the potential to improve secondary structure prediction after correcting for their contribution. We show that the β-sheet content prediction is improved upon subtraction of amino acid side chain contributions in the amide I–amide II spectral range. Improvement is relatively important, for instance, the error of prediction of β-sheet content decreases from 5.42 to 4.97% when evaluated by ascending stepwise regression. Other methods tested such as partial least square regression and support vector machine have also improved accuracy for β-sheet content evaluation. The other structures such as α-helix do not significantly benefit from side chain contribution subtraction, in some cases prediction is even degraded. We show that co-linearity between secondary structure content and amino acid composition is not a main limitation for improving secondary structure prediction. We also show that, even though based on different criteria, secondary structures defined by DSSP and XTLSSTR both arrive at the same conclusion: only the β-sheet structure clearly benefits from side chain subtraction. It must be concluded that side chain contribution subtraction benefit for the evaluation of other secondary structure contents is limited by the very rough description of side chain absorbance which does not take into account the variations related to their environment. The study was performed on a large protein set. To deal with the large number of proteins present, we worked on protein microarrays deposited on BaF2 slides and FTIR spectra were acquired with an imaging system.

Carbobenzoxy amino acids: structural requirements for cholecystokinin receptor antagonist activity

1985 ◽  
Vol 248 (4) ◽  
pp. G479-G484 ◽  
Author(s):  
P. N. Maton ◽  
V. E. Sutliff ◽  
R. T. Jensen ◽  
J. D. Gardner
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Receptor Antagonist ◽  
Amino Acid Derivative ◽  
Side Chain ◽  
Amylase Secretion ◽  
Amino Acid Side Chain ◽  
Antagonist Activity ◽  
Cholecystokinin Receptor ◽  
Stimulation Of

We used dispersed acini prepared from guinea pig pancreas to examine 28 carbobenzoxy (CBZ) amino acids for their abilities to function as cholecystokinin receptor antagonists. All amino acid derivatives tested, except for CBZ-alanine, CBZ-glycine, and N alpha-CBZ-lysine, were able to inhibit the stimulation of amylase secretion caused by the C-terminal octapeptide of cholecystokinin. In general, there was a good correlation between the ability of a carbobenzoxy amino acid to inhibit stimulated amylase secretion and the ability of the amino acid derivative to inhibit binding of 125I-cholecystokinin. The inhibition of cholecystokinin-stimulated amylase secretion was competitive, fully reversible, and specific for those secretagogues that interact with the cholecystokinin receptor. The potencies with which the various carbobenzoxy amino acids inhibited the action of cholecystokinin varied 100-fold and CBZ-cystine was the most potent cholecystokinin receptor antagonist. This variation in potency was primarily but not exclusively a function of the hydrophobicity of the amino acid side chain.

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