PSIQUE: Protein Secondary Structure Identification on the Basis of Quaternions and Electronic Structure Calculations

Conformational preference of dipeptide zwitterions in aqueous solvents

Proper description of solvent effects is challenging for theoretical methods, particularly if the solute is a zwitterion. Here, a series of theoretical procedures are used to determine the preferred solvated conformations of twelve hydrophobic dipeptides (Leu-Leu, Leu-Phe, Phe-Leu, Ile-Leu, Phe-Phe, Ala-Val, Val-Ala, Ala-Ile, Ile-Ala, Ile-Val, Val-Ile and Val-Val) in the zwitterionic state. First, the accuracy of density functional theory (DFT), combined with different implicit solvent models, for describing zwitterions in aqueous solvent is assessed by comparing the predicted against the experimental glycine tautomerization energy, i.e., the energetic difference between canonical and zwitterionic glycine in aqueous solvents. It is found that among the tested solvation schemes, the charge-asymmetric nonlocally determined local-electric solvation model (CANDLE) predicts an energetic difference in excellent agreement with the experimental value. Next, DFT-CANDLE is used to determine the most favorable solvated conformation for each of the investigated dipeptide zwitterions. The CANDLE-solvated structures are obtained by exploring the conformational space of each dipeptide zwitterion concatenating DFT calculations, in vacuum, with classical molecular dynamics simulations, in explicit solvents, and DFT calculations including explicit water molecules. It is found that the energetically most favorable conformations are similar to those of the dipeptide zwitterions in their respective crystal structures. Such structural agreement is indicative of the DFT-CANDLE accomplishment of the description of solvated zwitterions, and suggests that these biomolecules self-assemble as quasi-rigid objects.

A Perspective on the (Rise and Fall of) Protein β-Turns

The β-turn is the third defined secondary structure after the α-helix and the β-sheet. The β-turns were described more than 50 years ago and account for more than 20% of protein residues. Nonetheless, they are often overlooked or even misunderstood. This poor knowledge of these local protein conformations is due to various factors, causes that I discuss here. For example, confusion still exists about the assignment of these local protein structures, their overlaps with other structures, the potential absence of a stabilizing hydrogen bond, the numerous types of β-turns and the software's difficulty in assigning or visualizing them. I also propose some ideas to potentially/partially remedy this and present why β-turns can still be helpful, even in the AlphaFold 2 era.

Protein secondary structure assignment using residual networks

Proteins are constructed from amino acid sequences. Their structural classifications include primary, secondary, tertiary, and quaternary, with tertiary and quaternary structures influencing protein function. Because a protein's structure is inextricably connected to its biological function, machine learning algorithms that can better anticipate the structures have the potential to lead to new scientific discoveries in human health and improve our capacity to develop new treatments. Protein secondary structure assignment enriches the structural and functional understanding of proteins. It helps in protein structure comparison and classification studies, besides facilitating secondary and tertiary structure prediction systems. Several secondary structure assignment methods have been developed since the 1980s, most of which are based on hydrogen bond analysis and atomic coordinate features. However, the assignment process becomes complex when protein data includes missing atoms. Deep neural networks are often referred to as universal function approximators because they can approximate any function to produce the desired output when properly designed and trained. Optimised deep learning architectures have already proven their ability to increase performance in a wide range of problems. Recently, the ResNet architecture has garnered significant interest due to its applicability in various areas, including image classification and protein contact map prediction. The proposed model, which is based on the ResNet architecture, assigns secondary structures using Cα atom coordinates. The model achieved an accuracy of 94% when evaluated against the benchmark and independent test sets. The findings encourage the development of new deep learning-based methods that are more generalised across various protein learning tasks. Furthermore, it allows computational biologists to delve deeper into integrating these techniques with experimental methods. The model codes are available at: https://github.com/jisnava/ResNet_for_Structure_Assignments/ .

Enhancing stability and by-product tolerance of β-glucuronidase based on magnetic cross-linked enzyme aggregates

β-glucuronidase is an important catalyst which is highly specific for β-glucuronides. Here, we constructed magnetic cross-linking β-glucuronidase aggregates (MCLEAs) to for the production of glycyrrhetinic acid (GA). Before crosslinking via glutaraldehyde, we used carbodiimide to enhance the interaction between enzymes and carboxyl-functionalized Fe₃O₄, efficiently improving the activity recovery. Compared to free enzymes, both k_cat and k_cat/K_m enhanced, indicating that crosslinking and aggregation brought higher catalytic efficiency to enzymes. MCLEAs enhanced pH and thermal stabilities and retained 63.3% of catalytic activity after 6 cycles. More importantly, it was first found that the glucuronic acid tolerance of β-glucuronidase after the formation of MCLEAs enhanced 221.5% in 10 mM of glucuronic acid. According to the Raman spectroscopy, the ordered structure of β-glucuronidase increased from 43.9% to 50.6% after immobilization, which explained the increased stability and tolerance. To sum up, MCLEAs provided an efficient strategy for immobilization of enzymes, which enhanced stability and glucuronic acid tolerance of enzymes. It might be an effective solution to the serious inhibition caused by by-products during the preparation of aglycone from natural glycosides, having a significant applied prospect in industry.