Computational Study on DNA Repair: The Roles of Electrostatic Interactions Between Uracil-DNA Glycosylase (UDG) and DNA

A Comparative Analysis of SARS-CoV-2 Variants of Concern (VOC) Spike Proteins Interacting with hACE2 Enzyme

In late 2019, the emergence of a novel coronavirus led to its identification as SARS-CoV-2, precipitating the onset of the COVID-19 pandemic. Many experimental and computational studies were performed on SARS-CoV-2 to understand its behavior and patterns. In this research, Molecular Dynamic (MD) simulation is utilized to compare the behaviors of SARS-CoV-2 and its Variants of Concern (VOC)-Alpha, Beta, Gamma, Delta, and Omicron-with the hACE2 protein. Protein structures from the Protein Data Bank (PDB) were aligned and trimmed for consistency using Chimera, focusing on the receptor-binding domain (RBD) responsible for ACE2 interaction. MD simulations were performed using Visual Molecular Dynamics (VMD) and Nanoscale Molecular Dynamics (NAMD2), and salt bridges and hydrogen bond data were extracted from the results of these simulations. The data extracted from the last 5 ns of the 10 ns simulations were visualized, providing insights into the comparative stability of each variant's interaction with ACE2. Moreover, electrostatics and hydrophobic protein surfaces were calculated, visualized, and analyzed. Our comprehensive computational results are helpful for drug discovery and future vaccine designs as they provide information regarding the vital amino acids in protein-protein interactions (PPIs). Our analysis reveals that the Original and Omicron variants are the two most structurally similar proteins. The Gamma variant forms the strongest interaction with hACE2 through hydrogen bonds, while Alpha and Delta form the most stable salt bridges; the Omicron is dominated by positive potential in the binding site, which makes it easy to attract the hACE2 receptor; meanwhile, the Original, Beta, Delta, and Omicron variants show varying levels of interaction stability through both hydrogen bonds and salt bridges, indicating that targeted therapeutic agents can disrupt these critical interactions to prevent SARS-CoV-2 infection.

Toehold region triggered CRISPR/Cas12a trans-cleavage for detection of uracil-DNA glycosylase activity

DNA glycosylases are a group of enzymes that play a crucial role in the DNA repair process by recognizing and removing damaged or incorrect bases from DNA molecules, which maintains the integrity of the genetic information. The abnormal expression of uracil-DNA glycosylase (UDG), one of significant DNA glycosylases in the base-excision repair pathway, is linked to numerous diseases. Here, we proposed a simple UDG activity detection method based on toehold region triggered CRISPR/Cas12a trans-cleavage. The toehold region on hairpin DNA probe (HP) produced by UDG could induce the trans-cleavage of ssDNA with fluorophore and quencher, generating an obvious fluorescence signal. This protospacer adjacent motif (PAM)-free approach achieves remarkable sensitivity and specificity in detecting UDG, with a detection limit as low as 0.000368 U mL-1. Moreover, this method is able to screen inhibitors and measure UDG in complex biological samples. These advantages render it highly promising for applications in clinical diagnosis and drug discovery.

Exploring DNA Damage and Repair Mechanisms: A Review with Computational Insights

DNA damage is a critical factor contributing to genetic alterations, directly affecting human health, including developing diseases such as cancer and age-related disorders. DNA repair mechanisms play a pivotal role in safeguarding genetic integrity and preventing the onset of these ailments. Over the past decade, substantial progress and pivotal discoveries have been achieved in DNA damage and repair. This comprehensive review paper consolidates research efforts, focusing on DNA repair mechanisms, computational research methods, and associated databases. Our work is a valuable resource for scientists and researchers engaged in computational DNA research, offering the latest insights into DNA-related proteins, diseases, and cutting-edge methodologies. The review addresses key questions, including the major types of DNA damage, common DNA repair mechanisms, the availability of reliable databases for DNA damage and associated diseases, and the predominant computational research methods for enzymes involved in DNA damage and repair.

ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites

Lysine SUMOylation plays an essential role in various biological functions. Several approaches integrating various algorithms have been developed for predicting SUMOylation sites based on a limited dataset. Recently, the number of identified SUMOylation sites has significantly increased due to investigation at the proteomics scale. We collected modification data and found the reported approaches had poor performance using our collected data. Therefore, it is essential to explore the characteristics of this modification and construct prediction models with improved performance based on an enlarged dataset. In this study, we constructed and compared 16 classifiers by integrating four different algorithms and four encoding features selected from 11 sequence-based or physicochemical features. We found that the convolution neural network (CNN) model integrated with residue structure, dubbed ResSUMO, performed favorably when compared with the traditional machine learning and CNN models in both cross-validation and independent tests. The area under the receiver operating characteristic (ROC) curve for ResSUMO was around 0.80, superior to that of the reported predictors. We also found that increasing the depth of neural networks in the CNN models did not improve prediction performance due to the degradation problem, but the residual structure could be included to optimize the neural networks and improve performance. This indicates that residual neural networks have the potential to be broadly applied in the prediction of other types of modification sites with great effectiveness and robustness. Furthermore, the online ResSUMO service is freely accessible.

Using a comprehensive approach to investigate the interaction between Kinesin-5/Eg5 and the microtubule

Kinesins are microtubule-based motor proteins that play important roles ranging from intracellular transport to cell division. Human Kinesin-5 (Eg5) is essential for mitotic spindle assembly during cell division. By combining molecular dynamics (MD) simulations with other multi-scale computational approaches, we systematically studied the interaction between Eg5 and the microtubule. We find the electrostatic feature on the motor domains of Eg5 provides attractive interactions to the microtubule. Additionally, the folding and binding energy analysis reveals that the Eg5 motor domain performs its functions best when in a weak acidic environment. Molecular dynamics analyses of hydrogen bonds and salt bridges demonstrate that, on the binding interfaces of Eg5 and the tubulin heterodimer, salt bridges play the most significant role in holding the complex. The salt bridge residues on the binding interface of Eg5 are mostly positive, while salt bridge residues on the binding interface of tubulin heterodimer are mostly negative. Such salt bridge residue distribution is consistent with electrostatic potential calculations. In contrast, the interface between α and β-tubulins is dominated by hydrogen bonds rather than salt bridges. Compared to the Eg5/α-tubulin interface, the Eg5/β-tubulin interface has a greater number of salt bridges and higher occupancy for salt bridges. This asymmetric salt bridge distribution may play a significant role in Eg5's directionality. The residues involved in hydrogen bonds and salt bridges are identified in this work and may be helpful for anticancer drug design.

HIT web server: a hybrid method to improve electrostatic calculations for biomolecules

The electrostatic features of highly charged biomolecules are crucial and challenging tasks in computational biophysics. The electrostatic calculations by traditional implicit solvent methods are efficient but have difficulties on highly charged biomolecules. We have developed a Hybridizing Ion Treatment (HIT) tool, which successfully hybridizes the explicit ions and implicit solvation model to accurately calculate the electrostatic potential for highly charged biomolecules. Here we implemented the HIT tool into a web server. In this study, a training set was prepared to optimize the number of frames for the HIT web server. The results on tubulins, DNAs, and RNAs, reveal the mechanisms for the motor proteins, DNA of HIV, and tRNA. This HIT web server can be widely used to study highly charged biomolecules, including DNAs, RNAs, molecular motors, and other highly charged biomolecules.