Characterizing the Binding Interactions between DNA-Binding Proteins, XPA and XPE: A Molecular Dynamics Approach

The nucleotide excision repair proteins through the lens of molecular dynamics simulations

Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.

A deep learning-based method for the prediction of DNA interacting residues in a protein

DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcription, regulation and splicing of genes. In this study, we trained our models on a training dataset of 646 DNA-binding proteins having 15 636 DNA interacting and 298 503 non-interacting residues. Our trained models were evaluated on an independent dataset of 46 DNA-binding proteins having 965 DNA interacting and 9911 non-interacting residues. All proteins in the independent dataset have less than 30% of sequence similarity with proteins in the training dataset. A wide range of traditional machine learning and deep learning (1D-CNN) techniques-based models have been developed using binary, physicochemical properties and Position-Specific Scoring Matrix (PSSM)/evolutionary profiles. In the case of machine learning technique, eXtreme Gradient Boosting-based model achieved a maximum area under the receiver operating characteristics (AUROC) curve of 0.77 on the independent dataset using PSSM profile. Deep learning-based model achieved the highest AUROC of 0.79 on the independent dataset using a combination of all three profiles. We evaluated the performance of existing methods on the independent dataset and observed that our proposed method outperformed all the existing methods. In order to facilitate scientific community, we developed standalone software and web server, which are accessible from https://webs.iiitd.edu.in/raghava/dbpred.

Rad5 HIRAN domain: Structural insights into its interaction with ssDNA through molecular modeling approaches

The Rad5 protein is an SWI/SNF family ubiquitin ligase that contains an N-terminal HIRAN domain and a RING C3HC4 motif. The HIRAN domain is critical for recognition of the stalled replication fork during the replication process and acts as a sensor to initiate the damaged DNA checkpoint. It is a conserved domain widely distributed in eukaryotic organisms and is present in several DNA-binding proteins from all kingdoms. Here we showed that distant species have important differences in key residues that affect affinity for ssDNA. Based on these findings, we hypothesized that different HIRAN domains might affect fork reversal and translesion synthesis through different metabolic processes. To address this question, we predicted the tertiary structure of both yeast and human HIRAN domains using molecular modeling. Structural dynamics experiments showed that the yeast HIRAN domain exhibited higher structural denaturation than its human homolog, although both domains became stable in the presence of ssDNA. Analysis of atomic contacts revealed that a greater number of interactions between the ssDNA nucleotides and the Rad5 domain are electrostatic. Taken together, these results provide new insights into the molecular mechanism of the HIRAN domain of Rad5 and may guide us to further elucidate differences in the ancient eukaryotes HIRAN sequences and their DNA affinity. Communicated by Ramaswamy H. Sarma.