Molecular Dynamics (MD) Simulations is increasingly used as a powerful tool to study protein structure-related questions. Starting from the early simulation study on the photoisomerization in rhodopsin in 1976, MD Simulations has been used to study protein function, protein stability, protein–protein interaction, enzymatic reactions and drug–protein interactions, and membrane proteins. In this review, we provide a brief review for the history of MD Simulations application and the current status of MD Simulations applications in protein studies.

BRCA1 and BRCA2 play essential roles in maintaining the genome stability. Pathogenic germline mutations in these two genes disrupt their function, lead to genome instability and increase the risk of developing breast and ovarian cancers. BRCA mutations have been extensively screened in Caucasian populations, and the resulting information are used globally as the standard reference in clinical diagnosis, treatment and prevention of BRCA‐related cancers. Recent studies suggest that BRCA mutations can be ethnic‐specific, raising the question whether a Caucasian‐based BRCA mutation information can be used as a universal standard worldwide, or whether an ethnicity‐based BRCA mutation information system need to be developed for the corresponding ethnic populations. In this study, we used Chinese population as a model to test ethnicity‐specific BRCA mutations considering that China has one of the latest numbers of breast cancer patients therefore BRCA mutation carriers. Through comprehensive data mining, standardization and annotation, we collected 1,088 distinct BRCA variants derived from over 30,000 Chinese individuals, one of the largest BRCA data set from a non‐Caucasian population covering nearly all known BRCA variants in the Chinese population (https://dbBRCA-Chinese.fhs.umac.mo). Using this data, we performed multi‐layered analyses to determine the similarities and differences of BRCA variation between Chinese and non‐Chinese ethnic populations. The results show the substantial differences of BRCA data between Chinese and non‐Chinese ethnicities. Our study indicates that the current Caucasian population‐based BRCA data is not adequate to represent the BRCA status in non‐Caucasian populations. Therefore, ethnic‐based BRCA standards need to be established to serve for the non‐Caucasian populations.

Pathogenic mutation in BRCA1 gene is one of the most penetrant genetic predispositions towards cancer. Identification of the mutation provides important aspect in prevention and treatment of the mutation-caused cancer. Of the large quantity of genetic variants identified in human BRCA1, substantial portion is classified as Variant of Uncertain Significance (VUS) or unclassified variants due to the lack of functional evidence. In this study, we focused on the VUS and unclassified variants in BRCT repeat located at BRCA1 C-terminal. Utilizing the well-determined structure of BRCT repeats, we measured the influence of the variants on the structural conformations of BRCT repeats by using molecular dynamics simulation (MDS) consisting of RMSD (Root-mean-square-deviation), RMSF (Root-mean-square-fluctuations), Rg (Radius of gyration), SASA (Solvent accessible surface area), NH bond (hydrogen bond) and Covariance analysis. Using this approach, we analyzed 131 variants consisting of 89 VUS (Variant of Uncertain Significance) and 42 unclassified variants (unclassifiable by current methods) within BRCT repeats and were able to differentiate them into 78 Deleterious and 53 Tolerated variants. Comparing the results made by the saturation genome editing assay, multiple experimental assays, and BRCA1 reference databases shows that our approach provides high specificity, sensitivity and robust. Our study opens an avenue to classify VUS and unclassified variants in many cancer predisposition genes with known protein structure.

The wide application of new DNA sequencing technologies is generating vast quantities of genetic variation data at unprecedented speed. Developing methodologies to decode the pathogenicity of the variants is imperatively demanding. We hypothesized that as deleterious variants may function through disturbing structural stability of their affected proteins, information from structural change caused by genetic variants can be used to identify the variants with deleterious effects. In order to measure the structural change for proteins with large size, we designed a method named RP-MDS composed of Ramachandran plot (RP) and Molecular Dynamics Simulation (MDS). Ramachandran plot captures the variant-caused secondary structural change, whereas MDS provides a quantitative measure for the variant-caused globular structural change. We tested the method using variants in TP53 DNA binding domain of 219 residues as the model. In total, RP-MDS identified 23 of 38 (60.5%) TP53 known Pathogenic variants and 17 of 42 (41%) TP53 VUS that caused significant changes of P53 structure. Our study demonstrates that RP-MDS method provides a powerful protein structure-based tool to screen deleterious genetic variants affecting large-size proteins.

Prevalence and spectrum of BRCA germline variants in mainland Chinese familial breast and ovarian cancer patients

Background: Recent studies had found thousands of natural antisense transcripts originating from the same genomic loci of protein coding genes but from the opposite strand. It is unclear whether the majority of antisense transcripts are functional or merely transcriptional noise. Results: Using the Affymetrix Exon array with a modified cDNA synthesis protocol that enables genome-wide detection of antisense transcription, we conducted large-scale expression analysis of antisense transcripts in nine corresponding tissues from human, mouse and rat. We detected thousands of antisense transcripts, some of which show tissue-specific expression that could be subjected to further study for their potential function in the corresponding tissues/organs. The expression patterns of many antisense transcripts are conserved across species, suggesting selective pressure on these transcripts. When compared to protein-coding genes, antisense transcripts show a lesser degree of expression conservation. We also found a positive correlation between the sense and antisense expression across tissues. Conclusion: Our results suggest that natural antisense transcripts are subjected to selective pressure but to a lesser degree compared to sense transcripts in mammals.