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.

TP53 plays critical roles in maintaining genome stability. Deleterious genetic variants damage the function of TP53, causing genome instability and increased cancer risk. Of the large quantity of genetic variants identified in TP53, however, many remain functionally unclassified as variants of unknown significance (VUS) due to the lack of evidence. This is reflected by the presence of 749 (42%) VUS of the 1785 germline variants collected in the ClinVar database. In this study, we addressed the deleteriousness of TP53 missense VUS. Utilizing the protein structure-based Ramachandran Plot-Molecular Dynamics Simulation (RPMDS) method that we developed, we measured the effects of missense VUS on TP53 structural stability. Of the 340 missense VUS tested, we observed deleterious evidence for 193 VUS, as reflected by the TP53 structural changes caused by the VUS-substituted residues. We compared the results from RPMDS with those from other in silico methods and observed higher specificity of RPMDS in classification of TP53 missense VUS than these methods. Data from our current study address a long-standing challenge in classifying the missense VUS in TP53, one of the most important tumor suppressor genes.

The objective structured clinical examination (OSCE) is central to assessing clinical competence in undergraduate and postgraduate exams for medical and allied healthcare professions. A mock OSCE on the other hand is a simulation of the OSCE and a unique learning experience for the examinee. They benefit in a variety of ways; from enhancing their time management skills to receiving feedback that can improve their clinical skills. Unfortunately, opportunities to participate in simulated OSCEs remain limited. Reasons include difficulty in fulfilling organisational requirements and equipment-related costs. Mock OSCEs can be set up by undergraduate students or junior trainees for peers, without senior supervision or direct guidance. This article will discuss 12 tips regarding the arrangement of mock OSCEs to guide organisers, including accessing resources and establishing the content of the exam.

The COVID-19 pandemic, caused by SARS-CoV-2, has led to catastrophic damage for global human health. The initial step of SARS-CoV-2 infection is the binding of the receptor-binding domain (RBD) in its spike protein to the ACE2 receptor in the host cell membrane. Constant evolution of SARS-CoV-2 generates new mutations across its genome including the coding region for the RBD in the spike protein. In addition to the well-known single mutation in the RBD, the recent new mutation strains with an RBD “double mutation” are causing new outbreaks globally, as represented by the delta strain containing RBD L452R/T478K. Although it is considered that the increased transmissibility of double-mutated strains could be attributed to the altered interaction between the RBD and ACE2 receptor, the molecular details remain to be elucidated. Using the methods of molecular dynamics simulation, superimposed structural comparison, free binding energy estimation, and antibody escaping, we investigated the relationship between the ACE2 receptor and the RBD double mutants of L452R/T478K (delta), L452R/E484Q (kappa), and E484K/N501Y (beta, gamma). The results demonstrated that each of the three RBD double mutants altered the RBD structure and enhanced the binding of the mutated RBD to ACE2 receptor. Together with the mutations in other parts of the virus genome, the double mutations increase the transmissibility of SARS-CoV-2 to host cells.

Triple-negative breast cancer (TNBC) has poor clinical prognosis. Lack of TNBC-specific biomarkers prevents active clinical intervention. We reasoned that TNBC must have its specific signature due to the lack of three key receptors to distinguish TNBC from other types of breast cancer. We also reasoned that coupling methylation and gene expression as a single unit may increase the specificity for the detected TNBC signatures. We further reasoned that choosing the proper controls may be critical to increasing the sensitivity to identify TNBC-specific signatures. Furthermore, we also considered that specific drugs could target the detected TNBC-specific signatures. We developed a system to identify potential TNBC signatures. It consisted of (1) coupling methylation and expression changes in TNBC to identify the methylation-regulated signature genes for TNBC; (2) using TPBC (triple-positive breast cancer) as the control to detect TNBC-specific signature genes; (3) searching in the drug database to identify those targeting TNBC signature genes. Using this system, we identified 114 genes with both altered methylation and expression, and 356 existing drugs targeting 10 of the 114 genes. Through docking and molecular dynamics simulation, we determined the structural basis between sapropterin, a drug used in the treatment of tetrahydrobiopterin deficiency, and PTGS2, a TNBC signature gene involved in the conversion of arachidonic acid to prostaglandins. Our study reveals the existence of rich TNBC-specific signatures, and many can be drug target and biomarker candidates for clinical applications.

Pathogenic variation in DNA mismatch repair (MMR) gene MLH1 is associated with Lynch syndrome (LS), an autosomal dominant hereditary cancer. Of the 3798 MLH1 germline variants collected in the ClinVar database, 38.7% (1469) were missense variants, of which 81.6% (1199) were classified as Variants of Uncertain Significance (VUS) due to the lack of functional evidence. Further determination of the impact of VUS on MLH1 function is important for the VUS carriers to take preventive action. We recently developed a protein structure-based method named “Deep Learning-Ramachandran Plot-Molecular Dynamics Simulation (DL-RP-MDS)” to evaluate the deleteriousness of MLH1 missense VUS. The method extracts protein structural information by using the Ramachandran plot-molecular dynamics simulation (RP-MDS) method, then combines the variation data with an unsupervised learning model composed of auto-encoder and neural network classifier to identify the variants causing significant change in protein structure. In this report, we applied the method to classify 447 MLH1 missense VUS. We predicted 126/447 (28.2%) MLH1 missense VUS were deleterious. Our study demonstrates that DL-RP-MDS is able to classify the missense VUS based solely on their impact on protein structure.