Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

Association between missense variants of uncertain significance in the CHEK2 gene and hereditary breast cancer: a cosegregation and bioinformatics analysis

Inherited mutations in the CHEK2 gene have been associated with an increased lifetime risk of developing breast cancer (BC). We aim to identify in the study population the prevalence of mutations in the CHEK2 gene in diagnosed BC patients, evaluate the phenotypic characteristics of the tumor and family history, and predict the deleteriousness of the variants of uncertain significance (VUS). A genetic study was performed, from May 2016 to April 2020, in 396 patients diagnosed with BC at the University Hospital Lozano Blesa of Zaragoza, Spain. Patients with a genetic variant in the CHEK2 gene were selected for the study. We performed a descriptive analysis of the clinical variables, a bibliographic review of the variants, and a cosegregation study when possible. Moreover, an in-depth bioinformatics analysis of CHEK2 VUS was carried out. We identified nine genetic variants in the CHEK2 gene in 10 patients (two pathogenic variants and seven VUS). This supposes a prevalence of 0.75% and 1.77%, respectively. In all cases, there was a family history of BC in first- and/or second-degree relatives. We carried out a cosegregation study in two families, being positive in one of them. The bioinformatics analyses predicted the pathogenicity of six of the VUS. In conclusion, CHEK2 mutations have been associated with an increased risk for BC. This risk is well-established for foundation variants. However, the risk assessment for other variants is unclear. The incorporation of bioinformatics analysis provided supporting evidence of the pathogenicity of VUS.

The Role of Type 2 Diabetes Mellitus-Related Risk Factors and Drugs in Hepatocellular Carcinoma

With changes in modern lifestyles, type 2 diabetes mellitus (T2DM) has become a global epidemic metabolic disease, and hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. T2DM is a complex metabolic disorder and has been considered an independent risk factor for HCC. Growing evidence supports that T2DM-related risk factors facilitate hepatocarcinogenesis via abundant mechanisms. With the wide implementation of microbiomics, transcriptomics, and immunotherapy, the understanding of the complex mechanisms of intestinal flora and immune cell subsets have advanced tremendously in T2DM-related HCC, uncovering new findings in T2DM-related HCC patients. In addition, reports have indicated the different effects of anti-DM drugs on the progression of HCC. In this review, we summarize the effects of major T2DM-related risk factors (including hyperglycemia, hyperinsulinemia, insulin, chronic inflammation, obesity, nonalcoholic fatty liver disease, gut microbiota and immunomodulation), and anti-DM drugs on the carcinogensis and progression of HCC, as well as their potential molecular mechanisms. In addition, other factors (miRNAs, genes, and lifestyle) related to T2DM-related HCC are discussed. We propose a refined concept by which T2DM-related risk factors and anti-DM drugs contribute to HCC and discuss research directions prompted by such evidence worth pursuing in the coming years. Finally, we put forward novel therapeutic approaches to improve the prognosis of T2DM-related HCC, including exploiting novel diagnostic biomarkers, combination therapy with immunocheckpoint inhibitors, and enhancement of the standardized management of T2DM patients.

Calculation of Protein Folding Thermodynamics Using Molecular Dynamics Simulations

Despite advances in artificial intelligence methods, protein folding remains in many ways an enigma to be solved. Accurate computation of protein folding energetics could help drive fields such as protein and drug design and genetic interpretation. However, the challenge of calculating the state functions governing protein folding from first-principles remains unaddressed. We present here a simple approach that allows us to accurately calculate the energetics of protein folding. It is based on computing the energy of the folded and unfolded states at different temperatures using molecular dynamics simulations. From this, two essential quantities (ΔH and ΔCp) are obtained and used to calculate the conformational stability of the protein (ΔG). With this approach, we have successfully calculated the energetics of two- and three-state proteins, representatives of the major structural classes, as well as small stability differences (ΔΔG) due to changes in solution conditions or variations in an amino acid residue.

Classification of PTEN missense VUS through exascale simulations

Phosphatase and tensin homolog (PTEN), a tumor suppressor with dual phosphatase properties, is a key factor in PI3K/AKT signaling pathway. Pathogenic germline variation in PTEN can abrogate its ability to dephosphorylate, causing high cancer risk. Lack of functional evidence lets numerous PTEN variants be classified as variants of uncertain significance (VUS). Utilizing Molecular Dynamics (MD) simulations, we performed a thorough evaluation for 147 PTEN missense VUS, sorting them into 66 deleterious and 81 tolerated variants. Utilizing replica exchange molecular dynamic (REMD) simulations, we further assessed the variants situated in the catalytic core of PTEN's phosphatase domain and uncovered conformational alterations influencing the structural stability of the phosphatase domain. There was a high degree of agreement between our results and the variants classified by Variant Abundance by Massively Parallel Sequencing, saturation mutagenesis, multiplexed functional data and experimental assays. Our extensive analysis of PTEN missense VUS should benefit their clinical applications in PTEN-related cancer.

SIGNIFICANCE STATEMENT

Classification of PTEN variants affecting its lipid phosphatase activity is important for understanding the roles of PTEN variation in the pathogenesis of hereditary and sporadic malignancies. Of the 3000 variants identified in PTEN, 1296 (43%) were assigned as VUS. Here, we applied MD and REMD simulations to investigate the effects of PTEN missense VUS on the structural integrity of the PTEN phosphatase domain consisting the WPD, P and TI active sites. We classified a total of 147 missense VUS into 66 deleterious and 81 tolerated variants by referring to the control group comprising 54 pathogenic and 12 benign variants. The classification was largely in concordance with these classified by experimental approaches.

Protposer: the web server that readily proposes protein stabilizing mutations with high PPV

Protein stability is a requisite for most biotechnological and medical applications of proteins. As natural proteins tend to suffer from a low conformational stability ex vivo, great efforts have been devoted toward increasing their stability through rational design and engineering of appropriate mutations. Unfortunately, even the best currently used predictors fail to compute the stability of protein variants with sufficient accuracy and their usefulness as tools to guide the rational stabilisation of proteins is limited. We present here Protposer, a protein stabilising tool based on a different approach. Instead of quantifying changes in stability, Protposer uses structure- and sequence-based screening modules to nominate candidate mutations for subsequent evaluation by a logistic regression model, carefully trained to avoid overfitting. Thus, Protposer analyses PDB files in search for stabilization opportunities and provides a ranked list of promising mutations with their estimated success rates (eSR), their probabilities of being stabilising by at least 0.5 kcal/mol. The agreement between eSRs and actual positive predictive values (PPV) on external datasets of mutations is excellent. When Protposer is used with its Optimal kappa selection threshold, its PPV is above 0.7. Even with less stringent thresholds, Protposer largely outperforms FoldX, Rosetta and PoPMusiC. Indicating the PDB file of the protein suffices to obtain a ranked list of mutations, their eSRs and hints on the likely source of the stabilization expected. Protposer is a distinct, straightforward and highly successful tool to design protein stabilising mutations, and it is freely available for academic use at http://webapps.bifi.es/the-protposer.

PirePred: An Accurate Online Consensus Tool to Interpret Newborn Screening-Related Genetic Variants in Structural Context

PirePred is a genetic interpretation tool used for a variety of medical conditions investigated in newborn screening programs. The PirePred server retrieves, analyzes, and displays in real time genetic and structural data on 58 genes/proteins associated with medical conditions frequently investigated in the newborn. PirePred analyzes the predictions generated by 15 pathogenicity predictors and applies an optimized majority vote algorithm to classify any possible nonsynonymous single-nucleotide variant as pathogenic, benign, or of uncertain significance. PirePred predictions for variants of clear clinical significance are better than those of any of the individual predictors considered (based on accuracy, sensitivity, and negative predictive value) or are among the best ones (for positive predictive value and Matthews correlation coefficient). PirePred predictions also outperform the comparable in silico predictions offered as supporting evidence, according to American College of Medical Genetics and Genomics guidelines, by VarSome and Franklin. Also, PirePred has very high prediction coverage. To facilitate the molecular interpretation of the missense, nonsense, and frameshift variants in ClinVar, the changing amino acid residue is displayed in its structural context, which is analyzed to provide functional clues. PirePred is an accurate, robust, and easy-to-use tool for clinicians involved in neonatal screening programs and for researchers of related diseases. The server is freely accessible and provides a user-friendly gateway into the structural/functional consequences of genetic variants at the protein level.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure

MOTIVATION

Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction.

RESULTS

We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation.

CONCLUSIONS

The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease

TUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype–phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype–phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype–phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.

Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence

Bladder cancer (BLCA) is one of the most common cancers worldwide with high recurrence rate. Hence, we intended to establish a recurrence-related long non-coding RNA (lncRNA) model of BLCA as a potential biomarker based on multi-omics analysis. Multi-omics data including copy number variation (CNV) data, mutation annotation files, RNA expression profiles and clinical data of The Cancer Genome Atlas (TCGA) BLCA cohort (303 cases) and GSE31684 (93 cases) were downloaded from public database. With multi-omics analysis, twenty lncRNAs were identified as the candidates related with BLCA recurrence, CNVs and mutations in training set. Ten-lncRNA signature were established using least absolute shrinkage and selection operation (LASSO) and Cox regression. Then, various survival analysis was used to assess the power of lncRNA model in predicting BLCA recurrence. The results showed that the recurrence-free survival time of high-risk group was significantly shorter than that of low-risk group in training and testing sets, and the predictive value of ten-lncRNA signature was robust and independent of other clinical variables. Gene Set Enrichment Analysis (GSEA) showed this signature were associated with immune disorders, indicating this signature may be involved in tumor immunology. After compared with the other reported lncRNA signatures, ten-lncRNA signature was validated as a superior prognostic model in predicting the recurrence of BLCA. The effectiveness of the model was also evaluated in bladder cancer samples via qRT-PCR. Thus, the novel ten-lncRNA signature, constructed based on multi-omics data, had robust prognostic power in predicting the recurrence of BLCA and potential clinical implications as biomarkers.

Protein haploinsufficiency drivers identify MYBPC3 variants that cause hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease. Variants in MYBPC3, the gene encoding cardiac myosin-binding protein C (cMyBP-C), are the leading cause of HCM. However, the pathogenicity status of hundreds of MYBPC3 variants found in patients remains unknown, as a consequence of our incomplete understanding of the pathomechanisms triggered by HCM-causing variants. Here, we examined 44 nontruncating MYBPC3 variants that we classified as HCM-linked or nonpathogenic according to cosegregation and population genetics criteria. We found that around half of the HCM-linked variants showed alterations in RNA splicing or protein stability, both of which can lead to cMyBP-C haploinsufficiency. These protein haploinsufficiency drivers associated with HCM pathogenicity with 100% and 94% specificity, respectively. Furthermore, we uncovered that 11% of nontruncating MYBPC3 variants currently classified as of uncertain significance in ClinVar induced one of these molecular phenotypes. Our strategy, which can be applied to other conditions induced by protein loss of function, supports the idea that cMyBP-C haploinsufficiency is a fundamental pathomechanism in HCM.

Structural stability predictions and molecular dynamics simulations of RBD and HR1 mutations associated with SARS-CoV-2 spike glycoprotein

The COVID-19 pandemic is caused by human transmission and infection of Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2). There is no trusted drug against the virus; hence, efforts are on discovering novel inhibitors for the virus. The entry of a SARS-CoV-2 virus particle into a host cell is initiated by its spike glycoprotein and host Angiotensin-Converting Enzyme 2 (ACE2) receptor interaction. Spike glycoprotein domains, namely, the Receptor Binding Domain (RBD) and Heptad Repeat (HR) domains, are essential for this activity. We have studied the impact of mutations such as A348T, N354D, D364Y, G476S, V483A, S494D in the RBD (319-591), and S939F, S940T, T941A, S943P (912-984) in the HR1 domains of spike glycoprotein. Summarily, we utilized the computational screening algorithms to rank the deleterious, damaging and disease-associated spike glycoprotein mutations. Subsequently, to understand the changes in conformation, flexibility and function of the spike glycoprotein mutants, Molecular Dynamics (MD) simulations were performed. The computational predictions and analysis of the MD trajectories suggest that the RBD and HR1 mutations induce significant phenotypic effects on the pre-binding spike glycoprotein structure, which are presumably consequential to its binding to the receptor and provides lead to design inhibitors against the binding.Communicated by Ramaswamy H. Sarma.