Virtual screening on an α-helix to β-strand switchable region of the FGFR2 extracellular domain revealed positive and negative modulators

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

VarQ: A Tool for the Structural and Functional Analysis of Human Protein Variants

Understanding the functional effect of Single Amino acid Substitutions (SAS), derived from the occurrence of single nucleotide variants (SNVs), and their relation to disease development is a major issue in clinical genomics. Despite the existence of several bioinformatic algorithms and servers that predict if a SAS is pathogenic or not, they give little or no information at all on the reasons for pathogenicity prediction and on the actual predicted effect of the SAS on the protein function. Moreover, few actual methods take into account structural information when available for automated analysis. Moreover, many of these algorithms are able to predict an effect that no necessarily translates directly into pathogenicity. VarQ is a bioinformatic pipeline that incorporates structural information for the detailed analysis and prediction of SAS effect on protein function. It is an online tool which uses UniProt id and automatically analyzes known and user provided SAS for their effect on protein activity, folding, aggregation and protein interactions, among others. We show that structural information, when available, can improve the SAS pathogenicity diagnosis and more important explain its causes. We show that VarQ is able to correctly reproduce previous analysis of RASopathies related mutations, saving extensive and time consuming manual curation. VarQ assessment was performed over a set of previously manually curated RASopathies (diseases that affects the RAS/MAPK signaling pathway) related variants, showing its ability to correctly predict the phenotypic outcome and its underlying cause. This resource is available online at http://varq.qb.fcen.uba.ar/. Supporting Information & Tutorials may be found in the webpage of the tool.

Tumor suppressor properties of the splicing regulatory factor RBM10

RBM10 is an RNA binding protein and alternative splicing regulator frequently mutated in lung adenocarcinomas. Recent results indicate that RBM10 inhibits proliferation of lung cancer cells by promoting skipping of exon 9 of the gene NUMB, a frequent alternative splicing change in lung cancer generating a negative regulator of Notch signaling. Complementing these observations, we show that knock down of RBM10 in human cancer cells enhances growth of mouse tumor xenografts, confirming that RBM10 acts as a tumor suppressor, while knock down of an oncogenic mutant version of RBM10 reduces xenograft tumor growth. A RBM10 mutation found in lung cancer cells, V354E, disrupts RBM10-mediated regulation of NUMB alternative splicing, inducing the cell proliferation-promoting isoform. We now show that 2 natural RBM10 isoforms that differ by the presence or absence of V354 in the second RNA Recognition Motif (RRM2), display similar regulatory effects on NUMB alternative splicing, suggesting that V354E actively disrupts RBM10 activity. Structural modeling localizes V354 in the outside surface of one α-helix opposite to the RNA binding surface of RBM10, and we show that the mutation does not compromise binding of the RRM2 domain to NUMB RNA regulatory sequences. We further show that other RBM10 mutations found in lung adenocarcinomas also compromise regulation of NUMB exon 9. Collectively, our previous and current results reveal that RBM10 is a tumor suppressor that represses Notch signaling and cell proliferation through the regulation of NUMB alternative splicing.