Ataluren stimulates ribosomal selection of near-cognate tRNAs to promote nonsense suppression.

A premature termination codon (PTC) in the ORF of an mRNA generally leads to production of a truncated polypeptide, accelerated degradation of the mRNA, and depression of overall mRNA expression. Accordingly, nonsense mutations cause some of the most severe forms of inherited disorders. The small-molecule drug ataluren promotes therapeutic nonsense suppression and has been thought to mediate the insertion of near-cognate tRNAs at PTCs. However, direct evidence for this activity has been lacking. Here, we expressed multiple nonsense mutation reporters in human cells and yeast and identified the amino acids inserted when a PTC occupies the ribosomal A site in control, ataluren-treated, and aminoglycoside-treated cells. We find that ataluren's likely target is the ribosome and that it produces full-length protein by promoting insertion of near-cognate tRNAs at the site of the nonsense codon without apparent effects on transcription, mRNA processing, mRNA stability, or protein stability. The resulting readthrough proteins retain function and contain amino acid replacements similar to those derived from endogenous readthrough, namely Gln, Lys, or Tyr at UAA or UAG PTCs and Trp, Arg, or Cys at UGA PTCs. These insertion biases arise primarily from mRNA:tRNA mispairing at codon positions 1 and 3 and reflect, in part, the preferred use of certain nonstandard base pairs, e.g., U-G. Ataluren's retention of similar specificity of near-cognate tRNA insertion as occurs endogenously has important implications for its general use in therapeutic nonsense suppression.

High-throughput binding affinity calculations at extreme scales.

BACKGROUND: Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance. RESULTS: We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations. CONCLUSIONS: As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.