Factors Influencing the Prevalence of Resistance-Associated Substitutions in NS5A Protein in Treatment-Naive Patients with Chronic Hepatitis C

Direct-acting antivirals (DAAs) revolutionized treatment of hepatitis C virus (HCV) infection. Resistance-associated substitutions (RASs) present at the baseline impair response to DAA due to rapid selection of resistant HCV strains. NS5A is indispensable target of the current DAA treatment regimens. We evaluated prevalence of RASs in NS5A in DAA-naïve patients infected with HCV 1a (n = 19), 1b (n = 93), and 3a (n = 90) before systematic DAA application in the territory of the Russian Federation. Total proportion of strains carrying at least one RAS constituted 35.1% (71/202). In HCV 1a we detected only M28V (57.9%) attributed to a founder effect. Common RASs in HCV 1b were R30Q (7.5%), L31M (5.4%), P58S (4.4%), and Y93H (5.4%); in HCV 3a, A30S (31.0%), A30K (5.7%), S62L (8.9%), and Y93H (2.2%). Prevalence of RASs in NS5A of HCV 1b and 3a was similar to that worldwide, including countries practicing massive DAA application, i.e., it was not related to treatment. NS5A with and without RASs exhibited different co-variance networks, which could be attributed to the necessity to preserve viral fitness. Majority of RASs were localized in polymorphic regions subjected to immune pressure, with selected substitutions allowing immune escape. Altogether, this explains high prevalence of RAS in NS5A and low barrier for their appearance in DAA-inexperienced population.

A Quantitative Model to Estimate Drug Resistance in Pathogens

Pneumocystis pneumonia (PCP) is an opportunistic infection that occurs in humans and other mammals with debilitated immune systems. These infections are caused by fungi in the genus Pneumocystis, which are not susceptible to standard antifungal agents. Despite decades of research and drug development, the primary treatment and prophylaxis for PCP remains a combination of trimethoprim (TMP) and sulfamethoxazole (SMX) that targets two enzymes in folic acid biosynthesis, dihydrofolate reductase (DHFR) and dihydropteroate synthase (DHPS), respectively. There is growing evidence of emerging resistance by Pneumocystis jirovecii (the species that infects humans) to TMP-SMX associated with mutations in the targeted enzymes. In the present study, we report the development of an accurate quantitative model to predict changes in the binding affinity of inhibitors (K_i, IC₅₀) to the mutated proteins. The model is based on evolutionary information and amino acid covariance analysis. Predicted changes in binding affinity upon mutations highly correlate with the experimentally measured data. While trained on Pneumocystis jirovecii DHFR/TMP data, the model shows similar or better performance when evaluated on the resistance data for a different inhibitor of PjDFHR, another drug/target pair (PjDHPS/SMX) and another organism (Staphylococcus aureus DHFR/TMP). Therefore, we anticipate that the developed prediction model will be useful in the evaluation of possible resistance of the newly sequenced variants of the pathogen and can be extended to other drug targets and organisms.

Sequence analysis of an artificial family of RNA-binding peptides

Diverse peptide sequences recognizing the lambda boxB RNA hairpin were previously isolated from a library encoding the 22-residue lambda N peptide with random amino acids at positions 13-22 using mRNA display. We have statistically analyzed amino acid distributions in 65 unique sequences from rounds 11 and 12 of this selection and evaluated the resulting structural and functional predictions by alanine-scanning mutagenesis and circular dichroism spectrometry. This artificial sequence family has a consensus structure that continues the bent alpha helix of lambda N up to position 17 when bound to lambda boxB. A charge pair (E(14)R(15)) and hydrophobic patch (A(21)L(22) or V(21)L(22)) have important functional roles in this context. Notably, amino acid covariance reveals six specific pairs of random region positions with >95% significant linkage and strong overall helical (i+1, i+3, and i+4) couplings. The covariance analysis suggests that (1) the sequence context of every residue in each insert has been optimized, (2) selected sequences are local optima on a rugged fitness landscape, and (3) it is possible to detect more subtle structural features with artificial protein sequence families than natural homologs. Our results provide a framework for investigating the structures of in vitro selected proteins by functional minimization, reselection, and covariance analysis.