Protocol for predicting drug-resistant protein mutations to an ERK2 inhibitor using RESISTOR

Highly specific SARS-CoV-2 main protease (Mpro) mutations against the clinical antiviral ensitrelvir selected in a safe, VSV-based system

In the SARS-CoV-2 pandemic, the so far two most effective approved antivirals are the protease inhibitors nirmatrelvir, in combination with ritonavir (Paxlovid) and ensitrelvir (Xocova). However, antivirals and indeed all antimicrobial drugs are sooner or later challenged by resistance mutations. Studying such mutations is essential for treatment decisions and pandemic preparedness. At the same time, generating resistant viruses to assess mutants is controversial, especially with pathogens of pandemic potential like SARS-CoV-2. To circumvent gain-of-function research with non-attenuated SARS-CoV-2, a previously developed safe system based on a chimeric vesicular stomatitis virus dependent on the SARS-CoV-2 main protease (VSV-Mpro) was used to select mutations against ensitrelvir. Ensitrelvir is clinically especially relevant due to its single-substance formulation, avoiding drug-drug interactions by the co-formulated CYP3A4 inhibitor ritonavir in Paxlovid. By treating VSV-Mpro with ensitrelvir, highly-specific resistant mutants against this inhibitor were selected, while being still fully or largely susceptible to nirmatrelvir. We then confirmed several ensitrelvir-specific mutants in gold standard enzymatic assays and SARS-CoV-2 replicons. These findings indicate that the two inhibitors can have distinct viral resistance profiles, which could determine treatment decisions.

Study of key residues in MERS-CoV and SARS-CoV-2 main proteases for resistance against clinically applied inhibitors nirmatrelvir and ensitrelvir

The Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is an epidemic, zoonotically emerging pathogen initially reported in Saudi Arabia in 2012. MERS-CoV has the potential to mutate or recombine with other coronaviruses, thus acquiring the ability to efficiently spread among humans and become pandemic. Its high mortality rate of up to 35% and the absence of effective targeted therapies call for the development of antiviral drugs for this pathogen. Since the beginning of the SARS-CoV-2 pandemic, extensive research has focused on identifying protease inhibitors for the treatment of SARS-CoV-2. Our intention was therefore to assess whether these protease inhibitors are viable options for combating MERS-CoV. To that end, we used previously established protease assays to quantify inhibition of SARS-CoV-2, MERS-CoV and other main proteases. Nirmatrelvir inhibited several of these proteases, whereas ensitrelvir was less broadly active. To simulate nirmatrelvir's clinical use against MERS-CoV and subsequent resistance development, we applied a safe, surrogate virus-based system. Using the surrogate virus, we previously selected hallmark mutations of SARS-CoV-2-Mpro, such as T21I, M49L, S144A, E166A/K/V and L167F. In the current study, we selected a pool of MERS-CoV-Mpro mutants, characterized the resistance and modelled the steric effect of catalytic site mutants S142G, S142R, S147Y and A171S.

DexDesign: A new OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan, which enable exponential reductions in the size of the peptide sequence search space. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a new framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors

With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides-evaluation along a replication/restitution axis-and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.