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Abstract Introduction Respiratory tract infections are the major causes of morbidity and mortality in patients with cystic fibrosis. Nocardia are rarely implicated in these infections and few reports of the involvement of this species are found in the literature. Case presentation We describe a case of lung infection followed by chronic colonization of trimethoprim and sulfamethoxazole resistant Nocardia farcinica in a patient with cystic fibrosis. The chronic colonization of this uncommon bacterium in patients with cystic fibrosis was proved using a newly developed real-time polymerase chain reaction assay, which indicates that this bacterium, despite treatment, is difficult to eradicate. Conclusion Our case report confirms that this organism can be recovered in persons with cystic fibrosis. Its eradication is necessary especially if the patient is to undergo lung transplantation.

There is interest in peptide drug design, especially for targeting intracellular protein-protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.