Abstract
Biopharmaceuticals hold great promise for the future of drug discovery. Nevertheless, rational drug design strategies are mainly focused on the discovery of small synthetic molecules. Herein we present matched peptides, an innovative analysis technique for biological data related to peptide and protein sequences. It represents an extension of matched molecular pair analysis toward macromolecular sequence data and allows quantitative predictions of the effect of single amino acid substitutions on the basis of statistical data on known transformations. We demonstrate the application of matched peptides to a data set of major histocompatibility complex class II peptide ligands and discuss the trends captured with respect to classical quantitative structure–activity relationship approaches as well as structural aspects of the investigated protein–peptide interface. We expect our novel readily interpretable tool at the interface of cheminformatics and bioinformatics to support the rational design of biopharmaceuticals and give directions for further development of the presented methodology.
Highlights
Biopharmaceuticals are defined as pharmaceutical products consisting ofproteins and/or nucleic acids.[1]
We demonstrate the application of matched peptides to a data set of major histocompatibility complex class II peptide ligands and discuss the trends captured with respect to classical quantitative structure−activity relationship approaches as well as structural aspects of the investigated protein−peptide interface
We investigate peptide binding to the major histocompatibility complex class II (MHC II), a surface receptor crucial for T-cell activation in immune response.[33,34]
Summary
Biopharmaceuticals are defined as pharmaceutical products consisting of (glyco)proteins and/or nucleic acids.[1] this class of drugs mainly comprises peptide hormones, recombinant proteins, monoclonal antibodies, and therapeutic antibodies. Biopharmaceuticals generally pose new challenges for the drug discovery process, which has historically been focused on small molecules This includes their analytical characterization,[6] delivery and formulation[7,8] after optimization of the biotechnological production process,[9,10] and their molecular properties.[11] Computational modeling techniques hold great promise to handle the complexity of the generated data and, for example, to guide affinity optimization of therapeutic proteins[12] or peptides.[13,14] Peptide drugs are often considered as the border between small-molecule drugs and biopharmaceuticals, as their synthesis is mainly chemistry-driven.[15]
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