PEGylation of Proteins in Organic Solution: A Case Study for Interferon beta-1b

Effect of Mini-PEGs Modification on the Enzymatic Digestion of D-Amino Acid-Containing Peptides under the Action of PROK

It was previously reported that D-amino acid-containing peptides exhibited the ability to resist enzymatic hydrolysis. This study investigated the influence of mini-PEGs modification on enzymatic hydrolysis ability of D-amino acid-containing peptides. The results showed that PEGylation promoted enzymatic hydrolysis of the D-amino acid-containing peptide, especially, the cleavage rate of the D-amino acid-containing peptide 6-w with PEG₃ modification at the N-ends was up to 17 times higher in the presence of proteinase K (PROK) compared to those without PEG₃ modification. Moreover, analysis of the enzymatic cleavage sites demonstrated a similar cleavage pattern of the PEGylated D-amino acid-containing peptide to that of the unmodified peptide. The computational simulations further showed that the enhanced enzymatic hydrolysis ability can be attributed to the strong interaction between PROK and the peptide after PEG₃ modification and the resulting formation of a mature catalytic triad structure.

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

Peptide-based therapeutics are here to stay and will prosper in the future. A key step in identifying novel peptide-drugs is the determination of their bioactivities. Recent advances in peptidomics screening approaches hold promise as a strategy for identifying novel drug targets. However, these screenings typically generate an immense number of peptides and tools for ranking these peptides prior to planning functional studies are warranted. Whereas a couple of tools in the literature predict multiple classes, these are constructed using multiple binary classifiers. We here aimed to use an innovative deep learning approach to generate an improved peptide bioactivity classifier with capacity of distinguishing between multiple classes. We present MultiPep: a deep learning multi-label classifier that assigns peptides to zero or more of 20 bioactivity classes. We train and test MultiPep on data from several publically available databases. The same data are used for a hierarchical clustering, whose dendrogram shapes the architecture of MultiPep. We test a new loss function that combines a customized version of Matthews correlation coefficient with binary cross entropy (BCE), and show that this is better than using class-weighted BCE as loss function. Further, we show that MultiPep surpasses state-of-the-art peptide bioactivity classifiers and that it predicts known and novel bioactivities of FDA-approved therapeutic peptides. In conclusion, we present innovative machine learning techniques used to produce a peptide prediction tool to aid peptide-based therapy development and hypothesis generation.

Site Directed Disulfide PEGylation of Interferon-β-1b with Fork Peptide Linker

The attachment of PEG to biopharmaceuticals has been applied for enhancement of bioavailability and improved stability. The PEG polymer is highly hydrated; thus effective attachment to inaccessible sites could be hindered. We have devised a scheme to address this issue by introducing a considerable distance between PEG and protein by addition of a linear peptide, appended to long chained reactive linkers. Second, the position of PEG conjugation directly affects biological activity. Accordingly, a disulfide bond could be considered as an ideal choice for site directed PEGylation; but reactivity of both thiol moieties to bridging reagent is critical for maintenance of protein structure. In our design, a forked structure with two arms provides essential flexibility to account for dissociation of reduced cysteines. An efficient yield for disulfide PEGylation of IFN-β1b was attained and specificity, biophysical characterization, biological activity, and pharmacokinetics were surveyed.