Peptide library screening as a tool to derive potent therapeutics: current approaches and future strategies

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.

GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences

Although peptides are regarded as ideal therapeutic agents, only a small proportion of the marketed drugs are peptides. In the past decade, pharmacists have paid great attention to the development of peptide therapeutics. Except a few approved chemically/rationally designed peptides, most attempts failed due to unsatisfactory efficacy or safety. Luckily, computation methods, such as artificial intelligence, have been utilized to accelerate the discovery of therapeutic peptides by predicting the activity, toxicity, and absorption, distribution, metabolism, and excretion of polypeptides. Usually, a specific biological activity of a peptide could be accurately determined by an interest-oriented binary classification constructed of a positive set and another un-experimentally validated negative set regardless of other characteristics, which suggests that it could be challenging to realize the comprehensive evaluation of the research object in the early stage of drug research and development. Herein, we proposed an integrated method (GM-Pep) that contained a conditional variational autoencoder model (CVAE) and a positive sample training multiclassifier (Deep-Multiclassifier) to effectively generate a single bioactive peptide sequence without toxicity and referential side effects. The results showed that our Deep-Multiclassifier model gave a sequence accuracy of up to 96.41% [toxicity (94.48%), antifungal (96.58%), antihypertensive (97.18%), and antibacterial (96.91%), respectively]. The properties of Deep-Multiclassifier and CVAE were validated through 12 first synthesized antibacterial peptides or compared to random peptides. The source code and data sets are available at https://github.com/TimothyChen225/GM-Pep.

Peptide Affinity Chromatography Applied to Therapeutic Antibodies Purification

The interest in therapeutic monoclonal antibodies (mAbs) has significantly grown in the pharmaceutical industry, exceeding 100 FDA mAbs approved. Although the upstream processing of their industrial production has been significantly improved in the last years, the downstream processing still depends on immobilized protein A affinity chromatography. The high cost, low capacity and short half-life of immobilized protein A chromatography matrices, encouraged the design of alternative short-peptide ligands for mAb purification. Most of these peptides have been obtained by screening combinatorial peptide libraries. These low-cost ligands can be easily produced by solid-phase peptide synthesis and can be immobilized on chromatographic supports, thus obtaining matrices with high capacity and selectivity. Furthermore, matrices with immobilized peptide ligands have longer half-life than those with protein A due to the higher stability of the peptides. In this review the design and synthesis of peptide ligands, their immobilization on chromatographic supports and the evaluation of the affinity supports for their application in mAb purification is described.

Biosynthetic Strategies for Macrocyclic Peptides

Macrocyclic peptides are predominantly peptide structures bearing one or more rings and spanning multiple amino acid residues. Macrocyclization has become a common approach for improving the pharmacological properties and bioactivity of peptides. A variety of ribosomal-derived and non-ribosomal synthesized cyclization approaches have been established. The biosynthesis of backbone macrocyclic peptides using seven new emerging methodologies will be discussed with regard to the features and strengths of each platform rather than medicinal chemistry tools. The mRNA display variant, known as the random nonstandard peptide integrated discovery (RaPID) platform, utilizes flexible in vitro translation (FIT) to access macrocyclic peptides containing nonproteinogenic amino acids (NAAs). As a new discovery approach, the ribosomally synthesized and post-translationally modified peptides (RiPPs) method involves the combination of ribosomal synthesis and the phage screening platform together with macrocyclization chemistries to generate libraries of macrocyclic peptides. Meanwhile, the split-intein circular ligation of peptides and proteins (SICLOPPS) approach relies on the in vivo production of macrocyclic peptides. In vitro and in vivo peptide library screening is discussed as an advanced strategy for cyclic peptide selection. Specifically, biosynthetic bicyclic peptides are highlighted as versatile and attractive modalities. Bicyclic peptides represent another type of promising therapeutics that allow for building blocks with a heterotrimeric conjugate to address intractable challenges and enable multimer complexes via linkers. Additionally, we discuss the cell-free chemoenzymatic synthesis of macrocyclic peptides with a non-ribosomal catalase known as the non-ribosomal synthetase (NRPS) and chemo-enzymatic approach, with recombinant thioesterase (TE) domains. Novel insights into the use of peptide library tools, activity-based two-hybrid screening, structure diversification, inclusion of NAAs, combinatorial libraries, expanding the toolbox for macrocyclic peptides, bicyclic peptides, chemoenzymatic strategies, and future perspectives are presented. This review highlights the broad spectrum of strategy classes, novel platforms, structure diversity, chemical space, and functionalities of macrocyclic peptides enabled by emerging biosynthetic platforms to achieve bioactivity and for therapeutic purposes.