Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning

Phage Display Technology in Biomarker Identification with Emphasis on Non-Cancerous Diseases

In recent years, phage display technology has become vital in clinical research. It helps create antibodies that can specifically bind to complex antigens, which is crucial for identifying biomarkers and improving diagnostics and treatments. However, existing reviews often overlook its importance in areas outside cancer research. This review aims to fill that gap by explaining the basics of phage display and its applications in detecting and treating various non-cancerous diseases. We focus especially on its role in degenerative diseases, inflammatory and autoimmune diseases, and chronic non-communicable diseases, showing how it is changing the way we diagnose and treat illnesses. By highlighting important discoveries and future possibilities, we hope to emphasize the significance of phage display in modern healthcare.

The coming of age of cyclic peptide drugs: an update on discovery technologies

INTRODUCTION

Cyclic peptides are an established class of pharmaceuticals, with the ability to bind to a broader range of protein targets than traditional small molecules while also being capable of oral availability and cell penetration. Historically, cyclic peptide drugs have been discovered almost exclusively through natural product mining approaches; however, the last two decades have seen the development of display screening approaches capable of rapidly identifying de novo (i.e. not natural product derived) cyclic peptide ligands to targets of interest.

AREAS COVERED

In this review, the authors describe the current clinical landscape for cyclic peptide pharmaceuticals. This article focuses on the discovery approaches that have led to the development of different classes of molecules and how the development of newer technologies, particularly phage and mRNA display, has broadened the clinical applicability of such molecules.

EXPERT OPINION

The field of de novo cyclic peptide drug discovery is reaching maturity, with the first drugs identified through display screening approaches reaching the market in recent years. Many more are in clinical trials; however, significant technical challenges remain. Technological improvements will be required over the coming years to facilitate the identification of membrane permeable cyclic peptides capable of oral availability and targeting intracellular proteins.

Development of a new affinity maturation protocol for the construction of an internalizing anti-nucleolin antibody library

Over the last decades, monoclonal antibodies have substantially improved the treatment of several conditions. The continuous search for novel therapeutic targets and improvements in antibody's structure, demands for a constant optimization of their development. In this regard, modulation of an antibody's affinity to its target has been largely explored and culminated in the discovery and optimization of a variety of molecules. It involves the creation of antibody libraries and selection against the target of interest. In this work, we aimed at developing a novel protocol to be used for the affinity maturation of an antibody previously developed by our group. An antibody library was constructed using an in vivo random mutagenesis approach that, to our knowledge, has not been used before for antibody development. Then, a cell-based phage display selection protocol was designed to allow the fast and simple screening of antibody clones capable of being internalized by target cells. Next generation sequencing coupled with computer analysis provided an extensive characterization of the created library and post-selection pool, that can be used as a guide for future antibody development. With a single selection step, an enrichment in the mutated antibody library, given by a decrease in almost 50% in sequence diversity, was achieved, and structural information useful in the study of the antibody-target interaction in the future was obtained.

Extensive antibody search with whole spectrum black-box optimization

In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from. In our approach, multiple prediction models are trained by subsampling the training set and the multi-objective optimization problem, where one objective is the average activity and the other is the standard deviation, is solved. The Pareto front represents a list of sequences with the whole spectrum of activity and stability. Using this method, we designed VHH (Variable domain of Heavy chain of Heavy chain) antibodies based on the dataset obtained from deep mutational screening. To solve multi-objective optimization, we employed our sequence design software MOQA that uses quantum annealing. By applying several selection criteria to 19,778 designed sequences, five sequences were selected for wet-lab validation. One sequence, 16 mutations away from the closest training sequence, was successfully expressed and found to possess desired binding specificity. Our whole spectrum approach provides a balanced way of dealing with the prediction uncertainty, and can possibly be applied to extensive search of functional sequences.

Phage-based peptides for pancreatic cancer diagnosis and treatment: alternative approach

Pancreatic cancer is a devastating disease with a high mortality rate and a lack of effective therapies. The challenges associated with early detection and the highly aggressive nature of pancreatic cancer have limited treatment options, underscoring the urgent need for better disease-modifying therapies. Peptide-based biotherapeutics have become an attractive area of research due to their favorable properties such as high selectivity and affinity, chemical modifiability, good tissue permeability, and easy metabolism and excretion. Phage display, a powerful technique for identifying peptides with high affinity and specificity for their target molecules, has emerged as a key tool in the discovery of peptide-based drugs. Phage display technology involves the use of bacteriophages to express peptide libraries, which are then screened against a target of interest to identify peptides with desired properties. This approach has shown great promise in cancer diagnosis and treatment, with potential applications in targeting cancer cells and developing new therapies. In this comprehensive review, we provide an overview of the basic biology of phage vectors, the principles of phage library construction, and various methods for binding affinity assessment. We then describe the applications of phage display in pancreatic cancer therapy, targeted drug delivery, and early detection. Despite its promising potential, there are still challenges to be addressed, such as optimizing the selection process and improving the pharmacokinetic properties of phage-based drugs. Nevertheless, phage display represents a promising approach for the development of novel targeted therapies in pancreatic cancer and other tumors.