Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Ito, Tomoyuki; Nguyen, Thuy Duong; Saito, Yutaka; Kurumida, Yoichi; Nakazawa, Hikaru; Kawada, Sakiya; Nishi, Hafumi; Tsuda, Koji; Kameda, Tomoshi; Umetsu, Mitsuo
• Is Part Of: MAbs. Volume 15:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Machine learning -- antibody mimetics -- directed evolution -- deep sequencing analysis -- phage display; Monoclonal antibodies -- Therapeutic use -- Periodicals; Monoclonal antibodies -- Periodicals; Antibodies, Monoclonal -- Periodicals; 616.0798
• DOI: 10.1080/19420862.2023.2168470
• Languages: English
• ISSNs: 1942-0862

Abstract

ABSTRACT: Despite the advances in surface-display systems for directed evolution, variants with high affinity are not always enriched due to undesirable biases that increase target-unrelated variants during biopanning. Here, our goal was to design a library containing improved variants from the information of the "weakly enriched" library where functional variants were weakly enriched. Deep sequencing for the previous biopanning result, where no functional antibody mimetics were experimentally identified, revealed that weak enrichment was partly due to undesirable biases during phage infection and amplification steps. The clustering analysis of the deep sequencing data from appropriate steps revealed no distinct sequence patterns, but a Bayesian machine learning model trained with the selected deep sequencing data supplied nine clusters with distinct sequence patterns. Phage libraries were designed on the basis of the sequence patterns identified, and four improved variants with target-specific affinity (EC50 = 80–277 nM) were identified by biopanning. The selection and use of deep sequencing data without undesirable bias enabled us to extract the information on prospective variants. In summary, the use of appropriate deep sequencing data and machine learning with the sequence data has the possibility of finding sequence space where functional variants are enriched.