(B) Analysis of the interaction between AMG157 and TSLP (PDB ID: 5J13)
(B) Analysis of the interaction between AMG157 and TSLP (PDB ID: 5J13). (TIF) Click here to view.(1.1M, tif) S1 TableBinding sites of TSLPR, IL-7R, AMG157, and T6 on TSLP. Table: Results of the second round of single point mutations predicted by mCSM-PPI2. (XLSX) pcbi.1011984.s008.xlsx (16K) GUID:?7AD32BE2-614C-4873-BF9E-ED27C1E661A4 S8 Table: The IC50 values of the mutant strains in the second round of mutations. (XLSX) pcbi.1011984.s009.xlsx (11K) GUID:?481C8F2B-493C-4B84-A2E4-09993411B94D S9 Table: The IC50 values of the mutant strains in the third round of combinations. (XLSX) pcbi.1011984.s010.xlsx (10K) GUID:?4D846DB2-1EF4-462C-9251-B1C172B9FDB9 Attachment: Submitted filename: Review_response_PCOMPBIOL-D-23-01949.docx pcbi.1011984.s011.docx (23K) GUID:?AA663CE8-E946-4213-9938-15EF1B353A71 Data Availability StatementAll relevant data are within the manuscript and its Supporting Information files. Abstract Thymic stromal lymphopoietin is usually a key cytokine involved in the pathogenesis of asthma and other allergic diseases. Targeting TSLP and its signaling pathways is usually progressively recognized as MMAD an effective strategy for asthma treatment. This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-assisted techniques in expediting antibody affinity maturation, thereby reducing both the time and cost of experiments. The integration of computational methods with experimental methods holds great promise for the development of targeted therapeutic antibodies for TSLP-related diseases. Author summary Computer-assisted affinity maturation significantly reduces experimental time and lowers research costs. Targeting thymic stromal lymphopoietin and its signaling pathways with specific antibody drugs is usually widely recognized MMAD as an effective strategy for treating asthma. In our study, we successfully recognized a TSLP-targeting antibody from your fully synthetic human phage antibody libraries. We integrated computer-assisted methods to enhance the antibodys affinity. These techniques enabled efficient prediction of crucial amino acid residues, guiding targeted mutagenesis experiments. By combining computer-assisted methods with experimental methods, we have successfully developed a mature method for enhancing antibody affinity. Through this research, we have obtained an antibody with high affinity for TSLP, providing a new avenue for treating asthma and other TSLP-related diseases. The computer-assisted affinity maturation strategy brings hope for speeding up the drug development process. Introduction The cytokine thymic stromal lymphopoietin (TSLP), derived from epithelial cells, is usually involved in the initiation and persistence of asthma inflammatory pathways [1,2]. It has been found that TSLP forms a trimeric signaling complex with the thymic stromal lymphopoietin receptor (TSLPR) and Interleukin-7 receptor alpha chain (IL-7R), activating intracellular signaling via the STAT5 pathway [3C5], which leads to the release of inflammatory cytokines. Targeted antibody drugs against TSLP and its signaling pathway are considered effective strategies for asthma treatment [6]. Tezepelumab (AMG 157) is usually a fully human monoclonal antibody (immunoglobulin G2) that specifically targets TSLP, hindering its conversation with the TSLP receptor complex and effectively inhibiting multiple downstream inflammatory pathways [7]. X-ray crystallography studies have recognized the epitope binding sites between AMG157 and TSLP, exposing that AMG157 occupies the binding interfaces of TSLP and TSLPR, disrupting their conversation [3]. Based on the success MMAD of AMG157, our research aims to identify a novel antibody that not only binds effectively to TSLP but also possesses a higher affinity than AMG157, thereby blocking the activation of downstream pathways by TSLP. Antibodies undergo affinity optimization before they can be considered potential therapeutic drugs [8]. Techniques such as site-directed mutagenesis, chain shuffling, and error-prone PCR are commonly utilized for antibody affinity maturation [9,10]. However, this process is usually often time-consuming, spanning several months. The advancement in computational power has facilitated the development of various strategies for guiding the rational engineering of antibody binding and specificity. In silico methods such as structure-based and mini-library methods have played a crucial role in antibody affinity maturation by enabling the exploration and optimization of antibody-antigen interactions [8,11,12]. These techniques rely on high-quality co-crystal structure and algorithms capable of accurately computing the energy variations resulting from mutations. The development of machine learning and deep learning has opened new avenues for affinity maturation. Tools like mCSM-PPI2 and Geo-PPI integrate multiple factors, including graph-based signatures and atomic interactions, to predict the effects of mutations around the antibody-antigen affinity [13C15]. These tools have confirmed useful Slit1 in analyzing single-point and multi-point mutations and providing insights into changes in affinity. By utilizing these software tools, we aim to establish a computer-assisted approach for accelerating the maturation.