Using Graph-Based Signatures to Guide Rational Antibody Engineering

Overview

Journal Methods Mol Biol

Specialty Molecular Biology

Date 2022 Nov 8

PMID 36346604

Authors

David B Ascher

Lisa M Kaminskas

Yoochan Myung

Douglas E V Pires

Affiliations

Soon will be listed here.

Abstract

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

References

Crespi G, Ascher D, Parker M, Miles L . Crystallization and preliminary X-ray diffraction analysis of the Fab portion of the Alzheimer's disease immunotherapy candidate bapineuzumab complexed with amyloid-β. Acta Crystallogr F Struct Biol Commun. 2014; 70(Pt 3):374-7. PMC: 3944706. DOI: 10.1107/S2053230X14001642. View

Watt A, Crespi G, Down R, Ascher D, Gunn A, Perez K . Anti-Aβ antibody target engagement: a response to Siemers et al. Acta Neuropathol. 2014; 128(4):611-4. DOI: 10.1007/s00401-014-1333-8. View

Watt A, Crespi G, Down R, Ascher D, Gunn A, Perez K . Do current therapeutic anti-Aβ antibodies for Alzheimer's disease engage the target?. Acta Neuropathol. 2014; 127(6):803-10. DOI: 10.1007/s00401-014-1290-2. View

Wun K, Miles L, Crespi G, Wycherley K, Ascher D, Barnham K . Crystallization and preliminary X-ray diffraction analysis of the Fab fragment of WO2, an antibody specific for the Abeta peptides associated with Alzheimer's disease. Acta Crystallogr Sect F Struct Biol Cryst Commun. 2008; 64(Pt 5):438-41. PMC: 2376392. DOI: 10.1107/S1744309108011718. View

Pires D, C de Melo-Minardi R, Dos Santos M, da Silveira C, Santoro M, Meira Jr W . Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns. BMC Genomics. 2012; 12 Suppl 4:S12. PMC: 3287581. DOI: 10.1186/1471-2164-12-S4-S12. View

Copoiu L, Torres P, Ascher D, Blundell T, Malhotra S . ProCarbDB: a database of carbohydrate-binding proteins. Nucleic Acids Res. 2019; 48(D1):D368-D375. PMC: 6943041. DOI: 10.1093/nar/gkz860. View

Pires D, Blundell T, Ascher D . Platinum: a database of experimentally measured effects of mutations on structurally defined protein-ligand complexes. Nucleic Acids Res. 2014; 43(Database issue):D387-91. PMC: 4384026. DOI: 10.1093/nar/gku966. View

Xavier J, Nguyen T, Karmarkar M, Portelli S, Rezende P, Velloso J . ThermoMutDB: a thermodynamic database for missense mutations. Nucleic Acids Res. 2020; 49(D1):D475-D479. PMC: 7778973. DOI: 10.1093/nar/gkaa925. View

Pires D, Ascher D, Blundell T . mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics. 2013; 30(3):335-42. PMC: 3904523. DOI: 10.1093/bioinformatics/btt691. View

10.

Pires D, Ascher D, Blundell T . DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res. 2014; 42(Web Server issue):W314-9. PMC: 4086143. DOI: 10.1093/nar/gku411. View

11.

Rodrigues C, Pires D, Ascher D . DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res. 2018; 46(W1):W350-W355. PMC: 6031064. DOI: 10.1093/nar/gky300. View

12.

Pires D, Rodrigues C, Ascher D . mCSM-membrane: predicting the effects of mutations on transmembrane proteins. Nucleic Acids Res. 2020; 48(W1):W147-W153. PMC: 7319563. DOI: 10.1093/nar/gkaa416. View

13.

Pandurangan A, Ochoa-Montano B, Ascher D, Blundell T . SDM: a server for predicting effects of mutations on protein stability. Nucleic Acids Res. 2017; 45(W1):W229-W235. PMC: 5793720. DOI: 10.1093/nar/gkx439. View

14.

Pires D, Rodrigues C, Albanaz A, Karmakar M, Myung Y, Xavier J . Exploring Protein Supersecondary Structure Through Changes in Protein Folding, Stability, and Flexibility. Methods Mol Biol. 2019; 1958:173-185. DOI: 10.1007/978-1-4939-9161-7_9. View

15.

Rodrigues C, Pires D, Ascher D . DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Sci. 2020; 30(1):60-69. PMC: 7737773. DOI: 10.1002/pro.3942. View

16.

Pires D, Blundell T, Ascher D . pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J Med Chem. 2015; 58(9):4066-72. PMC: 4434528. DOI: 10.1021/acs.jmedchem.5b00104. View

17.

Pires D, Veloso W, Myung Y, Rodrigues C, Silk M, Rezende P . EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening. Bioinformatics. 2020; 36(14):4200-4202. DOI: 10.1093/bioinformatics/btaa480. View

18.

Kaminskas L, Pires D, Ascher D . dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction. Sci Rep. 2019; 9(1):15465. PMC: 6820739. DOI: 10.1038/s41598-019-51789-3. View

19.

Pires D, Kaminskas L, Ascher D . Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand. Methods Mol Biol. 2018; 1762:271-284. DOI: 10.1007/978-1-4939-7756-7_14. View

20.

Pires D, Portelli S, Rezende P, Veloso W, Xavier J, Karmakar M . A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods. Methods Mol Biol. 2020; 2112:91-106. DOI: 10.1007/978-1-0716-0270-6_7. View