Henry, K. A. & MacKenzie, C. R. Antigen recognition by single-domain antibodies: Structural latitudes and constraints. MAbs 10(6), 815–826 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wesolowski, J. et al. Single domain antibodies: Promising experimental and therapeutic tools in infection and immunity. Med. Microbiol. Immunol. 198, 157–174 (2009).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ventola, C. L. The antibiotic resistance crisis. Pharm. Therap. 40(4), 277–283 (2015).
Google ScholarÂ
Gould, I. M. & Bal, A. M. New antibiotic agents in the pipeline and how they can help overcome microbial resistance. Virulence 4(2), 185–191 (2013).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
McConnell, A. D. et al. A general approach to antibody thermostabilization. MAbs 6(5), 1274–1282 (2014).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ward, E. S. et al. Binding activities of a repertoire of single immunoglobulin variable domains secreted from Escherichia coli. Nature 341(6242), 544–546 (1989).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
Google ScholarÂ
Hamers-Casterman, C. et al. Naturally occurring antibodies devoid of light chains. Nature 363(6428), 446–448 (1993).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ovchinnikov, V. et al. Role of framework mutations and antibody flexibility in the evolution of broadly neutralizing antibodies. Elife 7, 1 (2018).ArticleÂ
Google ScholarÂ
Kiguchi, Y. et al. The VH framework region 1 as a target of efficient mutagenesis for generating a variety of affinity-matured scFv mutants. Sci. Rep. 11(1), 8201 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Nguyen, V. K. et al. Camel heavy-chain antibodies: Diverse germline V(H)H and specific mechanisms enlarge the antigen-binding repertoire. EMBO J. 19(5), 921–930 (2000).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Muyldermans, S. et al. Sequence and structure of VH domain from naturally occurring camel heavy chain immunoglobulins lacking light chains. Protein Eng. 7(9), 1129–1135 (1994).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ding, L. et al. Structural insights into the mechanism of single domain VHH antibody binding to cortisol. FEBS Lett. 593(11), 1248–1256 (2019).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Rudolph, M. J. et al. Contribution of an unusual CDR2 element of a single domain antibody in ricin toxin binding affinity and neutralizing activity. Protein Eng. Des. Select. 31(7–8), 277–287 (2018).ArticleÂ
CASÂ
Google ScholarÂ
Bever, C. S. et al. VHH antibodies: Emerging reagents for the analysis of environmental chemicals. Anal. Bioanal. Chem. 408(22), 5985–6002 (2016).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Polonelli, L. et al. Antibody complementarity-determining regions (CDRs) can display differential antimicrobial, antiviral and antitumor activities. PLoS ONE 3(6), e2371 (2008).ArticleÂ
ADSÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Liu, J. L. et al. Thermal stability and refolding capability of shark derived single domain antibodies. Mol. Immunol. 59(2), 194–199 (2014).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Kunz, P. et al. The structural basis of nanobody unfolding reversibility and thermoresistance. Sci. Rep. 8(1), 7934 (2018).ArticleÂ
ADSÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Bekker, G. J., Ma, B. & Kamiya, N. Thermal stability of single-domain antibodies estimated by molecular dynamics simulations. Protein Sci. 28(2), 429–438 (2019).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Jung, F. et al. DeepSTABp: A deep learning approach for the prediction of thermal protein stability. Int. J. Mol. Sci. 24(8), 7444 (2023).ArticleÂ
MathSciNetÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Li, M. et al. DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences. Comput. Struct. Biotechnol. J. 21, 5544–5560 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yang, Y. et al. ProTstab2 for prediction of protein thermal stabilities. Int. J. Mol. Sci. 23, 18 (2022).
Google ScholarÂ
Ku, T. et al. Predicting melting temperature directly from protein sequences. Comput. Biol. Chem. 33(6), 445–450 (2009).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Haselbeck, F. et al. Superior protein thermophilicity prediction with protein language model embeddings. NAR Genom. Bioinform. 5(4), 087 (2023).
Google ScholarÂ
Outeiral, C. & Deane, C. M. Codon language embeddings provide strong signals for use in protein engineering. Nat. Mach. Intell. 6(2), 170–179 (2024).ArticleÂ
Google ScholarÂ
Valdés-Tresanco, M. S. et al. NbThermo: A new thermostability database for nanobodies. Database 2023, 21 (2023).ArticleÂ
Google ScholarÂ
Kunz, P. et al. Exploiting sequence and stability information for directing nanobody stability engineering. Biochim. Biophys. Acta Gen. Subj. 1861(9), 2196–2205 (2017).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Osorio, D., Rondón-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. R J. 7(1), 4–14 (2015).ArticleÂ
Google ScholarÂ
Ikai, A. Thermostability and aliphatic index of globular proteins. J. Biochem. 88(6), 1895–1898 (1980).CASÂ
PubMedÂ
Google ScholarÂ
Guruprasad, K., Reddy, B. V. & Pandit, M. W. Correlation between stability of a protein and its dipeptide composition: A novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng. 4(2), 155–161 (1990).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Kyte, J. & Doolittle, R. F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1), 105–132 (1982).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Bannas, P., Hambach, J. & Koch-Nolte, F. Nanobodies and nanobody-based human heavy chain antibodies as antitumor therapeutics. Front. Immunol. 8, 1 (2017).ArticleÂ
Google ScholarÂ
Bhaskaran, R. & Ponnuswamy, P. K. Positional flexibilities of amino acid residues in globular proteins. Int. J. Peptide Protein Res. 32(4), 241–255 (1988).ArticleÂ
CASÂ
Google ScholarÂ
Dong, Y.-W. et al. Structural flexibility and protein adaptation to temperature: Molecular dynamics analysis of malate dehydrogenases of marine molluscs. Proc. Natl. Acad. Sci. 115(6), 1274–1279 (2018).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sheriff, S. et al. Influence of solvent accessibility and intermolecular contacts on atomic mobilities in hemerythrins. Proc. Natl. Acad. Sci. 82(4), 1104–1107 (1985).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sandberg, M. et al. New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J. Med. Chem. 41(14), 2481–2491 (1998).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Tesfaye, D. Y. et al. Targeting conventional dendritic cells to fine-tune antibody responses. Front. Immunol. 10, 1529 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Pervez, S. et al. Effect of polarity and differentiation on antibody localization in multicellular tumour spheroid and xenograft models and its potential importance for in vivo immunotargeting. Int. J. Cancer 44(5), 940–947 (1989).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Wang, Y. et al. Investigation of the small size of nanobodies for a sensitive fluorescence polarization immunoassay for small molecules: 3-Phenoxybenzoic acid, an exposure biomarker of pyrethroid insecticides as a model. J. Agric. Food Chem. 67(41), 11536–11541 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Moore, D. S. Amino acid and peptide net charges: A simple calculational procedure. Biochem. Educ. 13(1), 10–11 (1985).ArticleÂ
CASÂ
Google ScholarÂ
Lehninger, A. L. Lehninger Principles of Biochemistry 6th edn. (W.H. Freeman, 2013).
Google ScholarÂ
Rabia, L. A. et al. Net charge of antibody complementarity-determining regions is a key predictor of specificity. Protein Eng. Des. Select. 31(11), 409 (2018).ArticleÂ
CASÂ
Google ScholarÂ
Frank, S. A. Specificity and Cross-Reactivity, in Immunology and Evolution of Infectious Disease (Princeton University Press, 2002).
Google ScholarÂ
Ghisaidoobe, A. B. & Chung, S. J. Intrinsic tryptophan fluorescence in the detection and analysis of proteins: A focus on Förster resonance energy transfer techniques. Int. J. Mol. Sci. 15(12), 22518–22538 (2014).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Goldman, E. R. et al. Enhancing stability of camelid and shark single domain antibodies: An overview. Front. Immunol. 8, 1 (2017).ArticleÂ
Google ScholarÂ
Meitzler, J. L. et al. Conserved cysteine residues provide a protein-protein interaction surface in dual oxidase (DUOX) proteins. J. Biol. Chem. 288(10), 7147–7157 (2013).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wilkins, M. R. et al. Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol. 112, 531–552 (1999).CASÂ
PubMedÂ
Google ScholarÂ
Simonian, M. H. Spectrophotometric determination of protein concentration. Curr. Protoc. Toxicol. 1, 1–7 (2004).
Google ScholarÂ
Maity, H. et al. Comparison of predicted extinction coefficients of monoclonal antibodies with experimental values as measured by the Edelhoch method. Int. J. Biol. Macromol. 77, 260–265 (2015).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Holt, L. J. et al. Domain antibodies: Proteins for therapy. Trends Biotechnol. 21(11), 484–490 (2003).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Laimer, J. et al. MAESTRO—Multi agent stability prediction upon point mutations. BMC Bioinform. 16(1), 116 (2015).ArticleÂ
Google ScholarÂ
Høie, M. H. et al. NetSurfP-3.0: Accurate and fast prediction of protein structural features by protein language models and deep learning. Nucleic Acids Res. 50(W1), W510–W515 (2022).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cohen, T., Halfon, M. & Schneidman-Duhovny, D. NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning. Front. Immunol. 13, 958584 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ruffolo, J. A. & Gray, J. J. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Biophys. J. 121(3), 155–156 (2022).ArticleÂ
Google ScholarÂ
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637), 1123–1130 (2023).ArticleÂ
ADSÂ
MathSciNetÂ
CASÂ
PubMedÂ
Google ScholarÂ
Wu, R. et al. High-resolution de novo structure prediction from primary sequence. BioRxiv 21, 500999 (2022).
Google ScholarÂ
AlQuraishi, M. Machine learning in protein structure prediction. Curr. Opin. Chem. Biol. 65, 1–8 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873), 583–589 (2021).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Valdés-Tresanco, M. S. et al. Structural modeling of nanobodies: A benchmark of state-of-the-art artificial intelligence programs. Molecules 28(10), 3991 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Honegger, A. & Plückthun, A. Yet another numbering scheme for immunoglobulin variable domains: An automatic modeling and analysis tool. J. Mol. Biol. 309(3), 657–670 (2001).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Dunbar, J. & Deane, C. M. ANARCI: Antigen receptor numbering and receptor classification. Bioinformatics 32(2), 298–300 (2015).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Apweiler, R. et al. UniProt: The universal protein knowledgebase. Nucleic Acids Res. 32, 115–119 (2004).ArticleÂ
Google ScholarÂ
Pedregosa, F. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNetÂ
Google ScholarÂ
Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).Breiman, L. Random Forests. Mach. Learn. 45(1), 5–32 (2001).ArticleÂ
Google ScholarÂ
Hearst, M. A. et al. Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998).ArticleÂ
Google ScholarÂ
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996).ArticleÂ
MathSciNetÂ
Google ScholarÂ
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986).ArticleÂ
ADSÂ
Google ScholarÂ
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521(7553), 436–444 (2015).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
Google ScholarÂ
Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006).ArticleÂ
ADSÂ
MathSciNetÂ
CASÂ
PubMedÂ
Google ScholarÂ
Chollet, F. Deep Learning with Python (Simon and Schuster, 2021).
Google ScholarÂ
Waskom, M. Seaborn: Statistical data visualization. J. Open Source Softw. 6, 3021 (2021).ArticleÂ
ADSÂ
Google ScholarÂ
Kurgan, L. & Miri Disfani, F. Structural protein descriptors in 1-dimension and their sequence-based predictions. Curr. Protein Peptide Sci. 12(6), 470–489 (2011).ArticleÂ
CASÂ
Google ScholarÂ
Singh, H., Singh, S. & Raghava, G. P. Evaluation of protein dihedral angle prediction methods. PLoS ONE 9(8), e105667 (2014).ArticleÂ
ADSÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Jin, B. K. et al. NANOBODIES®: A review of diagnostic and therapeutic applications. Int. J. Mol. Sci. 24, 6 (2023).ArticleÂ
Google ScholarÂ
Natesan, R. et al. Heterogeneity in disulfide bond reduction in IgG1 antibodies is governed by solvent accessibility of the cysteines. Antibodies 12(4), 83 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yin, R. et al. Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants. Protein Sci. 31(8), e4379 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yin, R. & Pierce, B. G. Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy. Protein Sci. 33(1), e4865 (2024).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Saerens, D. et al. Disulfide bond introduction for general stabilization of immunoglobulin heavy-chain variable domains. J. Mol. Biol. 377(2), 478–488 (2008).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Zabetakis, D. et al. Evaluation of disulfide bond position to enhance the thermal stability of a highly stable single domain antibody. PLoS ONE 9(12), e115405 (2014).ArticleÂ
ADSÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hussack, G. et al. Engineered single-domain antibodies with high protease resistance and thermal stability. PLoS ONE 6(11), e28218 (2011).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tabares-da Rosa, S. et al. Competitive selection from single domain antibody libraries allows isolation of high-affinity antihapten antibodies that are not favored in the llama immune response. Anal. Chem. 83(18), 7213–7220 (2011).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Sturtz, J. et al. Deep learning approaches for the protein scaffold filling problem. In 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) (2022).Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180(4), 688–702 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
PudžiuvelytÄ—, I. et al. TemStaPro: Protein thermostability prediction using sequence representations from protein language models. Bioinformatics 40, 4 (2024).ArticleÂ
Google ScholarÂ
DeszyÅ„ski, P. et al. INDI—Integrated nanobody database for immunoinformatics. Nucleic Acids Res. 50(D1), D1273–D1281 (2021).ArticleÂ
PubMed CentralÂ
Google ScholarÂ
Legler, P. M. et al. Structure of a low-melting-temperature anti-cholera toxin: llama V(H)H domain. Acta Crystallogr Sect. F Struct. Biol. Cryst. Commun. 69, 90–93 (2013).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
George, J. et al. Structural and mutational analysis of a monomeric and dimeric form of a single domain antibody with implications for protein misfolding. Proteins Struct. Funct. Bioinform. 82(11), 3101–3116 (2014).ArticleÂ
CASÂ
Google ScholarÂ
Legler, P. M. et al. Stability of isolated antibody-antigen complexes as a predictive tool for selecting toxin neutralizing antibodies. mAbs 9(1), 43–57 (2017).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Krah, S. et al. Single-domain antibodies for biomedical applications. Immunopharmacol. Immunotoxicol. 38(1), 21–28 (2016).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Tomimoto, Y., Yamazaki, R. & Shirai, H. Increasing the melting temperature of VHH with the in silico free energy score. Sci. Rep. 13(1), 4922 (2023).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hagihara, Y., Mine, S. & Uegaki, K. Stabilization of an immunoglobulin fold domain by an engineered disulfide bond at the buried hydrophobic region. J. Biol. Chem. 282(50), 36489–36495 (2007).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Orlando, M. et al. CDR1 Composition can affect nanobody recombinant expression yields. Biomolecules 11, 9 (2021).ArticleÂ
Google ScholarÂ
Yang, K. K. et al. Learned protein embeddings for machine learning. Bioinformatics 34(15), 2642–2648 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yeung, W. et al. Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies. Brief. Bioinform. 24, 1 (2023).ArticleÂ
Google ScholarÂ
Littmann, M. et al. Protein embeddings and deep learning predict binding residues for various ligand classes. Sci. Rep. 11(1), 23916 (2021).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ferruz, N., Schmidt, S. & Höcker, B. ProtGPT2 is a deep unsupervised language model for protein design. Nat. Commun. 13(1), 4348 (2022).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Dean, S. N. et al. PepVAE: Variational autoencoder framework for antimicrobial peptide generation and activity prediction. Front. Microbiol. 12, 725727 (2021).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Saka, K. et al. Antibody design using LSTM based deep generative model from phage display library for affinity maturation. Sci. Rep. 11(1), 5852 (2021).ArticleÂ
ADSÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Humpe, A. & Peipp, M. Antibody engineering—Tailor-made next generation antibodies by molecular design. Transfus Med. Hemother. 44(5), 290–291 (2017).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ