TEMPRO: nanobody melting temperature estimation model using protein embeddings

Henry, K. A. & MacKenzie, C. R. Antigen recognition by single-domain antibodies: Structural latitudes and constraints. MAbs 10(6), 815–826 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wesolowski, J. et al. Single domain antibodies: Promising experimental and therapeutic tools in infection and immunity. Med. Microbiol. Immunol. 198, 157–174 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ventola, C. L. The antibiotic resistance crisis. Pharm. Therap. 40(4), 277–283 (2015).
Google Scholar 
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).Article 
PubMed 
PubMed Central 

Google Scholar 
McConnell, A. D. et al. A general approach to antibody thermostabilization. MAbs 6(5), 1274–1282 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Hamers-Casterman, C. et al. Naturally occurring antibodies devoid of light chains. Nature 363(6428), 446–448 (1993).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Ovchinnikov, V. et al. Role of framework mutations and antibody flexibility in the evolution of broadly neutralizing antibodies. Elife 7, 1 (2018).Article 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Ding, L. et al. Structural insights into the mechanism of single domain VHH antibody binding to cortisol. FEBS Lett. 593(11), 1248–1256 (2019).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 

Google Scholar 
Bever, C. S. et al. VHH antibodies: Emerging reagents for the analysis of environmental chemicals. Anal. Bioanal. Chem. 408(22), 5985–6002 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Polonelli, L. et al. Antibody complementarity-determining regions (CDRs) can display differential antimicrobial, antiviral and antitumor activities. PLoS ONE 3(6), e2371 (2008).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Liu, J. L. et al. Thermal stability and refolding capability of shark derived single domain antibodies. Mol. Immunol. 59(2), 194–199 (2014).Article 
CAS 
PubMed 

Google Scholar 
Kunz, P. et al. The structural basis of nanobody unfolding reversibility and thermoresistance. Sci. Rep. 8(1), 7934 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Jung, F. et al. DeepSTABp: A deep learning approach for the prediction of thermal protein stability. Int. J. Mol. Sci. 24(8), 7444 (2023).Article 
MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang, Y. et al. ProTstab2 for prediction of protein thermal stabilities. Int. J. Mol. Sci. 23, 18 (2022).
Google Scholar 
Ku, T. et al. Predicting melting temperature directly from protein sequences. Comput. Biol. Chem. 33(6), 445–450 (2009).Article 
CAS 
PubMed 

Google Scholar 
Haselbeck, F. et al. Superior protein thermophilicity prediction with protein language model embeddings. NAR Genom. Bioinform. 5(4), 087 (2023).
Google Scholar 
Outeiral, C. & Deane, C. M. Codon language embeddings provide strong signals for use in protein engineering. Nat. Mach. Intell. 6(2), 170–179 (2024).Article 

Google Scholar 
Valdés-Tresanco, M. S. et al. NbThermo: A new thermostability database for nanobodies. Database 2023, 21 (2023).Article 

Google Scholar 
Kunz, P. et al. Exploiting sequence and stability information for directing nanobody stability engineering. Biochim. Biophys. Acta Gen. Subj. 1861(9), 2196–2205 (2017).Article 
CAS 
PubMed 

Google Scholar 
Osorio, D., Rondón-Villarreal, P. & Torres, R. Peptides: A package for data mining of antimicrobial peptides. R J. 7(1), 4–14 (2015).Article 

Google Scholar 
Ikai, A. Thermostability and aliphatic index of globular proteins. J. Biochem. 88(6), 1895–1898 (1980).CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Kyte, J. & Doolittle, R. F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1), 105–132 (1982).Article 
CAS 
PubMed 

Google Scholar 
Bannas, P., Hambach, J. & Koch-Nolte, F. Nanobodies and nanobody-based human heavy chain antibodies as antitumor therapeutics. Front. Immunol. 8, 1 (2017).Article 

Google Scholar 
Bhaskaran, R. & Ponnuswamy, P. K. Positional flexibilities of amino acid residues in globular proteins. Int. J. Peptide Protein Res. 32(4), 241–255 (1988).Article 
CAS 

Google Scholar 
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).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Tesfaye, D. Y. et al. Targeting conventional dendritic cells to fine-tune antibody responses. Front. Immunol. 10, 1529 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore, D. S. Amino acid and peptide net charges: A simple calculational procedure. Biochem. Educ. 13(1), 10–11 (1985).Article 
CAS 

Google Scholar 
Lehninger, A. L. Lehninger Principles of Biochemistry 6th edn. (W.H. Freeman, 2013).
Google Scholar 
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).Article 
CAS 

Google Scholar 
Frank, S. A. Specificity and Cross-Reactivity, in Immunology and Evolution of Infectious Disease (Princeton University Press, 2002).
Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goldman, E. R. et al. Enhancing stability of camelid and shark single domain antibodies: An overview. Front. Immunol. 8, 1 (2017).Article 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wilkins, M. R. et al. Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol. 112, 531–552 (1999).CAS 
PubMed 

Google Scholar 
Simonian, M. H. Spectrophotometric determination of protein concentration. Curr. Protoc. Toxicol. 1, 1–7 (2004).
Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Holt, L. J. et al. Domain antibodies: Proteins for therapy. Trends Biotechnol. 21(11), 484–490 (2003).Article 
CAS 
PubMed 

Google Scholar 
Laimer, J. et al. MAESTRO—Multi agent stability prediction upon point mutations. BMC Bioinform. 16(1), 116 (2015).Article 

Google Scholar 
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).Article 
PubMed 
PubMed Central 

Google Scholar 
Cohen, T., Halfon, M. & Schneidman-Duhovny, D. NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning. Front. Immunol. 13, 958584 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 

Google Scholar 
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637), 1123–1130 (2023).Article 
ADS 
MathSciNet 
CAS 
PubMed 

Google Scholar 
Wu, R. et al. High-resolution de novo structure prediction from primary sequence. BioRxiv 21, 500999 (2022).
Google Scholar 
AlQuraishi, M. Machine learning in protein structure prediction. Curr. Opin. Chem. Biol. 65, 1–8 (2021).Article 
CAS 
PubMed 

Google Scholar 
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596(7873), 583–589 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Dunbar, J. & Deane, C. M. ANARCI: Antigen receptor numbering and receptor classification. Bioinformatics 32(2), 298–300 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Apweiler, R. et al. UniProt: The universal protein knowledgebase. Nucleic Acids Res. 32, 115–119 (2004).Article 

Google Scholar 
Pedregosa, F. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet 

Google Scholar 
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).Article 

Google Scholar 
Hearst, M. A. et al. Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998).Article 

Google Scholar 
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996).Article 
MathSciNet 

Google Scholar 
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986).Article 
ADS 

Google Scholar 
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521(7553), 436–444 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006).Article 
ADS 
MathSciNet 
CAS 
PubMed 

Google Scholar 
Chollet, F. Deep Learning with Python (Simon and Schuster, 2021).
Google Scholar 
Waskom, M. Seaborn: Statistical data visualization. J. Open Source Softw. 6, 3021 (2021).Article 
ADS 

Google Scholar 
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).Article 
CAS 

Google Scholar 
Singh, H., Singh, S. & Raghava, G. P. Evaluation of protein dihedral angle prediction methods. PLoS ONE 9(8), e105667 (2014).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Jin, B. K. et al. NANOBODIES®: A review of diagnostic and therapeutic applications. Int. J. Mol. Sci. 24, 6 (2023).Article 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin, R. et al. Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants. Protein Sci. 31(8), e4379 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin, R. & Pierce, B. G. Evaluation of AlphaFold antibody–antigen modeling with implications for improving predictive accuracy. Protein Sci. 33(1), e4865 (2024).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Saerens, D. et al. Disulfide bond introduction for general stabilization of immunoglobulin heavy-chain variable domains. J. Mol. Biol. 377(2), 478–488 (2008).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Hussack, G. et al. Engineered single-domain antibodies with high protease resistance and thermal stability. PLoS ONE 6(11), e28218 (2011).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pudžiuvelytė, I. et al. TemStaPro: Protein thermostability prediction using sequence representations from protein language models. Bioinformatics 40, 4 (2024).Article 

Google Scholar 
Deszyński, P. et al. INDI—Integrated nanobody database for immunoinformatics. Nucleic Acids Res. 50(D1), D1273–D1281 (2021).Article 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Krah, S. et al. Single-domain antibodies for biomedical applications. Immunopharmacol. Immunotoxicol. 38(1), 21–28 (2016).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
Orlando, M. et al. CDR1 Composition can affect nanobody recombinant expression yields. Biomolecules 11, 9 (2021).Article 

Google Scholar 
Yang, K. K. et al. Learned protein embeddings for machine learning. Bioinformatics 34(15), 2642–2648 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yeung, W. et al. Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies. Brief. Bioinform. 24, 1 (2023).Article 

Google Scholar 
Littmann, M. et al. Protein embeddings and deep learning predict binding residues for various ligand classes. Sci. Rep. 11(1), 23916 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferruz, N., Schmidt, S. & Höcker, B. ProtGPT2 is a deep unsupervised language model for protein design. Nat. Commun. 13(1), 4348 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dean, S. N. et al. PepVAE: Variational autoencoder framework for antimicrobial peptide generation and activity prediction. Front. Microbiol. 12, 725727 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Humpe, A. & Peipp, M. Antibody engineering—Tailor-made next generation antibodies by molecular design. Transfus Med. Hemother. 44(5), 290–291 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 

Hot Topics

Related Articles