Antibody complementarity determining region design using high-capacity machine learning

Liu, Ge; Zeng, Haoyang; Mueller, Jonas; Carter, Brandon; Wang, Ziheng; Schilz, Jonas; Horny, Geraldine; Birnbaum, Michael E.; Ewert, Stefan; Gifford, David K.

doi:10.1093/bioinformatics/btz895

Cited by 113 publications

(135 citation statements)

References 14 publications

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“…The data analysis of the NGS FastQ output files was performed as described 50 . For each panning output, 100'000 sequences were analyzed using the fixed flanking sequences on the boundary of variable region as template to locate and segment out the HCDR3 sequence.…”

Section: Ngs Data Analysismentioning

confidence: 99%

Protective anti-prion antibodies in human immunoglobulin repertoires

Senatore

Frontzek

Emmenegger

et al. 2020

Preprint

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Prion immunotherapy may hold great potential, but antibodies against certain PrP epitopes can be neurotoxic. Here we identified >6000 PrP-binding antibodies in a synthetic human Fab phage display library, 49 of which we characterized in detail. Antibodies directed against the flexible tail of PrP conferred neuroprotection against infectious prions. We then mined published repertoires of circulating B cells from healthy humans and found antibodies similar to the protective phage-derived antibodies. When expressed recombinantly, these antibodies exhibited anti-PrP reactivity. Furthermore, we surveyed 48'718 samples from 37'894 hospital patients for the presence of anti-PrP IgGs, and found 21 high-titer individuals. The clinical files of these individuals did not reveal any enrichment of specific pathologies, suggesting that anti-PrP autoimmunity is innocuous. The existence of protective anti-prion antibodies in unbiased human immunological repertoires, combined with the reported lack of such antibodies in carriers of disease-associated PRNP mutations, suggests a link to the low incidence of spontaneous prion diseases in human populations.We thank Rita Moos and Cinzia Tiberi for support in recombinant antigens and Fab purification, Irina Abakumova for TR-FRET antibody-fluorophore conjugation, and Stefan Schauer for the SPR experiments. We also thank the hospital patients supporting research, especially the individuals who generously agreed to grant us a second blood donation. We acknowledge the help provided by the Clinical

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Section: Ngs Data Analysismentioning

confidence: 99%

Protective anti-prion antibodies in human immunoglobulin repertoires

Senatore

Frontzek

Emmenegger

et al. 2020

Preprint

Self Cite

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“…Modern neural network (NN) approaches have had success generating sequences in other biological domains. Predictive models trained on experimental results have been used to successfully computationally evolve yeast 5′UTR sequences with higher protein expression 36 , 37 and antibody sequences with greater specificity 36 , 38 . Predictive ML models offer the opportunity to perform directed aptamer design: synthesizing and evaluating sequences in silico to dramatically reduce the number of sequences required to be experimentally screened.…”

Section: Introductionmentioning

confidence: 99%

Machine learning guided aptamer refinement and discovery

et al. 2021

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Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents.

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“…It is highly likely that this model can be applied to other display platforms that use bio-panning as the selection process, such as yeast display library for fluorescence-activated cell sorting screening [54]. Recently, artificial intelligence has been applied to predict the physicochemical properties of antibody sequences [55][56][57][58][59] and/or optimize them [60][61][62].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning

Yoo

Lee

Jung³

et al. 2020

Biomolecules

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c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth factor, and/or inducing receptor internalization have been clinically tested. To explore other therapeutic antibody mechanisms like Fc-mediated effector function, bispecific T cell engagement, and chimeric antigen T cell receptors, a diverse panel of antibodies is essential. We prepared a chicken immune scFv library, performed four rounds of bio-panning, obtained 641 clones using a high-throughput clonal retrieval system (TrueRepertoireTM, TR), and found 149 antigen-reactive scFv clones. We also prepared phagemid DNA before the start of bio-panning (round 0) and, after each round of bio-panning (round 1–4), performed next-generation sequencing of these five sets of phagemid DNA, and identified 860,207 HCDR3 clonotypes and 443,292 LCDR3 clonotypes along with their clonal abundance data. We then established a TR data set consisting of antigen reactivity for scFv clones found in TR analysis and the clonal abundance of their HCDR3 and LCDR3 clonotypes in five sets of phagemid DNA. Using the TR data set, a random forest machine learning algorithm was trained to predict the binding properties of in silico HCDR3 and LCDR3 clonotypes. Subsequently, we synthesized 40 HCDR3 and 40 LCDR3 clonotypes predicted to be antigen reactive (AR) and constructed a phage-displayed scFv library called the AR library. In parallel, we also prepared an antigen non-reactive (NR) library using 10 HCDR3 and 10 LCDR3 clonotypes predicted to be NR. After a single round of bio-panning, we screened 96 randomly-selected phage clones from the AR library and found out 14 AR scFv clones consisting of 5 HCDR3 and 11 LCDR3 AR clonotypes. We also screened 96 randomly-selected phage clones from the NR library, but did not identify any AR clones. In summary, machine learning algorithms can provide a method for identifying AR antibodies, which allows for the characterization of diverse antibody libraries inaccessible by traditional methods.

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Antibody complementarity determining region design using high-capacity machine learning

Cited by 113 publications

References 14 publications

Protective anti-prion antibodies in human immunoglobulin repertoires

Protective anti-prion antibodies in human immunoglobulin repertoires

Machine learning guided aptamer refinement and discovery

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning

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