Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning

Ashkenazy, Haim; Avram, Oren; Ryvkin, Arie; Roitburd-Berman, Anna; Weiss-Ottolenghi, Yael; Hada-Neeman, Smadar; Gershoni, Jonathan M.; Pupko, Tal

doi:10.1016/j.jmb.2021.167071

Cited by 8 publications

(4 citation statements)

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“…The present study focuses on the identification of short peptides that can bind with IgG using an ML classifier that was trained, tested, and validated on datasets that are tailored for biomimetic chromatography. Previous studies have focused mainly on the identification of peptides that exhibit affinity with antibodies within the context of therapeutics and pharmaceuticals. − …”

Section: Resultsmentioning

confidence: 99%

Machine Learning Model for Biomimetic Chromatography Peptide Ligands

Janairo

2022

ACS Appl. Bio Mater.

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Purification is an essential part of antibody production, which are important therapeutic biomolecules. Common methods of antibody purification rely on affinity chromatography (AC), wherein whole proteins are oftentimes used as ligands to catch the antibodies to be purified. While AC has been successful in purifying antibodies, it is associated with multiple challenges such as high cost and low stability, among others. A promising alternative is using short peptide sequences in place of whole proteins as the stationary phase for the chromatographic separation of the antibodies. In an effort to accelerate the discovery and development of short peptides for biomimetic chromatography, this study reports the creation of a machine learning classification which was trained and tested on 480 tetrapeptides. The optimized logistic regression model uses Cruciani properties as the input variables and can categorize peptides into one of two classes based on their binding affinity with immunoglobulin G (IgG). The externally validated model demonstrates satisfactory predictive performance and excellent discrimination as demonstrated by performance metrics such as AUC = 0.874, Balanced Accuracy = 0.874, F1 = 0.871, Precision = 0.884, and Recall = 0.859. Apart from this, the classifier has also provided valuable insights into important variables that influence the classification, such as electrostatic and hydrophobic interactions. Overall, the classifier can be regarded as a welcome development for biomimetic chromatography and is the first study that aims to integrate machine learning in the biomimetic chromatography peptide development process.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Model for Biomimetic Chromatography Peptide Ligands

Janairo

2022

ACS Appl. Bio Mater.

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“…Investigation of the vast diversity of the antibody repertoires is possible by direct sequencing of single cells’ BCR or by indirect methods based on high throughput generation of mimotopes ( 36 , 53 ). The latter yield big data sets of peptide sequences reflecting the repertoire reactivities referred to as Igome ( 36 , 39 ). The fact that small linear peptides mimic the mostly conformational B-cell epitopes is a limitation.…”

Section: Discussionmentioning

confidence: 99%

“…Most isospecific sets of mimotopes consisted of a large group of highly related sequences plus a small number of sequences unrelated to any of the rest. On the other hand, when the deep panning technique was used to create as big libraries of mimotopes of the same monoclonal as possible, no clear limit of the number of selected diverse sequences was observed but they were found to cluster in 14 to 48 motifs ( 39 ). Together this evidence points to a hypothetical topology of the footprint of a single antibody in the Igome.…”

Section: Discussionmentioning

confidence: 99%

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Restriction of the Global IgM Repertoire in Antiphospholipid Syndrome

et al. 2022

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The typical anti-phospholipid antibodies (APLA) in the anti-phospholipid syndrome (APS) are reactive with the phospholipid-binding protein β2GPI as well as a growing list of other protein targets. The relation of APLA to natural antibodies and the fuzzy set of autoantigens involved provoked us to study the changes in the IgM repertoire in APS. To this end, peptides selected by serum IgM from a 7-residue linear peptide phage display library (PDL) were deep sequenced. The analysis was aided by a novel formal representation of the Igome (the mimotope set reflecting the IgM specificities) in the form of a sequence graph. The study involved women with APLA and habitual abortions (n=24) compared to age-matched clinically healthy pregnant women (n=20). Their pooled Igomes (297 028 mimotope sequences) were compared also to the global public repertoire Igome of pooled donor plasma IgM (n=2 796 484) and a set of 7-mer sequences found in the J regions of human immunoglobulins (n=4 433 252). The pooled Igome was represented as a graph connecting the sequences as similar as the mimotopes of the same monoclonal antibody. The criterion was based on previously published data. In the resulting graph, identifiable clusters of vertices were considered related to the footprints of overlapping antibody cross-reactivities. A subgraph based on the clusters with a significant differential expression of APS patients’ mimotopes contained predominantly specificities underrepresented in APS. The differentially expressed IgM footprints showed also an increased cross-reactivity with immunoglobulin J regions. The specificities underexpressed in APS had a higher correlation with public specificities than those overexpressed. The APS associated specificities were strongly related also to the human peptidome with 1 072 mimotope sequences found in 7 519 human proteins. These regions were characterized by low complexity. Thus, the IgM repertoire of the APS patients was found to be characterized by a significant reduction of certain public specificities found in the healthy controls with targets representing low complexity linear self-epitopes homologous to human antibody J regions.

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