Random peptide sequences binding amyloid monoclonal antibodies
Cite this dataset
Glabe, Charles (2020). Random peptide sequences binding amyloid monoclonal antibodies [Dataset]. Dryad. https://doi.org/10.7280/D1QH5W
Antibodies against Aß amyloid are indispensable research tools and potential therapeutics for Alzheimer’s Disease, but display several unusual properties, such as specificity for aggregated forms of the peptide, ability to distinguish polymorphic aggregate structures and ability to recognize generic aggregation-related epitopes formed by unrelated amyloid sequences. Understanding the mechanisms underlying these unusual properties of anti-amyloid antibodies and the structures of their corresponding epitopes is crucial for the understanding why antibodies display different therapeutic activities and for the development of more effective therapeutic agents. Here we employed a novel “epitomic” approach to map the fine structure of the epitopes of 28 monoclonal antibodies against amyloid-beta using immunoselection of random sequences from a phage display library, deep sequencing and pattern analysis to define the critical sequence elements recognized by the antibodies. Although most of the antibodies map to major linear epitopes in the amino terminal 1-14 residues of Aß, the antibodies display differences in the target sequence residues that are critical for binding and in their individual preferences for non-target residues, indicating that the antibodies bind to alternative conformations of the sequence by different mechanisms. Epitomic analysis also identifies more discontinuous, non-overlapping sequence Aß segments than peptide array approaches that may constitute the conformational epitopes that underlie the aggregation specificity of antibodies. Aggregation specific antibodies recognize sequences that display a significantly higher predicted propensity for forming amyloid than antibodies that recognize monomer, indicating that the ability of random sequences to aggregate into amyloid is a critical element of their binding mechanism.
Twenty eight rabbit monoclonal antibodies consisting of 5 antibodies derived from A11 serum and 23 derived from OC serum were produced and characterized. One mg of each antibody was used to pan 1011 phage from a dodecapeptide library with a complexity of 109 sequences fused to the coat protein pIII of the M13 phage according to the manufacturer’s recommended protocol (PhD Phage display library, NE BioLabs). Phage library without adding antibody was used to control for sequences binding only to the beads (Dynabeads Protein A, 10002D obtained from Novex Life Technologies). Three rounds of panning and amplification were carried out. Phage obtained at each step were collected and stored for analysis. The nomenclature for the file names is: (antibody name) (panning number 1-3) (U-unamplified or A-amplified).
Phage DNA isolation and library preparation
Phage DNA was isolated using a standard phenol:chloroform method. Quality was assessed by visualization in a 1% agarose gel, and its concentration measured by spectrophotometry. One hundred ng of phage DNA were used as template for PCR amplification for the Next Generation Sequencing step. The phage DNA amplicons were barcoded, pooled and a 10 nM library was sequenced commercially on an Illumina MiSeq platform. (Laragen Inc, Culver City, California, USA).
The Illumina sequencing data was processed by a BASH script (See Supplemental Information for JBC paper JBC/2020/015501) that extracts the DNA sequence coding for the dodecapeptides and translates them to protein and counts how many times each unique sequence was found in the sequencing file (frequency) and removes common background sequences due to unspecific binding to the protein A beads. The peptide sequences were sorted by frequency and converted to FASTA format as previously described. The FASTA sequences were written with an identifier label that contains the antibody name, the unique sequence number and the number of times the sequence was observed in the following pattern: >(antibody)*(sequence #)_(frequency) to facilitate machine counting of the frequency. The sequences were analyzed to determine the amino acid sequence patterns they contain using Pratt 2.1 that was edited and recompiled to accommodate up to 200,000 sequences as previously described.
National Institute on Aging, Award: RF1AG056507