DOI: 10.7280/d1qh5w
title: Random peptide sequences binding amyloid monoclonal antibodies

description: 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.
description: Phage display 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 10<sup>11</sup> phage from a
 dodecapeptide library with a complexity of 10<sup>9</sup>
 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). Data
 analysis 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:
 &gt;(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.