Skip to main content


Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Figure 1 | BMC Bioinformatics

Figure 1

From: Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

Figure 1

Normalized mutation matrices of amyloidogenic (Column A) and non-amyloidogenic derivatives (Column B) of 12 antibody germlines. Original residues are in rows and corresponding replacement residues are in columns. The amino acids have been arranged according to increasing β-sheet forming propensities [54]. The intensity matrix of the difference between the amyloidogenic and non-amyloidogenic matrices (Column C) reflects the relative predominance of a mutation type in either amyloid or non-amyloid formers. A fourth matrix set (Column D) is used to indicate the mutations that occur exclusively in amyloidogenic derivatives. Separate matrices were generated for mutations in buried CDR, exposed CDR, buried FR and exposed FR positions.

Back to article page