ASAP-SML: An antibody sequence analysis pipeline using statistical testing and machine learning

• Published in: PLoS computational biology Vol. 16; no. 4; p. e1007779
• Main Authors: Li, Xinmeng, Van Deventer, James A, Hassoun, Soha
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2020; Public Library of Science (PLoS)
• Subjects: Amino acids; Antibodies; Antigens; Bioengineering; Biology and Life Sciences; Chemical inhibitors; Chemical properties; Computer and Information Sciences; Computer science; Datasets; Engineering and Technology; Feature extraction; Fingerprints; Health aspects; Inhibitors; Learning algorithms; Machine learning; Matrix metalloproteinase; Matrix metalloproteinases; Medicine and Health Sciences; Pipelines; Pipelines (Computing); Proteins; Research and Analysis Methods; Sequence analysis; Statistical analysis; Statistical hypothesis testing; Testing; United States; United States > US; Massachusetts
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007779

Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distinguish one set of antibody sequences from antibody sequences in a reference set. The pipeline extracts feature fingerprints from sequences. The fingerprints represent germline, CDR canonical structure, isoelectric point and frequent positional motifs. Machine learning and statistical significance testing techniques are applied to antibody sequences and extracted feature fingerprints to identify distinguishing feature values and combinations thereof. To demonstrate how it works, we applied the pipeline on sets of antibody sequences known to bind or inhibit the activities of matrix metalloproteinases (MMPs), a family of zinc-dependent enzymes that promote cancer progression and undesired inflammation under pathological conditions, against reference datasets that do not bind or inhibit MMPs. ASAP-SML identifies features and combinations of feature values found in the MMP-targeting sets that are distinct from those in the reference sets.