Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0178874 (tumor progression)
 40,807 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The serine protease hepsin is highly upregulated in prostate cancer and is implicated in tumor progression. Therefore, specific inhibition of hepsin enzymatic activity by an antibody constitutes an attractive therapeutic approach. Here, we report the identification of the anti-hepsin antibody Fab25 by screening of a Fab phage display library with a restricted chemical diversity at the complementary determining regions. Hepsin with its S1 pocket occupied by 3,4-dichloro-isocoumarin was used as the 'bait' for library screening. Fab25 was highly specific and it potently inhibited hepsin activity toward a panel of synthetic and macromolecular substrates. Biochemical and enzymatic studies with synthetic substrates of variable length suggested that Fab25 acts as an allosteric inhibitor based on non-competitive inhibition kinetics. Isothermal titration calorimetric experiments showed that the high-affinity (K(D) 6.1 nM) binding of Fab25 with hepsin is enthalpically driven. Despite an unusually long CDR-H3 loop with several potential hepsin cleavage sites (Lys, Arg residues), Fab25 was not processed by hepsin. Antibody-25 should be valuable for investigating hepsin's role in cancer progression and for potential therapeutic applications. Furthermore, the herein presented phage display strategy using an active site-modified protease should be widely applicable for identifying potential allosteric anti-protease antibodies.
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PMID:An allosteric anti-hepsin antibody derived from a constrained phage display library. 2225 74

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.
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PMID:ASAP-SML: An antibody sequence analysis pipeline using statistical testing and machine learning. 3233 64