Moreover, a reduction in size of the amino acid at position 396 decreased specific activity dramatically but increased coupling and switched the main product formation from 4HDPM towards
diphenylmethanol.”
“The strand-exchange engineered domain (SEED) platform was designed to generate asymmetric and bispecific antibody-like molecules, a capability that expands therapeutic applications of natural antibodies. This new protein engineered platform is based on exchanging structurally related sequences of immunoglobulin within the conserved CH3 domains. Alternating sequences from human BACE inhibitor IgA and IgG in the SEED CH3 domains generate two asymmetric but complementary domains, designated AG and GA. The SEED design allows efficient generation of AG/GA heterodimers, while disfavoring homodimerization of AG and GA SEED CH3 domains. Using a clinically validated antibody (C225), we tested whether Fab derivatives constructed on the SEED platform retain desirable therapeutic antibody features such as in vitro and in vivo stability, favorable pharmacokinetics, ligand binding and effector functions including antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity. In addition, we tested SEED with combinations of binder domains (scFv, VHH, Fab). Mono-and
bivalent Fab-SEED fusions retain full binding affinity, have excellent biochemical and biophysical stability, and retain desirable antibody-like characteristics check details conferred by Fc domains. Furthermore, SEED is compatible with different combinations of Fab, scFv and VHH domains. Our assessment shows that the new SEED platform expands therapeutic applications of natural antibodies by generating heterodimeric
next Fc-analog proteins.”
“Computational sequence design methods are used to engineer proteins with desired properties such as increased thermal stability and novel function. In addition, these algorithms can be used to identify an envelope of sequences that may be compatible with a particular protein fold topology. In this regard, we hypothesized that sequence-property prediction, specifically secondary structure, could be significantly enhanced by using a large database of computationally designed sequences. We performed a large-scale test of this hypothesis with 6511 diverse protein domains and 50 designed sequences per domain. After analysis of the inherent accuracy of the designed sequences database, we realized that it was necessary to put constraints on what fraction of the native sequence should be allowed to change. With mutational constraints, accuracy was improved vs. no constraints, but the diversity of designed sequences, and hence effective size of the database, was moderately reduced.