Bispecific Antibodies for IFN-ß Delivery to ErbB2+ Tumors.

The main aim of our work was to create a full-length bispecific antibody (BsAb) as a vehicle for the targeted delivery of interferon-beta (IFN-ß) to ErbB2+ tumor cells in the form of non-covalent complex of BsAb and IFN-ß. Such a construct is a CrossMab-type BsAb, consisting of an ErbB2-recognizing trastuzumab moiety, a part of chimeric antibody to IFN-ß, and human IgG1 Fc domain carrying knob-into-hole amino acid substitutions necessary for the proper assembly of bispecific molecules. The IFN-ß- recognizing arm of BsAb not only forms a complex with the cytokine but neutralizes its activity, thus providing a mechanism to avoid the side effects of the systemic action of IFN-ß by blocking IFN-ß Interaction with cell receptors in the process of cytokine delivery to tumor sites. Enzyme sandwich immunoassay confirmed the ability of BsAb to bind to human IFN-ß comparable to that of the parental chimeric mAb. The BsAb binds to the recombinant ErbB2 receptor, as well as to lysates of ErbB2+ tumor cell lines. The inhibition of the antiproliferative effect of IFN-ß by BsAb (IC50 = 49,3 µg/mL) was demonstrated on the HT29 cell line. It can be proposed that the BsAb obtained can serve as a component of the immunocytokine complex for the delivery of IFN-ß to ErbB2-associated tumor cells.

N-Terminal Tagging with GFP Enhances Selectivity of Agitoxin 2 to Kv1.3-Channel Binding Site.

Recently developed fluorescent protein-scorpion toxin chimeras (FP-Tx) show blocking activities for potassium voltage-gated channels of Kv1 family and retain almost fully pharmacological profiles of the parental peptide toxins (Kuzmenkov et al., Sci Rep. 2016, 6, 33314). Here we report on N-terminally green fluorescent protein (GFP)-tagged agitoxin 2 (GFP-L2-AgTx2) with high affinity and selectivity for the binding site of Kv1.3 channel involved in the pathogenesis of various (primarily of autoimmune origin) diseases. The basis for this selectivity relates to N-terminal location of GFP, since transposition of GFP to the C-terminus of AgTx2 recovered specific interactions with the Kv1.1 and Kv1.6 binding sites. Competitive binding experiments revealed that the binding site of GFP-L2-AgTx2 overlaps that of charybdotoxin, kaliotoxin 1, and agitoxin 2, the known Kv1.3-channel pore blockers. GFP-L2-AgTx2 was demonstrated to be applicable as a fluorescent probe to search for Kv1.3 pore blockers among individual compounds and in complex mixtures, to measure blocker affinities, and to visualize Kv1.3 distribution at the plasma membrane of Kv1.3-expressing HEK293 cells. Our studies show that definite combinations of fluorescent proteins and peptide blockers can result in considerable modulation of the natural blocker-channel binding profile yielding selective fluorescent ligands of certain channels.

Method to assess packing quality of transmembrane alpha-helices in proteins. 1. Parametrization using structural data.

Integral membrane proteins (MPs) are pharmaceutical targets of exceptional importance. Modern methods of three-dimensional protein structure determination often fail to supply the fast growing field of structure-based drug design with the requested MPs' structures. That is why computational modeling techniques gain a special importance for these objects. Among the principal difficulties limiting application of these methods is the low quality of the MPs' models built in silico. In this series of two papers we present a computational approach to the assessment of the packing "quality" of transmembrane (TM) alpha-helical domains in proteins. The method is based on the concept of protein environment classes, whereby each amino acid residue is described in terms of its environment polarity and accessibility to the membrane. In the first paper we analyze a nonredundant set of 26 TM alpha-helical domains and compute the residues' propensities to five predefined classes of membrane-protein environments. Here we evaluate the proposed approach only by various test sets, cross-validation protocols and ability of the method to delimit the crystal structure of visual rhodopsin, and a number of its erroneous theoretical models. More advanced validation of the method is given in the second article of this series. We assume that the developed "membrane score" method will be helpful in optimizing computer models of TM domains of MPs, especially G-protein coupled receptors.

Method to assess packing quality of transmembrane alpha-helices in proteins. 2. Validation by "correct vs misleading" test.

We describe a set of tests designed to check the ability of the new "membrane score" method (see the first paper of this series) to assess the packing quality of transmembrane (TM) alpha-helical domains in proteins. The following issues were addressed: (1) Whether there is a relation between the score (S(mem)) of a model and its closeness to the "nativelike" conformation? (2) Is it possible to recognize a correct model among misfolded and erroneous ones? (3) To what extent the score of a homology-built model is sensitive to errors in sequence alignment? To answer the first question, two test cases were considered: (i) Several models of bovine aquaporin-1 (target protein) were built on the structural templates provided by its homologs with known X-ray structure. (ii) Side chains in the spatial models of visual rhodopsin and cytochrome c oxidase were rebuilt based on the backbone scaffolds taken from their crystal structures, and the resulting models were iteratively fitted into the full-atom X-ray conformations. It was shown that the higher the S(mem) value of a model is, the lower its root-mean-square deviation is from the "correct" (crystal) structure of a target. Furthermore, the "membrane score" method successfully identifies the rhodopsin crystal structure in an ensemble of "rotamer-type" decoys, thus providing the way to optimize mutual orientations of alpha-helices in models of TM domains. Finally, being applied to a set of homology models of rhodopsin built on its crystal structure with systematically shifted alignment, the approach demonstrates a prominent ability to detect alignment errors. We therefore assume that the "membrane score" method will be helpful in optimization of in silico models of TM domains in proteins, especially those in GPCRs.