A Human-Derived Monoclonal Antibody Targeting Extracellular Connexin Domain Selectively Modulates Hemichannel Function

Connexin hemichannels, which are plasma membrane hexameric channels (connexons) composed of connexin protein protomers, have been implicated in a host of physiological processes and pathological conditions. A number of single point pathological mutations impart a "leaky" character to the affected hemichannels, i.e., make them more active or hyperactive, suggesting that normal physiological condition could be recovered using selective hemichannel inhibitors. Recently, a human-derived monoclonal antibody named abEC1.1 has been shown to inhibit both wild type and hyperactive hemichannels composed of human (h) connexin 26 (hCx26) subunits. The aims of this work were (1) to characterize further the ability of abEC1.1 to selectively modulate connexin hemichannel function and (2) to assess its in vitro stability in view of future translational applications. In silico analysis of abEC1.1 interaction with the hCx26 hemichannel identified critically important extracellular domain amino acids that are conserved in connexin 30 (hCx30) and connexin 32 (hCx32). Patch clamp experiments performed in HeLa DH cells confirmed the inhibition efficiency of abEC1.1 was comparable for hCx26, hCx30 and hCx32 hemichannels. Of note, even a single amino acid difference in the putative binding region reduced drastically the inhibitory effects of the antibody on all the other tested hemichannels, namely hCx30.2/31.3, hCx30.3, hCx31, hCx31.1, hCx37, hCx43 and hCx45. Plasma membrane channels composed of pannexin 1 were not affected by abEC1.1. Finally, size exclusion chromatography assays showed the antibody does not aggregate appreciably in vitro. Altogether, these results indicate abEC1.1 is a promising tool for further translational studies

LightDock: a new multi-scale approach to protein–protein docking

Computational prediction of protein–protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. We describe here a new multi-scale protein–protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases.B.J-G was supported by a FPI fellowship from the Spanish Ministry of Economy and Competitiveness. This work was supported by I+D+I Research Project grants BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness. This work is partially supported by the European Union H2020 program through HiPEAC (GA 687698), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programaciói Entorns d’Execució Paral·lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

MODELING PROTEIN INTERACTIONS THROUGH STRUCTURE ALIGNMENT

Rapid accumulation of the experimental data on protein-protein complexes drives the paradigm shift in protein docking from "traditional" template free approaches to template based techniques. Homology docking algorithms based on sequence similarity between target and template complexes can account for ~ 20% of known protein-protein interactions. When homologous templates for the target complex are not available, but the structure of the target monomers is known, docking through structural alignment may provide an adequate solution. Such an algorithm was developed based on the structural comparison of monomers to co-crystallized interfaces. A library of the interfaces was generated from the biological units. The success of the structure alignment of the interfaces depends on the way the interface is defined in terms of its structural content. We performed a systematic large-scale study to find the optimal definition/size of the interface for the structure alignment-based docking applications. The performance was the best when the interface was defined with a distance cutoff of 12 Å. The structure alignment protocol was validated, for both full and partial alignment, on the DOCKGROUND benchmark sets. Both protocols performed equally for higher-accuracy models (i-RMSD &le 5 Å). Overall, the partial structure alignment yielded more acceptable models than the full structure alignment (86 acceptable models were provided by partial structure alignment only, compared to 31 by full structure alignment only). Most templates identified by the partial structure alignment had very low sequence identity to targets and such templates were hard to detect by sequence-based methods. Detailed analysis of the models obtained for 372 test cases concluded that templates for higher-accuracy models often shared not only local but also global structural similarity with the targets. However, interface similarity even in these cases was more prominent, reflected in more accurate models yielded by partial structure alignment. Conservation of protein-protein interfaces was observed in very diverse proteins. For example, target complexes shared interface structural similarity not only with hetero- and homo-complexes but also, in few cases, with crystal packing interfaces. The results indicate that the structure alignment techniques provide a much needed addition to the docking arsenal, with the combined structure alignment and template free docking success rate significantly surpassing that of the free docking alone

IDENTIFICATION OF POTENTIAL SALMONELLA TYPHI BETA-LACTAMASE TEM 1 INHIBITORS USING PEPTIDOMIMETICS, VIRTUAL SCREENING, AND MOLECULAR DYNAMICS SIMULATIONS

Objective: The purpose of this study was to identify a potential peptidomimetic S. typhi Beta-lactamase TEM 1 inhibitor to tackle the antibiotic resistance among S. typhi.Methods: The potential peptidomimetic inhibitor was identified by in silico docking of the small peptide WFRKQLKW with S. typhi Beta-lactamase TEM 1. The 3D coordinate geometry of the residues of small peptide interacting with the active site of the receptor was generated and mimics were identified using PEP: MMs: MIMIC server. All the identified mimics were docked at the active site of the receptor using Autodock 4.2 and the best-docked complex was selected on the basis of binding energy and number of H-bonds. The complex was then subjected to molecular dynamics simulations of 30 ns using AMBER 12 software package. The stereochemical stability of the Beta-lactamase TEM 1-WFRKQLKW complex was estimated with the help of Ramachandran plot using PROCHECK tool.Results: In the present study, a new potential peptidomimetic inhibitor (ZINC05839264) of Beta-lactamase TEM 1 has been identified based on antimicrobial peptide WFRKQLKW by virtual screening of the MMsINC database. The docking and molecular simulation studies revealed that the mimic binds more tightly to the active site of the receptor than the peptide. The Ramachandran plot also shows that the Beta-lactamase TEM 1-mimic complex is stereo chemically more stable than Beta-lactamase TEM 1-WFRKQLKW complex as more number of residues (93.6%) are falling under the core region of the plot in case of the former.Conclusion: The study shows that the peptidomimetic compound can act as a potential inhibitor of S. typhi Beta-lactamase TEM 1 and further it can be developed into more effective therapeutic to tackle the problem of antibiotic resistance

Computational Modeling of Designed Ankyrin Repeat Protein Complexes with their Targets

Recombinant therapeutic proteins are playing an ever-increasing role in the clinic. High-affinity binding candidates can be produced in a high-throughput manner through the process of selection and evolution from large libraries, but the structures of the complexes with target protein can only be determined for a small number of them in a costly, low-throughput manner, typically by x-ray crystallography. Reliable modeling of complexes would greatly help to understand their mode of action and improve them by further engineering, for example, by designing bi-paratopic binders. Designed ankyrin repeat proteins (DARPins) are one such class of antibody mimetics that have proven useful in the clinic, in diagnostics and research. Here we have developed a standardized procedure to model DARPin–target complexes that can be used to predict the structures of unknown complexes. It requires only the sequence of a DARPin and a structure of the unbound target. The procedure includes homology modeling of the DARPin, modeling of the flexible parts of a target, rigid body docking to ensembles of the target and docking with a partially flexible backbone. For a set of diverse DARPin–target complexes tested it generated a single model of the complex that well approximates the native state of the complex. We provide a protocol that can be used in a semi-automated way and with tools that are freely available. The presented concepts should help to accelerate the development of novel bio-therapeutics for scaffolds with similar properties

Predicting multibody assembly of proteins

textThis thesis addresses the multi-body assembly (MBA) problem in the context of protein assemblies. [...] In this thesis, we chose the protein assembly domain because accurate and reliable computational modeling, simulation and prediction of such assemblies would clearly accelerate discoveries in understanding of the complexities of metabolic pathways, identifying the molecular basis for normal health and diseases, and in the designing of new drugs and other therapeutics. [...] [We developed] F²Dock (Fast Fourier Docking) which includes a multi-term function which includes both a statistical thermodynamic approximation of molecular free energy as well as several of knowledge-based terms. Parameters of the scoring model were learned based on a large set of positive/negative examples, and when tested on 176 protein complexes of various types, showed excellent accuracy in ranking correct configurations higher (F² Dock ranks the correcti solution as the top ranked one in 22/176 cases, which is better than other unsupervised prediction software on the same benchmark). Most of the protein-protein interaction scoring terms can be expressed as integrals over the occupied volume, boundary, or a set of discrete points (atom locations), of distance dependent decaying kernels. We developed a dynamic adaptive grid (DAG) data structure which computes smooth surface and volumetric representations of a protein complex in O(m log m) time, where m is the number of atoms assuming that the smallest feature size h is [theta](r[subscript max]) where r[subscript max] is the radius of the largest atom; updates in O(log m) time; and uses O(m)memory. We also developed the dynamic packing grids (DPG) data structure which supports quasi-constant time updates (O(log w)) and spherical neighborhood queries (O(log log w)), where w is the word-size in the RAM. DPG and DAG together results in O(k) time approximation of scoring terms where k << m is the size of the contact region between proteins. [...] [W]e consider the symmetric spherical shell assembly case, where multiple copies of identical proteins tile the surface of a sphere. Though this is a restricted subclass of MBA, it is an important one since it would accelerate development of drugs and antibodies to prevent viruses from forming capsids, which have such spherical symmetry in nature. We proved that it is possible to characterize the space of possible symmetric spherical layouts using a small number of representative local arrangements (called tiles), and their global configurations (tiling). We further show that the tilings, and the mapping of proteins to tilings on arbitrary sized shells is parameterized by 3 discrete parameters and 6 continuous degrees of freedom; and the 3 discrete DOF can be restricted to a constant number of cases if the size of the shell is known (in terms of the number of protein n). We also consider the case where a coarse model of the whole complex of proteins are available. We show that even when such coarse models do not show atomic positions, they can be sufficient to identify a general location for each protein and its neighbors, and thereby restricts the configurational space. We developed an iterative refinement search protocol that leverages such multi-resolution structural data to predict accurate high resolution model of protein complexes, and successfully applied the protocol to model gp120, a protein on the spike of HIV and currently the most feasible target for anti-HIV drug design.Computer Science

Structural Basis for the Recognition in an Idiotype-Anti-Idiotype Antibody Complex Related to Celiac Disease

Anti-idiotype antibodies have potential therapeutic applications in many fields, including autoimmune diseases. Herein we report the isolation and characterization of AIM2, an anti-idiotype antibody elicited in a mouse model upon expression of the celiac disease-specific autoantibody MB2.8 (directed against the main disease autoantigen type 2 transglutaminase, TG2). To characterize the interaction between the two antibodies, a 3D model of the MB2.8-AIM2 complex has been obtained by molecular docking. Analysis and selection of the different obtained docking solutions was based on the conservation within them of the inter-residue contacts. The selected model is very well representative of the different solutions found and its stability is confirmed by molecular dynamics simulations. Furthermore, the binding mode it adopts is very similar to that observed in most of the experimental structures available for idiotype-anti-idiotype antibody complexes. In the obtained model, AIM2 is directed against the MB2.8 CDR region, especially on its variable light chain. This makes the concurrent formation of the MB2.8-AIM2 complex and of the MB2.8-TG2 complex incompatible, thus explaining the experimentally observed inhibitory effect on the MB2.8 binding to TG2

Crystal Structure of Two Anti-Porphyrin Antibodies with Peroxidase Activity

We report the crystal structures at 2.05 and 2.45 Å resolution of two antibodies, 13G10 and 14H7, directed against an iron(III)-αααβ-carboxyphenylporphyrin, which display some peroxidase activity. Although these two antibodies differ by only one amino acid in their variable λ-light chain and display 86% sequence identity in their variable heavy chain, their complementary determining regions (CDR) CDRH1 and CDRH3 adopt very different conformations. The presence of Met or Leu residues at positions preceding residue H101 in CDRH3 in 13G10 and 14H7, respectively, yields to shallow combining sites pockets with different shapes that are mainly hydrophobic. The hapten and other carboxyphenyl-derivatized iron(III)-porphyrins have been modeled in the active sites of both antibodies using protein ligand docking with the program GOLD. The hapten is maintained in the antibody pockets of 13G10 and 14H7 by a strong network of hydrogen bonds with two or three carboxylates of the carboxyphenyl substituents of the porphyrin, respectively, as well as numerous stacking and van der Waals interactions with the very hydrophobic CDRH3. However, no amino acid residue was found to chelate the iron. Modeling also allows us to rationalize the recognition of alternative porphyrinic cofactors by the 13G10 and 14H7 antibodies and the effect of imidazole binding on the peroxidase activity of the 13G10/porphyrin complexes

Computational approaches in antibody-drug conjugate optimization for targeted cancer therapy

WOS: 000444683500007PubMed ID: 30068276Cancer has become one of the main leading causes of morbidity and mortality worldwide. One of the critical drawbacks of current cancer therapeutics has been the lack of the target-selectivity, as these drugs should have an effect exclusively on cancer cells while not perturbing healthy ones. In addition, their mechanism of action should be sufficiently fast to avoid the invasion of neighbouring healthy tissues by cancer cells. The use of conventional chemotherapeutic agents and other traditional therapies, such as surgery and radiotherapy, leads to off-target interactions with serious side effects. In this respect, recently developed target-selective Antibody-Drug Conjugates (ADCs) are more effective than traditional therapies, presumably due to their modular structures that combine many chemical properties simultaneously. In particular, ADCs are made up of three different units: a highly selective Monoclonal antibody (Mab) which is developed against a tumour-associated antigen, the payload (cytotoxic agent), and the linker. The latter should be stable in circulation while allowing the release of the cytotoxic agent in target cells. The modular nature of these drugs provides a platform to manipulate and improve selectivity and the toxicity of these molecules independently from each other. This in turn leads to generation of second-and third-generation ADCs, which have been more effective than the previous ones in terms of either selectivity or toxicity or both. Development of ADCs with improved efficacy requires knowledge at the atomic level regarding the structure and dynamics of the molecule. As such, we reviewed all the most recent computational methods used to attain all-atom description of the structure, energetics and dynamics of these systems. In particular, this includes homology modelling, molecular docking and refinement, atomistic and coarse-grained molecular dynamics simulations, principal component and cross-correlation analysis. The full characterization of the structure-activity relationship devoted to ADCs is critical for antibody-drug conjugate research and development.Fundacao para a Ciencia e a Tecnologia (FCT) Investigator programme [IF/00578/2014]; European Social Fund; Programa Operacional Potencial Humano; Marie Sklodowska-Curie Individual Fellowship MSCA-IF-2015 [MEMBRANEPROT 659826]; European Regional Development Fund (ERDF), through the Centro 2020 Regional Operational Programme [CENTRO-01-0145-FEDER-000008]; European Regional Development Fund (ERDF), COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation; Portuguese national funds via FCT [POCI-01-0145-FEDER-007440]; FCT [FCT-SFRH/BPD/97650/2013]; Fundacao para a Ciencia e Tecnologia (FCT), Portugal [UID/Multi/04349/2013]Irina S. Moreira acknowledges support by the Fundacao para a Ciencia e a Tecnologia (FCT) Investigator programme - IF/00578/2014 (co-financed by European Social Fund and Programa Operacional Potencial Humano), and a Marie Sklodowska-Curie Individual Fellowship MSCA-IF-2015 [MEMBRANEPROT 659826]. This work was also financed by the European Regional Development Fund (ERDF), through the Centro 2020 Regional Operational Programme under project CENTRO-01-0145-FEDER-000008: Brain-Health 2020, and through the COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation and Portuguese national funds via FCT, under project POCI-01-0145-FEDER-007440. Rita Melo acknowledges support from the FCT (FCT-SFRH/BPD/97650/2013). This work has been partially supported by the Fundacao para a Ciencia e Tecnologia (FCT), Portugal, through the UID/Multi/04349/2013 project in Centre for Nuclear Sciences and Technologies (C2TN)