Effective biologic therapeutics require binding affinities that are fine-tuned to their disease-related molecular target. The ADAPT (Assisted Design of Antibody and Protein Therapeutics) platform aids in the selection of mutants that improve/modulate the affinity of antibodies and other biologics. It uses a consensus z-score from three scoring functions and interleaves computational predictions with experimental validation, significantly enhancing the robustness of the design and selection of mutants. The platform was tested on three antibody Fab-antigen systems that spanned a wide range of initial binding affinities: bH1-VEGF-A (44 nM), bH1-HER2 (3.6 nM) and Herceptin-HER2 (0.058 nM). Novel triple mutants were obtained that exhibited 104-, 46- and 32-fold improvements in binding affinity for each system, respectively. Moreover, for all three antibody-antigen systems over 90% of all the intermediate single and double mutants that were designed and tested showed higher affinities than the parent sequence. The contributions of the individual mutants to the change in binding affinity appear to be roughly additive when combined to form double and triple mutants. The new interactions introduced by the affinity-enhancing mutants included long-range electrostatics as well as short-range nonpolar interactions. This diversity in the types of new interactions formed by the mutants was reflected in SPR kinetics that showed that the enhancements in affinities arose from increasing on-rates, decreasing off-rates or a combination of the two effects, depending on the mutation. ADAPT is a very focused search of sequence space and required only 20–30 mutants for each system to be made and tested to achieve the affinity enhancements mentioned above.

Molecular dynamics (MD) simulations are used to study the interaction of an anionic palmitoyl-oleoyl-phosphatidylglycerol (POPG) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with a POPG bilayer is employed as a model system for studying the details of membrane adsorption selectivity of cationic antimicrobial peptides. Seventy eight 4 ns MD production run trajectories of the equilibrated system, with six restrained orientations of LFCinB at 13 different separations from the POPG membrane, are generated to determine the free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the profile for this relatively large system, a variant of constrained MD and thermodynamic integration is used. A simplified method for relating the free energy profile to the LFCinB-POPG membrane binding constant is employed to predict a free energy of adsorption of -5.4+/-1.3 kcal/mol and a corresponding maximum adsorption binding force of about 58 pN. We analyze the results using Poisson-Boltzmann theory. We find the peptide-membrane attraction to be dominated by the entropy increase due to the release of counterions and polarized water from the region between the charged membrane and peptide, as the two approach each other. We contrast these results with those found earlier for adsorption of LFCinB on the mammalianlike palmitoyl-oleoyl-phosphatidylcholine membrane.

The free energies of adsorption of the monomer or dimer of the cationic β-hairpin antimicrobial peptide protegrin-1 (PG1) in a specific binding orientation on a lipid bilayer are determined using molecular dynamics (MD) simulations and Poisson-Boltzmann calculations. The bilayer is composed of anionic palmitoyl-oleoyl-phosphatidylglycerol (POPG) and palmitoyl-oleoyl-phosphatidylethanolamine (POPE) with ratio 1:3 (POPG:POPE). PG1 is believed to kill bacteria by binding on their membranes. There it forms pores that lyse the bacteria. Herein we focus on the thermodynamics of binding. In particular, we explore the role of counterion release from the lipid bilayer upon adsorption of either the monomeric or the dimeric form of PG1. Twenty two 4ns-long MD trajectories of equilibrated systems are generated to determine the free energy profiles for the monomer and dimer as a function of the distance between the peptide(s) and the membrane surface. The MD simulations are conducted at eleven different separations from the membrane for each of the two systems, one with PG1, the second with PG1 dimer only of a specific orientation of the monomer and dimer without taking into account the change of entropy for the peptide. To calculate the potential of mean force for each peptide/membrane system, a variant of constrained MD and thermodynamic integration is used. We observed that PG1 dimer binds more favorably to the POPG:POPE membrane. A simple method for relating the free energy profile to the PG1-membrane binding constant is employed to predict a free energy of adsorption of −2.4 ± 0.8 kcal/mol. A corresponding PG1-dimer-membrane binding constant is calculated as −3.5 ± 1.1 kcal/mol. Free energy profiles from MD simulation were extensively analyzed and compared with results of Poisson-Boltzmann theory. We find the peptide-membrane attraction to be dominated by the entropy increase due to the release of counterions in a POPG:POPE lipid bilayer.

Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05+/-0.39 kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. Questions? Contact the NRC Publications Archive team atPublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. ABSTRACT: Affinity modulation of antibodies and antibody fragments of therapeutic value is often required in order to improve their clinical efficacies. Virtual affinity maturation has the potential to quickly focus on the critical hotspot residues without the combinatorial explosion problem of conventional display and library approaches. However, this requires a binding affinity scoring function that is capable of ranking single-point mutations of a starting antibody. We focus here on assessing the solvated interaction energy (SIE) function that was originally developed for and is widely applied to scoring of protein−ligand binding affinities. To this end, we assembled a structure−function data set calledS i n g l e -P o i n tM u t a n t Antibody Binding (SiPMAB) comprising several antibody− antigen systems suitable for this assessment, i.e., based on high-resolution crystal structures for the parent antibodies and coupled with high-quality binding affinity measurements for sets of single-point antibody mutants in each system. Using this data set, we tested the SIE function with several mutation protocols based on the popular methods SCWRL, Rosetta, and FoldX. We found that the SIE function coupled with a protocol limited to sampling only the mutated side chain can reasonably predict relative binding affinities with a Spearman rank-order correlation coefficient of about 0.6, outperforming more aggressive sampling protocols. Importantly, this performance is maintained for each of the seven system-specific component subsets as well as for other relevant subsets including non-alanine and charge-altering mutations. The transferability and enrichment in affinityimproving mutants can be further enhanced using consensus ranking over multiple methods, including the SIE, Talaris, and FOLDEF energy functions. The knowledge gained from this study can lead to successful prospective applications of virtual affinity maturation.