RosettaAntibodyDesign (RAbD): A general framework for computational antibody design.

A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

[Presumption of the energy-spectrum of high-energy electron beam based on the beta-distribution model].

The energy spectra of high-energy electron beams used in radiotherapy are the most important data for evaluating absorbed doses and/or dose distributions in the body of a patient. However, it is impossible to measure the actual spectra of a high-energy electron beam. In this study, we suggest a method to presume the spectra of high-energy electron beams by use of the beta distribution model. The procedure of this method is as follows: (1) the spectrum of the high-energy electron beam was assumed to have a maximum energy Emax, and α, ß parameters of the beta probability density function. (2) The percentage depth dose (PDD) based on the assumed spectrum was calculated by a Monte Carlo simulation. (3) The best matching energy spectrum was searched in comparison with the experimental PDD curves. Finally, the optimal energy spectrum of the electron beam was estimated after reiterating the process from (1) to (3). With our method, the measured PDD curves were optimally simulated following the experimental data. It appeared that the assumed spectra approximated well to the actual spectra. However, the error between the assumed and experimental data was observed in the region under the incident surface. We believe this was due to the influence of low-energy electrons scattered at installed collimators, etc. In order to simulate PDDs in this region accurately, a further correction process is required for a spectrum based on the beta distribution model.