Antibody-based therapeutics provides novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal or directed evolution of antibodies with low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.
In this study we introduce a computationally-driven enzyme redesign workflow for altering cofactor specificity from NADPH to NADH. By compiling and comparing data from previous studies involving cofactor switching mutations, we show that their effect cannot be explained as straightforward changes in volume, hydrophobicity, charge, or BLOSUM62 scores of the residues populating the cofactor binding site. Instead, we find that the use of a detailed cofactor binding energy approximation is needed to adequately capture the relative affinity towards different cofactors. The implicit solvation models Generalized Born with molecular volume integration and Generalized Born with simple switching were integrated in the iterative protein redesign and optimization (IPRO) framework to drive the redesign of Candida boidinii xylose reductase (CbXR) to function using the non-native cofactor NADH. We identified 10 variants, out of the 8,000 possible combinations of mutations, that improve the computationally assessed binding affinity for NADH by introducing mutations in the CbXR binding pocket. Experimental testing revealed that seven out of ten possessed significant xylose reductase activity utilizing NADH, with the best experimental design (CbXR-GGD) being 27-fold more active on NADH. The NADPH-dependent activity for eight out of ten predicted designs was either completely abolished or significantly diminished by at least 90%, yielding a greater than 10 4 -fold change in specificity to NADH (CbXR-REG). The remaining two variants (CbXR-RTT and CBXR-EQR) had dual cofactor specificity for both nicotinamide cofactors.
Antibodies are an important class of proteins with many biomedical and biotechnical applications. Although there are a plethora of experimental techniques geared toward their efficient production, there is a paucity of computational methods for their de novo design. OptCDR is a general computational method to design the binding portions of antibodies to have high specificity and affinity against any targeted epitope of an antigen. First, combinations of canonical structures for the antibody complementarity determining regions (CDRs) that are most likely to be able to favorably bind the antigen are selected. This is followed by the simultaneous refinement of the CDR structures' backbones and optimal amino acid selection for each position. OptCDR is applied to three computational test cases: a peptide from the capsid of hepatitis C, the hapten fluorescein and the protein vascular endothelial growth factor. The results demonstrate that OptCDR can efficiently generate diverse antibody libraries of a pre-specified size with promising antigen affinity potential as exemplified by computationally derived binding metrics.
Disease-specific antibodies can serve as highly effective biomarkers but have been identified for only a relatively small number of autoimmune diseases. A method was developed to identify disease-specific binding motifs through integration of bacterial display peptide library screening, next-generation sequencing (NGS) and computational analysis. Antibody specificity repertoires were determined by identifying bound peptide library members for each specimen using cell sorting and performing NGS. A computational algorithm, termed Identifying Motifs Using Next- generation sequencing Experiments (IMUNE), was developed and applied to discover disease- and healthy control-specific motifs. IMUNE performs comprehensive pattern searches, identifies patterns statistically enriched in the disease or control groups and clusters the patterns to generate motifs. Using celiac disease sera as a discovery set, IMUNE identified a consensus motif (QPEQPF[PS]E) with high diagnostic sensitivity and specificity in a validation sera set, in addition to novel motifs. Peptide display and sequencing (Display-Seq) coupled with IMUNE analysis may thus be useful to characterize antibody repertoires and identify disease-specific antibody epitopes and biomarkers.
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