HSymM-guided engineering of the immunodominant p53 transactivation domain putative peptide antigen for improved binding to its anti-p53 monoclonal antibody.

A novel engineering strategy to improve autoantibody detection with peptide fragments derived from the parent antigen is presented. The model system studied was the binding of the putative p53 TAD peptide antigen (residues 46-55) to its cognate anti-p53 antibody, ab28. Each engineered peptide contained the full decapeptide epitope and differed only in the flanking regions. Since minimal structural information was available to guide the design, a simple epitope:paratope binding model was applied. The Hidden Symmetry Model, which we recently reported, was used to guide peptide design and estimate per-residue contributions to interaction free energy as a function of added C- and N-terminal flanking peptides. Twenty-four peptide constructs were designed, synthesized, and assessed for binding affinity to ab28 by surface plasmon resonance, and a subset of these peptides were evaluated in a simulated immunoassay for limit of detection. Many peptides exhibited over 200-fold enhancements in binding affinity and improved limits of detection. The epitope was reevaluated and is proposed to be the undecapeptide corresponding to residues 45-55. HSymM calculated binding free energy and experimental data were found to be in good agreement (R2 > 0.75).

A protein interaction free energy model based on amino acid residue contributions: Assessment of point mutation stability of T4 lysozyme.

Here we present a model to estimate the interaction free energy contribution of each amino acid residue of a given protein. Protein interaction energy is described in terms of per-residue interaction factors, µ. Multibody interactions are implicitly captured in µ through the combination of amino acid terms (γ) guided by local conformation indices (σ). The model enables construction of an interaction factor heat map for a protein in a given fold, allows prima facie assessment of the degree of residue-residue interaction, and facilitates a qualitative and quantitative evaluation of protein association properties. The model was used to compute thermal stability of T4 bacteriophage lysozyme mutants across seven sites. Qualitative assessment of mutational effects provides a straightforward rationale regarding whether a particular site primarily perturbs native or non-native states, or both. The presented model was found to be in good agreement with experimental mutational data (R 2 = 0.73) and suggests an approach by which to convert structure space into energy space.

The growing role of precision and personalized medicine for cancer treatment.

Cancer is a devastating disease that takes the lives of hundreds of thousands of people every year. Due to disease heterogeneity, standard treatments, such as chemotherapy or radiation, are effective in only a subset of the patient population. Tumors can have different underlying genetic causes and may express different proteins in one patient versus another. This inherent variability of cancer lends itself to the growing field of precision and personalized medicine (PPM). There are many ongoing efforts to acquire PPM data in order to characterize molecular differences between tumors. Some PPM products are already available to link these differences to an effective drug. It is clear that PPM cancer treatments can result in immense patient benefits, and companies and regulatory agencies have begun to recognize this. However, broader changes to the healthcare and insurance systems must be addressed if PPM is to become part of standard cancer care.