Optimal selection of suitable templates in protein interface prediction.

MOTIVATION: Molecular-level classification of protein-protein interfaces can greatly assist in functional characterization and rational drug design. The most accurate protein interface predictions rely on finding homologous proteins with known interfaces since most interfaces are conserved within the same protein family. The accuracy of these template-based prediction approaches depends on the correct choice of suitable templates. Choosing the right templates in the immunoglobulin superfamily (IgSF) is challenging because its members share low sequence identity and display a wide range of alternative binding sites despite structural homology. RESULTS: We present a new approach to predict protein interfaces. First, template-specific, informative evolutionary profiles are established using a mutual information-based approach. Next, based on the similarity of residue level conservation scores derived from the evolutionary profiles, a query protein is hierarchically clustered with all available template proteins in its superfamily with known interface definitions. Once clustered, a subset of the most closely related templates is selected, and an interface prediction is made. These initial interface predictions are subsequently refined by extensive docking. This method was benchmarked on 51 IgSF proteins and can predict nontrivial interfaces of IgSF proteins with an average and median F-score of 0.64 and 0.78, respectively. We also provide a way to assess the confidence of the results. The average and median F-scores increase to 0.8 and 0.81, respectively, if 27% of low confidence cases and 17% of medium confidence cases are removed. Lastly, we provide residue level interface predictions, protein complexes, and confidence measurements for singletons in the IgSF. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at: https://gitlab.com/fiserlab.org/interdct_with_refinement.

Preclinical Evidence of an Allogeneic Dual CD20xCD22 CAR to Target a Broad Spectrum of Patients with B-cell Malignancies.

Despite the remarkable success of autologous chimeric antigen receptor (CAR) T cells, some patients relapse due to tumor antigen escape and low or uneven antigen expression, among other mechanisms. Therapeutic options after relapse are limited, emphasizing the need to optimize current approaches. In addition, there is a need to develop allogeneic "off-the-shelf" therapies from healthy donors that are readily available at the time of treatment decision and can overcome limitations of current autologous approaches. To address both challenges simultaneously, we generated a CD20xCD22 dual allogeneic CAR T cell. Herein, we demonstrate that allogeneic CD20x22 CAR T cells display robust, sustained and dose-dependent activity in vitro and in vivo, while efficiently targeting primary B-cell non-Hodgkin lymphoma (B-NHL) samples with heterogeneous levels of CD22 and CD20. Altogether, we provide preclinical proof-of-concept data for an allogeneic dual CAR T cell to overcome current mechanisms of resistance to CAR T-cell therapies in B-NHL, while providing a potential alternative to CD19 targeting.

INTERCAAT: identifying interface residues between macromolecules.

SUMMARY: The Interface Contact definition with Adaptable Atom Types (INTERCAAT) was developed to determine the atomic interactions between molecules that form a known three dimensional structure. First, INTERCAAT creates a Voronoi tessellation where each atom acts as a seed. Interactions are defined by atoms that share a hyperplane and whose distance is less than the sum of each atoms' Van der Waals radii plus the diameter of a solvent molecule. Interacting atoms are then classified and interactions are filtered based on compatibility. INTERCAAT implements an adaptive atom classification method; therefore, it can explore interfaces between a variety macromolecules. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at: https://gitlab.com/fiserlab.org/intercaat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.