Predictive Modeling for Geometric RuleBased Methods Previously, we proposed a method for incorporating molecular geometry in a biological rulebased model by encoding molecular curvature into the rules and associated binding rate constants. We combined this method with a 3D rigidbody Monte Carlo simulation to model antigenantibody aggregation. In this work, we use our geometric rulebased method to develop a model for predicting the output of the fullresolution Monte Carlo simulation given the output of lower resolution simulations. The purpose of this predictive model is to reduce the computational cost of the Monte Carlo simulation. We develop this model by first choosing a rule set for each molecular geometry and varying only the binding rate constant for each Monte Carlo resolution, and then fitting the resulting data to a function. We examine the calculation time needed for each predictive model to demonstrate how this model is more efficient than running a fullresolution simulation. We find that this method can reduce the computational time of the Monte Carlo simulation by up to 20%.

StructureBased Antibody Paratope Prediction with 3D Zernike Descriptors and SVM Antibodies currently represent the most valuable category of biopharmaceuticals for both diagnostic and therapeutic applications. They are a class of Yshaped proteins capable of specifically recognizing and binding to a virtually infinite number of antigens. Being able to identify the antigenbinding residues in an antibody structure is crucial for all antibody design methods and for shedding light on the complex mechanisms that govern antigen recognition and binding. This paper presents a method for antibody interface prediction from their experimentallysolved structures based on 3D Zernike Descriptors. Rototranslationally invariant descriptors are computed from circular patches of the antibody surface enriched with a chosen subset of physicochemical properties from the AAindex1 amino acid index set, and are used as samples for a binary classification problem. An SVM classifier is used to distinguish interface surface patches from noninterface ones. The proposed method outperforms other antigenbinding interface prediction software, namely Paratome, Antibody iPatch and Parapred, on a novel dataset of experimentallysolved antibodyxe2x80x93antigen complex structures.