End-point methods such as Linear Interaction Energy (LIE) analysis, Molecular Mechanics Generalized Born Solvent Accessible Surface (MM/GBSA) and Solvent Interaction Energy (SIE) analysis have become popular techniques to calculate the free energy associated with protein-ligand binding. Such methods typically use molecular dynamics (MD) simulations to generate an ensemble of protein structures that encompasses the bound and unbound states. The energy evaluation method (LIE, MM/GBSA or SIE) is subsequently used to calculate the energy of each member of the ensemble, thus providing an estimate of the average free energy difference between the bound and unbound states. The workflow requiring both MD simulation and energy calculation for each frame and each trajectory proves to be computationally expensive. In an attempt to reduce the high computational cost associated with end-point methods, we study several methods by which frames may be intelligently selected from the MD simulation including clustering and address the question how the number of selected frames influences the accuracy of the SIE calculations.

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Distance-based statistical potentials have long been used to model condensed matter systems, e.g. as scoring functions in differentiating native-like protein structures from decoys. These scoring functions are based on the assumption that the total free energy of the protein can be calculated as the sum of pairwise free energy contributions derived from a statistical analysis of pair-distribution functions. However, this fundamental assumption has been challenged theoretically. In fact the free energy of a system with N particles is only exactly related to the N-body distribution function. Based on this argument coarse-grained multi-body statistical potentials have been developed to capture higher-order interactions. Having a coarse representation of the protein and using geometric contacts instead of pairwise interaction distances renders these models insufficient in modeling details of multi-body effects. In this study, we investigated if extending distance-dependent pairwise atomistic statistical potentials to corresponding interaction functions that are conditional on a third interacting body, defined as quasi-three-body statistical potentials, could model details of three-body interactions. We also tested if this approach could improve the predictive capabilities of statistical scoring functions for protein structure prediction. We analyzed the statistical dependency between two simultaneous pairwise interactions and showed that there is surprisingly little if any dependency of a third interacting site on pairwise atomistic statistical potentials. Also the protein structure prediction performance of these quasi-three-body potentials is comparable with their corresponding two-body counterparts. The scoring functions developed in this study showed better or comparable performances compared to some widely used scoring functions for protein structure prediction.

The cognitive radio (CR) field currently lacks a standardized end-user test methodology that is repeatable, flexible, and effective across multiple CR architectures. Furthermore, the CR field lacks a suitable deviceagnostic framework that allows testing of an integrated cognitive radio system (CRS) and not solely specific components. This research presents a CR test methodology, known as Cognitive RAdio Test Methodology (CRATM), to address these issues. CRATM proposes to use behaviorbased testing in which cognition may be measured by evaluating both primary user (PU) and secondary user (SU) (i.e. the CR under test) performance. Data on behaviorbased testing is collected and evaluated. A SU pair and PU radio pair are implemented using the Wireless Open-Acess Research Platform (WARP) platform and WARPLab software running in MATLAB. The PU is used to create five distinct radio frequency (RF) environments utilizing narrowband, wideband, and non-contiguous waveforms. The SU response to the PU created environments is measured. The SU implements a simple cognitive engine (CE) that incorporates energy-detection spectrum sensing. The effect of the CE on both SU and PU performance is measured and evaluated.

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