Over the last two decades, mass spectrometry (MS) has emerged as a key approach for addressing challenging problems in structural biochemistry. Sophisticated MS techniques in conjunction with covalently-labeled protein residues yield exposure information useful in protein structure prediction. Alternatively, surface induced dissociation (SID) allows for estimation of the dissociation energy of mostly non-covalent interactions at protein-protein interfaces, as well as complex stoichiometry. However, easy and reliable translation of this information into accurate structural protein and protein complex models remains particularly challenging, due to a lack of standardized and automated methods. There remains a critical need for computer programs that can facilitate the rapid and unambiguous structural interpretation of MS labeling data. We developed and validated software tools that allow covalent labeling MS data to be effectively used to guide protein structure prediction and that allow SID data to improve protein-protein docking in Rosetta. We developed a custom MS-scoring function that favorably scores exposure of labeled residues in protein models and this functionality was implemented into the Rosetta protein structure prediction software. In combination with Rosetta's powerful energy function, we can accurately predict protein structure from MS labeling data. Additionally, we used SID data to develop a structure-based method for calculating interface energy. This allows for discrimination of different quaternary structural models based on SID measurements using a novel SID based scoring GGfunction, which is used in combination with Rosetta's scoring function.

When people make simple decisions, for example choosing between entrees on a menu, they tend to look back and forth between the alternatives, as if gathering evidence to support each alternative. Indeed, many types of decision can be modeled as noisy evidence accumulation, where the support for one option (over the other) diffuses randomly over time until a preset decision criterion is reached. This diffusion-to-bound process has theoretically attractive optimality properties and, more importantly, fits behavior quite accurately. In this talk, I will review the research on diffusion models of decision making. I will discuss data from my own lab on how eye-movements influence the diffusion-process dynamics, biasing people's choices towards alternatives that attract more attention. I will also present models and evidence from neuroscience for how this diffusion process is actually implemented in the brain, relying on the buildup of brain activity (neuronal spike rates) in cortical regions such as the dorsomedial prefrontal cortex.