Leveraging Structural Flexibility to Predict Protein Function

Proteins are essentially versatile and flexible molecules and understanding protein function plays a fundamental role in understanding biological systems. Protein structure comparisons are widely used for revealing protein function. However,with rigidity or partial rigidity assumption, most existing comparison methods do not consider conformational flexibility in protein structures. To address this issue, this thesis seeks to develop algorithms for flexible structure comparisons to predict one specific aspect of protein function, binding specificity. Given conformational samples as flexibility representation, we focus on two predictive problems related to specificity: aggregate prediction and individual prediction.For aggregate prediction, we have designed FAVA (Flexible Aggregate Volumetric Analysis). FAVA is the first conformationally general method to compare proteins with identical folds but different specificities. FAVA is able to correctly categorize members of protein superfamilies and to identify influential amino acids that cause different specificities. A second method PEAP (Point-based Ensemble for Aggregate Prediction) employs ensemble clustering techniques from many base clustering to predict binding specificity. This method incorporates structural motions of functional substructures and is capable of mitigating prediction errors.For individual prediction, the first method is an atomic point representation for representing flexibilities in the binding cavity. This representation is able to predict binding specificity on each protein conformation with high accuracy, and it is the first to analyze maps of binding cavity conformations that describe proteins with different specificities. Our second method introduces a volumetric lattice representation. This representation localizes solvent-accessible shape of the binding cavity by computing cavity volume in each user-defined space. It proves to be more informative than point-based representations. Last but not least, we discuss a structure-independent representation. This representation builds a lattice model on protein electrostatic isopotentials. This is the first known method to predict binding specificity explicitly from the perspective of electrostatic fields.The methods presented in this thesis incorporate the variety of protein conformations into the analysis of protein ligand binding, and provide more views on flexible structure comparisons and structure-based function annotation of molecular design

Stochastic Derivative-Free Optimization of Noisy Functions

Optimization problems with numerical noise arise from the growing use of computer simulation of complex systems. This thesis concerns the development, analysis and applications of randomized derivative-free optimization (DFO) algorithms for noisy functions. The first contribution is the introduction of DFO-VASP, an algorithm for solving the problem of finding the optimal volumetric alignment of protein structures. Our method compensates for noisy, variable-time volume evaluations and warm-starts the search for globally optimal superposition. These techniques enable DFO-VASP to generate practical and accurate superpositions in a timely manner. The second algorithm, STARS, is aimed at solving general noisy optimization problems and employs a random search framework while dynamically adjusting the smoothing step-size using noise information. rate analysis of this algorithm is provided in both additive and multiplicative noise settings. STARS outperforms randomized zero-order methods in both additive and multiplicative settings and has an advantage of being insensitive to the level noise in terms of number of function evaluations and final objective value. The third contribution is a trust-region model-based algorithm STORM, that relies on constructing random models and estimates that are sufficiently accurate with high probability. This algorithm is shown to converge with probability one. Numerical experiments show that STORM outperforms other stochastic DFO methods in solving noisy functions

Reports to the President

A compilation of annual reports including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans

Superposition of Protein Structures Using Electrostatic Isopotentials

Abstract-Algorithms for comparing protein structures are widely used to identify proteins with similar functions and to examine the mechanisms of binding specificity. In order to make accurate comparisons, two structures must first be superposed, so that differences in position and orientation do not create misleading dissimilarities. Most algorithms generate these superpositions by aligning atoms of the peptide backbone. This approach is rapid, but it may not reflect similarities or differences in all mechanisms that proteins use to bind other molecules. Electric fields, for example, play a large role in recognition and their substantial range can interact with other molecules long before backbone contacts occur. To compare proteins based on their electric fields, we have developed the first algorithm designed to superpose protein structures using electric fields alone. Our method works by searching rotational and translational space for a superposition that maximizes the overlapping volume between electrostatic isopotentials. Applying this method to compare the serine protease and enolase superfamilies, our results demonstrate that our electrostatic superposition algorithm can distinguish very similar proteins with different binding preferences