From Cellular to Holistic: Development of Algorithms to Study Human Health and Diseases

The development of theoretical computational methods and their application has become widespread in the world today. In this dissertation, I present my work in the creation of models to detect and describe complex biological and health related problems. The first major part of my work centers around the creation and enhancement of methods to calculate protein structure and dynamics. To this end, substantial enhancement has been made to the software package REDCRAFT to better facilitate its usage in protein structure calculation. The enhancements have led to an overall increase in its ability to characterize proteins under difficult conditions such as high noise and low data density. Secondly, a database that allows for easy and comprehensive mining of protein structures has been created and deployed. We show preliminary results for its application to protein structure calculation. This database, among other applications, can be used to create input sets for computational models for prediction of protein structure. Lastly, I present my work on the creation of a theoretical model to describe discrete state protein dynamics. The results of this work can be used to describe many real-world dynamic systems. The second major part of my work centers around the application of machine learning techniques to create a system for the automated detection of smoking using accelerometer data from smartwatches. The first aspect of this work that will be presented is binary detection of smoking puffs. This model was then expanded to perform full cigarette session detection. Next, the model was reformulated to perform quantification of smoking (such as puff duration and the time between puffs). Lastly, a rotational matrix was derived to resolve ambiguities of smartwatches due to position of the watch on the wrist

Concurrent Identification, Characterization, and Reconstruction Of Protein Structure and Mixed-Mode Dynamics From RDC Data Using Redcraft

A complete understanding of the structure-function relationship of proteins requires an analysis of their dynamic behaviors and the static structure. However, all current approaches to studying dynamics in proteins have their shortcomings. A conceptually attractive and alternative approach simultaneously characterizes a protein\u27s structure and its intrinsic dynamics⁠. Ideally, such an approach could solely rely on RDC data-carrying both structural and dynamical information. The major bottleneck in utilizing RDC data in recent years has been attributed to a lack of RDC analysis tools capable of extracting the pertinent information embedded within this complex data source. Here we present a comprehensive strategy for structure calculation and reconstruction of discrete state dynamics from RDC data based on the SVD method of order tensor estimation. In addition to structure determination, we provide a mechanism of producing an ensemble of conformations for the dynamical regions of a protein from RDC data. The developed methodology has been tested on simulated RDC data with ۫Hz of error from an 83 residue α protein (PDB ID 1A1Z). In nearly all instances, our method reproduced the protein structure, including the conformational ensemble, within less than 2Å. Based on our investigations, arc motions with more than 30° of rotation are recognized as internal dynamics and are reconstructed with sufficient accuracy. Furthermore, states with relative occupancies above 20% are consistently recognized and reconstructed successfully. Arc motions with a magnitude of 15° or relative occupancy of less than 10% are consistently unrecognizable as dynamical regions within the context of ± 1Hz of error. We also introduce a computational approach named REDCRAFT that allows for uncompromised and concurrent characterization of protein structure and dynamics. We have subjected DHFR (PDB-ID 1RX2), a 159-residue protein, to a fictitious but plausible, mixed-mode internal dynamics model. In this simulation, DHFR was segmented into seven regions. The two dynamical and rigid-body segments experienced an average orientational modification of 7˚ and 12˚, respectively. Observable RDC data for backbone C\u27-N, N-H, and C\u27-H were generated from 102 frames that described the molecular trajectory. The Dynamic Profile generated by REDCRAFT allowed for the recovery of individual fragments with bb-rmsd of less than 1Å and the identification of different dynamical regions of the protein. Following the recovery of fragments, structural assembly correctly assembled the four rigid fragments with respect to each other, categorized the two domains that underwent rigid-body dynamics, and identified one dynamical region for which no conserved structure can be defined. In conclusion, our approach successfully identified dynamical domains, recovery of structure where it is meaningful, and relative assembly of the domains when possible