Structural Chemistry & Crystallography Communication

REAL SPACE MODELING OF SOLUTION-STATE SAXS USING INFORMATION THEORY

3^rd Edition of International Conference on Advanced Spectroscopy, Crystallography and Applications in Modern Chemistry
June 04-05, 2018 London, UK

Robert P Rambo

Diamond Light Source, Harwell Science and Innovation Campus, UK

Posters & Accepted Abstracts: Struct Chem Crystallogr Commun

DOI: 10.21767/2470-9905-C1-006

Abstract

Small angle X-ray scattering measurements of dilute, homogenous particles in solution are resolution limited measurements of the thermodynamic ensemble. Similar to X-ray crystallography and electron microscopy, SAXS observations made at higher resolutions imply a greater detail in the structural measurement. Here, I present a new approach to understanding bioSAXS data using two fundamental properties of Information Theory (namely, the Shannon Sampling and Noisy-Coding Channel theorems). These theorems allow for the error-free recovery of the SAXS signal, in the form of a real-space, cross-validated pairdistance, P(r), distribution function. The P(r)-distribution contains the structural assessment of the thermodynamic ensemble. I will show that the Information theory framework can be used to develop structural modeling algorithms for shape determination and docking. Specifically, I will demonstrate an adaptive simulated-annealing, density modeling algorithm that targets the P(r)-distribution using the Kullback-Liebler divergence, an Information Theory difference metric. The algorithm scales with resolution. Using a SAXS dataset of a 25 base-pair, doublestranded DNA, the volumetric model illustrates features of the major and minor groove as the resolution of the SAXS dataset increases. Further tests on SAXS of the P4-P6 group I intron RNA domain reveal the large solvent channels observed in the X-ray crystal structure. Furthermore, I will show the Information Theory approach can be used in antibody-antigen studies to uniquely determine the structure of the complex in the solution state.  Our approach shows that modeling can be made more reliable by exploiting theorems from Information Theory. robert.rambo@diamond.ac.uk