Article Details New 3D Modeling and Data Extraction Technique

March 29, 2016
Pharmaceutical Technology Editors

PTSM: Pharmaceutical Technology Sourcing and Management

PTSM: Pharmaceutical Technology Sourcing and Management-04-06-2016, Volume 11, Issue 4

A new 3D modeling and data-extraction technique Improves X-ray crystallography analysis of proteins.

An article published in the Proceedings of the National Academy of Sciences on March 28, 2016 describes a new 3D modeling and data-extraction technique for the improved blending of experimentation and computer modeling, extracting valuable information from diffuse, previously discarded data. According to a press announcement from Los Alamos National Laboratory (LANL), the new technique is an important advance in the field of X-ray crystallography.

“The accomplishment here is to demonstrate that we can analyze conventionally collected protein crystallography data and pull out background features that are usually thrown away,” said Michael E. Wall, a scientist in the Computer, Computational, and Statistical Sciences Division at LANL and co-corresponding author of the article with James S. Fraser, Assistant Professor in the Department of Bioengineering and Therapeutic Sciences at University of California, San Francisco, in a press release. “What’s been reclaimed is information about how the protein moves.”

Traditional crystallography data provide a blurred picture of how the protein moves. Wall notes their approach sharpens the picture, providing information about which atoms are moving in a concerted way, such as ones on the swinging arm of the protein or on opposite sides of a hinge opening or closing, and which ones are moving more independently.

“This is a method that will eventually change the way X-ray crystallography is done, bringing in this additional data stream in addition to the sharply peaked Bragg scattering, which is the traditional analysis method,” he said. “We’re working toward using both data sets simultaneously to increase the clarity of the crystallography model and more clearly map how proteins are moving.”

According to LANL, the researchers measured the 3D diffuse scattering data from crystals of the enzymes cyclophilin A and trypsin (an enzyme that acts to degrade protein) at Stanford Synchrotron Radiation Lightsource (SSRL), a US Department of Energy (DOE) Office of Science user facility. The measurements were extracted and movements were modeled using computers at LANL, Lawrence Berkeley National Laboratory, and the University of California, San Francisco. The ongoing computational work includes simulations on Conejo and Mustang, supercomputing clusters in LANL’s Institutional Computing Program.

Averaging relatively weak features in the data improves the clarity of the imaging of diffuse features, which has value as researchers have had an increasing interest in the role of protein motions. According to LANL, with better modeling, adapted to more closely match experimental diffuse data, the steps toward a new pharmaceutical product can be reduced by more accurately accounting for protein motions in drug interactions. The new approach can improve new and ongoing experiments and could potentially be used to explore data from previously conducted crystallography experiments if the level of background noise is not too severe.

According to LANL, with this new method, scientists can experimentally validate predictions of detailed models of protein motions, such as computationally expensive all-atom molecular dynamics simulations, and less-expensive normal mode analysis, in which the protein motions are modeled as vibrations in a network of atoms interconnected by soft springs. A key finding is that normal modes models of both cyclophilin A and trypsin resemble the diffuse data; this creates an avenue for adjusting detailed models of protein motion to better agree with the data. This use of diffuse scattering data illustrates the potential to increase understanding of protein structure variations in any X-ray crystallography experiment.

Source: LANL

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