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Protein and Protein Structure

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme can...

Globular and Fibrous Proteins

Globular and Fibrous Proteins

Many proteins can be classified into two distinct subtypes - globular or fibrous. These two types differ in their shapes and solubilities.
Globular proteins are also known as spheroproteins and typically are approximately round in shape. They contain a mix of amino acid types and contain differing sequences in their primary structures. Globular proteins have many different functions, such as enzymes, cellular messengers, and molecular transporters. These roles often require the proteins to be...

Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.

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Related Experiment Video

Updated: Jun 13, 2026

$Microcrystallography of Protein Crystals and In Cellulo Diffraction$

Microcrystallography of Protein Crystals and In Cellulo Diffraction

Published on: July 21, 2017

Data reduction in protein serial crystallography.

Marina Galchenkova¹, Alexandra Tolstikova², Bjarne Klopprogge¹

¹Center for Free-Electron Laser Science CFEL, Deutsche Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany.

|February 8, 2024

Summary

This summary is machine-generated.

Serial crystallography (SX) generates massive datasets. This study evaluates lossless and lossy compression methods and proposes data quality metrics to manage SX data effectively.

Keywords:

data compression data quality evaluation data reduction protein serial crystallography

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Published on: December 1, 2020

Area of Science:

Structural biology
Biophysics
Data science

Background:

Serial crystallography (SX) is crucial for determining protein structures, especially for small or radiation-sensitive crystals and dynamic processes.
Advanced X-ray detectors capture data at high speeds, leading to terabytes (TB) or petabytes (PB) of experimental data.
The significant storage costs necessitate efficient data reduction strategies without compromising scientific integrity.

Purpose of the Study:

To assess the effectiveness of various lossless and lossy compression techniques for serial crystallography data.
To identify and describe suitable metrics for evaluating the quality of serial crystallography data after compression.

Main Methods:

Application of diverse lossless and lossy compression algorithms to serial crystallography datasets.
Development and utilization of specific data quality metrics to evaluate the impact of compression on scientific outcomes.
Comparative analysis of compression ratios and data quality preservation across different methods.

Main Results:

Lossless compression methods preserve data integrity but yield limited compression ratios on noisy SX data.
Lossy compression methods offer significant data volume reduction but require careful evaluation of their impact on data quality.
Established metrics are essential for quantitatively assessing the effects of lossy compression on SX data.

Conclusions:

Effective data management strategies are vital for serial crystallography due to large data volumes.
Both lossless and lossy compression techniques have roles, with lossy methods requiring rigorous quality assessment.
The proposed data quality metrics provide a framework for evaluating compression strategies in SX research.