Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

A CPU benchmark for protein crystallographic refinement.

P E Bourne¹, W A Hendrickson

¹Howard Hughes Medical Institute, Department of Biochemistry, Columbia University, New York, NY 10032.

Computers in Biology and Medicine

|January 1, 1990

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Real-world evidence from over one million COVID-19 vaccinations is consistent with reactivation of the varicella-zoster virus.

Journal of the European Academy of Dermatology and Venereology : JEADV·2022

Same author

Optics Concept for a Pair of Undulator Beamlines for MX.

Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment·2011

Same author

Diffraction analysis of motion in proteins.

Biophysical journal·2009

Same author

A case study of high-throughput biological data processing on parallel platforms.

Bioinformatics (Oxford, England)·2004

Same author

The status of structural genomics defined through the analysis of current targets and structures.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2004

Same author

The apoptosis database.

Cell death and differentiation·2003

Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026

Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026

Same journal

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

Computers in biology and medicine·2026

Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026

Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026

Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026

See all related articles

This study benchmarks protein structure refinement using FORTRAN codes on 48 processors. Performance varied significantly across hardware architectures, highlighting the impact of specific optimizations for scientific computing.

Area of Science:

Computational biology
Structural biology
Scientific computing

Background:

X-ray crystallography is crucial for determining protein structures.
Computational efficiency in structure refinement impacts research timelines.
FORTRAN codes like PROTIN and PROLSQ are widely used in structural biology.

Purpose of the Study:

To benchmark the CPU time for protein structure refinement using PROTIN and PROLSQ.
To compare the performance of various hardware architectures (sequential, vector, VLIW, multiprocessor, RISC) on these computations.
To assess the impact of hardware-specific coding on run-time performance.

Main Methods:

Benchmarking the restrained least-squares refinement cycle of protein structures.
Utilizing X-ray crystallographic data and FORTRAN codes (PROTIN, PROLSQ).

Related Experiment Videos

Testing on 48 different processors, from workstations to supercomputers, across diverse architectures.

Main Results:

CPU times varied substantially across the 48 tested processors.
Performance differences were observed for both small and large protein structures.
Compile times and run-time improvements due to hardware-specific coding were documented.

Conclusions:

Hardware architecture significantly influences the computational time for protein structure refinement.
Optimizing code for specific architectures can yield substantial performance gains.
These findings are relevant for a wide range of scientific disciplines employing similar computational methods.