I work with scientists around the world to remove the barriers to life sciences research and, as important, to enable questions to be asked that weren’t possible even six months ago. Cryo-electron microscopy or Cryo-EM is one of the most interesting examples of how the intersection of technology and research can widen the scope of inquiry and accelerate discovery. It is delivering significant insights in areas like immunology and cancer research, as well as cardiovascular and neurodegenerative diseases. Eva Nogales, a structural biologist at the University of California, Berkeley, notes that “Whether you call [Cryo-EM] a revolution or a quantum leap, the fact is that the gates have opened.”1
Cryo-EM is revolutionizing medical research because of its capability to study whole classes of proteins and molecules that were not accessible through prior methods with x-ray crystallography. Due to the variety of protein molecule shapes, alignment, classification, and processing are complex. Cryo-EM allows visualization of proteins near the atomic level, and that opens up capabilities that simply weren’t possible before. For example, scientists can see exactly how a protein is interacting with a membrane or look at a virus and see how it’s behaving and then design drug targets that might interfere with those proteins.
So let’s take a brief look at some of the enabling technologies. Intel® Scalable System Framework (Intel® SSF) is a highly dense, scalable HPC analytics cluster design featuring optimized commercial and open source software. Intel has worked with industry experts to optimize top life sciences workloads, scale to keep pace with high throughput instruments, and maximize the use of efficient available infrastructure. Intel® SSF delivers high performance, balanced, power-efficient, and reliable systems capable of supporting a wide range of compute-intensive and data-intensive life sciences analytics workloads, including genomics, molecular dynamics and molecular imaging, deep learning and visualization.
The results are exciting and are having a direct impact on time to discovery. Recent benchmarks using ROME* to perform 2D alignment and classification, found near-linear scaling performance with the Intel® Xeon Phi™ processor, and 3x faster performance with the Intel® Xeon Phi™ processor (vs. the Intel Xeon E5-2600 v4 family) on Dell EMC PowerEdge C6320p.3
Demos at SC16 running on the latest Intel® Xeon® and Intel® Xeon Phi™ processors displayed some of the considerable potential. Here’s a sampling and you can find details in the event Demo Guide:
- Visualizing Protein Structures: 3D Imaging and Deep Learning with Cryo-Electron Microscopy. Watch the video
- Examining the Movement of Proteins: 3D Molecular Dynamics (MD) Simulation. Watch the video
- Identifying Effective Compounds: Deep Learning for Virtual Cancer Screening. Watch the video >
- Using Genome Analytics to Identify Patients for Clinical Trials. Watch the video
- Monitoring Cancer Treatment Effectiveness: High Fidelity 3D Visualization of Evolving Brain Tumors. Watch the video
I want more scientists to be able to take advantage of the latest algorithms and democratize the use of HPC. Intel has done a lot of work in genomics, molecular imaging, and Cryo-EM, and also with emerging models of machine learning, deep learning, and visualization. The reality is that once you take a look at the latest Intel Scalable System Framework for Life Sciences, you’ll find that you can run multiple diverse workloads using the same cluster infrastructure and it will scale with you over time. You can fine-tune workload performance by adjusting the mix of processor types, from the latest Intel Xeon and Intel Xeon Phi™ processors, to the Intel Xeon with integrated FPGA in the future, without having to completely reengineer the cluster.
These are just a few of the ways Intel is enabling the science that will benefit us all—from more accurate cancer screening to timely interventions to more targeted treatments selected on the basis of an individual’s molecular profile.
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- Garima Kochhar and Kihoon Yoon. Del l EMC HPC Innovation Lab. October 2016, published November 11, 2016, Configuration details: http://en.community.dell.com/techcenter/high-performance-computing/b/general_hpc/archive/2016/11/11/cryo-em-in-hpc-with-knl. Benchmark datasets: ebi.ac.uk/pdbe/emdb/empiar/entry/10069/.
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