Tag 1 | Beckstein Lab

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Tag Archives: python
Research Experience for Undergraduates (Summer 2017)

Research Experience for Undergraduates (Summer 2017)

The Beckstein Lab offers a fully funded ten-week research program in computational biophysics for a highly motivated undergraduate student. This is a NSF-sponsored Research Experience for Undergraduates (REU). Deadline for applications is May 5, 2017.

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Bidstrup Foundation Undergraduate Fellowship for Kacey Clark

Bidstrup Foundation Undergraduate Fellowship for Kacey Clark

Physics freshwoman undergraduate student Kacey Clark was selected as a Bidstrup Undergraduate Fellow for the 2016-2017 academic year. The Bidstrup Fellowship is awarded by Barrett, The Honors College at Arizona State University where Kacey is a student.

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Talks from Scipy 2016

Talks from Scipy 2016

This year at SciPy 2016 in Austin, TX, David Dotson presented his work on datreant : Persistent, Pythonic Trees for Heterogeneous Data and Oliver Beckstein talked about MDAnalysis : A Python Package for the Rapid Analysis of Molecular Dynamics Simulations. Video recordings of both talks are available here.

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SciPy 2016

SciPy 2016

Members of the Becksteinlab will present talks at SciPy 2016. This year the biggest conference on Python in science takes place from July 11-17 in Austin, TX. We will talk about datreant and MDAnalysis, get inspired, meet old and new friends, and hand out MDAnalysis stickers.

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Robert Delgado

Robert Delgado

Robert Delgado is an undergraduate pursuing a Bachelor’s of Science in Cornell’s Applied and Engineering Physics Program who is spending the summer of 2016 in the Beckstein lab as a REU student.

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Research Experience for Undergraduates (Summer 2016)

Research Experience for Undergraduates (Summer 2016)

The Beckstein Lab offers a fully funded ten-week research program in computational biophysics for a highly motivated undergraduate student. This is a NSF-sponsored Research Experience for Undergraduates. Deadline for applications is May 30, 2016.

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PSA: A Method for Quantifying Macromolecular Pathways

PSA: A Method for Quantifying Macromolecular Pathways

Transition pathways in high dimensional spaces, such as the ones produced by advanced algorithms to sample large conformational changes in macromolecules, are difficult to analyze quantitatively. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA is implemented in the MDAnalysis library.

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PHY494 — Topic: Computational Methods in Physics

PHY494 — Topic: Computational Methods in Physics

The course provides an introduction to using the computer as a tool to solve problems in physics. Students will learn to analyze problems, select appropriate numerical algorithms, implement them using Python, a programming language widely used in scientific computing, and critically evaluate their numerical results. Problems will be drawn from diverse areas of physics.

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SciPy 2015

SciPy 2015

SciPy is an annual conference bringing together scientists and software developers that use, develop, and maintain the scientific Python ecosystem. This year the conference took place from July 7-12 on the campus of the University of Texas at Austin. David Dotson presented a poster on MDSynthesis: a Python package enabling data-driven molecular dynamics research.

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CECAM Macromolecular simulation software workshop

CECAM Macromolecular simulation software workshop

The CECAM Macromolecular simulation software workshop will be held from Mon 12 Oct, 2015 to Sat 24 Oct 2015 at Forschungszentrum Jülich, Germany. David Dotson and Oliver Beckstein will participate and will teach how to use MDAnalysis productively.

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Research Experience for Undergraduates (Summer 2015)

Research Experience for Undergraduates (Summer 2015)

The Beckstein Lab offers a fully funded ten-week research program in computational biophysics for a highly motivated undergraduate student. This is a NSF-sponsored Research Experience for Undergraduates. Deadline for applications is May 31, 2015.

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AdK apo PMF

AdK apo PMF

The enzyme adenylate kinase (AdK) undergoes a large hinge-like motion. In 2009, we studied the conformational transition between open and closed E. coli AdK without substrate, i.e. “apo AdK”, with a variety of computational methods. As part of the study we also produced a free energy landscape (a potential of mean force or PMF) as a function of the two domain angles. Here we make the data of the underlying free energy landscape available to other researchers so that they can use them in their own research.

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Learning Python

Learning Python

The Python programming language is used everywhere in this lab, the computational physics, biology communities, and big companies such as Google and Facebook. You don’t know Python? Start here to learn it.

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Lennard van der Feltz

Lennard van der Feltz

Lennard graduated with a Masters of Science in Physics in Fall 2014. His work is concerned with the electrostatics of ion channels and pores. He is primarily using Poisson-Boltzmann calculations to obtain experimentally measurable observables in a semi-quantitative manner.

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Tutorial: MDAnalysis

Tutorial: MDAnalysis

A short introductory tutorial on MDAnalysis

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Tutorial: Simulating AdK with Gromacs

Tutorial: Simulating AdK with Gromacs

A tutorial that teaches you to perform and analyze a molecular dynamics simulation of the the enzyme adenylate kinase (AdK) with the Gromacs simulation package.

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Submitting strings of jobs on supercomputers

Submitting strings of jobs on supercomputers

The script qsub_dependents.py automates the process of submitting strings of dependents jobs. One either tells it how many jobs to run or lets the script calculate the number of jobs based on the benchmarked performance in ns/d and the desired simulation run time in ns. It simplifies running long simulations on supercomputers that limit the run time of jobs.

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Project: Predicting protonation states in proteins

Project: Predicting protonation states in proteins

In this rotation project you will predict known and unknown pKa values of key residues in proteins with a new method that combines molecular dynamics simulations with fast heuristic predictions. You will learn to write programs in the Python language and contribute to the open source MDAnalysis library.

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GromacsWrapper

GromacsWrapper

GromacsWrapper is a python package that allows one to call the standard Gromacs tools in Python scripts. It is object-oriented and encourages code reuse and is therefore suitable for scripting complex work flows for setting up or analyzing Gromacs MD simulations.

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MDAnalysis

MDAnalysis

MDAnalysis is an open source, versatile, object-oriented Python library for analyzing molecular dynamics trajectories. It makes it easy to access trajectory data from Python code by interfacing trajectory readers (and writers) with NumPy arrays and to select atoms via a expressive selection syntax. The CHARMM/NAMD, Amber, Gromacs trajectory formats are all supported as well as PDB, GRO, CRD, PQR, and a range of others.

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Our software on GitHub and elsewhere

Our software on GitHub and elsewhere

Most of the code that we are writing and using is accessible through the lab web pages. However, there are odds and ends and small pieces of code that barely justify a web page of their own. Typically, such code can be found in the Becksteinlab GitHub repository

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