ADCP | AutoDock CrankPep for peptide and disordered protein | Genomics library
kandi X-RAY | ADCP Summary
kandi X-RAY | ADCP Summary
AutoDock CrankPep or ADCP is an AutoDock docking engine specialized for docking peptides. It combines technology form the protein folding filed with an efficient representation of a rigid receptor as affinity grids to fold the peptide in the context of the energy landscape created by the receptor. A Monte-Carlo search is used to fold the peptide while concurrently optimizing the interaction between the peptide and the receptor molecule, yielding docked peptides. The program can dock peptides provides as 3D structures in PDB files or from a sequence string. It has been show to successfully re-dock peptides with up to 20 amino acids in length. ADCP is developed based on CRANKITE. Podtelezhnikov,A.A. and Wild,D.L. (2008) Source Code Biol. Med., 3, 12. ADCP is available under the GNU LGPL v2.0 OpenSource license. Please visit adcp.scripps.edu for more details. There are two ways of using ADCP. The first and recommended way is to use the prepared Python wrapper runADCP.py. runADCP works directly with a zipped target file (.trg) prepared by AGFR. Please see adcp.scripps.edu/tutorial for more details.
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QUESTION
I´m using Jupyter Lab!
I opened a '.mat' file, using hdf5storage. This file was generated by a software which operates an instrument of data acquisition (ADCP by Rowe, for who may ask! Oceanographic stuff...)
I can access the keys and extract the data from all keys but one... the 'Gps' contains latitude and longitude data, supposely in a two columns and many rows array... but its shape is (1x1), and when I print it I can see the data, but don´t know how to access it!
How can I access this data?
The file is here....
https://drive.google.com/file/d/15JEA5y5_Zt52FpPa--Cp_wwl2TYrnlQZ/view?usp=sharing
This is the notebook (what fits on the screen) but you can see what I mean.
...ANSWER
Answered 2021-Mar-04 at 22:36Looks like this the 'old' style MATLAB mat file, which I can load with scipy.io.loadmat
QUESTION
I have an array of numbers (e.g 279.341, 279.345, 279.348) which relate to the date and time in 2017 (its supposed to be October 6th 2017). To be able to compare this data to another dataset I need to convert that array into an array of UNIX timestamps.
I have successfully done something similar in matlab (code below) but don't know how to translate this to Python.
MatLab:
...ANSWER
Answered 2021-Jan-06 at 14:48assuming these numbers are fractional days of the year (UTC) and the year is 2017, in Python you would do
QUESTION
For this particular shiny example I am trying to apply a circular model and display and summarize it within the ggplot and a summary table. This is straightforward up until trying to add in reactive 'brushplot' capabilities. Each of the data points represent a date and the point of the selective graph is to be able to discard undesirable dates. As far as I've figured out, this requires the filtering and model fitting to be within a renderPlot
which then leads to complications (unable to find the data/model) trying to call the filtered data and the circular model's statistical outputs outside the function and/or within another reactive function. This yields the Error: object 'k_circ.lm' not found
So my questions are:
- How can I read the filtered data from the
renderPlot
function to thesummarytable
matrix? - How could I similarly add the fitted model values and residuals from
k_circ.lm
? - Is there a better or simpler way to arrange app to avoid this?
Alternatative code lines are commented out for a working (if poorly formatted) summary table.
...ANSWER
Answered 2020-Feb-06 at 03:11Here's a few ideas - but there are multiple approaches to handling this, and you probably want to restructure your server
function a bit more after working with this further.
First, you probably want a reactive
expression that will update your model based on vals$keeprows
as this changes with your clicks. Then, you can access the model results from this expression from both your plot and data table.
Here is an example:
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