Doing rigid receptor docking? Consider using multiple structures!

Here it is. It’s finally happening. I’m actually writing a blog post about docking. Are the end times upon us? Perhaps. If by my next post I’m not back to my usual techie self, the horsemen may well be on their way.

If you’ve ever used, read about, or listened to a lab mate complain about protein-ligand docking, you’re likely familiar with the rigid receptor assumption. In this model, the active site of the protein is treated as completely rigid, with no side chain flexibility, and only the rotatable bonds in the ligand are allowed to move. The motivation behind this assumption is simple. The computational cost of sampling the conformational space of a ligand within a protein’s active site, and doing so with sufficient rigour so as to sample a near-native binding mode, grows rapidly with the number of rotatable bonds in the ligand. Further increasing the degrees of freedom in the system by incorporating receptor side chain flexibility compounds this problem, making the sampling of accurate binding modes for the ligand an incredibly expensive and difficult problem.

One compromise, if multiple structures with different active site conformations are available for the target protein, is to simply dock your ligands into multiple structures, and trust your scoring function (!!!) to pick out the best binding mode from across the different structures. This is a crude approximation to true flexible receptor docking which won’t capture fully any induced fit effects due to a particular ligand, but if the structures are available, this may offer a more computationally-feasible alternative to flexible docking.

A study earlier this year by Cleves and Jain illustrates this approach nicely. They dock the ligands of the DUD-E database into multiple structures for each target, in each case treating the receptor strucutre as completely rigid. Unsurprisingly, when the target is rigid and there is little structural variation in the active site across the structures, the choice of structure has little influence on the docking results. However, when the receptor is flexible, with clear structural variation across the active sites in the different structures, there is a strong impact on the poses generated by rigid-receptor docking. This effect translates directly into improved virtual screening performance when docking into multiple different structures, illustrating the value of considering the conformational space of the receptor, even when it is treated as rigid during the docking process.

PyMOL: colour by residue

PyMOL is a handy free way of viewing three dimensional protein structures. It allows you to toggle between different representations of the protein – such as cartoon, surface, sticks, etc. – which all have their own pros and cons.

However one thing I felt that PyMOL lacked was an easy way to visually distinguish residues based on type. Whist you can easily differentiate atom types based on colour in the colour menu, and even choose which colour you wish carbons to show up as whilst keeping heteroatoms different colours, this assigned carbon colour would be constant throughout the entire protein.

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Enhance your presentation by using virtual webcams and scene transitions

If you haven’t checked out Matt’s post on using OBS for recording video, I highly recommend doing so. OBS is a terrific way to present your work online. It can provide (amongst many, many other things) the ability to create live picture-in-picture scenes, so you can move through your powerpoint deck whilst overlaying video of your tiny talking head in the corner.

Continuing on from Matt’s post, I’d like to promote the OBS virtual camera plugin and Touch Portal.

Every company, department or course has its own favourite video conferencing application and whilst they all have their strengths, consistency is not one of them. If you want to consistently display your presentation and your live video regardless of the platform in use, this quickly gets into the “messy” territory. This is where the virtual camera comes in.

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Graphical abstracts that spark joy on a gloomy day

Have you ever read a paper just because it had a funny, endearing, or utterly bizarre graphical abstract? Ever since a colleague showed me the ‘Graphical abstracts that I gone and found’ Facebook page, I have definitely come across a few, and I thought I would share some of my favourite ones below. If you enjoy this kind of thing, I strongly suggest visiting their page for more – it makes for a wonderful distraction from pretty much anything. Continue reading

A new Graduate students (unexperienced) guide to academic literature.

Given this is my first ever attempt at a blog post, let alone one on such a highly regarded platform I feel it’s proper that I introduce myself. Hi, my name is Maranga, I am a second-year SABS student starting my DPhil project in Small molecules, and honestly, I really don’t like reading. Especially, scientific journals. Now I can appreciate this does not bode well given my chosen career path, however, my aversion for reading is not new (shoutout to Biff, Chip and Kipper) and hopefully not permanent.

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3 Useful UNIX commands you might not know

nohup

The command nohup (stands for “No hang up”) allows your script to run even if you quit the terminal. It can be very useful, especially if your terminal has been opened through ssh and you have a dodgy connection. It can be used as follows:

nohup python my_script.py > log.out &

nohup will automatically append the output from your script to a file named nohup.out. By adding the > log.out part of the command you can save the output to a different file of your choice.

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NeurIPS 2020: Chemistry / Biology papers

Another blog post, another look at accepted papers for a major ML conference. NeurIPS joins the other major machine learning conferences (and others) in moving virtual this year, running from 6th – 12th December 2020. In a continuation of past posts (ICML 2020, NeurIPS 2019), I will highlight several of potential interest to the chem-/bio-informatics communities

The list of accepted papers can be found here, with 1,903 papers accepted out of 9,467 submissions (20% acceptance rate).

In addition to the main conference, there are several workshops highly related to the type of research undertaken in OPIG: Machine Learning in Structural Biology and Machine Learning for Molecules.

The usual caveat: given the large number of papers, these were selected either by “accident” (i.e. I stumbled across them in one way or another) or through a basic search (e.g. Ctrl+f “molecule”). If you find any I have missed, please reach out and I will update accordingly.

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