A match made in heavenhell: academic writing with latex and git
Procrastinating writing by over-engineering my workflow
If you are like me, you can happily write code for hours and hours on end but as soon as you need to write a paper you end up staring at a blank page. Luckily, I have come up with a fool proof way to trick myself into thinking I am coding when in reality I am finalling getting around to writing up the work my supervisor has been wanting for the last month. Introducing Latex and git- this was my approach to draft a review paper recently and in this blopig post I will go through some of the ups and downs I had using these tools.
The Observed Antibody Space (OAS) [1,2] is an amazing resource for investigating observed antibodies or as a resource for training antibody specific models, however; its size (over 2.4 billion unpaired and 1.5 million paired antibody sequences as of June 2023) can make it painful to work with. Additionally, OAS is extremely information rich, having nearly 100 columns for each antibody heavy or light chain, further complicating how to handle the data.
From spending a lot of time working with OAS, I wanted to share a few tricks and insights, which I hope will reduce the pain and increase the joy of working with OAS!
In this post I will cover how to calculate sequence identity and Tanimoto similarity between any pairs of complexes in PDBbind 2020. I used RDKit in python for Tanimoto similarity and the MMseqs2 software for sequence identity calculations.
A few weeks back I wanted to cluster the protein-ligand complexes in PDBbind 2020, but to achieve this I first needed to precompute the sequence identity between all pairs sequences in PDBbind, and Tanimoto similarity between all pairs of ligands. PDBbind 2020 includes 19.443 complexes but there are much fewer distinct ligands and proteins than that. However, I kept things simple and calculated the similarities for all 19.443*19.443 pairs. Calculating the Tanimoto similarity is relatively easy thanks to the BulkTanimotoSimilarity function in RDKit. The following code should do the trick:
from rdkit.Chem import AllChem, MolFromMol2File
from rdkit.DataStructs import BulkTanimotoSimilarity
import numpy as np
import os
fps = []
for pdb in pdbs:
mol = MolFromMol2File(os.path.join('data', pdb, f'{pdb}_ligand.mol2'))
fps.append(AllChem.GetMorganFingerprint(mol, 3))
sims = []
for i in range(len(fps)):
sims.append(BulkTanimotoSimilarity(fps[i],fps))
arr = np.array(sims)
np.savez_compressed('data/tanimoto_similarity.npz', arr)
Sequence identity calculations in python with Biopandas turned out to be too slow for this amount of data so I used the ultra fast MMseqs2. The first step to running MMseqs2 is to create a .fasta file of all the sequences, which I call QUERY.fasta. This is what the first few lines look like:
If you’ve ever found yourself typing out the same long commands over and over again, or if you’ve ever wished you could teleport directly to your favourite directories, then this post is for you.
Before we jump into some useful examples, let’s go over what bash functions and aliases are, and how to set them up.
Bash Functions vs Aliases
A bash function is like a mini script stored in your .bashrc or .bash_profile file. It can accept arguments, execute a series of commands, and even return a value.
The PyMOL Python API is a useful resource for most people doing research in OPIG, whether focussed on antibodies, small molecule drug design or protein folding. However, the documentation is poorly structured and difficult to interpret without first having understood the structure of the module. In particular, the differences between use of the PyMOL command line and the API can be unclear, leading to a much longer debugging process for code than you’d like.
While I’m reluctant to continue the recent theme of ChatGPT-related posts, this is a use for ChatGPT that would have been incredibly useful to me when I was first getting to grips with the PyMOL API.
Disclaimer: I used ChatGPT to improve the writing style of this article, in combination with some personal curation before obtaining a final version.
You’ve probably heard it all already, from ChatGPT writing code and doing proofreading for you to a rap battle between OPIG’s Antibodies and Small Molecules groups, and more.
Whether you like it or not, ChaGPT has unleashed people’s creative side regarding applications and attempts to find shortcuts. Questionable? Absolutely!
In this BLOPIG post, I show how I used ChatGPT to easily write a post summarising some material of my own intellectual property, which I presented as part of my group meeting talk. Mainly, I list some personal thoughts on the ethical concerns around using ChatGPT to assist your writing.
To start off, I passed on content from my own publication draft to ChatGPT, asking to generate a blog post in plain English for BLOPIG. The outcome:
Not bad.
But, it made me realise a number of things:
With great power comes great responsibility [Uncle Ben – Spiderman]. You are responsible for the ethics that go into using ChatGPT. Are you faking expertise? Are you being actually lazy or just being efficient? Think twice (or many more times) if you’re doing the right thing.
It can significantly reduce the number of writing iterations but don’t take it at face value. Can you actually trust the plain output? No. Never take its output as the ground truth, as Large Language Models such as ChatGPT often produce biased writing outputs. Keep in mind that whatever you produce as a scientist will be picked up by others, and prone to drive misinformation, if incorrect. It is OK to reduce mechanical iterations, but it’s NOT OK to skip quality control.
Be open about it. You don’t want to set the wrong example for your colleagues. So, mention if you use it, how you used it, and it is fine to encourage efficiency, but not incentivising a culture of scientific misconduct and plagiarism. Don’t skip the step of producing quality ideas on your own. This is such a concern that publishers like Elsevier have already reacted by publishing guidelines contemplating this possibility. While Nature Springer is working on ways to spot AI-generated outputs.
The bottom line
What are the dos and don’ts of using ChatGPT?
Yes, use it to have fun. Yes, use it to proofread or polish your writing. Yes, use it to summarise your own ideas. No, don’t use it to do the analysis and interpretation of your results. No, don’t copy and paste its direct output into your publication. No, don’t hide that you used it. Finally, NO, you can’t add ChatGPT as a contributing author!
Language models have token the world by storm recently and, given the already explored analogies between protein primary sequence and text, there’s been a lot of interest in applying these models to protein sequences. Interest is not only coming from academia and the pharmaceutical industry, but also some very unlikely suspects such as ByteDance – yes the same ByteDance of TikTok fame. So if you also fancy trying your hand at building a protein language model then read on, it’s surprisingly easy.
Training your own protein language model from scratch is made remarkably easy by the HuggingFace Transformers library, which allows you to specify a model architecture, tokenise your training data, and train a model in only a few lines of code. Under the hood, the Transformers library uses PyTorch (or optionally Tensorflow) models, allowing you to dig deeper into customising training or model architecture, or simply leave it to the highly abstracted Transformers library to handle it all for you.
For this article, I’ll assume you already understand how language models work, and are now looking to implement one yourself, trained from scratch.
It’s not always easy to live in an Anglophone scientific world when English isn’t your first language. When careers are built upon the ability to communicate ideas clearly and eloquently, struggling to find the right words can be a real hindrance to explain your science in a way that is taken seriously. Contrary to popular belief, it’s not something you can simply “work” on. Often, it doesn’t matter how many books you’ve read, how many years of education you have, or how articulate you are in your original language — your brain will refuse to summon the right expression, or get stuck in a construction that a native speaker would never use. Struggling with a second language is very much a biological phenomenon.
The standard recommendation for ESL (English as a Second Language) speakers has long been to ask a native colleague to read through any text that needs to be published or submitted somewhere (such as an article or a grant application). Well-intentioned as this advice may be, there are multiple problems with it. Lingua franca or not, only 15% of the world population speaks English, of which only 5% are native speakers — meaning that for most scientists not working in Anglophone countries, the option is rarely available. Even when available, it is unreasonable to expect these colleagues to add charitable proof-reading to their workload simply because they happened to be born speaking a different language. But, most importantly, I have always felt — and I want to emphasize that I truly believe most people who issue this kind of advice to be well-intentioned — that the underliying message sounds too much like “you need vetting by a member of our select linguistic club if you want your ideas to be taken seriously“.
Alternative Title: The tragic story of how I got trapped making slides with latex.
Typically after giving a presentation at least one person will approach me and ask if they could have access to my custom latex template to make slides with beamer that don’t look rubbish.
Personal websites are a great and increasingly important way to build your online presence. Along with professional social media pages, such as on LinkedIn and Twitter, a website can provide a boost to your career and/or job search.
This blog post is based on my recent experience creating a personal website, following guidelines from Lewis’ talk at the OPIG Retreat last year (thank you Lewis!). The method I used and will cover here, based on an HTML5 UP! template and GitHub pages, is free and fast.
Why have a personal website?
Improves your online presence and brand
Boost for your career, including by allowing potential future employers to find you
Share things you have accomplished or are interested in
