Category Archives: Protein Structure

KAUST Computational Advances in Structural Biology

Last month, I had the privilege of being invited to the KAUST Research Conference on Computational Advances in Structural Biology, held from May 1-3, 2023. This gave me the opportunity to present some of the latest OPIG works on small molecules while visiting an exceptional campus with state-of-the-art facilities in one of those corners of the world that are not widely known. Moreover, the experience went beyond the impressive surroundings as I had the chance to attend a highly engaging conference and meet many scientists from different backgrounds.

KAUST Library (left) and Dinning Hall (right)

The conference brought together experts in the field to explore cutting-edge developments in computational structural biology. It had a primary focus on advancements in protein structure prediction, multi-scale simulations, and integrative structural biology. Cryo-electron microscopy (cryo-EM) was the most popular experimental technique, with more than a third of the talks dedicated to its applications. These talks showcased impressive examples where structure prediction, simulations, and mid-resolution cryo-EM maps were combined to construct atomic models of large macromolecular complexes.

Notable examples of integrative works were presented by Jan Kosinski and Thomas Miller, among others. Jan Kosinski shared insights into the model of the human nuclear pore complex, highlighting the integration of cryo-electron tomography (cryo-ET), prior experimental knowledge, and AlphaFold predictions. Thomas Miller, on the other hand, presented his work on EM-based visual biochemistry, which combines single-particle cryo-electron microscopy (cryo-EM), and time-resolved experiments, as a tool to study the molecular mechanisms of eukaryotic DNA replication.

There were also several talks about novel algorithms. Nazim Bouatta presented some less-known details about OpenFold and introduced some of their approaches to tackling the problem of multimer modelling. He also announced the future release of folding methods for predicting protein-ligand complexes. Jianlin Cheng presented MULTICOM, their new protein structure predictor based on consensus predictions from Alphafold. Sergei Grudinin showed deep-learning tools able to predict protein dynamics as well as some integrative modelling tools driven by low-resolution experimental observations, such as small-angle scattering.

On the cryo-EM methods side, Mikhail Kudryashev presented TomoBEAR and SUSAN, cryoEM tools developed to automatize the analysis of tomographic data. Johannes Schwab presented dynamight, a deep learning-based approach for heterogeneity analysis in single particle cryo-EM. While, on the ComChem side, Haribabu Arthanari showed their ultra-large Virtual screening platform and Jean-Louis Reymond talked about tools to enumerate, visualize and search the vast chemical space of drug-like molecules

Overall, the conference provided a quite diverse set of talks that facilitated multidisciplinary views and discussions. From protein structure prediction to integrative approaches combining experimental and computational methods, the talks showed the transformative potential of computational analysis in unravelling the complexities of biological macromolecules.

9th Joint Sheffield Conference on Cheminformatics

Over the next few days, researchers from around the world will be gathering in Sheffield for the 9th Joint Sheffield Conference on Cheminformatics. As one of the organizers (wearing my Molecular Graphics and Modeling Society ‘hat’), I can say we have an exciting array of speakers and sessions:

  • De Novo Design
  • Open Science
  • Chemical Space
  • Physics-based Modelling
  • Machine Learning
  • Property Prediction
  • Virtual Screening
  • Case Studies
  • Molecular Representations

It has traditionally taken place every three years, but despite the global pandemic it is returning this year, once again in person in the excellent conference facilities at The Edge. You can download the full programme in iCal format, and here is the conference calendar:

Continue reading →

Checking your PDB file for clashing atoms

Detecting atom clashes in protein structures can be useful in a number of scenarios. For example if you are just about to start some molecular dynamics simulation, or if you want to check that a structure generated by a deep learning model is reasonable. It is quite straightforward to code, but I get the feeling that these sort of functions have been written from scratch hundreds of times. So to save you the effort, here is my implementation!!!

Continue reading →

Train Your Own Protein Language Model In Just a Few Lines of Code

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.

Continue reading →

Can AlphaFold predict protein-protein interfaces?

Since its release, AlphaFold has been the buzz of the computational biology community. It seems that every group in the protein science field is trying to apply the model in their respective areas of research. Already we are seeing numerous papers attempting to adapt the model to specific niche domains across a broad range of life sciences. In this blog post I summarise a recent paper’s use of the technology for predicting protein-protein interfaces.

Continue reading →

histo.fyi: A Useful New Database of Peptide:Major Histocompatibility Complex (pMHC) Structures

pMHCs are set to become a major target class in drug discovery; unusual peptide fragments presented by MHC can be used to distinguish infected/cancerous cells from healthy cells more precisely than over-expressed biomarkers. In this blog post, I will highlight a prototype resource: Dr. Chris Thorpe’s new database of pMHC structures, histo.fyi.

histo.fyi provides a one-stop shop for data on (currently) around 1400 pMHC complexes. Similar to our dedicated databases for antibody/nanobody structures (SAbDab) and T-cell receptor (TCR) structures (STCRDab), histo.fyi will scrape the PDB on a weekly basis for any new pMHC data and process these structures in a way that facilitates their analysis.

Continue reading →

How to build a Python dictionary of residues for each molecule in PyMOL

Sometimes it can be handy to work with multiple structures in PyMOL using Python.

Here’s a snippet of code you might find useful: we iterate over all the α-carbon atoms in a protein and append to a list tuples such as (‘GLY’, 1). The dictionary, ‘reslist’, returns a list of residue names and indices for each molecule, where the key is a string containing the name of the molecule. 

from pymol import cmd

# Create a list of all the objects, called 'mpls':
mols = cmd.get_object_list('*')

# Create an empty dictionary that will return a list of residues
# given the name of the molecule object
reslist = {}

# Set the dictionaries to be empty lists
for m in mols:  reslist[m] = []

# Use PyMOL's iterate command to go over every α-Carbon and append 
# a tuple consisting of the each residue's residue name ('resn') and
# residue index ('resi '):
for m in mols:  cmd.iterate('%s and n. ca'%m, 'reslist["%s"].append((resn,int(resi)))'%m)

This script assumes you only have protein molecules loaded, and ignores things like chain ID and insertion codes.

Once you have your list of residues, you can use it with the cmd.align command, e.g., to align a particular residue to a reference structure.

Do you have cis peptide bonds in your simulation inputs?

People who run molecular simulations quickly become familiar with all of the things about a PDB file – missing residues, missing heavy atoms in residues, missing hydrogens, non-standard amino acids, multiple conformations, crystallization ligands, etc. – that might need to be fixed before setting up a simulation. This blog post is a reminder to check, after you have “fixed” your PDB, if you have accidentally introduced aberrant cis peptide bonds into your structure during rebuilding.

Continue reading →

Retrieving AlphaFold models from AlphaFoldDB

There are now nearly a million AlphaFold [1] protein structure predictions openly available via AlphaFoldDB [2]. This represents a huge set of new data that can be used for the development of new methods. The options for downloading structures are either in bulk (sorted by genome), or individually from the webpage for a prediction.

If you want just a few hundred or a few thousand specific structures, across different genomes, neither of these options are particularly practical. For example, if you have several thousand experimental structures for which you have their PDB [3] code, and you want to obtain the equivalent AlphaFold predictions, there is another way!

If we take the example of the PDB’s current molecule of the month, pyruvate kinase (PDB code 4FXF), this is how you can go about downloading the equivalent AlphaFold prediction programmatically.

  1. Query UniProt [4] for the corresponding accession number – an example python script is shown below:
Continue reading →