An Overview of Clustering Algorithms

During the first 6 months of my DPhil, I worked on clustering antibodies and I thought I would share what I learned about these algorithms. Clustering is an unsupervised data analysis technique that groups a data set into subsets of similar data points. The main uses of clustering are in exploratory data analysis to find hidden patterns or data compression, e.g. when data points in a cluster can be treated as a group. Clustering algorithms have many applications in computational biology, such as clustering antibodies by structural similarity. Actually, this is objectively the most important application and I don’t see why anyone would use it for anything else.

There are several types of clustering algorithms that offer different advantages.

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PLIP on PDBbind with Python

Today’s blog post is about using PLIP to extract information about interactions between a protein and ligand in a bound complex, using data from PDBbind. The blog post will cover how to combine the protein pdb file and the ligand mol2 file into a pdb file, and how to use PLIP in a high-throughput manner with python.

In order for PLIP to consider the ligand as one molecule interacting with the protein, we need to modify the mol2 file of the ligand. The 8th column of the atom portion of a mol2 file (the portion starts with @<TRIPOS>ATOM) includes the ID of the ligand that the atom belongs to. Most often all the atoms have the same ligand ID, but for peptides for instance, the atoms have the ID of the residue they’re part of. The following code snippet will make the required changes:

ligand_file = 'data/5oxm/5oxm_ligand.mol2'

with open(ligand_file, 'r') as f:
    ligand_lines = f.readlines()

mod = False
for i in range(len(ligand_lines)):
    line = ligand_lines[i]
    if line == '@&lt;TRIPOS&gt;BOND\n':
        mod = False
        
    if mod:
        ligand_lines[i] = line[:59] + 'ISK     ' + line[67:]
        
    if line == '@&lt;TRIPOS&gt;ATOM\n':
        mod = True

with open('data/5oxm/5oxm_ligand_mod.mol2', 'w') as g:
    for j in ligand_lines:
        g.write(j)
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Molecular conformation generation with a DL-based force field

Deep learning (DL) methods in structural modelling are outcompeting force fields because they overcome the two main limitations to force fields methods – the prohibitively large search space for large systems and the limited accuracy of the description of the physics [4].

However, the two methods are also compatible. DL methods are helping to close the gap between the applications of force fields and ab initio methods [3]. The advantage of DL-based force fields is that the functional form does not have to be specified explicitly and much more accurate. Say goodbye to the 12-6 potential function.

In principle DL-based force fields can be applied anywhere where regular force fields have been applied, for example conformation generation [2]. The flip-side of DL-based methods commonly is poor generalization but it seems that force fields, when properly trained, generalize well. ANI trained on molecules with up to 8 heavy atoms is able to generalize to molecules with up to 54 atoms [1]. Excitingly for my research, ANI-2 [2] can replace UFF or MMFF as the energy minimization step for conformation generation in RDKit [5].

So let’s use Auto3D [2] to generated low energy conformations for the four molecules caffeine, Ibuprofen, an experimental hybrid peptide, and Imatinib:

CN1C=NC2=C1C(=O)N(C(=O)N2C)C CFF
CC(C)Cc1ccc(cc1)C(C)C(O)=O IBP
Cc1ccccc1CNC(=O)[C@@H]2C(SCN2C(=O)[C@H]([C@H](Cc3ccccc3)NC(=O)c4cccc(c4C)O)O)(C)C JE2
Cc1ccc(cc1Nc2nccc(n2)c3cccnc3)NC(=O)c4ccc(cc4)CN5CCN(CC5)C STI
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The ultimate modulefile for conda

Environment modules is a great tool for high-performance computing as it is a modular system to quickly and painlessly enable preset configurations of environment variables, for example a user may be provided with modulefile for an antiquated version of a tool and a bleeding-edge alpha version of that same tool and they can easily load whichever they wish. In many clusters the modules are created with a tool called EasyBuild, which delivered an out-of-the-box installation. This works for things like a single binary, but for conda this severely falls short as there are many many configuration changes needed.

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BRICS Decomposition and Synthetic Accessibility

Recently I’ve been thinking a lot about how to decompose a compound into smaller fragments specifically for a retrosynthetic purpose. My question is: given a compound, can I return building blocks that are likely to synthesize together to produce this compound simply by breaking likely bonds formed in a reaction? A method that is nearly 15 years old named, breaking of retrosynthetically interesting chemical substructures (BRICS), is one approach to do this. Here I’ll explore how BRICS can reflect synthetic accessibility.

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Experience at a Keystone Symposium

From 19th-22nd February I was fortunate enough to participate in the joint Keystone Symposium on Next-Generation Antibody Therapeutics and Multispecific Immune Cell Engagers, held in Banff, Canada. Now in their 51st year, the Keystone Symposia are a comprehensive programme of scientific conferences spanning the full range of topics relating to human health, from studies on fundamental bodily processes through to drug discovery.

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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.

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LaTeX Beamer Template with Logos

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.

TL;DR Yes you can: https://github.com/npqst/latex-beamer-template

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Atom mapping with RXNMapper

When recently looking at some reaction data, I was confronted with the problem of atom-to-atom mapping (AAM) and what tools are available to tackle it. AAM refers to the process of mapping individual atoms in reactants to their corresponding atoms in the products, which is important for defining a reaction template and identifying which bonds are being formed and broken. This has many downstream uses for computational chemists, such as for reaction searching and forward and retrosynthesis planning1. The problem is that many reaction databases do not contain these mappings, and annotation by expert chemists is impractical for databases containing thousands (or more) data points.

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