Histograms are frequently used to visualize the distribution of a data set or to compare between multiple distributions. Python, via matplotlib.pyplot, contains convenient functions for plotting histograms; the default plots it generates, however, leave much to be desired in terms of visual appeal and clarity.
The two code blocks below generate histograms of two normally distributed sets using default matplotlib.pyplot.hist settings and then, in the second block, I add some lines to improve the data presentation. See the comments to determine what each individual line is doing.
BRICS Decomposition in 3D
Inspired by this blog post by the lovely Kate, I’ve been doing some BRICS decomposing of molecules myself. Like the structure-based goblin that I am, though, I’ve been applying it to 3D structures of molecules, rather than using the smiles approach she detailed. I thought it may be helpful to share the code snippets I’ve been using for this: unsurprisingly, it can also be done with RDKit!
I’ll use the same example as in the original blog post, propranolol.
First, I import RDKit and load the ligand in question:
Continue readingAI-pril Fools
As my turn to write a blog post has fallen a few days after April 1st, I decided I would write an April Fools’ Day-inspired post and ask everyone’s favourite chatbot to tell me some jokes.
I asked ChatGPT to tell me a knock-knock joke, prompting it with various topics relevant to OPIG (including AI, antibodies, drug discovery and proteins) to see what it could come up with. I’d argue that we’re playing fast and loose with the definition of a joke (several of these just made me cringe), but here are some of the results…
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.
Continue readingPLIP 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 == '@<TRIPOS>BOND\n':
mod = False
if mod:
ligand_lines[i] = line[:59] + 'ISK ' + line[67:]
if line == '@<TRIPOS>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 STIContinue reading
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.
Continue readingBRICS 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.
Continue readingExperience 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.
Continue readingLigands of CASF-2016
CASF-2016 is a commonly used benchmark for docking tools. Unfortunately, some of the provided ligand files cannot be loaded using RDKit (version 2022.09.1) but there is an easy remedy.
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