Category Archives: Proteins

NeurIPS 2019: Chemistry/Biology papers

NeurIPS is the largest machine learning conference (by number of participants), with over 8,000 in 2017. This year, the conference will be held in Vancouver, Canada from 8th-14th December.

Recently, the list of accepted papers was announced, with 1430 papers accepted. Here, I will highlight several of potential interest to the chem-/bio-informatics communities. 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”).

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A new way of eating too much

Fresh off the pages of Therapeutic Advances in Endocrinology and Metabolism comes a warning no self-respecting sweet tooth should ignore.

“Liquorice is not just a candy,” write a team of ten from Chicago. “Life-threatening complications can occur with excess use.” Hold on to your teabags. Liquorice – the Marmite of sweets – is about to become a lot more sinister.

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When OPIGlets leave the office

Hi everyone,

My blogpost this time around is a list of conferences popular with OPIGlets. You are highly likely to see at least one of us attending or presenting at these meetings! I’ve tried to make it as exhaustive as possible (with thanks to Fergus Imrie!), listing conferences in upcoming chronological order.

(Most descriptions are slightly modified snippets taken from the official websites.)

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Learning dynamical information from static protein and sequencing data

I would like to advertise the research from Pearce et al. (https://doi.org/10.1101/401067) whose talk I attended at ISMB 2019. The talk was titled ‘Learning dynamical information from static protein and sequencing data’. I got interested in it as my field of research is structural biology which deals with dynamics systems, e.g. proteins, but data is often static, e.g. structures from X-ray crystallography. They presented a general protocol to infer transition rates between states in a dynamical system that can be represented with an energy landscape.

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Two Tools for Systematically Compiling Ensembles of Protein Structures

In order to know how a protein works, we generally want to know its 3-dimensional structure. We then can either try to solve it ourselves (which requires considerable time, skill, and resources), or look for it in the Protein Data Bank, in case it has already been solved. The vast majority of structures in the Protein Data Bank (PDB) are solved through protein crystallography, and represent a “snapshot” of the conformational space available to our protein of interest. Continue reading →

What is the hydrophobic-polar (HP) model?

Proteins are fascinating. They are ubiquitous in living organisms, carrying out all kinds of functions: from structural support to unbelievably powerful catalysis. And yet, despite their ubiquity, we are still bemused by their functioning, not to mention by how they came to be. As computational scientists, our research at OPIG is mostly about modelling proteins in different forms. We are a very heterogeneous group that leverages approaches of diverse scale: from modelling proteins as nodes in a complex interaction network, to full atomistic models that help us understand how they behave.

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What can you do with the OPIG Antibody Suite?

OPIG has now developed a whole range of tools for antibody analysis. I thought it might be helpful to summarise all the different tools we are maintaining (some of which are brand new, and some are not hosted at opig.stats), and what they are useful for.

Immunoglobulin Gene Sequencing (Ig-Seq/NGS) Data Analysis

1. OAS
Link: http://antibodymap.org/
Required Input: N/A (Database)
Paper: http://www.jimmunol.org/content/201/8/2502

OAS (Observed Antibody Space) is a quality-filtered, consistently-annotated database of all of the publicly available next generation sequencing (NGS) data of antibodies. Here you can:

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