Exciting new studies in OAS

Hi everyone!

Today is the day for another blog post from me. Here, I would like to give you an update on new studies, which were deposited in the Observed Antibody Space (OAS) resource and take a closer look at one of these studies. To date, we have curated 57 studies in OAS, where we provide raw nucleotide and numbered amino acid sequences for download. These amino acid sequences have been filtered using ANARCI parsing, which ensures that the sequences align to respective species HMM profiles and do not have unusual indels and frameshifts. More than 660 million numbered amino acid sequences are deposited in OAS, where every sequence keeps a link to its corresponding nucleotide sequence. Recently we added two more studies to OAS: Sheng et al., (2017) and Setliff et al., (2018). We numbered roughly 2.8 and 46 million sequences in Sheng et al., and Setliff et al., studies respectively. In this blog post, I would like to talk more about the uniqueness of Setliff et al., data.

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Finding the lowest energy conformation of given molecule!

Generating low-energy molecular conformers is important for many areas of computational chemistry, molecular modeling and cheminformatics. Many tools have been developed to generate conformers, including BALLOON (1), Confab (2), FROG2 (3),  MOE (4), OMEGA (5) and RDKit (6). The search algorithm implemented in these tools can be broadly classified as either systematic or stochastic. These algorithms primarily focus on generating geometrically diverse low-energy conformers. Here, we are interested in finding lowest energy conformation of a molecule instead of achieving geometric diversity and Bayesian optimization is used to find the lowest energy conformation (7). Continue reading →

Check My Blob

A brief overview and discussion of: Automatic recognition of ligands in electron density by machine learning .This paper aims to reduce the bias of crystallographers fitting ligands into electron density for protein ligand complexes. The authors train a supervised machine learning model using known ligand sites across the whole protein databank, to produce a classifier that can identify which common ligands could fit to that electron density.

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OPIG Putts Up

Tonight, post-OPIG Group Meeting, most of us visited the local crazy golf course “Junkyard Golf” for some serious fun. Three groups of us teed off at different times, negotiating dimly lit Heath-Robinson/Rube Goldberg-style courses leading into bathtubs, past bears and through volcanoes. We’re not competitive at all (Serenity & Crunch) so it was a great surprise to learn at the end of our games that CW had won…

Post-putting OPIGlets

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