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.
Continue readingCategory Archives: Proteins
Custom coffee mugs for OPIG
As of last week, many members of OPIG have custom mugs. Each comes with an illustration of its owner’s favourite protein, as well as, the OPIG logo. There is an additional `unofficial’ OPIG logo on the backstamp (the outside bottom).
Continue readingTwo 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
How to Iterate in PyMOL
Sometimes pointing-and-clicking just doesn’t cut it. With PyMOL’s built-in Python interpreter, repetitive actions are made simple.
Continue readingWhat 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.
Continue readingWhat 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:
Mol2vec: Finding Chemical Meaning in 300 Dimensions
2D projections (t-SNE) of Mol2vec vectors of amino acids (bold arrows). These vectors were obtained by summing the vectors of the Morgan substructures (small arrows) present in the respective molecules (amino acids in the present example). The directions of the vectors provide a visual representation of similarities. Magnitudes reflect importance, i.e. more meaningful words. [Figure from Ref. 1]
A recent publication of one of my former InhibOx-colleagues, Simone Fulle, and her co-workers, Sabrina Jaeger and Samo Turk, shows how we can embed molecular substructures and chemical compounds into a similarly high-dimensional, continuous vectorial representation, which they dubbed “mol2vec“.1 They also released a Python implementation, available on Samo Turk’s GitHub repository.
Protein Structure Classification: Order in the Chaos
The number of known protein structures has increased exponentially over the past decades; there are currently over 127,000 structures deposited in the PDB [1]. To bring order to this large volume of data, and to further our understanding of protein function and evolution, these structures are systematically classified according to sequence and structural similarity. Downloadable classification data can be used for annotating datasets, exploring the properties of proteins and for the training and benchmarking of new methods [2].
Yearly growth of structures in the PDB (adapted from [1])
Typically, proteins are grouped by structural similarity and organised using hierarchical clustering. Proteins are sorted into classes based on overall secondary structure composition, and grouped into related families and superfamilies. Although this process could originally be manually curated, as with Structural Classification of Proteins (SCOP) [3] (last updated in June 2009), the growing number of protein structures now requires semi- or fully-automated methods, such as SCOP-extended (SCOPe) [4] and Class, Architecture, Topology, Homology (CATH) [5]. These resources are comprehensive and widely used, particularly in computational protein research. There is a large proportion of agreement between these databases, but subjectivity of protein classification is to be expected. Variation in methods and hierarchical structure result in differences in classifications. For example, different criteria for defining and classifying domains results in inconsistencies between CATH and SCOPe.
The arrangements of secondary structure elements in space are known as folds. As a result of evolution, the number of folds that exist in nature is thought to be finite, predicted to be between 1000-10,000 [6]. Analysis of currently known structures appears to support this hypothesis, although solved structures in the PDB are likely to be a skewed sample of all protein structures space. Some folds are extremely commonly observed in protein structures.
In his ‘periodic table for protein structures’, William Taylor went one step further in his goal to find a comprehensive, non-hierarchical method of protein classification [7]. He attempted to identify a minimal set of building blocks, referred to as basic Forms, that can be used to assemble as many globular protein structures as possible. These basic Forms can be combined systematically in layers in a way analogous to the combination of electrons into valence shells to form the periodic table. An individual protein structure can then be described as the closest matching combination of these basic Forms. Related proteins can be identified by the largest combination of basic Forms they have in common.
The ‘basic Forms’ that make up Taylor’s ‘periodic table of proteins’. These secondary structure elements accounted for, on average, 80% of each protein in a set of 2,230 structures (all-alpha proteins were excluded from the dataset) [7]
- RCSB PDB Statistics
- Fox, N.K., Brenner, S.E., Chandonia, J.-M., 2015. The value of protein structure classification information-Surveying the scientific literature. Proteins Struct. Funct. Bioinforma. 83, 2025–2038.
- Murzin AG, Brenner SE, Hubbard T, Chothia C., 1995. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol. 247, 536–540.
- Fox, N.K., Brenner, S.E., Chandonia, J.-M., 2014. SCOPe: Structural Classification of Proteins–extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Res. 42, 304-9.
- Dawson NL, Lewis TE, Das S, et al., 2017. CATH: an expanded resource to predict protein function through structure and sequence. Nucleic Acids Research. 45, 289-295.
- Derek N Woolfson, Gail J Bartlett, Antony J Burton, Jack W Heal, Ai Niitsu, Andrew R Thomson, Christopher W Wood,. 2015. De novo protein design: how do we expand into the universe of possible protein structures?, Current Opinion in Structural Biology, 33, 16-26.
- Taylor, W.R., 2002. A “periodic table” for protein structures. Nature. 416, 657–660.
- Ahnert, S.E., Marsh, J.A., Hernandez, H., Robinson, C. V., Teichmann, S.A., 2015. Principles of assembly reveal a periodic table of protein complexes. Science. 80, 350
Start2Fold: A database of protein folding and stability data
Hydrogen/deuterium exchange (HDX) experiments are used to probe the tertiary structures and folding pathways of proteins. The rate of proton exchange between a given residue’s backbone amide proton and the surrounding solvent depends on the solvent exposure of the residue. By refolding a protein under exchange conditions, these experiments can identify which regions quickly become solvent-inaccessible, and which regions undergo exchange for longer, providing information about the refolding pathway.
Although there are many examples of individual HDX experiments in the literature, the heterogeneous nature of the data has deterred comprehensive analyses. Start2Fold (Start2Fold.eu) [1] is a curated database that aims to present protein folding and stability data derived from solvent-exchange experiments in a comparable and accessible form. For each protein entry, residues are classified as early/intermediate/late based on folding data, or strong/medium/weak based on stability data. Each entry includes the PDB code, length, and sequence of the protein, as well as details of the experimental method. The database currently includes 57 entries, most of which have both folding and stability data. Hopefully, this database will grow as scientists add their own experimental data, and reveal useful information about how proteins refold.
The folding data available in Start2Fold is visualised in the figure below, with early, intermediate and late folding residues coloured light, medium and dark blue, respectively.
[1] Pancsa, R., Varadi, M., Tompa, P., Vranken, W.F., 2016. Start2Fold: a database of hydrogen/deuterium exchange data on protein folding and stability. Nucleic Acids Res. 44, D429-34.
Seventh Joint Sheffield Conference on Cheminformatics Part 1 (#ShefChem16)
In early July I attended the the Seventh Joint Sheffield Conference on Cheminformatics. There was a variety of talks with speakers at all stages of their career. I was lucky enough to be invited to speak at the conference, and gave my first conference talk! I have written two blog posts about the conference: part 1 briefly describes a talk that I found interesting and part 2 describes the work I spoke about at the conference.
One of the most interesting parts of the conference was the active twitter presence. #ShefChem16. All of the talks were live tweeted which provided a summary of each talk and also included links to software or references. It also allowed speakers to gain insight and feedback on their talk instantly.
One of the talks I found most interesting presented the Protein-Ligand Interaction Profiler (PLIP). It is a method for the detection of protein-ligand interactions. PLIP is open-source and has a web-based online tool and a command-line tool. Unlike PyMol which only calculates polar contacts, and not the type of interaction, PLIP calculates 8 different types of interactions: hydrogen bonding, hydrophobic, π-π stacking, π-cation interactions, salt bridges, water bridges, halogen bonds, metal complexes. For a given pdb file the interactions are calculated and shown in a publication quality figure shown here.
The display can also be downloaded as a PyMol session so the display can be modified.
This tool is an extremely useful way to calculate protein-ligand interactions and can be used to find the types of interactions formed by the protein-ligand complex.
PLIP can be found here: https://projects.biotec.tu-dresden.de/plip-web/plip/