In our latest immunoinformatics review, OPIG has teamed up with experienced antibody consultant Dr. Anthony Rees to outline the evidence for BCR/antibody repertoire convergence on common epitopes post-pathogen exposure, and all the ways we can go about detecting it from repertoire gene sequencing data. We highlight the new advances in the repertoire functional analysis field, including the role for OPIG’s latest tools for structure-aware antibody analytics: Structural Annotation of AntiBody repertoires+ (SAAB+), Paratyping, Ab-Ligity, Repertoire Structural Profiling & Structural Profiling of Antibodies to Cluster by Epitope (‘SPACE’).
Continue readingCategory Archives: Protein Structure
Unraveling the role of entanglement in protein misfolding
Proteins that fail to fold correctly may populate misfolded conformations with disparate structure and function. Misfolding is the focus of intense research interest due to its putative and confirmed role in various diseases, including neurodegenerative diseases such as Parkinson’s and Alzheimer’s Diseases as well as cystic fibrosis (PMID: 16689923).
Many open questions about protein misfolding remain to be answered. For example, how do misfolded proteins evade cellular quality control mechanisms like chaperones to remain soluble but non-functional for long timescales? How long do misfolded states persist on average? How widespread is misfolding? Experiments indicate that misfolding can even be caused by synonymous mutations that alter the speed of protein translation but not the sequence of the protein produced (PMID: 23417067), introducing the additional puzzle of how the protein maintains a “memory” of its translation kinetics after synthesis is complete.
A series of four recent preprints (Preprints 1, 2, 3, and 4, see below) suggests that these questions can be answered by the partitioning of proteins into long-lived self-entangled conformations that are structurally similar to the native state but with perturbed function. Simulation of the synthesis, termination, and post-translational dynamics of a large dataset of E. coli proteins suggests that misfolding and entanglement are widespread, with two thirds of proteins misfolding some of the time (Preprint 1). Many misfolded conformations may bypass proteostasis machinery to remain soluble but non-functional due to their structural similarity to the native state. Critically, entanglement is associated with particularly long-lived misfolded states based on simulated folding kinetics.
Coarse-grain and all-atom simulation results indicate that these misfolded conformations interact with chaperones like GroEL and HtpG to a similar extent as does the native state (Preprint 2). These results suggest an explanation for why some protein always fails to refold while remaining soluble, even in the presence of multiple folding chaperones – it remains trapped in entangled conformations that resemble the native state and thus fail to recruit chaperones.
Finally, simulations indicate that changes to the translation kinetics of oligoribonuclease introduced by synonymous mutations cause a large change in its probability of entanglement at the dimerization interface (Preprint 3). These entanglements localized at the interface alter its ability to dimerize even after synthesis is complete. These simulations provide a structural explanation for how translation kinetics can have a long-timescale influence on protein behavior.
Together, these preprints suggest that misfolding into entangled conformations is a widespread phenomenon that may provide a consistent explanation for many unanswered question in molecular biology. It should be noted that entanglement is not exclusive to other types of misfolding, such as domain swapping, that may contribute to misfolding in cells. Experimental validation of the existence of entangled conformations is a critical aspect of testing this hypothesis; for comparisons between simulation and experiment, see Preprint 4.
Preprint 1: https://www.biorxiv.org/content/10.1101/2021.08.18.456613v1
Preprint 2: https://www.biorxiv.org/content/10.1101/2021.08.18.456736v1
Preprint 3: https://www.biorxiv.org/content/10.1101/2021.10.26.465867v1
Preprint 4: https://www.biorxiv.org/content/10.1101/2021.08.18.456802v1
2021 likely to be a bumper year for therapeutic antibodies entering clinical trials; massive increase in new targets
Earlier this month the World Health Organisation (WHO) released Proposed International Nonproprietary Name List 125 (PL125), comprising the therapeutics entering clinical trials during the first half of 2021. We have just added this data to our Therapeutic Structural Antibody Database (Thera-SAbDab), bringing the total number of therapeutic antibodies recognised by the WHO to 711.
This is up from 651 at the end of 2020, a year which saw 89 new therapeutic antibodies introduced to the clinic. This rise of 60 in just the first half of 2021 bodes well for a record-breaking year of therapeutics entering trials.
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AlphaFold 2 is here: what’s behind the structure prediction miracle
Nature has now released that AlphaFold 2 paper, after eight long months of waiting. The main text reports more or less what we have known for nearly a year, with some added tidbits, although it is accompanied by a painstaking description of the architecture in the supplementary information. Perhaps more importantly, the authors have released the entirety of the code, including all details to run the pipeline, on Github. And there is no small print this time: you can run inference on any protein (I’ve checked!).
Have you not heard the news? Let me refresh your memory. In November 2020, a team of AI scientists from Google DeepMind indisputably won the 14th Critical Assessment of Structural Prediction competition, a biennial blind test where computational biologists try to predict the structure of several proteins whose structure has been determined experimentally but not publicly released. Their results were so astounding, and the problem so central to biology, that it took the entire world by surprise and left an entire discipline, computational biology, wondering what had just happened.
Continue readingAutomated intermolecular interaction detection using the ODDT Python Module
Detecting intermolecular interactions is often one of the first steps when assessing the binding mode of a ligand. This usually involves the human researcher opening up a molecular viewer and checking the orientations of the ligand and protein functional groups, sometimes aided by the viewer’s own interaction detecting functionality. For looking at single digit numbers of structures, this approach works fairly well, especially as more experienced researchers can spot cases where the automated interaction detection has failed. When analysing tens or hundreds of binding sites, however, an automated way of detecting and recording interaction information for downstream processing is needed. When I had to do this recently, I used an open-source Python module called ODDT (Open Drug Discovery Toolkit, its full documentation can be found here).
My use case was fairly standard: starting with a list of holo protein structures as pdb files and their corresponding ligands in .sdf format, I wanted to detect any hydrogen bonds between a ligand and its native protein crystal structure. Specifically, I needed the number and name of the the interacting residue, its chain ID, and the name of the protein atom involved in the interaction. A general example on how to do this can be found in the ODDT documentation. Below, I show how I have used the code on PDB structure 1a9u.
Continue readingThe Smallest Allosteric System
Allostery is still a badly understood but very general mechanism in the protein world. In principle, an allosteric event occurs when a ligand (small or big) binds to a certain site of a protein and something (activity or function) changes at a different, distant site. A well-known example would be G-protein-coupled receptors that transport such an allosteric signal even across a membrane. But it does not have to be that far apart. As part of the Protein Folding and Dynamics series, I have recently watched a talk by Peter Hamm (Zurich) who presented work on an allosteric system that I thought was very interesting because it was small and most importantly, controllable.
PDZ domains are peptide-binding domains, often part of multi-domain proteins. For the work presented the researchers used the PDZ3 domain which is a bit special and has an additional (third) C-terminal α-helix (α3-helix) which is packing to the other side of the binding pocket. Previous work (Petit et al. 2009) had shown that removal of the α3-helix had changed ligand affinity but not PDZ structure, major changes were of an entropic nature instead. Peter Hamm’s group linked an azobenzene-derived photoswitch to that α3-helix; in its cis configuration stabilizing the α3-helix and destabilising in trans (see Figure 1).
The Coronavirus Antibody Database: 10 months on, 10x the data!
Back in May 2020, we released the Coronavirus Antibody Database (‘CoV-AbDab’) to capture molecular information on existing coronavirus-binding antibodies, and to track what we anticipated would be a boon of data on antibodies able to bind SARS-CoV-2. At the time, we had found around 300 relevant antibody sequences and a handful of solved crystal structures, most of which were characterised shortly after the SARS-CoV epidemic of 2003. We had no idea just how many SARS-CoV-2 binding antibody sequences would come to be released into the public domain…
10 months later (2nd March 2021), we now have tracked 2,673 coronavirus-binding antibodies, ~95% with full Fv sequence information and ~5% with solved structures. These datapoints originate from 100s of independent studies reported in either the academic literature or patent filings.
Ribosome occupancy profiles are conserved between structurally and evolutionarily related yeast domains
Shameless plug for any OPIG blog readers to take a look at our recent publication in Bioinformatics. Consider giving it a read if the below summary grabs your attention.
Many proteins are now known to fold during their synthesis through the process known as co-translational folding. Translation is an inherently non-equilibrium process – one consequence of this fact is that the speed of translation can radically influence the ability of proteins to fold and function. In this paper we compare ribosome occupancy profiles between related domains in yeast to test the hypothesis that evolutionarily related proteins with similar native folds should tend to have similar translation speed profiles to preserve efficient co-translational folding. We find strong evidence in support of this hypothesis at the level of individual protein domains and across a set of 664 pairs of related domains for which we are able to compute high-quality ribosome occupancy profiles.
To find out more, view the Advance Article at Bioinformatics.
Miniproteins – small but mighty!
Proteins come in all shapes and sizes, ranging from thousands of amino acids in length to less than 20. However, smaller size does not correlate with reduced importance. Miniproteins, which are commonly defined as being less than 100 amino acids long, are receiving increased attention for their potential roles as pharmaceuticals. A recent paper by David Baker’s group put miniproteins into the spotlight, as the study authors were able to design miniproteins that bind the SARS-CoV-2 spike protein with as strong affinity as an antibody would – but in a tiny fraction of the size (Cao et al., 2020). These miniproteins are much cheaper to manufacture than antibodies (as they can be expressed in bacteria) and can be highly stable (with melting temperatures of >90º possible, meaning they can easily be stored at room temperature). The most promising miniprotein developed by the Baker group (LCB1) is currently undergoing testing to be used as a prophylactic nasal spray that provides protection against SARS-CoV-2 infection. These promising results – and the speed in which progress was made – brings the vast potential of miniproteins in healthcare to the fore.
Continue readingMaking Pretty Pictures with PyMOL
There’s few things I like more in our field than the opportunity to make a really nice image of a protein structure. Don’t judge me, but I’ve been known to spend the occasional evening in front of the TV with a cup of tea and PyMOL open in front of me! I’ve presented on the subject at a couple of our research group retreats, and have wanted to type it up into a blog post for a while – and this is the last opportunity I will have, since I will be leaving in just a few weeks time, after nearly eight years (!) as an OPIGlet. So, here goes – my tips and tricks for making pretty pictures with PyMOL!
Ray Tracing
set ray_trace_mode, number
I always ray trace my images to make them higher quality. It can take a while for large proteins, but it’s always worth it! My favourite setting is 1, but 3 can be fun to make things a bit more cartoon-ish.
You can also improve the quality of the image by increasing the ‘surface_quality’ and ‘cartoon_sampling’ settings.