Category Archives: Protein Structure

Monoclonal antibody PRNP100 therapy for Creutzfeldt–Jakob disease

Recently, University College London Hospitals (UCLH) received a “Specials License” to allow the treatment of six patients suffering from Creutzfeldt–Jakob Disease (CJD), by way of a novel antibody known as PRN100. The results of this treatment have now been published in The Lancet.

There is currently no cure for CJD, yet over 100 people per year develop it either spontaneously or through external means including (but not limited to) growth hormones, cataract surgery or infected neurosurgical implements [1]. “There is no UK legislation which implements a compassionate use programme as set out in Article 83 of the relevant EU regulation. But the UK has implemented an exemption process known as the “Specials” in light of the requirement to be able to deal with special needs.” [2]

As there is no known cure, the request for use of PRN100 was put before the court as in Law “Some treatment decisions are so serious that the court has to make them.”

Continue reading →

CryoEM is now the dominant technique for solving antibody structures

Last year, the Structural Antibody Database (SAbDab) listed a record-breaking 894 new antibody structures, driven in no small part by the continued efforts of the researchers to understand SARS-CoV-2.

Fig. 1: The aggregate growth in antibody structure data (all methods) over time. Taken from http://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab/stats/ on 25th May 2022.

In this blog post I wanted to highlight the major driving force behind this curve – the huge increase in cryo electron microscopy (cryoEM) data – and the implications of this for the field of structure-based antibody informatics.

Continue reading →

MM(PB/GB)SA – a quick start guide

The MMPBSA.py program distributed Open Source in the AmberTools21 package is a powerful tool for end-point free energy calculations on molecular dynamics simulations. In its most simple application, MMPBSA.py is used to calculate the free energy difference between the bound and unbound states of a protein-ligand complex. In order to use it, however, you need to have an Amber-compliant trajectory file, which means you need to setup and run your simulation fairly carefully.

While the Amber Manual and the MMPBSA tutorial provide lots of helpful information, putting everything together into a full pipeline taking you from structure to a free energy is another story. The goal for this guide is to provide a schematic you can follow to get started. This guide assumes you are familiar with molecular dynamics simulations and the theory of MMPBSA.

The easiest way I have found to do this, using only Open Source software, is:

(1) Download your raw PDB file. If you are lucky and it contains a complete set of heavy atoms (excepting perhaps a terminal OXT here and there, which tleap will add for you in step 3) you are good to go.

(2) Use the H++ webserver to determine the protonation states of each residue and add hydrogens as needed. This webserver is particularly convenient because it will allow you to directly download a PQR file that you can use to generate your starting topology and coordinates. Note that you have various options to choose the pH and internal/external dielectric constants for the calculation.

(3) Use tleap to generate your topology (prmtop) and coordinate (mdcor) files for your simulations. Do not forget that you will need not only the prmtop for the solvated complex, but also a dry prmtop for each of the complex, receptor, and ligand. Load the PQR file from H++ and do not forget to set PBRadii *to the same value for all prmtops*. A typical tleap script for setting up your solvated complex would look something like:

Continue reading →

OpenMM Setup: Start Simulating Proteins in 5 Minutes

Molecular dynamics (MD) simulations are a good way to explore the dynamical behaviour of a protein you might be interested in. One common problem is that they often have a relatively steep learning curve when using most MD engines.

What if you just want to run a simple, one-off simulation with no fancy enhanced sampling methods? OpenMM Setup is a useful tool for exactly this. It is built on the open-source OpenMM engine and provides an easy to install (via conda) GUI that can have you running a simulation in less than 5 minutes. Of course, running a simulation requires careful setting of parameters and being familiar with best practices and while this is beyond the scope of this post, there are many guides out there that can easily be found. Now on to the good stuff: using OpenMM Setup!

When you first run OpenMM Setup, you’ll be greeted by a browser window asking you to choose a structure to use. This can be a crystal structure or a model. Remember, sometimes these will have problems that need fixing like missing density or charged, non-physiological termini that would lead to artefacts, so visual inspection of the input is key! You can then choose the force field and water model you want to use, and tell OpenMM to do some cleaning up of the structure. Here I am running the simulation on hen egg-white lysozyme:

Continue reading →

Fragment Based Drug Discovery with Crystallographic Fragment Screening at XChem and Beyond

Disclaimer: I’m a current PhD student working on PanDDA 2 for Frank von Delft and Charlotte Deane, and sponsored by Global Phasing, and some of this is my opinion – if it isn’t obvious in one of the references I probably said it so take it with a pinch of salt

Fragment Based Drug Discovery

Principle

Fragment based drugs discovery (FBDD) is a technique for finding lead compounds for medicinal chemistry. In FBDD a protein target of interest is identified for inhibition and a small library, typically of a few hundred compounds, is screened against it. Though these typically bind weakly, they can be used as a starting point for chemical elaboration towards something more lead-like. This approach is primarily contrasted with high throughput screening (HTS), in which an enormous number of larger, more complex molecules are screened in order to find ones which bind. The key idea is recognizing that the molecules in these HTS libraries can typically be broken down into a much smaller number of common substructures, fragments, so screening these ought to be more informative: between them they describe more of the “chemical space” which interacts with the protein. Since it first appeared about 25 years ago, FBDD has delivered four drugs for clinical use and over 40 molecules to clinical trials.

Continue reading →

Model validation in Crystallographic Fragment Screening

Fragment based drug discovery is a powerful technique for finding lead compounds for medicinal chemistry. Crystallographic fragment screening is particularly useful because it informs one not just about whether a fragment binds, but has the advantage of providing information on how it binds. This information allows for rational elaboration and merging of fragments.

However, this comes with a unique challenge: the confidence in the experimental readout, if and how a fragment binds, is tied to the quality of the crystallographic model that can be built. This intimately links crystallographic fragment screening to the general statistical idea of a “model”, and the statistical ideas of goodness of fit and overfitting.

Continue reading →

New review on BCR/antibody repertoire analysis out in MAbs!

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 reading →

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.

Continue reading →

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 reading →