Tag Archives: Protein Structure Prediction

The First OpenBind Blind Challenge

At OpenBind, we’re working to generate large-scale, open experimental datasets of protein-ligand structures and binding measurements to support the development and evaluation of computational methods for drug discovery. One thing I’m particularly excited about is using these data not just for retrospective benchmarking, but for prospective, blind evaluation.

So, I’m very pleased that we’re launching the first OpenBind blind challenge on 14 October 2026, in partnership with OpenADMET and the ASAP Discovery Consortium. The interim submission deadline is 11 November, with final submissions due by 16 December 2026 at 23:59 UTC.

The challenge asks participants to predict protein-ligand complexes of 400 compounds bound to Zika virus NS2B-NS3 protease, ranging from small fragments to larger, lead-like molecules.

A key challenge will be using the structural information that already exists to accurately predict the binding modes of new, unseen ligands. There are already a number of publicly available structures of Zika NS2B-NS3 protease in the PDB that participants can use.

We’re also setting a high bar for success. While many protein-ligand benchmarks use an RMSD threshold of 2 Å, we’re tightening this to 1.5 Å, along with additional checks on interaction accuracy (lDDT-PLI > 0.8) and physical validity (PoseBusters validity checks). I’m particularly excited to see how docking, cofolding, and other approaches compare!

If you work on protein-ligand structure prediction, I’d strongly encourage you to join the challenge. You can find all the details, including how to register and join the challenge Discord, in the OpenBind announcement.

Update: We’re now live! Check out the challenge on Hugging Face.

Spin Lattices and Proteins – How state-based discretisations have enabled modern protein modelling

I got into protein modelling not long before AlphaFold2 first released. At that time some of the prevailing methods for protein structure prediction came from highly interpretable energy functionals that arose from a particularly beautiful intersection of statistical mechanics and biology. These “Potts” models are going to be the centre of a larger discussion in this blog on state-based discretisations of proteins, how they’ve shaped modern deep learning methods and whether there is still more to learn from them.

In the age of black box deep learning, does the Potts model still have a place?

The Potts/Ising Model

The Ising model is a well established popular theoretical physics model of ferromagnetism. Simply put, given a lattice of atoms each capable of adopting 1 of 2 spins (up and down) ferromagnetism arises when their spins align and their associated magnetic moments point in the same direction. The Ising model tries to parameterise the local and non-local relationships between atoms and their spin states such that we can learn the Hamiltonian of the system and its different configurations under the magnetic field. The Hamiltonian takes the following form for a system of N atoms


$$
E = -\sum_{i}^Nh_ix_i – \sum_{i<j}^N J_{ij}x_i x_j,
$$

where J is the “coupling energy” between any two atoms x_i and x_j, and h represents the magnetic field, or more appropriately for our purposes it can be framed as a single-site field dictating how an individual atom independently acts within the model. You might recognise the form this binary spin model takes as it arises naturally across the sciences including in Hopfield networks and graphical models.

Everything is an Ising-like model if you’re brave enough

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Accelerating AlphaFold 3 for high-throughput structure prediction

Introduction

Recently, I have been conducting a project in which I need to predict the structures of a dataset comprising a few thousand protein sequences using AlphaFold 3. Taking a naive approach, it was taking an hour or two per entry to get a predicted structure. With a few thousand structures, it seemed that it would take months to be able to run…

In this blog post, I will go through some tips I found to help accelerate the structure predictions and make all of the predictions I needed in under a week. In general, following the tips in the AlphaFold 3 performance documentation is a useful starting place. Most of the tips I provide are related to accelerating the MSA generation portion of the predictions because this was the biggest bottleneck in my case.

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Estimating the Generalisability of Machine Learning Models in Drug Discovery

Machine learning (ML) has significantly advanced key computational tasks in drug discovery, including virtual screening, binding affinity prediction, protein-ligand structure prediction (co-folding), and docking. However, the extent to which these models generalise beyond their training data is often overestimated due to shortcomings in benchmarking datasets. Existing benchmarks frequently fail to account for similarities between the training and test sets, leading to inflated performance estimates. This issue is particularly pronounced in tasks where models tend to memorise training examples rather than learning generalisable biophysical principles. The figure below demonstrates two examples of model performance decreasing with increased dissimilarity between training and test data, for co-folding (left) and binding affinity prediction (right).

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Conference summary: Generative AI in Life Science

This year I attended the second edition of Generative AI in Life Science (GenLife – https://genlife.dk/) and it was an enriching experience that I thoroughly enjoyed. Held in Copenhagen, the event brought together researchers from different areas of AI applied to the life sciences and provided a fantastic platform for networking, learning and sharing ideas. The programme included a mix of long and short talks from experts in the field, but also had a significant presence of emerging PIs, making the conference a perfect place to discover emerging groups in the field. Here I have collected some highlights of the talks I have enjoyed the most at the conference.

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Pyrosetta for RFdiffusion

I will not lie: I often struggle to find a snippet of code that did something in PyRosetta or I spend hours facing a problem caused by something not working as I expect it to. I recently did a tricky project involving RFdiffusion and I kept slipping on the PyRosetta side. So to make future me, others, and ChatGTP5 happy, here are some common operations to make working with PyRosetta for RFdiffusion easier.

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Useful metrics and their meanings

Short and selfish blog here. Probably been done before, but I shall carry on regardless. I am going to review some metrics relevant to our area of Immunoinformatics. In other words, I will try dissect things such as perplexity, logits, pTM, pLDDT and the ABodyBuilder2 confidence score. These numbers can help inform us on the likelihood of predictions, and whether we should have confidence in them.

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Current strategies to predict structures of multiple protein conformational states

Since the release of AlphaFold2 (AF2), the problem of protein structure prediction is widely believed to be solved. Current structure prediction tools, such as AF2, are able to model most proteins with high accuracy. These methods, however, have a major limitation as they have been trained to predict a single structure for a given protein. Proteins are highly dynamic molecules, and their function often depends on transitions between several conformational states. Despite research focusing on the task of predicting the structures of multiple conformations of a protein, currently, no accurate and reliable method is available. In this blog post, I will provide a short overview of the strategies developed for predicting protein conformations. I have grouped these into three sets of related approaches. To conclude, I will also demonstrate how to run one of these strategies on your own.

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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.

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Journal Club: Large-scale structure prediction by improved contact predictions and model quality assessment.

With the advent of statistical techniques to infer protein contacts from multiple sequence alignments (which you can read more about here), accurate protein structure prediction in the absence of a template has become possible. Taking advantage of this fact, there have been efforts to brave the sea of protein families for which no structure is known (about 8,500 – over 50% of known protein families) in an attempt to predict their topology. This is particularly exciting given that protein structure prediction has been an open problem in biology for over 50 years and, for the first time, the community is able to perform large-scale predictions and have confidence that at least some of those predictions are correct.

Based on these trends, last group meeting I presented a paper entitled “Large-scale structure prediction by improved contact predictions and model quality assessment”. This paper is the culmination of years of work, making use of a large number of computational tools developed by the Elofsson Lab at Stockholm University. With this blog post, I hope to offer some insights as to the innovative findings reported in their paper.

Let me begin by describing their structure prediction pipeline, PconsFold2. Their method for large-scale structure prediction can be broken down into three components: contact prediction, model generation and model quality assessment. As the very name of their article suggests, most of the innovation of the paper stems from improvements in contact prediction and the quality assessment protocols used, whereas for their model generation routine, they opted to sacrifice some quality in favour of speed. I will try and dissect each of these components over the next paragraphs.

Contact prediction relates to the process in which residues that share spatial proximity in a protein’s structure are inferred from multiple sequence alignments by co-evolution. I will not go into the details of how these protocols work, as they have been previously discussed in more detail here and here. The contact predictor used in PconsFold2 is PconsC3, which is another product of the Elofsson Lab. There was some weirdness with the referencing of PconsC3 on the PconsFold2 article, but after a quick google search, I was able to retrieve the article describing PconsC3 and it was worth a read. Other than showcasing PconsC3’s state-of-the-art contact prediction capabilities, the original PconsC3 paper also provides figures for the number of protein families for which accurate contact prediction is possible (over 5,000 of the ~8,500 protein families in Pfam without a member of known structure). I found the PconsC3 article feels like a prequel to the paper I presented. The bottom line here is that PconsC3 is a reliable tool for predicting contacts from multiple sequence alignments and is a sensible choice for the PconsFold2 pipeline.

Another aspect of contact prediction that the authors explore is the idea that the precision of contact prediction is dependent on the quality of the underlying multiple sequence alignment (MSA). They provide a comparison of the Positive Predicted Value (PPV) of PconsC3 using different MSAs on a test set of 626 protein domains from Pfam. To my knowledge, this is the first time I have encountered such a comparison and it serves to highlight the importance the MSA has on the quality of resulting contact predictions. In the PconsFold2 pipeline, the authors use consensus approach; they identify the consensus of four predicted contact maps each using a different alignment. Alignments were generated using Jackhmmer and HHBlits at E-Value cutoffs of 1 and 10^-4.

Now, moving on to the model generation routine. PconsFold2 makes use of CONFOLD to perform model generation. CONFOLD, in turn, uses the simulated annealing routine of the Crystallographic and NMR System (CNS) to produce models based on spatial and geometric constraints. To derive those constraints, predicted secondary structure and the top 2.5 L predicted contacts are given as input. The authors do note that the refinement stage of CONFOLD is omitted, which is a convenience I assume was adopted to save computational time. The article also acknowledges that models generated by CONFOLD are likely to be less accurate than the ones produced by Rosetta, yet a compromise was made in order to make the large-scale comparison feasible in terms of resources.

One particular issue that we often discuss when performing structure prediction is the number of models that should be produced for a particular target. The authors performed a test to assess how many decoys should be produced and, albeit simplistic in their formulation, their results suggest that 50 models per target should be sufficient. Increasing this number further did not lead to improvements in the average quality of the best models produced for their test set of 626 proteins.

After producing 50 models using CONFOLD, the final step in the PconsFold2 protocol is to select the best possible model from this ensemble. Here, they present a novel method, PcombC, for ranking models. PcombC combines the clustering-based method Pcons, the single-model deep learning method ProQ3D, and the proportion of predicted contacts that are present in the model. These three scores are combined linearly, and are given weights that were optimised via a parameter sweep. One of my reservations relating to this paper is that little detail is given regarding the data set that was used to perform this training. It is unclear from their methods section if the parameter sweep was trained on the test set with 626 proteins used throughout the manuscript. Given that no other data set (with known structures) is ever introduced, this scenario seems likely. Therefore, all the classification results obtained by PcombC, and all of the reported TM-score Top results should be interpreted with care since performance on validation set tends to be poorer than on a training set.

Recapitulating the PconsFold2 pipeline:

  • Step 1: generate four multiple sequence alignments using HHBlits and Jackhmmer.
  • Step 2: generate four predicted contact maps using PconsC3.
  • Step 3: Use CONFOLD to produce 50 models using a consensus of the contact maps from step 2.
  • Step 4: Use PCombC to rank the models based on a linear combination of the Pcons and ProQ3D scores and the proportion of predicted contacts that are present in the model.

So, how well does PconsFold2 perform? The conclusion is that it depends on the quality of the contact predictions. For the protein families where abundant sequence information is available, PconsFold2 produces a correct model (TM-Score > 0.5) for 51% of the cases. This is great news. First, because we know which cases have abundant sequence information beforehand. Second, because this comprises a large number of protein families of unknown structure. As the number of effective sequence (a common way to assess the amount of information available on an MSA) decreases, the proportion of families for which a correct model has been generated also decreases, which restricts the applicability of their method to protein families with abundant sequence information. Nonetheless, given that protein sequence databases are growing exponentially, it is possible that over the next years, the number of cases where protein structure prediction achieves success is likely to increase.

One interesting detail that I was curious about was the length distribution of the cases where modelling was successful. Can we detect the cases for which good models were produced simply by looking at a combination of length and number of effective sequences? The authors never address this question, and I think it would provide some nice insights as to which protein features are correlated to modelling success.

We are still left with one final problem to solve: how do we separate the cases for which we have a correct model from the ones where modelling has failed? This is what the authors address with the last two subsections of their Results. In the first of these sections, the authors compare four ways of ranking decoys: PcombC, Pcons, ProQ3D, and the CNS contact score. They report that, for the test set of 626 proteins, PcombC obtains the highest Pearson’s Correlation Coefficient (PCC) between the predicted and observed TM-Score of the highest ranking models. As mentioned before, this measure could be overestimated if PcombC was, indeed, trained on this test set. Reported PCCs are as follows: PcombC = 0.79, Pcons = 0.73, ProQ3D = 0.67, and CNS-contact = -0.56.

In their final analysis, the authors compare the ability of each of the different Quality Assessment (QA) scores to discern between correct and incorrect models. To do this, they only consider the top-ranked model for each target according to different QA scores. They vary the false positive rate and note the number of true positives they are able to recall. At a 10% false positive rate, PcombC is able to recall about 50% of the correct models produced for the test set. This is another piece of good news. Bottomline is: if we have sufficient sequence information available, PconsFold2 can generate a correct model 51% of the time. Furthermore, it can detect 50% of these cases, meaning that for ~25% of the cases it produced something good and it knows the model is good. This opens the door for looking at these protein families with no known structure and trying to accurately predict their topology.

That is exactly what the authors did! On the most interesting section of the paper (in my opinion), the authors predict the topology of 114 protein families (at FPR of 1%) and 558 protein families (at FPR of 10%). Furthermore, the authors compare the overlap of their results with the ones reported by a similar study from the Baker group (previously presented at group meeting here) and find that, at least for some cases, the predictions agree. These large-scale efforts  force us to revisit the way we see template-free structure prediction, which can no longer be dismissed as a viable way of obtaining structural models when sufficient sequences are available. This is a remarkable achievement for the protein structure prediction community, with the potential to change the way we conduct structural biology research.