Neoantigen-targeting vaccines have achieved breakthrough success in cancer immunotherapy by eliciting immune responses against neoantigens, which are proteins uniquely produced by cancer cells. During the immune response, the interactions between peptides and major histocompatibility complexes (MHC) play an important role as peptides must be bound and presented by MHC to be recognised by the immune system. However, only limited experimentally determined peptide-MHC (pMHC) structures are available, and in-silico structure modelling is therefore used for studying their interactions. Current approaches mainly use Monte Carlo sampling and energy minimisation, and are often computationally expensive. On the other hand, the advent of large high-quality proteomic data sets has led to an unprecedented opportunity for deep learning-based methods with pMHC structure prediction becoming feasible with these trained protein folding models. In this work, we present a graph neural network-based model for pMHC structure prediction, which takes an amino acid-level pMHC graph and an atomic-level peptide graph as inputs and predicts the peptide backbone conformation. With a novel weighted reconstruction loss, the trained model achieved a similar accuracy to AlphaFold 2, requiring only 1.7M learnable parameters compared to 93M, representing a more than 98% reduction in the number of required parameters.
The Madden-Julian oscillation (MJO: Madden & Julian, 1971) is an envelope of enhanced tropical convection with associated changes to the atmospheric circulation. It is characterized by its period of 40-50 days, its planetary scale, and its Eastward propagation at speeds of 4-8 ms −1 . It is the major source of predictability on sub-seasonal timescales in the Tropics (Zhang, 2013) and influences phenomena such as the North Atlantic Oscillation and Arctic sea ice cover through global teleconnections (
The Madden-Julian oscillation (MJO: Madden & Julian, 1971) is an envelope of enhanced tropical convection with associated changes to the atmospheric circulation. It is characterized by its period of 40-50 days, its planetary scale, and its Eastward propagation at speeds of 4-8 ms −1 . It is the major source of predictability on sub-seasonal timescales in the Tropics (Zhang, 2013) and influences phenomena such as the North Atlantic Oscillation and Arctic sea ice cover through global teleconnections (
<p>The Madden&#8211;Julian Oscillation (MJO) is the dominant source of sub-seasonal variability in the tropics. It consists of an Eastward moving region of enhanced convection coupled to changes in zonal winds. It is not possible to predict the precise evolution of the MJO, so subseasonal forecasts are generally probabilistic. Ideally the spread of the forecast probability distribution would vary from day to day depending on the instantaneous predictability of the MJO. Operational subseasonal forecasting models do not have this property. We present a deep convolutional neural network that produces skilful state-dependent probabilistic MJO forecasts. This statistical model accounts for intrinsic chaotic uncertainty by predicting the standard deviation about the mean, and model uncertainty using a Monte-Carlo dropout approach. Interpretation of the mean forecasts from the neural network highlights known MJO mechanisms, providing confidence in the model, while interpretation of the predicted uncertainty indicates new physical mechanisms governing MJO predictability.</p>
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