| Résumé | Protein-protein interactions play a fundamental role in drug design, gene therapy and vaccine development. The study of protein-protein interactions relies heavily on complex and time-consuming experiments, which has a severe impact on research throughputs. Thus, it is important to provide the experimentalist with the most promising cases by screening rapidly through a very large number of potential candidates. We propose a new deep neural network architecture that allows the binding probability for two proteins to be predicted instantly based solely on their amino acid sequences. Subsequently, screenings are performed based on the binding probabilities. The novelty of our approach lies in the fact that we consider self-binding and folding amino acid sequences, rather than just looking at these sequences per se. Our novel Siamese Pyramid Network (SPNet) architecture is inspired by Feature Pyramid Networks and consists of a multi-level Siamese neural network with an attention mechanism and a multilevel, trainable binding probability prediction network. Our experimental evaluation is performed on a strict dataset and shows that SPNet outperforms the state-of-the-art architectures. In addition, we employ SPNet to find the proteins that are most likely to bind with the Covid-2019 spike, thus providing a small and potentially valuable set of candidates for a future therapeutic vaccine. |
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