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MotivationAs more data of experimentally determined protein structures is becoming available, data-driven models to describe protein sequence-structure relationship become more feasible. Within this space, the amino acid sequence design of protein-protein interactions has still been a rather challenging sub-problem with very low success rates - yet it is central for the most biological processes.ResultsWe developed an attention-based deep learning model inspired by algorithms used for image-caption assignments for sequence design of peptides or protein fragments. These interaction fragments are derived from and represent core parts of protein-protein interfaces. Our trained model allows the one-sided design of a given protein fragment which can be applicable for the redesign of protein-interfaces or the de novo design of new interactions fragments. Here we demonstrate its potential by recapitulating naturally occurring protein-protein interactions including antibody-antigen complexes. The designed interfaces capture essential native interactions with high prediction accuracy and have native-like binding affinities. It further does not need precise backbone location, making it an attractive tool for working with de novo design of protein-protein interactions.AvailabilityThe source code of the method is available at https://github.com/strauchlab/iNNterfaceDesignSupplementary informationSupplementary data are available at Bioinformatics online.

Protein-protein interactions are part of most processes in life and thereby the ability to generate new ones to either control, detect or inhibit them has universal applications. However, to develop a new binding protein to bind to a specific site at atomic detail without any additional input is a challenging problem. After DeepMind entered the protein folding field, we have seen rapid advances in protein structure predictions thanks to the implementation of machine learning algorithms. Neural networks are part of machine learning and they can learn the regularities from their input data. Here, we took advantage of their capabilities by training multiple neural networks on co-crystal structures of natural protein complexes. Inspired by image caption algorithms, we developed an extensive set of NN-based models, referred to as iNNterfaceDesign. It predicts the positioning and the secondary structure for the new binding motifs and then designs the backbone atoms followed by amino acid sequence design. Our methods are capable of recapitulating native interactions, including antibody-antigen interactions, while they also capable to produce more diverse solutions to binding at the same sites. As it was trained on natural complexes, it learned their features and can therefore also highlight preferential binding sites, as found in natural protein-protein interactions. Our method is generally applicable, and we believe that this is the first deep learning model for one-sided design of protein-protein interactions.Abstract figure

Polyacrylonitrile, poly(styrene-acrylonitrile) and poly(acrylonitrile-butadiene-styrene) complexes with transition metal salts have been synthesized. The synthesis were attempted by carrying out free radical polymerization reactions in aqueous transition metal salt solutions (ZnCl2, CuCl2, CoCl2, CoSO4, NiSO4, CuSO4) and by addition of the transition metal salts to the reaction mixture in the final stage of the polymerization. It is shown experimentally and theoretically that the first method is successful only when zinc chloride is used, while the cobalt, nickel, copper complexes are formed through the addition of the salts to the ready polymers. Reactions that take place during studied complexations were investigated by means of ab initio quantum chemistry calculations, thermodynamic parameters for these reactions were determined.