Proteins are macromolecules responsible for essential functions in almost all living organisms. Designing reasonable proteins with desired functions is crucial. A protein's sequence and structure are strongly correlated and they together determine its function. In this paper, we propose NAEPro, a model to jointly design Protein sequence and structure based on automatically detected functional sites. NAEPro is powered by an interleaving network of attention and equivariant layers, which can capture global correlation in a whole sequence and local influence from nearest amino acids in three dimensional (3D) space. Such an architecture facilitates effective yet economic message passing at two levels. We evaluate our model and several strong baselines on two protein datasets, $\beta$-lactamase and myoglobin. Experimental results show that our model consistently achieves the highest amino acid recovery rate, TM-score, and the lowest RMSD among all competitors. These findings prove the capability of our model to design protein sequences and structures that closely resemble their natural counterparts. Furthermore, in-depth analysis further confirms our model's ability to generate highly effective proteins capable of binding to their target metallocofactors. We provide code, data and models in Github.

Genetic mutations take place deep inside our DNA and can be challenging to identify, let alone treat. Scientists hope that a new deep learning approach will help doctors better combat these disease-causing mutations. Thanks to their data-crunching abilities, deep learning and A.I. have become increasingly important medical tools in recent years. These models are able to digest and make use of reams of medical data created by the human body by learning patterns from a test data-set and applying those rules to new, incoming data. Far from replacing a physician, these medical machines simply help physicians make connections quicker and more accurately.