Project description

Protein structure is essential for understanding their function as well as for developing drugs targeting proteins. Recently, a deep learning method that can predict the structure of most proteins was made freely available and a database with predicted structure was released. However, proteins do not act alone – they act together with other proteins. Therefore, the next major challenge is to use these types of methods for predicting protein–protein interactions. Initial studies from us have shown that it is possible to predict accurate structures of a large part of dimeric proteins using either a modified version of AlphaFold2 or AlphaFold-multimer. However, there are still many proteins that cannot be built accurately, nor are we able to always distinguish interacting from non-interacting protein pairs and to build larger complexes accurately is still an unsolved problem. In this project, we are recruiting two postdocs to leverage recent advances in the field of machine learning to build better deep-learning models for predicting protein–protein interactions and to apply these methods to biologically relevant problems.

Environment

The Elofsson group is located at the Science for Life Laboratory. Elofsson has worked on protein structure predictions for more than two decades. He has worked on various techniques, both using machine learning and other computational techniques. His most important contributions for this work are the methods he has developed to identify the quality of protein models, Pcons and various versions of ProQ. The group consists currently of 5 PhD students and one senior researcher.

Azizpour’s group is part of the KTH division of Robotics, Perception and Learning. He has extensive experience in computer vision and deep learning. The main research directions pursued in Azizpour’s group have direct relevance to this project which includes robustness and estimation of uncertainty, transfer learning including knowledge distillation techniques, non-standard deep networks e.g., graph networks and transformers, and interpretable deep learning. Furthermore, the group has extensive experience in deploying large experiments in GPU clusters. It consists of 4 PhD students, 1 postdoc, and several master students/interns.

This position is part of a joint collaboration between the two largest research programs in Sweden, the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), with the ultimate goal of solving ground-breaking research questions across disciplines.

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