Deep Learning for Protein Crystallography: Improving Structure Determination
What is it about?
The application of deep learning techniques in protein crystallography has shown promise in improving all stages of protein structure determination. These techniques can provide automated and efficient approaches to protein crystallography, leading to significant advancements. However, deep learning models face challenges in handling the multi-stage process of protein structure determination through crystallography, and there is a risk of bias in training data. Recent trends involve the use of synthetic data sets for supervised learning, which may result in better-performing models when compared with models fine-tuned with real data. A good-quality data collection, assessment and labeling workflow is crucial in ensuring the model's robustness, reliability and reproducibility.
Why is it important?
Deep learning techniques have the potential to provide automated approaches to experimental protein structure determination, which can lead to improved efficiency in structural biology research, freeing up structural biologists' time to focus on the biological implications of their structural data. This can in turn lead to acceleration in connected translational science fields, such as drug discovery. Key Takeaways: 1. Deep learning algorithms can extract relevant features from protein crystallographic data and learn to recognize patterns, which can improve the success rate of crystallization and the quality of crystal structures. 2. Deep learning architectures, which are explained in the article’s supplement, can provide automated and efficient approaches to all stages of protein structure determination, which can lead to significant advancements in the field of structural biology. 3. The use of synthetic data sets for supervised learning, which involves creating photorealistic images of virtual protein crystals, has been found to result in better-performing models when compared with models fine-tuned with real data. However, including real data in the training can significantly improve model robustness.