Deep Learning in Protein Structural Modeling and Design
arXiv:2007.08383
Abstract
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling, and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence -> structure -> function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.
References in corpus (37)
- Distributed Representations of Sentences and Documents
- Geometric deep learning: going beyond Euclidean data
- Axiomatic Attribution for Deep Networks
- Learning Important Features Through Propagating Activation Differences
- Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
- SchNet - a deep learning architecture for molecules and materials
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
- Semi-Supervised Learning with Deep Generative Models
- Graph Neural Networks: A Review of Methods and Applications
- Recent Trends in Deep Learning Based Natural Language Processing
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
- Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
- ProtVec: A Continuous Distributed Representation of Biological Sequences
- Less is more: sampling chemical space with active learning
- SmoothGrad: removing noise by adding noise
- Sanity Checks for Saliency Maps
- Autonomous discovery in the chemical sciences part II: Outlook
- Computational Protein Design with Deep Learning Neural Networks
- Chip Placement with Deep Reinforcement Learning
- Design of metalloproteins and novel protein folds using variational autoencoders
- Deep convolutional networks for quality assessment of protein folds
- Generating and designing DNA with deep generative models
- Multiplicative LSTM for sequence modelling
- Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
- Evaluating Protein Transfer Learning with TAPE
- Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions
- Distributed Representations for Biological Sequence Analysis
- Variational auto-encoding of protein sequences
- Conditioning by adaptive sampling for robust design
- Deep Robust Framework for Protein Function Prediction using Variable-Length Protein Sequences
- Design by adaptive sampling
- A Large-Scale Study on Regularization and Normalization in GANs
- Structural biology meets data science: Does anything change?
- Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks
- How to Hallucinate Functional Proteins
- Generating protein sequences from antibiotic resistance genes data using Generative Adversarial Networks
- Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations