Variational auto-encoding of protein sequences

2017/12/09 by Sam Sinai, Eric D. Kelsic, Eric Kelsic +6 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Artificial intelligence #Autoencoder #Biology #Computational biology #Computer science #Deep learning #Embedding #Encoding (memory) #FOS: Biological sciences #FOS: Computer and information sciences #Function (biology) #Gene #Generative grammar #Genetics #Genomics and Phylogenetic Studies #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Mathematics #Peptide sequence #Protein design #Protein function #Protein function prediction #Protein sequencing #Protein structure #Quantitative Methods (q-bio.QM) #RNA and protein synthesis mechanisms #Sequence (biology) #Sequence space #Set (abstract data type) #cs.LG #q-bio.QM

paper · pdf · doi:10.48550/arxiv.1712.03346

Abstract for oral presentation at NIPS 2017 Workshop on Machine Learning in Computational Biology

openalex publication_date 2017/12/09 · arxiv created 2018/01/03 · arxiv updated 2018/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

Proteins are responsible for the most diverse set of functions in biology. The ability to extract information from protein sequences and to predict the effects of mutations is extremely valuable in many domains of biology and medicine. However the mapping between protein sequence and function is complex and poorly understood. Here we present an embedding of natural protein sequences using a Variational Auto-Encoder and use it to predict how mutations affect protein function. We use this unsupervised approach to cluster natural variants and learn interactions between sets of positions within a protein. This approach generally performs better than baseline methods that consider no interactions within sequences, and in some cases better than the state-of-the-art approaches that use the inverse-Potts model. This generative model can be used to computationally guide exploration of protein sequence space and to better inform rational and automatic protein design.

Citations

• Adam: A Method for Stochastic Optimization
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Cited by

• RNAGenScape: Property-guided Optimization and Interpolation of mRNA Sequences with Manifold Langevin Dynamics
• Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids

Related

• A joint embedding of protein sequence and structure enables robust variant effect predictions
• How to Hallucinate Functional Proteins
• Multi-level Protein Representation Learning for Blind Mutational Effect Prediction
• SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions
• Generative power of a protein language model trained on multiple sequence alignments