Learning protein constitutive motifs from sequence data
Statistical analysis of evolutionary-related protein sequences provides insights about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families from sequence information. We apply RBM to two protein domains, Kunitz and WW, and to synthetic lattice proteins for benchmarking. The features inferred by the RBM can be biologically interpreted in terms of structural modes, including residue-residue tertiary contacts and extended secondary motifs ($\alpha$-helix and $\beta$-sheet), of functional modes controlling activity and ligand specificity, or of phylogenetic identity. In addition, we use RBM to design new protein sequences with putative properties by composing and turning up or down the different modes at will. Our work therefore shows that RBM are a versatile and practical tool to unveil and exploit the genotype-phenotype relationship for protein families.
Code (1)
Tasks
BenchmarkingSpecificitySimilar Papers 제목 키워드 기반
idMotif: An Interactive Motif Identification in Protein Sequences
This article introduces idMotif, a visual analytics framework designed to aid domain experts in the identification of motifs within protein sequences. Motifs, short sequences of amino acids, are critical for understandin…
Deep LearningMemory Matching Networks for Genomic Sequence Classification
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complex…
ClassificationGeneral ClassificationTERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs
Computational protein design has the potential to deliver novel molecular structures, binders, and catalysts for myriad applications. Recent neural graph-based models that use backbone coordinate-derived features show ex…
Protein DesignGPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models
With the rise of Transformers and Large Language Models (LLMs) in Chemistry and Biology, new avenues for the design and understanding of therapeutics have opened up to the scientific community. Protein sequences can be m…
Attention based convolutional neural network for predicting RNA-protein binding sites
RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recogn…