A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information

AbstractMotivationAlmost all critical functions and processes in cells are sustained by the cellular networks of protein-protein interactions (PPIs), understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack high-quality PPI data for constructing the networks, which makes it challenging to study the functions of association of proteins. High-throughput experimental techniques have produced abundant data for systematically studying the cellular networks of a biological system and the development of computational method for PPI identification.ResultsWe have developed a deep learning-based framework, named iPPI, for accurately predicting PPI on a proteome-wide scale depended only on sequence information. iPPI integrates the amino acid properties and compositions of protein sequence into a unified prediction framework using a hybrid deep neural network. Extensive tests demonstrated that iPPI can greatly outperform the state-of-the-art prediction methods in identifying PPIs. In addition, the iPPI prediction score can be related to the strength of protein-protein binding affinity and further showed the biological relevance of our deep learning framework to identify PPIs.Availability and ImplementationiPPI is available as an open-source software and can be downloaded from https://github.com/model-lab/[email protected]

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A Review of Protein Structure Prediction using Deep Learning

BIO Web of Conferences ◽

10.1051/bioconf/20214104003 ◽

2021 ◽

Vol 41 ◽

pp. 04003

Author(s):

Meredita Susanty ◽

Tati Erawati Rajab ◽

Rukman Hertadi

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Protein Interactions ◽

Structure Prediction ◽

Protein Complexes ◽

Dimensional Structure ◽

Evolutionary Information ◽

Learning Approaches ◽

Predict Protein Structure ◽

Specific Order

Proteins are macromolecules composed of 20 types of amino acids in a specific order. Understanding how proteins fold is vital because its 3-dimensional structure determines the function of a protein. Prediction of protein structure based on amino acid strands and evolutionary information becomes the basis for other studies such as predicting the function, property or behaviour of a protein and modifying or designing new proteins to perform certain desired functions. Machine learning advances, particularly deep learning, are igniting a paradigm shift in scientific study. In this review, we summarize recent work in applying deep learning techniques to tackle problems in protein structural prediction. We discuss various deep learning approaches used to predict protein structure and future achievements and challenges. This review is expected to help provide perspectives on problems in biochemistry that can take advantage of the deep learning approach. Some of the unanswered challenges with current computational approaches are predicting the location and precision orientation of protein side chains, predicting protein interactions with DNA, RNA and other small molecules and predicting the structure of protein complexes.

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A deep-learning framework for multi-level peptide–protein interaction prediction

Nature Communications ◽

10.1038/s41467-021-25772-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Yipin Lei ◽

Shuya Li ◽

Ziyi Liu ◽

Fangping Wan ◽

Tingzhong Tian ◽

...

Keyword(s):

Deep Learning ◽

Protein Interaction ◽

Protein Interactions ◽

Comprehensive Evaluation ◽

Interaction Prediction ◽

Learning Framework ◽

Binding Residue ◽

Protein Interaction Prediction ◽

Binding Residues ◽

Multi Level

AbstractPeptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Here, we present a deep learning framework for multi-level peptide-protein interaction prediction, called CAMP, including binary peptide-protein interaction prediction and corresponding peptide binding residue identification. Comprehensive evaluation demonstrated that CAMP can successfully capture the binary interactions between peptides and proteins and identify the binding residues along the peptides involved in the interactions. In addition, CAMP outperformed other state-of-the-art methods on binary peptide-protein interaction prediction. CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can thus facilitate the peptide drug discovery process.

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