Protein–protein interaction and site prediction using transfer learning

Large language models generate functional protein sequences across diverse families

(2023) Ali Madani et al.   NATURE BIOTECHNOLOGY

Rational redesign of thermophilic PET hydrolase LCCICCG to enhance hydrolysis of high crystallinity polyethylene terephthalates

(2023) Zundan Ding et al.   JOURNAL OF HAZARDOUS MATERIALS

Accurate medium-range global weather forecasting with 3D neural networks

(2023) Kaifeng Bi et al.   NATURE

ProteinBERT: a universal deep-learning model of protein sequence and function

(2022) Nadav Brandes et al.   BIOINFORMATICS

Using deep learning to annotate the protein universe

(2022) Maxwell L. Bileschi et al.   NATURE BIOTECHNOLOGY

Protein interaction networks in neurodegenerative diseases: from physiological function to aggregation

(2022) Gaetano Calabrese et al.   JOURNAL OF BIOLOGICAL CHEMISTRY

Improved prediction of protein-protein interactions using AlphaFold2

(2022) Patrick Bryant et al.   Nature Communications

TimeTree 5: An Expanded Resource for Species Divergence Times

(2022) Sudhir Kumar et al.   MOLECULAR BIOLOGY AND EVOLUTION

De novo sequence redesign of a functional Ras‐binding domain globally inverted the surface charge distribution and led to extreme thermostability

(2021) Ruicun Liu et al.   BIOTECHNOLOGY AND BIOENGINEERING

LSTM-PHV: prediction of human-virus protein–protein interactions by LSTM with word2vec

(2021) Sho Tsukiyama et al.   BRIEFINGS IN BIOINFORMATICS

Highly accurate protein structure prediction with AlphaFold

(2021) John Jumper et al.   NATURE

DeepTrio: a ternary prediction system for protein–protein interaction using mask multiple parallel convolutional neural networks

(2021) Xiaotian Hu et al.   BIOINFORMATICS

The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

(2020) Damian Szklarczyk et al.   NUCLEIC ACIDS RESEARCH

Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

(2019) Muhao Chen et al.   BIOINFORMATICS

An integration of deep learning with feature embedding for protein–protein interaction prediction

(2019) Yu Yao et al.   PeerJ

Protein–protein interaction site prediction through combining local and global features with deep neural networks

(2019) Min Zeng et al.   BIOINFORMATICS

Small protein–protein interfaces rich in electrostatic are often linked to regulatory function

(2019) Christina Nilofer et al.   JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

(2019) P. Gainza et al.   NATURE METHODS

The BioGRID interaction database: 2019 update

(2018) Rose Oughtred et al.   NUCLEIC ACIDS RESEARCH

HPIDB 2.0: a curated database for host–pathogen interactions

(2016) Mais G. Ammari et al.   Database-The Journal of Biological Databases and Curation

UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

(2014) B. E. Suzek et al.   BIOINFORMATICS

Protein-protein interaction networks: probing disease mechanisms using model systems

(2013) Uros Kuzmanov et al.   Genome Medicine

Interaction landscape of membrane-protein complexes in Saccharomyces cerevisiae

(2012) Mohan Babu et al.   NATURE

Structural basis for cell surface patterning through NetrinG-NGL interactions

(2011) Elena Seiradake et al.   EMBO JOURNAL

Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites

(2010) Yoichi Murakami et al.   BIOINFORMATICS

Progress and challenges in predicting protein-protein interaction sites

(2009) I. Ezkurdia et al.   BRIEFINGS IN BIOINFORMATICS

Principles of Protein−Protein Interactions: What are the Preferred Ways For Proteins To Interact?

(2008) Ozlem Keskin et al.   CHEMICAL REVIEWS

Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

(2008) Yanzhi Guo et al.   NUCLEIC ACIDS RESEARCH