Recent advances in deep learning for protein-protein interaction analysis: A comprehensive review
M Lee - Molecules, 2023 - mdpi.com
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative
imprint across multiple disciplines. Within computational biology, it is expediting progress in …
imprint across multiple disciplines. Within computational biology, it is expediting progress in …
Recent advances and challenges in protein structure prediction
Artificial intelligence has made significant advances in the field of protein structure prediction
in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated …
in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated …
Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes
Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain
residue-residue contact information is important for structure prediction of membrane protein …
residue-residue contact information is important for structure prediction of membrane protein …
Protein–protein contact prediction by geometric triangle-aware protein language models
Abstract Information regarding the residue–residue distance between interacting proteins is
important for modelling the structures of protein complexes, as well as being valuable for …
important for modelling the structures of protein complexes, as well as being valuable for …
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
Residue-residue distance information is useful for predicting tertiary structures of protein
monomers or quaternary structures of protein complexes. Many deep learning methods …
monomers or quaternary structures of protein complexes. Many deep learning methods …
Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15
Abstract To enhance the AlphaFold-Multimer-based protein complex structure prediction, we
developed a quaternary structure prediction system (MULTICOM) to improve the input fed to …
developed a quaternary structure prediction system (MULTICOM) to improve the input fed to …
DeepHomo2. 0: improved protein–protein contact prediction of homodimers by transformer-enhanced deep learning
Protein–protein interactions play an important role in many biological processes. However,
although structure prediction for monomer proteins has achieved great progress with the …
although structure prediction for monomer proteins has achieved great progress with the …
Deep graph learning of inter-protein contacts
Motivation Inter-protein (interfacial) contact prediction is very useful for in silico structural
characterization of protein–protein interactions. Although deep learning has been applied to …
characterization of protein–protein interactions. Although deep learning has been applied to …
Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15
Abstract Since the 14th Critical Assessment of Techniques for Protein Structure Prediction
(CASP14), AlphaFold2 has become the standard method for protein tertiary structure …
(CASP14), AlphaFold2 has become the standard method for protein tertiary structure …
A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers
Motivation Deep learning has revolutionized protein tertiary structure prediction recently.
The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy …
The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy …