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Molecular docking in organic, inorganic, and hybrid systems: a tutorial review
Molecular docking simulation is a very popular and well-established computational
approach and has been extensively used to understand molecular interactions between a …
approach and has been extensively used to understand molecular interactions between a …
Protein structure prediction: conventional and deep learning perspectives
Protein structure prediction is a way to bridge the sequence-structure gap, one of the main
challenges in computational biology and chemistry. Predicting any protein's accurate …
challenges in computational biology and chemistry. Predicting any protein's accurate …
Independent se (3)-equivariant models for end-to-end rigid protein docking
Protein complex formation is a central problem in biology, being involved in most of the cell's
processes, and essential for applications, eg drug design or protein engineering. We tackle …
processes, and essential for applications, eg drug design or protein engineering. We tackle …
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
The inapplicability of amino acid covariation methods to small protein families has limited
their use for structural annotation of whole genomes. Recently, deep learning has shown …
their use for structural annotation of whole genomes. Recently, deep learning has shown …
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 …
Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13
Predicting residue‐residue distance relationships (eg, contacts) has become the key
direction to advance protein structure prediction since 2014 CASP11 experiment, while …
direction to advance protein structure prediction since 2014 CASP11 experiment, while …
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 …
Interdependence of a mechanosensitive anion channel and glutamate receptors in distal wound signaling
J Moe-Lange, NM Gappel, M Machado, MM Wudick… - Science …, 2021 - science.org
Glutamate has dual roles in metabolism and signaling; thus, signaling functions must be
isolatable and distinct from metabolic fluctuations, as seen in low-glutamate domains at …
isolatable and distinct from metabolic fluctuations, as seen in low-glutamate domains at …
Recent progress of protein tertiary structure prediction
The prediction of three-dimensional (3D) protein structure from amino acid sequences has
stood as a significant challenge in computational and structural bioinformatics for decades …
stood as a significant challenge in computational and structural bioinformatics for decades …
Protein design with deep learning
Computational Protein Design (CPD) has produced impressive results for engineering new
proteins, resulting in a wide variety of applications. In the past few years, various efforts have …
proteins, resulting in a wide variety of applications. In the past few years, various efforts have …