AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Protein Conformation

Showing 121 to 130 of 496 articles

Clear Filters

Harnessing deep learning for enhanced ligand docking.

Trends in pharmacological sciences
Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how d...

Interpreting forces as deep learning gradients improves quality of predicted protein structures.

Biophysical journal
Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a c...

DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction.

Nature methods
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, the...

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.

Nature methods
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other...

Predicting multiple conformations via sequence clustering and AlphaFold2.

Nature
AlphaFold2 (ref. ) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates, and disease-causing point mutations often ca...

Role of environmental specificity in CASP results.

BMC bioinformatics
BACKGROUND: Recently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical Assessment of Structure Predict...

Equivariant Flexible Modeling of the Protein-Ligand Binding Pose with Geometric Deep Learning.

Journal of chemical theory and computation
Flexible modeling of the protein-ligand complex structure is a fundamental challenge for in silico drug development. Recent studies have improved commonly used docking tools by incorporating extra-deep learning-based steps. However, such strategies l...

The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.

Acta crystallographica. Section D, Structural biology
Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedu...

CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.

Proteins
Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted...

Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures.

Nature communications
Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite...