AIMC Topic: Models, Molecular

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In silico design of ankyrin repeat proteins that bind to the insulin-like growth factor type 1 receptor.

Journal of molecular graphics & modelling
Ankyrins are proteins widely distributed in nature that mediate protein‒protein interactions. Owing to their outstanding stability and ability to recognize targets, ankyrins have been used as therapeutic and diagnostic tools in several diseases, incl...

Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation.

Journal of chemical information and modeling
The development of new materials is a time-consuming and resource-intensive process. Deep learning has emerged as a promising approach to accelerate this process. However, accurately predicting crystal structures using deep learning remains a signifi...

Deep learning tools predict variants in disordered regions with lower sensitivity.

BMC genomics
BACKGROUND: The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined ...

Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem.

Journal of chemical information and modeling
Deep learning has revolutionized difficult tasks in chemistry and biology, yet existing language models often treat these domains separately, relying on concatenated architectures and independently pretrained weights. These approaches fail to fully e...

DeepAssembly2: A Web Server for Protein Complex Structure Assembly Based on Domain-Domain Interactions.

Journal of molecular biology
Proteins often perform biological functions by forming complexes, thereby accurately predicting the structure of protein complexes is crucial to understanding and mastering their functions, as well as facilitating drug discovery. Protein monomeric st...

A Specialized and Enhanced Deep Generation Model for Active Molecular Design Targeting Kinases Guided by Affinity Prediction Models and Reinforcement Learning.

Journal of chemical information and modeling
Kinases are critical regulators in numerous cellular processes, and their dysregulation is linked to various diseases, including cancer. Thus, protein kinases have emerged as major drug targets at present, with approximately a quarter to a third of g...

Critical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game.

Journal of chemical information and modeling
Computational predictions of biomolecular structure via artificial intelligence (AI) based approaches, as exemplified by AlphaFold software, have the potential to model of all life's biomolecules. We performed oligonucleotide structure prediction and...

Fitting Atomic Structures into Cryo-EM Maps by Coupling Deep Learning-Enhanced Map Processing with Global-Local Optimization.

Journal of chemical information and modeling
With the breakthroughs in protein structure prediction technology, constructing atomic structures from cryo-electron microscopy (cryo-EM) density maps through structural fitting has become increasingly critical. However, the accuracy of the construct...

Atomic context-conditioned protein sequence design using LigandMPNN.

Nature methods
Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme and small-molecule binder and sensor design, but current state-of-the-art deep-learning-based sequence design methods are unable to model nonprote...

A critical address to advancements and challenges in computational strategies for structural prediction of protein in recent past.

Computational biology and chemistry
Protein structure prediction has undergone significant advancements, driven by the limitations of experimental techniques like X-ray crystallography, NMR, and cryo-EM, which are costly and time-consuming. To bridge the gap between protein sequences a...