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Protein Conformation

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AI-Driven Deep Learning Techniques in Protein Structure Prediction.

International journal of molecular sciences
Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established pr...

Progress in the application of artificial intelligence in molecular generation models based on protein structure.

European journal of medicinal chemistry
The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyz...

Context-aware geometric deep learning for protein sequence design.

Nature communications
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning appr...

MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics.

Journal of computer-aided molecular design
Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand-protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and an...

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction.

Topics in current chemistry (Cham)
In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the i...

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

Nature communications
The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and...

Deep-learning map segmentation for protein X-ray crystallographic structure determination.

Acta crystallographica. Section D, Structural biology
When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification...

Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums.

PLoS computational biology
AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predi...

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

Journal of chemical information and modeling
The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have eme...

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

eLife
Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D st...