AIMC Topic: Protein Conformation

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Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.

BMC systems biology
BACKGROUND: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome...

Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps.

Computational biology and chemistry
Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have sp...

Results of the Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design.

Proteins
The grand challenge of protein engineering is the development of computational models to characterize and generate protein sequences for arbitrary functions. Progress is limited by lack of (1) benchmarking opportunities, (2) large protein function da...

How good is generative diffusion model for enhanced sampling of protein conformations across scales and in all-atom resolution?

The Journal of chemical physics
Molecular dynamics (MD) simulations are fundamental for probing the structural dynamics of biomolecules, yet their efficiency is limited by the high computational cost of exploring long-timescale events. Generative machine learning (ML) models, parti...

AlphaFold modeling uncovers global structural features of class I and class II fungal hydrophobins.

Protein science : a publication of the Protein Society
Hydrophobins are a family of small fungal proteins that self-assemble at hydrophobic-hydrophilic interfaces. Hydrophobins not only play crucial roles in filamentous fungal growth and development but also have attracted substantial attention due to th...

Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants.

NAR genomics and bioinformatics
Genetic variants can impact the structure of the corresponding protein, which can have detrimental effects on protein function. While the effect of protein-truncating variants is often easier to evaluate, most genetic variants that affect the protein...

BindUP-Alpha: A Webserver for Predicting DNA-and RNA-binding Proteins based on Experimental and Computational Structural Models☆.

Journal of molecular biology
Structural data provides important information on the proteins' function. Recent development of advanced machine learning and artificial intelligence tools, such as AlphaFold, have led to an explosion of predicted protein structures. However, many of...

PEGASUS: Prediction of MD-derived protein flexibility from sequence.

Protein science : a publication of the Protein Society
Protein flexibility is essential to its biological function. However, experimental methods for its assessment, such as X-ray crystallography and nuclear magnetic resonance spectroscopy, are often limited by experimental variability and high cost, lea...

Artificial intelligence and first-principle methods in protein redesign: A marriage of convenience?

Protein science : a publication of the Protein Society
Since AlphaFold2's rise, many deep learning methods for protein design have emerged. Here, we validate widely used and recognized tools, compare them with first-principle methods, and explore their combinations, focusing on their effectiveness in pro...

Relationship between immunogenicity and protein structure at amino acid substitution sites of blood group antigens.

Blood
Polypeptide blood group antigens, many of which are created by single exofacial amino acid substitutions, have varying immunogenicities. Why some amino acid substitutions are more immunogenic than others is little understood. Using AlphaFold2, an art...