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Proteins

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Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data.

Nature methods
Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepM...

ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.

Journal of chemical information and modeling
Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for p...

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...

Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of comp...

Evaluation of DNA-protein complex structures using the deep learning method.

Physical chemistry chemical physics : PCCP
Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental...

De novo design of high-affinity binders of bioactive helical peptides.

Nature
Many peptide hormones form an α-helix on binding their receptors, and sensitive methods for their detection could contribute to better clinical management of disease. De novo protein design can now generate binders with high affinity and specificity ...

A comparative study of protein structure prediction tools for challenging targets: Snake venom toxins.

Toxicon : official journal of the International Society on Toxinology
Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several pr...

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...

Regulated Behavior in Living Cells with Highly Aligned Configurations on Nanowrinkled Graphene Oxide Substrates: Deep Learning Based on Interplay of Cellular Contact Guidance.

ACS nano
Micro-/nanotopographical cues have emerged as a practical and promising strategy for controlling cell fate and reprogramming, which play a key role as biophysical regulators in diverse cellular processes and behaviors. Extracellular biophysical facto...

Bonds and bytes: The odyssey of structural biology.

Current opinion in structural biology
Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively r...