In the rapidly advancing field of computational biology, a newly peer-reviewed review explores the transformative role of deep learning techniques in revolutionizing protein structure prediction. The ...

Protein design, a crucial aspect of biological sciences, involves creating amino acid sequences that fold into desired protein structures. This process, known as the protein inverse folding problem, ...

In a recent study published in the journal Nature Methods, a group of researchers developed a novel method called Ribonucleic Acid (RNA) High-Order Folding Prediction Plus (RhoFold+). This deep ...

WEST LAFAYETTE, Ind. – Proteins are often called the working molecules of the human body. A typical body has more than 20,000 different types of proteins, each of which are involved in many functions ...

Protein design is crucial for the treatment of human diseases, but traditional protein design methods have some limitations. Site-directed mutagenesis is highly dependent on the physiological ...

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...

Researchers have developed a deep-learning model, called PepFlow, that can predict all possible shapes of peptides -- chains of amino acids that are shorter than proteins, but perform similar ...

With the advent of AI-powered protein design tools, protein-based therapeutics may no longer be constrained by the limits imposed by natural selection. Protein-based therapeutics have been ...

Researchers create Disobind, an AI tool predicting protein interactions, advancing disease biology and drug design applications.

We know the genes, but not their functions—to resolve this long-standing bottleneck in microbial research, a joint research team has proposed a cutting-edge research strategy that leverages artificial ...