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AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. [1]
Interpretation of PAE values allows scientists to understand the level of confidence in the predicted structure of a protein: Lower PAE values between residue pairs from different domains indicate that the model predicts well-defined relative positions and orientations for those domains.
n/a Ensembl ENSG00000214717 n/a UniProt O96006 n/a RefSeq (mRNA) NM_004729 NM_001171135 NM_001171136 n/a RefSeq (protein) NP_001164606 NP_001164607 NP_004720 n/a Location (UCSC) Chr X: 2.49 – 2.5 Mb n/a PubMed search n/a Wikidata View/Edit Human Full Name: Zinc finger BED domain-containing protein 1 is a protein that in humans is encoded by the ZBED1 gene. ZBED1 regulates the expression of ...
Structure prediction software such as AlphaFold rely on co-evolutionary data derived from multiple sequence alignment (MSA) and homologous protein sequences to predict structures of proteins. However, per definition, de novo proteins lack homologous sequences, as they are evolutionarily new. [17]
Graph neural networks are one of the main building blocks of AlphaFold, an artificial intelligence program developed by Google's DeepMind for solving the protein folding problem in biology. AlphaFold achieved first place in several CASP competitions.
There has been rapid development in computational ability to determine protein structure with programs such as AlphaFold, [2] and the demand for the corresponding protein-ligand docking predictions is driving implementation of software that can find accurate models. Once the protein folding can be predicted accurately along with how the ligands ...
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TM-score between two protein structures (e.g., a template structure and a target structure) is defined by = [+ (())] where is the length of the amino acid sequence of the target protein, and is the number of residues that appear in both the template and target structures.