Structural bioinformatics

Computational analysis of biological three-dimensional structures. It connects structural comparison, prediction and annotation with questions about molecular relationships or function, while retaining the distinction between a predicted model and experimental evidence.

Inference of a protein’s three-dimensional structure from information such as sequence, related sequences and known structures. A useful prediction includes an assessment of uncertainty; a confident local fold does not automatically establish a molecular interaction or biological function.

An AlphaFold confidence score for local structural accuracy, usually reported on a 0–100 scale. It is not a probability that a complete structure is correct.

A family of computational systems for predicting biomolecular structures. Its second major version showed that learned sequence and structural relationships can support highly accurate protein models, while confidence and biological context remain necessary for interpretation.^[ref]

The protein-structure prediction system evaluated in CASP14 and reported in 2021. It combines information from related sequences with learned structural relationships, producing coordinates together with confidence estimates rather than a direct experimental structure.^[ref]

Expected positional error at one residue after predicted and true structures are aligned on another residue. Low within-domain values with high between-domain values support more confident internal geometry than relative domain placement. PAE is not a physical residue–residue distance and need not be symmetric.^[ref]