A single model method combining statistical potentials and agreement terms
in a linear manner

A single model method combining statistical potentials and agreement terms
with a distance constraints (DisCo) score. DisCo evaluates consistencies of
pairwise CA-CA distances from a model with constraints extracted from
homologous structures. All scores are combined using a neural network
trained to predict per-residue lDDT scores.

QMEANBrane is a combination of statistical potentials targeted at local
quality estimation of membrane protein models in their naturally occurring
oligomeric state: after identifying the transmembrane region using an
implicit solvation model, specifically trained statistical potentials get
applied on the different regions of a protein model

Reference sequence (SEQRES) of submitted protein model. This sequence is
used for secondary structure and solvent accessibility predictions.
If not provided, the sequence gets directly extracted from the model.
See the help page for further input information.

The plot relates the obtained global QMEAN4 value to scores calculated from
a set of high-resolution X-ray structures.

Local quality is either estimated using the raw QMEAN scoring function or
one of the two specialized functions QMEANBrane and QMEANDisCo.
They all provide scores in range [0,1] with one being good.

QMEAN4 is a linear combination of four statistical potential terms.
It is trained to predict global lDDT score in range [0,1]. The value displayed
here is transformed into a Z-score to relate it with what one would expect
from high resolution X-ray structures.

The QMEANDisCo global score is the average
per-residue score and the provided error estimate is based on global
QMEANDisCo scores estimated for a large set of models and represents
the root mean squared difference (i.e. standard deviation) between
QMEANDisCo global score and lDDT (the ground truth).
As the reliability of the prediction heavily depends on model size, the
provided error estimate is calculated based on models of similar size to
the input.