| 2022 |
AGGRESCAN3D
2.0 |
Extends A3D to previously inaccessible
proteins and incorporates
new modules. |
Prediction and optimization of protein
solubility |
(763) |
| 2015 |
AGGRESCAN3D |
Protein 3D structure as input, energetically minimized using
the FoldX force field. |
β-aggregate-prone region
prediction |
(762) |
| 2007 |
AGGRESCAN |
Aggregation prediction based on an aggregation-propensity scale
for natural amino acids. |
Aggregation-prone segment prediction |
(761) |
| 2022 |
TAPASS |
A pipeline for annotation of protein
amyloidogenicity in the
context of other structural states. |
Amyloidogenicity
prediction |
(779) |
| 2021 |
SolupHred |
The first dedicated software for prediction of pH-dependent
protein aggregation. |
pH-dependent protein aggregation
prediction |
(780) |
| 2021 |
TISIGNER.com |
Including TIsigner, SoDoPE and Razor for production improvement. |
Protein expression and solubility optimization |
(781) |
| 2021 |
iAMY-SCM |
A scoring card method-based predictor. |
Amyloid protein prediction |
(776) |
| 2021 |
ANuPP |
Taking into account atomic-level features of hexapeptides. |
Aggregation nucleating region prediction |
(775) |
| 2021 |
AbsoluRATE |
SVM based regression model to
predict absolute rates of aggregation
using experimental conditions and sequence-based properties. |
Protein aggregation rate prediction |
(97) |
| 2020 |
WALTZ-DB
2.0 |
The largest open-access repository for
amyloid fibril formation
determinants. |
Aggregation-prone sequence prediction |
(765) |
| 2010 |
WALTZ |
Combining the position-specific amyloid sequence with structural
information. |
Amyloid-forming sequence prediction |
(764) |
| 2020 |
CORDAX |
Use crystal contact information to generate fibril cores from
isolated PDB structures. |
Aggregation-prone region prediction |
(777) |
| 2020 |
PATH |
Comparative modeling, query sequence threaded into seven templates
representing different structural classes. |
Amyloidogenicity
prediction |
(778) |
| 2020 |
AgMata |
Unsupervised tool to predict β-aggregation in proteins. |
β-aggregate-prone region
prediction |
(756) |
| 2018 |
RFAmy |
Feature extraction algorithms and classification algorithms
improvement. |
Amyloid protein prediction |
(767) |
| 2015 |
APPNN |
Based on recursive feature selection and feed-forward
neural
networks. |
Amyloid formation prediction |
(769) |
| 2014 |
PASTA
2.0 |
Energy function rederived on a larger
data set of globular
protein domains. |
β-sheet structure aggregation
prediction |
(772) |
| 2007 |
PASTA |
An energy function from the hydrogen bonding statistics on
β-strands. |
β-sheet structure aggregation
prediction |
(771) |
| 2014 |
FISH
Amyloid |
Based on site specific co-occurrence
of amino acids. |
Amyloidogenic segment prediction |
(766) |
| 2011 |
AmyloidMutants |
Discrimination of topologically dissimilar amyloid
conformations. |
β-sheet structure aggregation
prediction |
(773) |
| 2010 |
FoldAmyloid |
Packing density and the probability of hydrogen bond
formation. |
Amyloidogenic region prediction in protein
chains |
(760) |
| 2009 |
NetCSSP |
Latest version of CSSP algorithm and a Flash chart-based graphic
interface. |
Chameleon sequence and amyloid fibril formation
prediction |
(774) |
| 2006 |
3D profile |
Base on the crystal structure of the peptide NNQQNY. |
Fibril-forming segment prediction |
(799) |
| 2005 |
Zyggregator |
Protein aggregation
prediction based on α-helix and β-sheet
propensities, hydrophobicity, net charge of polypeptide, hydrophobic/hydrophilic
patterns, and presence of Gatekeeper residues. |
Aggregate-prone
region prediction |
(758, 759) |
| 2005 |
PAGE |
Aromaticity,
β-propensity, charge, polar-nonpolar surfaces,
and solubility are the factors employed for APR identification. |
Protein aggregation rate and β-aggregate-prone region
prediction |
(768) |
| 2004 |
TANGO |
Protein
aggregation prediction based on physicochemical principles
of β-sheet formation in term of concentration, pH, ionic strength
and TFE content. |
β-aggregate-prone region
prediction |
(770) |
