BioDesign Research / 2022 / Article / Tab 8

Review Article

Design of Protein Segments and Peptides for Binding to Protein Targets

Table 8

A general overview of learning-based methods that have been applied to the task of designing functional peptides.

TaskInput dataModelOutput

Binders for Bcl-xL, Mcl-1, or Bfl-1Set of 10,000 previously generated bindersSORTCERY [171]: Support vector machine for prediction, applied to a larger sequence library36 high affinity binders for each target with Ki as low as 5.71.2 nM for Mcl-1, 1.320.18 nM for Bcl-xL, and 6.61.0 nM for Bfl-1
Finding peptide hits for Sfp and AcpSExperimental data of known hits and fragments that don’t bind, each round new data was addedPOOL [172]: Naive BayesianSelective binders for Sfp or AcpS after 4 rounds
Design of cell permeable peptides600 PMO-miniprotein conjugatesRNN for generation and CNN for prediction [173]Synthesized and characterized 12. Highly active for macromolecule delivery
Peptides with anticancer propertiesCurated peptides from CancerPPD that target breast or lung cancer, pepCAST descriptors were used to find featuresCPANN [174]: counter propagation artificial neural network for prediction and modLAMP for generation6/15 peptides predicted to have anticancer activity indeed showed anticancer activity
Design of chitin binding peptides21 million amino acid sequences taken from the Pfam database and 20000 sequences from the SCOPe databaseTwo Bi-LSTM each with 1024 hidden layers [175]Interactive residues in the two predicted peptides matched experimentally known ones
Designing nonhemolytic AMPsDBAASP databaseRNN for classifying active, inactive, hemolytic, and nonhemolytic sequences [176]12/28 were active