MPA-Pred: A machine learning approach for predicting the binding affinity of membrane protein-protein complexes.
CoDe: a web-based tool for codon deoptimization.
PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features.
DeepBSRPred: deep learning-based binding site residue prediction for proteins.
Identification of potential driver mutations in glioblastoma using machine learning.
Predicting potential residues associated with lung cancer using deep neural network
Prediction of protein–carbohydrate complex binding affinity using structural features
ANuPP: A Versatile Tool to Predict Aggregation Nucleating Regions in Peptides and Proteins
MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins
Pred‐MutHTP: Prediction of disease‐causing and neutral mutations in human transmembrane proteins
VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning
AggreRATE-Pred: A mathematical model for the prediction of change in aggregation rate upon point mutation.
ProAffiMuSeq: sequence-based prediction of change in binding affinity upon mutation in protein-protein complexes.
Seq2Feature: a comprehensive web-based feature extraction tool.
Importance of functional groups in predicting the activity of small molecule inhibitors of Bcl-2 and Bcl-xL.
AggreRATE-Disc Identifying aggregation rate enhancer or mitigator mutations.
PDBparam: Online Resource for Computing Structural Parameters of Proteins.
GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies.
Protein-protein binding affinity prediction from amino acid sequence.
Prediction of protein disorder upon amino acid substitutions
Prediction of Change in Protein Unfolding Rates upon Point Mutations in Two State Proteins
Discrimination of driver and passenger mutations in epidermal growth factor receptor in cancer.
Folding RaCe: A Robust Method for Predicting Changes in Protein Folding Rates upon Point Mutations.
Discrimination of beta-barrel membrane proteins using statistical methods.
Discrimination of outer membrane proteins using support vector machines
Prediction of membrane spanning beta-strands in outer membrane proteins.
Prediction of solvent accessibility using neural networks
Real value prediction of solvent accessibility
Discrimination of DNA binding proteins and prediction of their binding sites
Identifying the stabilizing residues in protein structures.
Structure based prediction of protein stability upon mutation
Prediction of protein mutant stability from amino acid sequence
Prediction of protein folding rates from amino acid sequence
Identification of DNA-binding proteins
Prediction of RNA binding sites
Functional discrimination of membrane proteins using machine learning techniques
