DSIMB

SWORD2

SWift and Optimized Recognition of protein Domains

MEDUSA

A Deep Learning based protein flexibility prediction tool.

PoincareMSA

Detection of the structural, evolutionary and functional relationship between different proteins and protein families requires comparative analysis of their amino acid sequences through multiple sequence alignment. While a direct analysis of the resulting data allows to identify conserved regions, it does not provide a general and broad view on the proteins’ hierarchical organization and their distribution in the sequence, structural or functional spaces. Our project aims to develop a novel strategy for multiple sequence alignment visualization and interpretation using a powerful method demonstrated its efficiency for the hierarchy detection in multi-dimensional data sets called Poincaré maps.

ICARUS

Flexible protein structural alignment based on Protein Units

PYTHIA

Deep Learning Approach For Local Protein Conformation Prediction

pepit

The PepIT program proposes peptides that may interact with a given protein. PepIT is based on a non-sequential alignment algorithm to identify peptide binding sites that share geometrical and physicochemical properties with regions on the target protein.

AMBROSIA

ANN-Glycolysis-Flux-Prediction

Flux prediction using artificial neural network (ANN) for the upper part of glycolysis.

GC-ANN-Enzyme-Concentration-Selection

openfold

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

test_fork

This is a fork of my test repo

CompareCBS

Comparison of carbohydrate binding site (CBS) geometry using a graph-based method

VEP-Benchmark