protein-structure-prediction

There are 75 repositories under protein-structure-prediction topic.

  • Django_for_Prediction_of_Proteins_PPII

    Language:C2

  • ProteiNN-Structure-Predictor

    A transformer network trained to predict end-to-end single sequence protein structure as a set of angles given amino acid sequences.

    Language:Python4

  • PSPHybridDE

    Protein Structure prediction using Hybrid Differential Evolution (HybridDE)

    Language:Python4

  • RosettaEvolution

    Evolutionary Computation methods applied to protein structure prediction and protein folding modeling with the Rosetta Software Suite

    Language:C++4

  • Awsome-CompBio-Bioinformatics

    A curated list of FREE courses available online from top universities of the world on Computational Biology and Bioinformatics.

    3

  • rosettafold-pytorch

    An unofficial re-implementation of RoseTTAFold, a three-track deep learning model for protein structure prediction.

    Language:Python3

  • GANcon-pytorch

    personal RE-implementation of GANcon paper

    Language:Python3

  • SPECS

    Superposition-based Protein Embedded CA SC score

    Language:C++3

  • fragfold

    Fragment assembly ab initio protein folding

    Language:C3

  • rf2na-packages

    Script to solve the package dependency issues for RoseTTAFold2NA

    Language:Shell2

  • deepab-pytorch

    An unofficial re-implementation of DeepAb, an interpretable deep learning model for antibody structure prediction.

    Language:Python2

  • igfold-pytorch

    Unofficial re-implementation of IgFold, a fast antibody structure prediction method, in PyTorch.

    Language:Python2

  • AlphaFold_Workshop

    Predict protein folding structures using ColabFold. Gain a deeper understanding of protein folding prediction with AlphaFold2 and MMseqs2. Run the Jupyter notebook on UCloud, learn to interpret results, predict protein structures of interest. Technical requirements provided. Enhance your knowledge of protein folding and AlphaFold2's principles. Fam

    Language:Jupyter Notebook2

  • modelsREV

    Reticuloendotheliosis virus ENV, GAG and POL protein models.

    2

  • adversarial-protein-sequences

    Code for paper "Adversarial Attacks on Protein Language Models", Ginevra Carbone, Francesca Cuturello, Luca Bortolussi, Alberto Cazzaniga (2022).

    Language:Python2

  • ResNetQA

    ResNetQA: Improved protein model quality assessment by integrating sequential and pairwise features using deep learning

    Language:Python2

  • DConStruct

    Hybridized distance- and contact-based hierarchical protein folding

    Language:Python2

  • protein-threading

    ⚛️📈Protein threading implementation using double dynamic programming👥💻

    Language:Python1

  • ProteinSecondaryStructurePrediction

    A repo containing all the material for the examination exercise from the 2023 Deep Learning course, held by Professor Paolo Frasconi, at the University of Florence, Italy. The exercise focuses on using a Transformer architecture for Protein Secundary Structure Prediction on the CullPDB dataset.

    Language:Python1

  • Protein_Structure_Prediction

    Repository with scripts and data generated during my internship at Institut Pasteur of Paris

    Language:Python1

  • Multi-S3P

    Multi-S3P: Protein Secondary Structure Prediction with Specialized Multi-Network and Self-Attention-based Deep Learning Model

    Language:Python1

  • PSSP

    Protein Secondary Structure Prediction project with RNNs and Transformers

    Language:Python1

  • rrQNet

    Protein contact map quality estimation by evolutionary reconciliation

    Language:Python1

  • psifold

    Transformer model for protein structure prediction

    Language:Python1

  • FingerprintContacts

    Source code for the manuscript: FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

    Language:Python1

  • PEPSGO

    Peptide structure prediction by global optimization of a potential energy function

    Language:C++1

  • SAXS-guidedAdaptiveSampling

    Python implementation of Markov state model-based adaptive sampling guided by SAXS and hybrid information.

    Language:Jupyter Notebook1

  • ProteinStructurePrediction-ComparativeStudy

    Comparison of protein structure prediction methods: Rosetta Ab Initio, which uses fragment assembly, Monte Carlo optimization and energy minimization, and a Linear Algebra Algorithm based on Cayley-Menger matrices.

    Language:Jupyter Notebook

  • QuantumHoneycombPSP

    QuantumHoneycombPSP is a Python-based project designed to model protein structure prediction (PSP) using D-Wave quantum annealing. Uses novel protein lattices and energy functions.

    Language:Python
  • .github

    .github

    Praetorius Lab for Biomolecular Design

  • PyAA---Programming-for-Computational-Biology

    A package-like tool that analyzes protein sequences, categorizes a protein of interest into one of two groups, predicts secondary structure based on dihedral angles/Ramachandran plots and Chou and Fasman's statistical propensity method, and does pattern search by Naive, KMP, and Bayer-Moore

    Language:Python

  • HP_model

    Implementation of the HP protein folding model with commands line interface

    Language:Python
  • Protein-Tertiary-Structure

    Protein-Tertiary-Structure

    Performing a regression task for estimating residue size based on given physicochemical properties of protein tertiary structures (CASP 5-9).

    Language:Jupyter Notebook

  • deep-learning-protein-structure-prediction

    Deep Learning for Protein Structure Prediction

    Language:Jupyter Notebook

  • DebruijnExtend

    This repository contains the source code for the DebruijnExtend tool. This tool uses known secondary structures for common protein kmers to predict the most likely secondary structure. This is done using a debruijn graph approach to incorporate local homology for predicting the most probable secondary structure along a protein. Tested on mac/linux

    Language:Jupyter Notebook

  • rgn

    Recurrent Geometric Networks for end-to-end differentiable learning of protein structure

    Language:Python