/tools

LightDock is a docking framework that utilizes the Glowworm Swarm Optimization algorithm to facilitate protein-protein, protein-peptide, and protein-DNA docking. It allows users to define custom scoring functions and supports various simulation options, making it a versatile tool for molecular docking studies.

PMGen is a comprehensive pipeline designed for predicting peptide-MHC complex structures and optimizing peptide sequences. It utilizes advanced techniques like AlphaFold for structure prediction and includes features for iterative peptide optimization and mutation screening.

DeepSeqPanII is a sequence-based model designed to predict the binding affinity of peptides to MHC II molecules using a recurrent neural network with an attention mechanism. It includes implementation code and datasets for training and evaluation, making it a useful tool for researchers in computational biology and drug discovery.

JAMUN is a tool that bridges smoothed molecular dynamics and score-based learning to efficiently generate conformational ensembles of peptides. It enhances the sampling of molecular dynamics by operating in a smoothed space, allowing for faster and more transferable results in molecular simulations.

MD-AlanineDipeptide is a tool for performing molecular dynamics simulations of the alanine dipeptide, analyzing its conformational states and energy landscape. It utilizes GROMACS for simulations and provides insights into dihedral angle distributions and transition states.

UniSim is a unified simulator designed for time-coarsened dynamics of biomolecules, utilizing generative models to simulate molecular interactions. It provides tools for training and evaluating models on various datasets related to small molecules and proteins.

AMPTrans-lstm is a deep generative model application designed to discover novel and diverse functional peptides that can combat microbial resistance. It utilizes advanced machine learning techniques, specifically LSTM and transformer models, to generate and optimize peptide sequences.

This repository contains a Python script that generates helical wheel visualizations based on input peptide sequences. It is useful for visualizing the arrangement of amino acids in alpha-helices, aiding in protein design and analysis.

USMPep is a tool designed for predicting the binding affinity of peptides to the Major Histocompatibility Complex (MHC) using a recurrent neural network. It demonstrates competitive performance against state-of-the-art methods and provides predictions on benchmark datasets.

GPT_protein_design is a pipeline for de novo protein design that utilizes a GPT-based generator and a transfer learning-based discriminator. It aims to generate novel proteins, such as antimicrobial peptides, by leveraging advanced machine learning techniques.

pLM4ACE is a tool that utilizes a protein language model to predict the bioactivity of antihypertensive peptides. It allows users to train their own models and prepare datasets for peptide screening, making it a valuable resource in molecular biology and drug discovery.

Peplib Generator is a model focused on the in silico generation and directed evolution of peptide ligands. It integrates various computational methods to create and filter peptide sequences, making it a valuable tool in peptide drug design.

PeptideSim is an automated tool designed for simulating peptides using GROMACS. It facilitates various molecular dynamics simulations, including energy minimization and equilibration, making it a valuable resource for researchers in molecular biology and computational chemistry.

The hsm-web repository provides a website for the biophysical prediction of protein-peptide interactions and signaling networks using machine learning techniques. It serves as a resource for understanding these interactions and is linked to a scientific publication detailing the methodology.

The 'peptide-ai' repository contains code related to the research on active learning and meta-learning for the iterative design of peptides. It aims to enhance the design process of peptides, making it relevant to molecular design and generation tasks.

High-PepBinder is a framework designed for the de novo generation of peptide sequences that are specific to target proteins, utilizing a latent diffusion model. It includes functionalities for training models on peptide datasets and generating new peptide sequences based on input target sequences.

HyMD-tutorial offers a collection of Jupyter Notebook examples demonstrating the use of HylleraasMD for simulating various molecular systems, including monoatomic particles, polymers, and peptides. It covers different types of interactions, such as bonded, field, and electrostatic interactions, making it a useful resource for molecular dynamics studies.

The genome-tools repository provides code for the exploration and analysis of biochemical data, especially focusing on proteins. It includes classes for reading PDB files and measuring molecular properties, making it useful for molecular simulations and structural analysis.

This repository provides Jupyter Notebooks and simulation data for modeling the interactions of titrating peptides with lipid bilayers using coarse-grained methods. It includes tools for running simulations, analyzing density distributions, and training models based on simulation data.

gmx_cyclizer.py is a Python script that automates the generation of cyclic peptide topologies for use in GROMACS simulations. It streamlines the process of preparing cyclic peptides by reordering atom indices and merging coordinate files, facilitating molecular dynamics studies.

The gmx_saltbridge script evaluates the formation of salt bridges in peptides and proteins over Gromacs molecular dynamics simulation trajectories. It calculates the occupancy rate of salt bridges and provides results in a structured format for further analysis.

ImmunoPeptideDesigner automates the generation of immunogenic peptides from protein structures and performs molecular docking analysis using AlphaFold2 and AutodockVina. It allows for the design and optimization of peptide sequences based on their immunogenicity, making it a valuable tool in molecular biology and immunology.

NNAASynth provides a framework for analyzing the synthetic accessibility of non-natural amino acids and their protected forms. It employs cheminformatics tools and deep learning models to generate amino acid derivatives, perform retrosynthetic analysis, and evaluate synthetic routes.

Morphoscanner is a library designed for the analysis of molecular dynamics simulations of self-assembling peptides. It provides tools for recognizing patterns in peptide networks and analyzing trajectory data, making it useful for researchers in molecular dynamics and peptide studies.