〘 Biophysics 〙

Language Requirement(s): None. Usage Requirements: Java-Based Operating System

Java-based image processing program with Java plugins and recordable macros useful for image processing and analysis problems in biophysics. ImageJ can be run in browser!

Contributor: Jin Oueslati, Indiana University - Bloomington

Usage Requirements: Only runs on Windows; requires a subscription but most universities have access.

World’s leading mapping and GIS processing software; essential for almost any GIS project. Analyze complex structures, conduct geospacial analysis & data management.

Contributor: Allison Loper, Purdue University - West Lafayette

Language Requirement(s): C++, C, Fortran, or Python for pytraj. Usage Requirements: AMBER suite, compilers for appropriate languages, Linux or Unix terminal & Bash, Python-Compatable IDE for pytraj.

Amber allows for the simulation of biological macromolecules' dynamics through the use of an array of pre-programmed "force fields" from which the user can select. The program developers have created a detailed series of introductory courses for the suite found here and yearly reference manuals are published. The program central to AMBER is CPPTRAJ, which developers have created a Python wrapper for called pytraj.

Contributor: Amelia Binau, Indiana University - Bloomington

Usage Requirements: Python-compatible interpreter/IDE, internet connection. Language Requirements: Python.

Biopython is an all-in-one Python package for all things bioinformatics and biology-related computation. Documentation is complete and engaging to read, many examples of major functionalities are detailed, including gene sequencing, working with sequences, and loading/accessing major biological data repositories.

Contributor: Amelia Binau, Indiana University - Bloomington

Language Requirement(s): Writen in C++, features native Python (and other) bindings for development. Usage Requirements: Cross-platform. Requires CMake to build from source, or can be installed via pip or Conda. Works offline, requires more packages for GPU support, like CUDA/OpenCL.

A high-performance toolkit for molecular simulations. Researchers can interface with OpenMM (typically via Python) to implement custom forces, integration algorithms, and machine-learning potentials without rewriting CUDA or C++ code. OpenMM has two parts: (i) a set of libraries for those who want to design and add molecular simulations to their programs, and (ii) an application layer for users who simply want to run simulations.

Contributor: Neil Ghugare, University of Florida

Language Requirement(s): Written in C++, includes Python bindings via PyRosetta. Usage Requirements: Runis natively on UNIX environments. Requires a free academic license to download source code. Compiles via SCons or CMake, works offline on laptops or HPC clusters.

A comprehensive software suite for macromolecular modeling, structure prediction, and de novo protein design. It enables researchers to predict 3D structures from primary amino acids, model protein-protein infterfaces, and design novel enzymes.

Contributor: Neil Ghugare, University of Florida

Language Requirement(s): Written in C and Python, features Python command line. Usage Requirements: Works cross-platform on standard user hardware, works offline. Available as an open-source build or incentive-supported binary.

A user-sponsored molecular visualization system, used to render high-resolution 3D models of proteins/DNA/RNA. The module is lightweight and fast, and is able to produce publication-quality representations and ray-traced images of biochemical molecules.

Contributor: Neil Ghugare, University of Florida

Language Requirement(s): Written in Python, utilizes C extensions/Cython for performance. Usage Requirements: Standard Python environment via pip or conda. Works cross-platform on standard hardware and operates offline.

An open-source tool used for the analysis of molecular simulation data to study molecular structures and dynamics. It enables users to efficiently analyze molecular dynamics (MD), including particle-based trajectories and individual coordinate frames. With numpy, you can also access atomic coordinates, which help extract subsets of structures and support trajectory transformations.

Contributor: Neil Ghugare, University of Florida

Language Requirement(s): Typically accessed via API using Python, R, or JavaScript, or through direct downloads of standard .pdb files and other file formats. Usage Requirements: Web-based database query platform, requires internet connection to download for later offline viewing/analysis.

A single-archive repository containing experimentally determined 3D structures of biological macromolecules. It serves as a foundational database for structural biology and computational biophysics research.

Contributor: Neil Ghugare, University of Florida

Language Requirement(s): Acess via APIs or standard file format downloads like .pdb files. Usage Requirements: Web browser interface hosted by EMBL-EBI. Internet connection required to search sequences or download predictions, no local computational hardware required for data entries themselves.

An open-access database developed by Google DeepMind and EMBL-EBI, containing high-confidence, AI-predicted 3D protein structures for over 200 million sequence entries. It covers almost the entire known protein universe, allowing researchers to study structures that have not yet been solved experimentally.

Contributor: Neil Ghugare, University of Florida