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a tool for membrane segmentation in 3D for cryo-electron tomography (cryo-ET).

(Diffraction Integration for Advanced Light Sources) a software project for the analysis of crystallographic diffraction images for many new methods from shutterless, fine-sliced rotation scans to randomly-oriented snapshots of serial crystallography. It uses multiple CPU and GPU machines to increase speed and accuracy based on a comprehensive physical model.

a multi-GPU accelerated software package that enables real-time fully automated reconstruction of cryoET tomograms in parallel with cryoET data collection.

a Python package and simple command-line program to automatically fit high-resolution atomic structures in cryo-EM densities.

is a Python pipeline for evaluating AlphaFold2 and AlphaFold3 protein complex predictions by combining model confidence metrics such as ipTM, pTM, pLDDT, and PAE with interface-level biophysical descriptors including contacts, hydrogen bonds, salt bridges, buried area, solvation proxy, and shape complementarity.

(Python-based Hierarchical Environment for INtegrated Xtallography) a software suite for the automated determination of molecular structures using X-ray crystallography and other methods.

is a graphical interface for quantifying membrane ultrastructure in cryo-electron tomography data. It supports generating surface meshes from segmentations, measuring membrane curvature, distances, and relative orientations, and interactively visualizing the results in 3D.

a tool for scaling data analyses in cellular cryoET using comprehensive segmentation.

a graph neural network framework for scoring protein-protein interfaces that incorporates ESM-2 language model embeddings. The tool provides both a command-line interface for direct complex scoring and a Python framework for custom applications, generating predictions of interface quality (fnat values) from PDB structures. It includes pre-trained models and supports both CPU and GPU environments for analyzing protein-protein binding interfaces.

is a Python pipeline for evaluating AlphaFold2 and AlphaFold3 protein complex predictions by combining model confidence metrics such as ipTM, pTM, pLDDT, and PAE with interface-level biophysical descriptors including contacts, hydrogen bonds, salt bridges, buried area, solvation proxy, and shape complementarity.