Kembhavi, A. | bioRxiv Science

bioRxiv2020

CASSPER: A Semantic Segmentation based Particle Picking Algorithm for Single Particle Cryo-Electron Microscopy

Single-particle cryo-electron microscopy has emerged as the method of choice for structure determination of proteins and protein complexes. However, particle identification and selection which is a prerequisite for achieving high-resolution still poses a major bottleneck for automating the steps of structure determination. Here, we present a generalised deep learning tool, CASSPER, for the automated detection and isolation of protein particles in transmission microscope images. This deep learning tool uses Semantic Segmentation and a collection of visually prepared training samples to capture the differences in the transmission intensities of protein, ice, carbon and other impurities found in the micrograph. CASSPER is the first method to do pixel level classification and completely eliminates the need of manual particle picking. Integration of Contrast Limited Adaptive Histogram Equalization (CLAHE) in CASSPER enables high-fidelity particle detection even in micrographs with variable ice thickness and contrast. In addition, our generalized model for cross molecule picking works with high efficiency on unseen datasets and can potentially pick particles on-the-fly, thereby, enabling automation of data processing.

biophysics↗

bioRxiv2019

CoRNeA: A pipeline to decrypt the protein-protein interaction from amino acid sequence information

Computational methods have been devised in the past to predict the interface residues using amino acid sequence information but have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence are different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here we report a new hybrid pipeline for the prediction of protein-protein interaction interfaces from the amino acid sequence information alone based on the framework of Co-evolution, machine learning (Random forest) and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We incorporate the intra contact information of the individual proteins to eliminate false positives from the predictions as the amino acid sequence also holds information for its own folding along with the interface propensities. Our prediction on various case studies shows that CoRNeA can successfully identify minimal interacting regions of two partner proteins with higher precision and recall.

bioinformatics↗