Simpler protein domain identification using spectral clustering

The decomposition of a biomolecular complex into domains is an important step to investigate biological functions and ease structure determination. A successful approach to do so is the SPECTRUS algorithm, which provides a segmentation based on spectral clustering applied to a graph coding interatomic fluctuations derived from an elastic network model. We present SPECTRALDOM , which makes three straightforward and useful additions to SPECTRUS . For single structures, we show that high quality partitionings can be obtained from a graph Laplacian derived from pairwise interactions–without normal modes. For sets of homologous structures, we introduce a Multiple Sequence Alignment mode, exploiting both the sequence based information (MSA) and the geometric information embodied in experimental structures. Finally, we propose to analyse the clusters/- domains delivered using the so-called D -family-matching algorithm, which establishes a correspondence between domains yielded by two decompositions, and can be used to handle fragmentation issues. Our domains compare favorably to those of the original SPECTRUS , and those of the deep learning based method Chainsaw . Using two complex cases, we show in particular that SPECTRALDOM is the only method handling complex conformational changes involving several sub-domains. Finally, a comparison of SPECTRALDOM and Chainsaw on the manually curated domain classification ECOD as a reference shows that high quality domains are obtained without using any evolutionary related piece of information. SPECTRALDOM is provided in the Structural Bioinformatics Library, see http://sbl.inria.fr and https://sbl.inria.fr/doc/Spectral_domain_explorer-user-manual.html .