Biology subjects

Grant, A. R.

Publications and source records attributed to Grant, A. R..

2 recordsLinked to original sources

bioRxiv2023

Nano-computed tomography reveals repeated phenotypic divergence in parasites to escape host defense

Understanding how selective pressures drive morphological change is a central question in evolutionary biology. Feather lice have repeatedly diversified into convergent ecomorphs, based on how they escape from host defenses in different microhabitats. Here, we used nano-computed tomography scan data of 89 specimens of feather lice, belonging to four ecomorph groups to quantify variation of functional traits, including mandibular muscle volume, limb length, and three-dimensional head shape data in these tiny insects. The results suggest that the shape of the head, the proportional volume of the chewing muscles, and the length of the leg segments in feather lice are all strongly associated with specific host-habitats. Further, species that co-occur on hosts have increased rates of morphological evolution, suggesting competition for host space is one of the drivers of morphology. This supports previous work indicating that the phenotypic diversity of feather lice is the result of repeated convergence resulting from resource partitioning, microhabitat specialization, and selection pressures imposed by host defense.

evolutionary biology↗

bioRxiv2018

Deep Semantic Protein Representation for Annotation, Discovery, and Engineering

Computational assignment of function to proteins with no known homologs is still an unsolved problem. We have created a novel, function-based approach to protein annotation and discovery called D-SPACE (Deep Semantic Protein Annotation Classification and Exploration), comprised of a multi-task, multi-label deep neural network trained on over 70 million proteins. Distinct from homology and motif-based methods, D-SPACE encodes proteins in high-dimensional representations (embeddings), allowing the accurate assignment of over 180,000 labels for 13 distinct tasks. The embedding representation enables fast searches for functionally related proteins, including homologs undetectable by traditional approaches. D-SPACE annotates all 109 million proteins in UniProt in under 35 hours on a single computer and searches the entirety of these in seconds. D-SPACE further quantifies the relative functional effect of mutations, facilitating rapid in silico mutagenesis for protein engineering applications. D-SPACE incorporates protein annotation, search, and other exploratory efforts into a single cohesive model.

bioinformatics↗