Biology subjects

Burley, S.

Publications and source records attributed to Burley, S..

3 recordsLinked to original sources

bioRxiv2025

Multi-scale structural similarity embedding search across entire proteomes

The rapid expansion of three-dimensional (3D) biomolecular structure information, driven by breakthroughs in artificial intelligence/deep learning (AI/DL)-based structure predictions, has created an urgent need for scalable and efficient structure similarity search methods. Traditional alignment-based approaches, such as structural superposition tools, are computationally expensive and challenging to scale with the vast number of available macromolecular structures. Herein, we present a scalable structure similarity search strategy designed to navigate extensive repositories of experimentally determined structures and computed structure models predicted using AI/DL methods. Our approach leverages protein language models and a deep neural network architecture to transform 3D structures into fixed-length vectors, enabling efficient large-scale comparisons. Although trained to predict TM-scores between single-domain structures, our model generalizes beyond the domain level, accurately identifying 3D similarity for full-length polypeptide chains and multimeric assemblies. By integrating vector databases, our method facilitates efficient large-scale structure retrieval, addressing the growing challenges posed by the expanding volume of 3D biostructure information.

bioinformatics↗

bioRxiv2021

Selective Vulnerability of Tripartite Synapses in Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder. Separate lines of evidence suggest that synapses and astrocytes play a role in the pathological mechanisms underlying ALS. Given that astrocytes make specialised contacts with some synapses, called tripartite synapses, we hypothesise that tripartite synapses could act as the fulcrum of disease in ALS. To test this hypothesis, we have performed an extensive microscopy-based investigation of synapses and tripartite synapses in the spinal cord of ALS model mice and post-mortem human tissue from ALS cases. We reveal widescale synaptic changes at the early symptomatic stages of the SOD1G93a mouse model. Super-resolution microscopy reveals that large complex postsynaptic structures are lost in ALS mice. Most surprisingly, tripartite synapses are selectively lost while non-tripartite synapses remain in equal number to healthy controls. Finally, we also observe a similar selective loss of tripartite synapses in human post-mortem ALS spinal cords. From these data we conclude that tripartite synaptopathy is a key hallmark of ALS.

neuroscience↗

bioRxiv2018

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

1Docking calculations can be used to accelerate drug discovery by providing predictions of the poses of candidate ligands bound to a targeted protein. However, studies in the literature use varied docking methods, and it is not clear which work best, either in general or for specific protein targets. In addition, a complete docking calculation requires components beyond the docking algorithm itself, such as preparation of the protein and ligand for calculations, and it is difficult to isolate which aspects of a method are most in need of improvement. To address such issues, we have developed the Continuous Evaluation of Ligand Protein Predictions (CELPP), a weekly blinded challenge for automated docking workflows. Participants in CELPP create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new (never before released) protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflows predictions and posts the scores online. CELPP is a new cyberinfrastructure resource to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

biophysics↗