Biology subjects

Lau, A. M.

Publications and source records attributed to Lau, A. M..

5 recordsLinked to original sources

bioRxiv2025

Metagenomic-scale analysis of the predicted protein structure universe

Protein structure prediction breakthroughs, AlphaFold2 and ESMfold, have led to an unprecedented influx of computationally derived structures. The AlphaFold Protein Structure Database provides over 200 million predictions, while the ESM Metagenomic Atlas (ESMatlas) includes over 600 million predictions from uncultured microbes. We combine these into AFESM, an 820-million-entry dataset, and cluster them using a scalable pipeline based on sequence and structure similarity, yielding 5.12 million non-singleton structural clusters. We identify common ancestors and biomes for these clusters to explore their environmental diversity and specificity, and we investigate their structural novelties. From non-singleton clusters unique to ESMatlas, we identified 12 novel domain folds, repredicting a subset ([~]45%) of low-quality domains with ColabFold yielded 33 additional novel folds. This underscores the importance of prediction quality in structural novelty discovery. We also identified 11,941 previously unseen domain combinations, highlighting the untapped structural diversity and importance of metagenomic data for illuminating underexplored regions of the protein structural universe. An interactive webserver and data are available at afesm.foldseek.com.

bioinformatics↗

bioRxiv2023

Merizo: A rapid and accurate domain segmentation method using invariant point attention

AO_SCPLOWBSTRACTC_SCPLOWThe AlphaFold Protein Structure Database (AFDB), containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the information within the AFDB is precluded by an urgent need of tools that allow the efficient traversal, discovery and documentation of its contents. Identifying the regions that correspond to domains in the AFDB is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for accurate domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on a subset of AFDB models via self-distillation, enabling it to be applicable to both experimental and AFDB models. As proof of concept, we apply Merizo to models of the human proteome, and identify 40,818 putative domains that can be matched to CATH representative domains. Merizo is available at https://github.com/psipred/Merizo.

bioinformatics↗

bioRxiv2020

Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments

Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologues of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable computational effort is spent deriving these input features. We present a method that greatly reduces the amount of preprocessing required for a target MSA, while producing main chain coordinates as a direct output of a deep neural network. The network makes use of just three recurrent networks and a stack of residual convolutional layers, making the predictor very fast to run, and easy to install and use. Our approach constructs a directly learned representation of the sequences in an MSA, starting from a one-hot encoding of the sequences. When supplemented with an approximate precision matrix, the learned representation can be used to produce structural models of comparable or greater accuracy as compared to our original DMPfold method, while requiring less than a second to produce a typical model. This level of accuracy and speed allows very large-scale 3-D modelling of proteins on minimal hardware, and we demonstrate that by producing models for over 1.3 million uncharacterized regions of proteins extracted from the BFD sequence clusters. After constructing an initial set of approximate models, we select a confident subset of over 30,000 models for further refinement and analysis, revealing putative novel protein folds. We also provide updated models for over 5,000 Pfam families studied in the original DMPfold paper. Significance StatementWe present a deep learning-based predictor of protein tertiary structure that uses only a multiple sequence alignment (MSA) as input. To date, most emphasis has been on the accuracy of such deep learning methods, but here we show that accurate structure prediction is also possible in very short timeframes (a few hundred milliseconds). In our method, the backbone coordinates of the target protein are output directly from the neural network, which makes the predictor extremely fast. As a demonstration, we generated over 1.3 million models of uncharacterised proteins in the BFD, a large sequence database including many metagenomic sequences. Our results showcase the utility of ultrafast and accurate tertiary structure prediction in rapidly exploring the "dark space" of proteins.

bioinformatics↗

bioRxiv2020

Perturbed structural dynamics underlie inhibition and altered specificity of the multidrug efflux pump AcrB

Resistance-nodulation-division (RND) efflux pumps play a key role in inherent and evolved multidrug-resistance (MDR) in bacteria. AcrB is the prototypical member of the RND family and acts to recognise and export a wide range of chemically distinct molecules out of bacteria, conferring resistance to a variety of antibiotics. Although high resolution structures exist for AcrB, its conformational fluctuations and their putative role in function are largely unknown, preventing a complete mechanistic understanding of efflux and inhibition. Here, we determine these structural dynamics in the presence of AcrB substrates using hydrogen/deuterium exchange mass spectrometry, complemented by molecular modelling, drug binding and bacterial susceptibility studies. We show that the well-studied efflux pump inhibitor phenylalanine-arginine-{beta}-naphthylamide (PA{beta}N) potentiates antibiotic activity by restraining drug-binding pocket dynamics, rather than preventing antibiotic binding. We also reveal that a drug-binding pocket substitution discovered within an MDR clinical isolate, AcrBG288D, modifies the plasticity of the transport pathway, which could explain its altered substrate specificity. Our results provide molecular insight into drug export and inhibition of a major MDR-conferring efflux pump and the important directive role of its dynamics.

biochemistry↗

bioRxiv2020

Hydrogen-deuterium exchange mass spectrometry captures distinct dynamics upon substrate and inhibitor binding to a transporter

Proton-coupled transporters use transmembrane proton gradients to power active transport of nutrients inside the cell. High-resolution structures often fail to capture the coupling between proton and ligand binding, and conformational changes associated with transport. We combine HDX-MS with mutagenesis and MD simulations to dissect the molecular mechanism of the prototypical transporter XylE. We show that protonation of a conserved aspartate triggers conformational transition from outward-facing to inward-facing state. This transition only occurs in the presence of substrate xylose, while the inhibitor glucose locks the transporter in the outward-facing state. MD simulations corroborate the experiments by showing that only the combination of protonation and xylose binding, and not glucose, sets up the transporter for conformational switch. Overall, we demonstrate the unique ability of HDX-MS to distinguish between the conformational dynamics of inhibitor and substrate binding, and show that a specific allosteric coupling between substrate binding and protonation is a key step to initiate transport.

biochemistry↗