Biology subjects

Snowden, J.

Publications and source records attributed to Snowden, J..

6 recordsLinked to original sources

bioRxiv2024

DoRIAT: A Bayesian Framework For Interpreting And Annotating Docking Runs.

The advent of sequence-to-structure deeplearning models have transformed protein engineering landscape by providing an accurate and cost effective way to determine crystal structures. Despite their accuracy, deep-learning predictions tend to give limited insights around protein dynamics. To improve conformation exploration we have developed a machine learning pipeline that combines deep-learning predictions with molecular docking. In this report, we propose Docking Run Intepretation and Annotation Tool (DoRIAT). In contrast to frameworks that score models based on interface interactions, DoRIAT uses a set of parameters that summarize binding conformation. We use DoRIAT to score output from docking runs, identify complexes close to the native structure and create ensembles of models with similar binding conformations. Our results demonstrate that the single structural model DoRIAT selects to be the closest representation of the crystal structure lies within the top 10 of docked models, ranked by Root Mean Squared Distance(RMSD), in around 80% of cases.

systems biology↗

bioRxiv2022

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule-based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and results were acquired through model simulations. Conforming to previous reports, we confirm that the Kappa model recapitulated the general dynamics of its ODE counterpart with minor differences. These differences occur whenever molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one but can be corrected by revision of the rate constants used in the relevant rules. As in previous reports on other molecular systems, we find that, also in the case of the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, facilitating model reuse. In parallel with these analyses, this manuscript reports a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of a more complex interaction network to study this important molecular system.

systems biology↗

bioRxiv2020

The allosteric modulation of Complement C5 by knob domain peptides

To overcome limited germline combinatorial diversity, bovines have evolved a subset of antibodies with ultra-long CDRH3 regions that harbour cysteine-rich knob domains. To produce affinity-maturated peptides, we previously isolated autonomous 3-6 kDa knob domains from bovine antibodies. Here, we show that binding of four knob domain peptides elicits a range of effects on the clinically validated drug target complement C5. Allosteric mechanisms predominated, with one peptide selectively inhibiting C5 cleavage by the alternative pathway C5 convertase, revealing a targetable mechanistic difference between the classical and alternative pathway C5 convertases. Taking a hybrid biophysical approach, we present C5-knob domain co-crystal structures and, by solution methods, observed allosteric effects propagating >50 [A] from the binding sites. This study expands the therapeutic scope of C5, presents new inhibitors and introduces knob domains as new, low molecular weight antibody fragments, with therapeutic potential.

immunology↗

bioRxiv2020

Ab-Ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope

Solving the structure of an antibody-antigen complex gives atomic level information of the interactions between an antibody and its antigen, but such structures are expensive and hard to obtain. Alternative experimental sources include epitope mapping and binning experiments which can be used as a surrogate to identify key interacting residues. However, their resolution is usually not sufficient to identify if two antibodies have identical interactions. Computational approaches to this problem have so far been based on the premise that antibodies with similar sequences behave similarly. Such approaches will fail to identify sequence-distant antibodies that target the same epitope. We present Ab-Ligity, a structure-based similarity measure tailored to antibody-antigen interfaces. Using predicted paratopes on model antibody structures, we assessed its ability to identify those antibodies that target highly similar epitopes. Most antibodies adopting similar binding modes can be identified from sequence similarity alone, using methods such as clonotyping. In the challenging subset of antibodies whose sequences differ significantly, Ab-Ligity is still able to predict antibodies that would bind to highly similar epitopes (precision of 0.95 and recall of 0.69). We compared Ab-Ligitys performance to an existing tool for comparing general protein interfaces, InterComp, and showed improved performance on antibody cases alongside a significant speed-up. These results suggest that Ab-Ligity will allow the identification of diverse (sequence-dissimilar) antibodies that bind to the same epitopes from large datasets such as immune repertoires. The tool is available at http://opig.stats.ox.ac.uk/resources.

bioinformatics↗

bioRxiv2019

Structural Diversity of B-Cell Receptor Repertoires along the B-cell Differentiation Axis in Humans and Mice

Most current analysis tools for antibody next-generation sequencing data work with primary sequence descriptors, leaving accompanying structural information unharnessed. We have used novel rapid methods to structurally characterize the paratopes of more than 180 million human and mouse B-cell receptor (BCR) repertoire sequences. These structurally annotated paratopes provide unprecedented insights into both the structural predetermination and dynamics of the adaptive immune response. We show that B-cell types can be distinguished based solely on these structural properties. Antigen-unexperienced BCR repertoires use the highest number and diversity of paratope structures and these patterns of naive repertoire paratope usage are highly conserved across subjects. In contrast, more differentiated B-cells are more personalized in terms of paratope structure usage. Our results establish the paratope structure differences in BCR repertoires and have applications for many fields including immunodiagnostics, phage display library generation, and \"humanness\" assessment of BCR repertoires from transgenic animals.

immunology↗

bioRxiv2018

Observed Antibody Space: a resource for data mining next generation sequencing antibody repertoires

Antibodies are immune system proteins that recognize noxious molecules for elimination. Their sequence diversity and binding versatility have made antibodies the primary class of biopharmaceuticals. Recently it has become possible to query their immense natural diversity using next-generation sequencing of immunoglobulin gene repertoires (Ig-seq). However, Ig-seq outputs are currently fragmented across repositories and tend to be presented as raw nucleotide reads, which means nontrivial effort is required to reuse the data for analysis. To address this issue, we have collected Ig-seq outputs from 53 studies, covering more than half a billion antibody sequences across diverse immune states, organisms and individuals. We have sorted, cleaned, annotated, translated and numbered these sequences and make the data available via our Observed Antibody Space (OAS) resource at antibodymap.org. The data within OAS will be regularly updated with newly released Ig-seq datasets. We believe OAS will facilitate data mining of immune repertoires for improved understanding of the immune system and development of better biotherapeutics.

immunology↗