Biology subjects

Buchanan, A.

Publications and source records attributed to Buchanan, A..

10 recordsLinked to original sources

bioRxiv2025

Recessive but damaging alleles of muscle-specific ribosomal protein gene RPL3L drive neonatal dilated cardiomyopathy

The heart employs a specialized ribosome in its muscle cells to translate genetic information into proteins, a fundamental adaptation with an elusive physiological role1-3. Its significance is underscored by the discovery of neonatal patients suffering from often fatal heart failure caused by severe dilated cardiomyopathy when both copies of the gene RPL3L are mutated4-9. RPL3L is a muscle-specific paralog1-3 of the ubiquitous ribosomal protein L3 (RPL3), which makes the closest contact of any protein to the ribosomes RNA-based catalytic center10. RPL3L-linked heart failure represents the only known human disease associated with tissue-specific ribosomes, yet the underlying pathogenetic mechanisms remain poorly understood. Intriguingly, disease is linked to a large number of mostly missense variants in RPL3L, and RPL3L-knockout resulted in no severe heart defect in either human or mice3, 11-13, challenging the prevailing view that autosomal recessive diseases are caused by loss-of-function mutations. Here, we report three new cases of RPL3L-linked severe neonatal heart failure and present a unifying pathogenetic mechanism by which a large number of variants in the muscle-specific ribosome led to disease. Specifically, affected families often carry one of two recurrent toxic gain-of-function variants alongside a family-specific putative loss-of-function variant. While the non-recurrent variants often trigger partial compensation of RPL3 similar to Rpl3l-knockout mice, both recurrent variants exhibit increased affinity for the RPL3/RPL3L chaperone GRWD114-16 and 60S biogenesis factors, sequester 28S rRNA in the nucleus, disrupt ribosome biogenesis, and trigger severe cellular toxicity that extends beyond the loss of ribosomes. These findings provide critical insights for genetic screening and therapeutic development of neonatal heart failure. Our results suggest that gain-of-toxicity mechanisms may be more prevalent in autosomal recessive diseases, and a combination of gain-of-toxicity and loss-of-function mechanisms could underlie many diseases involving genes with paralogs.

molecular biology↗

bioRxiv2024

Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/590325v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1c5e607org.highwire.dtl.DTLVardef@1810ee5org.highwire.dtl.DTLVardef@1f8f1acorg.highwire.dtl.DTLVardef@c28053_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIReliable calculation of relative binding free energy changes for most protein mutations to within [~]1 kcal/mol. C_LIO_LIAutomated Protein FEP+ Groups treatment of alternate protonation states for titratable residues. C_LIO_LIApplication of FEP+ methodology to "real-world" protein design projects. C_LI

biophysics↗

bioRxiv2022

Crowd-sourced benchmarking of single-sample tumour subclonal reconstruction

Tumours are dynamically evolving populations of cells. Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumour evolution, allowing assessment of how cancers initiate, progress and respond to selective pressures. A plethora of subclonal reconstruction algorithms have been created, but their relative performance across the varying biological and technical features of real-world cancer genomic data is unclear. We therefore launched the ICGC-TCGA DREAM Somatic Mutation Calling -- Tumour Heterogeneity and Evolution Challenge. This seven-year community effort used cloud-computing to benchmark 31 containerized subclonal reconstruction algorithms on 51 simulated tumours. Each algorithm was scored for accuracy on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance significantly more than tumour features, but purity-adjusted read-depth, copy number state and read mappability were associated with performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were surprisingly unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumour evolution.

bioinformatics↗

bioRxiv2021

DLAB - Deep learning methods for structure-based virtual screening of antibodies

Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time-and cost-intensive and heavily reliant on in-vivo and in-vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders. We demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies.

bioinformatics↗

bioRxiv2020

Data mining patented antibody sequences

Patent literature should be a reflection of thirty years of engineering efforts in developing monoclonal antibody therapeutics. Such information is potentially valuable for rational antibody design. Patents however are not designed to convey scientific knowledge, but rather legal protection. It is unclear whether antibody information from patent documents, such as antibody sequences could be useful for the therapeutic antibody sphere in conveying engineering know-how rather than act as legal reference only. To assess the utility of patent data for therapeutic antibody engineering, we quantified the amount of antibody sequences in patents destined for medicinal purposes and how well they reflect the primary sequences of therapeutic antibodies in clinical use. We identified 16,526 patent families from major jurisdictions (e.g. USPTO and WIPO) that contained antibody sequences. These families held 245,109 unique antibody chains (135,397 heavy chains and 109,712 light chains) that we compiled in our Patented Antibody Database (PAD, http://naturalantibody.com/pad). We find that antibodies make up a non-trivial proportion of all patent amino acid sequence depositions (e.g. 10.95% of USPTO Full Text database). Our analysis of the 16,526 families demonstrates that the volume of patent documents with antibody sequences is growing with the majority of documents classified as containing antibodies for medicinal purposes. We further studied the 245,109 antibody chains from patent literature to reveal that they very well reflect the primary sequences of antibody therapeutics in clinical use. This suggests that patent literature could serve as a reference of previous engineering efforts to improve rational antibody design.

bioinformatics↗

bioRxiv2020

Precision targeting of the Plasminogen Activator Inhibitor-1 mechanism increases efficacy of fibrinolytic therapy in empyema.

Plasminogen activator inhibitor-1 (PAI-1) is an endogenous irreversible inhibitor of tissue-type (tPA) and urokinase (uPA) plasminogen activators. PAI-1-targeted fibrinolytic therapy (PAI-1-TFT) is designed to decrease the therapeutic dose of tPA and uPA to attenuate the risk of bleeding and other complications. The docking site peptide (DSP) is a part of the PAI-1 reactive center loop, which interacts with plasminogen activators, thus affecting the PAI-1 mechanism. We used DSP for PAI-1-TFT in two rabbit models: chemically-induced pleural injury and Streptococcus pneumoniae induced empyema. PAI-1-TFT with DSP combined with single chain uPA or tPA resulted in an up to 8-fold decrease in the minimal effective therapeutic dose of plasminogen activator and induced no bleeding. An increase in the level of PAI-1 in infectious pleural injury, when compared to chemically-induced injury, coincided with an increase in the minimal effective dose of plasminogen activator and DSP. PAI-1 is a valid molecular target in S. pneumoniae empyema model in rabbits, which closely recapitulates key characteristics of empyema in humans. Low dose PAI-1-TFT is a novel precise interventional strategy that may improve fibrinolytic therapy of empyema in clinical practice.

biochemistry↗

bioRxiv2020

In vitro Evolution of Antibody Affinity via Insertional Mutagenesis Scanning of an Entire Antibody Variable Region

We report the first systematic combinatorial exploration of affinity enhancement of antibodies by insertions and deletions (InDels). Transposon-based introduction of InDels via the method TRIAD was used to generate large libraries with random in-frame InDels across the entire scFv gene that were further recombined and screened by ribosome display. Knowledge of potential insertion points from TRIAD libraries formed the basis of exploration of length and sequence diversity of novel insertions by insertional-scanning mutagenesis (ISM). An overall 256-fold affinity improvement of an anti-IL-13 antibody BAK1 as a result of InDel mutagenesis and combination with known point mutations validates this approach and suggests that the results of this InDel approach and conventional exploration of point mutations can synergize to generate antibodies with higher affinity. SignificanceInsertion/deletion (InDel) mutations play key roles in genome and protein evolution. Despite their prominence in evolutionary history, the potential of InDels for changing function in protein engineering by directed evolution remains unexplored. Instead point mutagenesis is widely used. Here we create antibody libraries containing InDels and demonstrate that affinity maturation can be achieved in this way, establishing an alternative to the point mutation strategies employed in all previous in vitro selections. These InDels mirror the observation of considerable length variation in loops of natural antibodies originating from the same germline genes and be combined with point mutations, making both natural sources of functional innovation available for artificial evolution in the test tube.

biochemistry↗

bioRxiv2019

Exploring Integrative Analysis using the BioMedical Evidence Graph

The analysis of cancer biology data involves extremely heterogeneous datasets including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenomic regulation, somatic mutations from whole-exome or whole-genome analyses, pathology estimates from imaging sections or subtyping, drug response or other treatment outcomes, and various other clinical and phenotypic measurements. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrative analysis. We introduce a graph database and query engine for discovery and analysis of cancer biology, called the BioMedical Evidence Graph (BMEG). The BMEG is unique from other biological data graphs in that sample level molecular information is connected to reference knowledge bases. It combines gene expression and mutation data, with drug response experiments, pathway information databases and literature derived associations. The construction of the BMEG has resulted in a graph containing over 36M vertices and 29M edges. The BMEG system provides a graph query based API to enable analysis, with client code available for Python, Javascript and R, and a server online at bmeg.io. Using this system we have developed several forms of integrated analysis to demonstrate the utility of the system. The BMEG is an evolving resource dedicated to enabling integrative analysis. We have demonstrated queries on the system that illustrate mutation significance analysis, drug response machine learning, patient level knowledge base queries and pathway level analysis. We have compared the resulting graph to other available integrated graph systems, and demonstrated that it is unique in the scale of the graph and the type of data it makes available.\n\nHighlightsO_LIData resource connected extremely diverse set of cancer data sets\nC_LIO_LIGraph query engine that can be easily deployed and used on new datasets\nC_LIO_LIEasily installed python client\nC_LIO_LIServer online at bmeg.io\nC_LI\n\nSummaryThe analysis of cancer biology data involves extremely heterogeneous datasets including information. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrative analysis. We introduce a graph database and query engine for discovery and analysis of cancer biology, called the BioMedical Evidence Graph (BMEG). The construction of the BMEG has resulted in a graph containing over 36M vertices and 29M edges. The BMEG system provides a graph query based API to enable analysis, with client code available for Python, Javascript and R, and a server online at bmeg.io. Using this system we have developed several forms of integrated analysis to demonstrate the utility of the system.

cancer biology↗

bioRxiv2019

Cdc48/VCP and endocytosis regulate TDP-43 and FUS toxicity and turnover

Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron degenerative disease. TDP-43 (TAR DNA-binding protein 43) and FUS (fused in sarcoma) are aggregation-prone RNA-binding proteins that in ALS can mis-localize to the cytoplasm of affected motor neuron cells, often forming cytoplasmic aggregates in the process. Such mis-localization and aggregation are implicated in ALS pathology, though the mechanisms of TDP-43 and FUS cytoplasmic toxicity remains unclear. Recently, we determined that the endocytic function aids turnover of TDP-43 and reduces TDP-43 toxicity. Here, we identified that Cdc48 and Ubx3, a Cdc48 co-factor implicated in endocytic function, regulates the turnover and toxicity of TDP-43 and FUS expressed in S. cerevisiae. Cdc48 physically interacts and co-localizes with TDP-43, as does VCP in ALS patient tissue. In yeast, FUS toxicity also depends strongly on endocytic function, but not autophagy under normal conditions. FUS expression also impairs endocytic function, as previously observed with TDP-43. Taken together, our data identifies a role for Cdc48/VCP and endocytosis function in regulating TDP-43 and FUS toxicity and turnover. Furthermore, endocytic dysfunction may be a common defect affecting cytoplasmic clearance of ALS aggregation-prone proteins and may represent a novel therapeutic target of promise.

molecular biology↗

bioRxiv2018

Creating Standards for Evaluating Tumour Subclonal Reconstruction

Tumours evolve through time and space. Computational techniques have been developed to infer their evolutionary dynamics from DNA sequencing data. A growing number of studies have used these approaches to link molecular cancer evolution to clinical progression and response to therapy. There has not yet been a systematic evaluation of methods for reconstructing tumour subclonality, in part due to the underlying mathematical and biological complexity and to difficulties in creating gold-standards. To fill this gap, we systematically elucidated the key algorithmic problems in subclonal reconstruction and developed mathematically valid quantitative metrics for evaluating them. We then created approaches to simulate realistic tumour genomes, harbouring all known mutation types and processes both clonally and subclonally. We then simulated 580 tumour genomes for reconstruction, varying tumour read-depth and benchmarking somatic variant detection and subclonal reconstruction strategies. The inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis; this study creates a baseline for its evaluation.

bioinformatics↗