Biology subjects

Farrell, D.

Publications and source records attributed to Farrell, D..

9 recordsLinked to original sources

bioRxiv2022

Robust residue-level error detection in cryo-electron microscopy models

Building accurate protein models into moderate resolution (3-5[A]) cryo-electron microscopy (cryo-EM) maps is challenging and error-prone. While the majority of solved cryo-EM structures are at these resolutions, there are few model validation metrics that can precisely evaluate the local quality of atomic models built into these maps. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model to identify residue-level errors in protein structures built into cryo-EM maps. Trained on a set of errors from obsoleted protein structures, our model draws off two major sources of information to predict errors: the local agreement of model and map compared to expected, and how "native-like" the neighborhood around a residue looks, as predicted by a deep learning model. MEDIC is validated on a set of 28 structures that were subsequently solved to higher-resolutions, where our model identifies the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to rebuild 12 deposited structures, fixing 2 sequence registration errors, 51 areas with improper secondary structure, 51 incorrect loops, and 16 incorrect carbonyls, showing the value of this approach to guide model building.

biochemistry↗

bioRxiv2021

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

bioinformatics↗

bioRxiv2021

epitopepredict: A tool for integrated MHC binding prediction

A key step in the cellular adaptive immune response is the presentation of antigen to T cells. During this process short peptides processed from self or foreign proteins may be presented on the surface bound to MHC molecules for binding to T cell receptors. Those that bind and activate an immune response are called epitopes. Computational prediction of T cell epitopes has many applications in vaccine design and immuno-diagnostics. This is the basis of immunoinformatics which allows in silico screening of peptides before experiments are performed. The most effective approach is to estimate the binding affinity of a given peptide fragment to MHC class I or II molecules. With the availability of whole genomes for many microbial species it is now feasible to computationally screen whole proteomes for candidate peptides. epitopepredict is a programmatic framework and command line tool designed to aid this process. It provides access to multiple binding prediction algorithms under a single interface and scales for whole genomes using multiple target MHC alleles. A web interface is provided to assist visualization and filtering of the results. The software is freely available under an open source license from https://github.com/dmnfarrell/epitopepredict

bioinformatics↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗

bioRxiv2020

Structural basis for lipid transport by the MLA complex

Multi-resistant bacteria are a major threat in modern medicine. The gram-negative coccobacillus Acinetobacter baumannii currently leads the WHO list of pathogens in critical need for new therapeutic development. The maintenance of lipid asymmetry (MLA) protein complex is one of the core machineries that transport lipids from/to the outer membrane in gram-negative bacteria. It also contributes to broad-range antibiotic resistance in several pathogens, most prominently in A. baumannii. Nonetheless, the molecular details of its role in lipid transport has remained largely elusive. Here, we report the cryo-EM structures of the core MLA complex, MlaBDEF, from the pathogen A. baumannii, in the apo-, ATP- and ADB-bound states. These structures reveal multiple lipid binding sites, in the cytosolic and periplasmic side of the complex. Molecular dynamics simulations suggest their potential trajectory across the membrane. Collectively with the recently-reported structures of the E.coli orthologue, these data also allows us to propose a molecular mechanism of lipid transport by the MLA system.

biochemistry↗

bioRxiv2020

Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM

Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 [A]), with visible secondary structure elements but poorly resolved loops, making model-building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 [A] resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it is comprised of 6557 residues, only 1897 of which are covered by homology models. In the published structure built from this map, only 387 residues could be assigned to specific subunits. By building and placing into density 42 deep-learning guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.

biochemistry↗

bioRxiv2019

Updated functional annotation of the Mycobacterium bovis AF2122/97 reference genome.

Mycobacterium bovis AF2122/97 is the reference strain for the bovine tuberculosis bacillus. We here report an update to the M. bovis AF2122/97 genome annotation to reflect 616 new protein identifications which replace many of the old hypothetical coding sequences and proteins of unknown function in the genome. These changes integrate information from functional assignments of orthologous coding sequences in the Mycobacterium tuberculosis H37Rv genome. We have also added 69 additional new gene names.

microbiology↗

bioRxiv2017

smallrnaseq: Short Non Coding RNA-Seq Analysis With Python

The use of next generation sequencing is now a standard approach to elucidate the small non-coding RNA species (sncRNAs) present in tissue and biofluid samples. This has revealed the wide variety of RNAs with regulatory functions the best studied of which are microRNAs. Profiling of sncRNAs by deep sequencing allows measures of absolute abundance and for the discovery of novel species that have eluded previous methods. Specific considerations must be made when quantifying and cataloging sncRNAs and multiple algorithms are now available, mostly focused on miRNA analysis. smallrnaseq is a Python package that implements some of the standard approaches for quantification and analysis of sncRNAs. This includes miRNA quantification and novel miRNA prediction. A command line interface makes the software accessible for general users.

bioinformatics↗

bioRxiv2016

Updated reference genome sequence and annotation of Mycobacterium bovis AF2122/97

We report an update to the reference genome of the bovine tuberculosis bacillus Mycobacterium bovis AF2122/97 generated using an integrative multi- omics approach. Updates include 42 new CDS, 14 modified annotations, 26 SNP corrections, and disclosure that the RD900 locus, previously described as absent from the genome, is in fact present.

genomics↗