Biology subjects

Mura, C.

Publications and source records attributed to Mura, C..

7 recordsLinked to original sources

bioRxiv2022

Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data

BackgroundMachine learning (ML) has a rich history in structural bioinformatics, and modern approaches, such as deep learning, are revolutionizing our knowledge of the subtle relationships between biomolecular sequence, structure, function, dynamics and evolution. As with any advance that rests upon statistical learning approaches, the recent progress in biomolecular sciences is enabled by the availability of vast volumes of sufficiently-variable data. To be useful, such data must be well-structured, machine-readable, intelligible and manipulable. These and related requirements pose challenges that become especially acute at the computational scales typical in ML. Furthermore, in structural bioinformatics such data generally relate to protein three-dimensional (3D) structures, which are inherently more complex than sequence-based data. A significant and recurring challenge concerns the creation of large, high-quality, openly-accessible datasets that can be used for specific training and benchmarking tasks in ML pipelines for predictive modeling projects, along with reproducible splits for training and testing. ResultsHere, we report Prop3D, a platform that allows for the creation, sharing and extensible reuse of libraries of protein domains, featurized with biophysical and evolutionary properties that can range from detailed, atomically-resolved physicochemical quantities (e.g., electrostatics) to coarser, residue-level features (e.g., phylogenetic conservation). As a community resource, we also supply a Prop3D-20sf protein dataset, obtained by applying our approach to CATH. We have developed and deployed the Prop3D framework, both in the cloud and on local HPC resources, to systematically and reproducibly create comprehensive datasets via the Highly Scalable Data Service (HSDS). Our datasets are freely accessible via a public HSDS instance, or they can be used with accompanying Python wrappers for popular ML frameworks. ConclusionProp3D and its associated Prop3D-20sf dataset can be of broad utility in at least three ways. Firstly, the Prop3D workflow code can be customized and deployed on various cloud-based compute platforms, with scalability achieved largely by saving the results to distributed HDF5 files via HSDS. Secondly, the linked Prop3D-20sf dataset provides a hand-crafted, already-featurized dataset of protein domains for 20 highly-populated CATH families; importantly, provision of this pre-computed resource can aid the more efficient development (and reproducible deployment) of ML pipelines. Thirdly, Prop3D-20sfs construction explicitly takes into account (in creating datasets and data-splits) the enigma of data leakage, stemming from the evolutionary relationships between proteins.

bioinformatics↗

bioRxiv2022

Explainable Deep Generative Models, Ancestral Fragments, and Murky Regions of the Protein Structure Universe

Modern proteins did not arise abruptly, as singular events, but rather over the course of at least 3.5 billion years of evolution. Can machine learning teach us how this occurred? The molecular evolutionary processes that yielded the intricate three-dimensional (3D) structures of proteins involve duplication, recombination and mutation of genetic elements, corresponding to short peptide fragments. Identifying and elucidating these ancestral fragments is crucial to deciphering the interrelationships amongst proteins, as well as how evolution acts upon protein sequences, structures & functions. Traditionally, structural fragments have been found using sequence-based and 3D structural alignment approaches, but that becomes challenging when proteins have undergone extensive permutations--allowing two proteins to share a common architecture, though their topologies may drastically differ (a phenomenon termed the Urfold). We have designed a new framework to identify compact, potentially-discontinuous peptide fragments by combining (i) deep generative models of protein superfamilies with (ii) layerwise relevance propagation (LRP) to identify atoms of great relevance in creating an embedding during an allsuperfamilies x alldomains analysis. Our approach recapitulates known relationships amongst the evolutionarily ancient small {beta}-barrels (e.g. SH3 and OB folds) and amongst P-loop-containing proteins (e.g. Rossmann and P-loop NTPases), previously established via manual analysis. Because of the generality of our deep models approach, we anticipate that it can enable the discovery of new ancestral peptides. In a sense, our framework uses LRP as an explainable AI approach, in conjunction with a recent deep generative model of protein structure (termed DeepUrfold), in order to leverage decades worth of structural biology knowledge to decipher the underlying molecular bases for protein structural relationships--including those which are exceedingly remote, yet discoverable via deep learning.

bioinformatics↗

bioRxiv2022

Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space

Our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and understand protein structure, function and evolution. For instance, is there an optimal granularity in viewing protein structural similarities (e.g., architecture, topology or some other level)? Similarly, the discrete/ continuous dichotomy of fold space is central, but remains unresolved. Discrete views of fold space bin similar folds into distinct, non-overlapping groups; unfortunately, such binning can miss remote relationships. While hierarchical systems like CATH are indispensable resources, less heuristic and more conceptually flexible approaches could enable more nuanced explorations of fold space. Building upon an Urfold model of protein structure, here we present a deep generative modeling framework, termed DeepUrfold, for analyzing protein relationships at scale. DeepUrfolds learned embeddings occupy high-dimensional latent spaces that can be distilled for a given protein in terms of an amalgamated representation uniting sequence, structure and biophysical properties. This approach is structure-guided, versus being purely structure-based, and DeepUrfold learns representations that, in a sense, define superfamilies. Deploying DeepUrfold with CATH reveals evolutionarily-remote relationships that evade existing methodologies, and suggests a new, mostly-continuous view of fold space--a view that extends beyond simple geometric similarity, towards the realm of integrated sequence{leftrightarrow}structure{leftrightarrow}function properties.

bioinformatics↗

bioRxiv2019

The Urfold: Structural Similarity Just above the Superfold Level?

OverviewWe suspect that there is a level of granularity of protein structure intermediate between the classical levels of architecture and topology, as reflected in such phenomena as extensive 3D structural similarity above the level of (super)folds. Here, we examine this notion of architectural identity despite topological variability, starting with a concept that we call the Urfold. We believe that this model could offer a new conceptual approach for protein structural analysis and classification: indeed, the Urfold concept may help reconcile various phenomena that have been frequently recognized or debated for years, such as the precise meaning of significant structural overlap and the degree of continuity of fold space. More broadly, the role of structural similarity in sequence/structure/function evolution has been studied via many models over the years; the Urfold may help synthesize these models into a generalized, consistent framework, by addressing a conceptual gap that we believe exists between the architecture and topology levels of structural classification schemes.

bioinformatics↗

bioRxiv2019

A stimuli-responsive, pentapeptide, nanofiber hydrogel for tissue engineering

Short peptides are uniquely versatile building blocks for self-assembly. Supramolecular peptide assemblies can be used to construct functional hydrogel biomaterials--an attractive approach for neural tissue engineering. Here, we report a new class of short, five-residue peptides that form hydrogels with nanofiber structures. Using rheology and spectroscopy, we describe how sequence variations, pH, and peptide concentration alter the mechanical properties of our pentapeptide hydrogels. We find that this class of seven unmodified peptides forms robust hydrogels from 0.2-20 kPa at low weight percent (less than 3 wt. %) in cell culture media, and undergoes shear-thinning and rapid self-healing. The peptides self-assemble into long fibrils with sequence-dependent fibrillar morphologies. These fibrils exhibit a unique twisted ribbon shape, as visualized by TEM and Cryo-EM imaging, with diameters in the low tens of nanometers and periodicities similar to amyloid fibrils. Experimental gelation behavior corroborates our molecular dynamics simulations, which demonstrate peptide assembly behavior, an increase in {beta}-sheet content, and patterns of variation in solvent accessibility. Our Rapidly Assembling Pentapeptides for Injectable Delivery (RAPID) hydrogels are syringe-injectable and support cytocompatible encapsulation of oligodendrocyte progenitor cells (OPCs), as well as their proliferation and three-dimensional process extension. Furthermore, RAPID gels protect OPCs from mechanical membrane disruption and acute loss of viability when ejected from a syringe needle, highlighting the protective capability of the hydrogel as potential cell carriers for trans-plantation therapies. The tunable mechanical and structural properties of these supramolecular assemblies are shown to be permissive to cell expansion and remodeling, making this hydrogel system suitable as an injectable material for cell delivery and tissue engineering applications.

bioengineering↗

bioRxiv2018

Guidance of zoospores by potassium gradient sensing mediates aggregation

Biflagellate zoospores of some phytopathogenic Phytophthora species spontaneously aggregate within few minutes in suspension. Depending on species auto-aggregate formation results from bioconvection or from a sequence bioconvection-positive chemotaxis. In this study, we show that P. parasitica zoospores may form aggregates upon application of a K+ gradient in particular geometric arrangements. Based on the use of macro- and microfluidic devices, in addition to time-lapse live imaging both in the vertical and horizontal planes, we defined (i) the spatiotemporal and concentration scale evolution within the gradient in correlation with (ii) cell distribution and (iii) metrics of zoospore motion (velocity, trajectory). The results indicated that K+-induced aggregates result from a single bi-phasic temporal sequence involving negative chemotaxis and then bioconvection in a K+ gradient concentration scale [0-17 mM]. Each K+-sensing cell undergoes a forward-to-backward movement within a threshold range of 1-4mM, thereby forming progressively a swarm. Once a critical population density is achieved zoospores form a plume which undergoes a downward migration leading to aggregate formation on the support surface. We discuss putative sources for K+ gradient generation in natural environment (zoospore population, microbiota, plant roots, soil particles), and implication for events preceding inoculum formation on host plant.

microbiology↗

bioRxiv2017

Producing Hfq/Sm Proteins and sRNAs for Structural and Biophysical Studies of Ribonucleoprotein Assembly

Hfq is a bacterial RNA-binding protein that plays key roles in the post-transcriptional regulation of gene expression. Like other Sm proteins, Hfq assembles into toroidal discs that bind RNAs with varying affinities and degrees of sequence specificity. By simultaneously binding to a regulatory small RNA (sRNA) and an mRNA target, Hfq hexamers facilitate productive RNA[···]RNA interactions; the generic nature of this chaperone-like functionality makes Hfq a hub in many sRNA-based regulatory networks. That Hfq is crucial in diverse cellular pathways--including stress response, quorum sensing and biofilm formation-- has motivated genetic and RNAomic studies of its function and physiology (in vivo), as well as biochemical and structural analyses of Hfq[···]RNA interactions (in vitro). Indeed, crystallographic and bio-physical studies first established Hfq as a member of the phylogenetically-conserved Sm superfamily. Crystallography and other biophysical methodologies enable the RNA-binding properties of Hfq to be elucidated in atomic detail, but such approaches have stringent sample requirements, viz.: reconstituting and characterizing an Hfq*RNA complex requires ample quantities of well-behaved (sufficient purity, homogeneity) specimens of Hfq and RNA (sRNA, mRNA fragments, short oligoribonucleotides, or even single nucleotides). The production of such materials is covered in this Chapter, with a particular focus on recombinant Hfq proteins for crystallization experiments.\n\nAbbreviations\n\nJournal formatMethods in Molecular Biology (Springer Protocols series); this volume is entitled \"Bacterial Regulatory RNA: Methods and Protocols\"; an author guide is linked at http://www.springer.com/series/7651

biochemistry↗