Biology subjects

Das, R.

Publications and source records attributed to Das, R..

At least 145 records · Page 8Linked to original sources

bioRxiv2019

Crumbs 2 mediates ventricular layer remodelling to form the adult spinal cord central canal

In the spinal cord, the adult central canal forms through a poorly-understood process termed dorsal collapse that involves attrition and remodelling of the pseudostratified dorsal ventricular layer. Here we show, in mouse, that dorsal ventricular layer cells adjacent to midline Nestin(+) radial glia downregulate the apical polarity proteins Crumbs2 (CRB2) and aPKC and delaminate in a step-wise manner; concomitantly, Nestin(+) radial glial end-feet ratchet down, to repeat this process. Nestin(+) radial glia secrete a factor that promotes cell delamination. This activity is mimicked by a secreted variant of CRB2 (CRB2S), which is specifically expressed by dorsal midline Nestin(+) radial glia. In cultured cells, CRB2S associated with apical membranes and decreased cell cohesion. Analysis of Crb2F/F/Nestin-Cre+/- mice further confirmed an essential role for CRB2 in dorsal collapse. We propose a model in which CRB2S promotes the progressive attrition of the ventricular layer without loss of overall integrity. This novel mechanism may operate more widely to promote orderly progenitor delamination.

developmental biology↗

bioRxiv2019

A Fyn - specific biosensor reveals localized, pulsatile kinase activity and spatially regulated signaling crosstalk

Cell behavior is controlled through spatio-temporally localized protein activity. Despite unique and often contradictory roles played by Src-family-kinases (SFKs) in regulating cell physiology, activity patterns of individual SFKs have remained elusive. Here, we report a biosensor for specifically visualizing active conformation of SFK-Fyn in live cells. We deployed combinatorial library screening to isolate a binding-protein (F29) targeting activated Fyn. Nuclear-magnetic-resonance (NMR) analysis provides the structural basis of F29 specificity for Fyn over homologous SFKs. Using F29, we engineered a sensitive, minimally-perturbing fluorescence-resonance-energy-transfer (FRET) biosensor (FynSensor) that reveals cellular Fyn activity to be spatially localized, pulsatile and sensitive to adhesion/integrin signaling. Strikingly, growth factor stimulation further enhanced Fyn activity in pre-activated intracellular zones. However, inhibition of focal-adhesion-kinase activity not only attenuates Fyn activity, but abolishes growth-factor modulation. FynSensor imaging uncovers spatially-organized, sensitized signaling clusters, direct crosstalk between integrin and growth-factor-signaling, and clarifies how compartmentalized Src-kinase activity may drive cell fate.

cell biology↗

bioRxiv2019

Ribosolve: Rapid determination of three-dimensional RNA-only structures

The discovery and design of biologically important RNA molecules is dramatically outpacing three-dimensional structural characterization. To address this challenge, we present Ribosolve, a hybrid method integrating moderate-resolution cryo-EM maps, chemical mapping, and Rosetta computational modeling, and demonstrate its application to thirteen previously unknown 119-to 338-nucleotide protein-free RNA-only structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length V. cholerae and F. nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and computer-designed spinach-TTR-3, eterna3D-JR_1, and ATP-TTR-3 with and without AMP. Blind challenges, prospective compensatory mutagenesis, internal controls, and simulation benchmarks validate the Ribosolve models and establish that modeling convergence is quantitatively predictive of model accuracy. These results demonstrate that RNA-only 3D structure determination can be rapid and routine.

biophysics↗

bioRxiv2019

The viral SUMO-targeted Ubiquitin Ligase ICP0 is phosphorylated and activated by host kinase Chk2

When the Herpes Simplex virus (HSV) genome enters the nucleus for replication and transcription, phase-segregated nuclear protein bodies called PML Nuclear Bodies (PML NBs) colocalize with the genome and repress it. HSV encodes a SUMO-targeted Ubiquitin ligase ICP0 that degrades PML NBs to alleviate the repression. The molecular mechanism used by ICP0 to target PML NBs is unclear. For reasons unknown, the growth of HSV is dependent on the ATM/Chk2 pathway. Here we identify a bonafide SUMO-Interacting motif in ICP0 (SLS4) that is essential and sufficient to target SUMOylated proteins in PML NBs like PML and Sp100. Phosphorylation of SLS4 creates new salt-bridges between SUMO and SLS4, increases the SUMO/SLS4 affinity and switches ICP0 into a potent STUbL. We also report that ICP0 exploits the kinase Chk2 to phosphorylate SLS4 and enhance its STUbL activity. Our results uncover how a viral STUbL counters antiviral response by exploiting an unprecedented mechanism involving three post-translational modifications; ubiquitination, SUMOylation, and phosphorylation.

biochemistry↗

bioRxiv2019

Deamidation disrupts native and transient contacts to weaken the interaction between UBC13 and RING-finger E3 ligases

The deamidase OspI from enteric bacteria Shigella flexneri deamidates a glutamine residue in the host ubiquitin-conjugating enzyme UBC13 and converts it to glutamate (Q100E). Consequently, its polyubiquitination activity in complex with the RING-finger ubiquitin ligase TRAF6 and the downstream NF-{kappa}B inflammatory response is inactivated. The precise role of deamidation in inactivating the UBC13/TRAF6 complex is unknown. We report that deamidation inhibits the interaction between UBC13 and TRAF6 RING-domain (TRAF6RING) by perturbing both the native and transient interactions. Deamidation creates a new intramolecular salt-bridge in UBC13 that competes with a critical intermolecular salt-bridge at the native UBC13/TRAF6RING interface. Moreover, the salt-bridge competition prevents transient interactions necessary to form a typical UBC13/RING complex. Repulsion between E100 and the negatively charged surface of RING also prevents transient interactions in the UBC13/RING complex. Our findings highlight a mechanism where a post-translational modification perturbs the conformation and stability of transient complexes to inhibit protein-protein association.

biochemistry↗

bioRxiv2019

Phosphorylation by CK2 increases the SUMO-dependent activity of Cytomegalovirus transactivator IE2

Viral factors manipulate the host post-translational modification (PTM) machinery for replication. Distinctly, phosphorylation and SUMOylation can regulate the activity of human cytomegalovirus (HCMV) protein IE2. However, the molecular mechanism of this process is unknown. Taking a structural, biochemical and cellular approach, we uncover a cross-talk of phosphorylation and SUMOylation exploited by IE2. A scan for the SUMO Interacting Motifs (SIMs) revealed two SIMs in IE2. A real-time SUMOylation assay indicated that the N-terminal SIM (IE2-SIM1) enhanced IE2 SUMOylation up to 4-fold. Kinetic analysis and structural studies proved that IE2 is a SUMO cis-E3 ligase. Two putative CK2 sites adjacent to IE2-SIM1 are phosphorylated in-vitro and in cellular conditions. Phosphorylation drastically increased the IE2/SUMO affinity, IE2-SUMOylation and cis-E3 activity of IE2. Additional salt-bridges between the phosphoserines and SUMO account for the higher IE2/SUMO affinity. Phosphorylation also enhances the SUMO-dependent transactivation activity and auto-repression activity of IE2. Together, our findings highlight a novel mechanism where SUMOylation and phosphorylation of the viral cis-E3 ligase and transactivator protein IE2, works in tandem to enable transcriptional regulation of viral genes.\n\nAuthor summaryThe host protein SUMO is a crucial regulator of cellular processes. Conjugation of other proteins to SUMO by a process called SUMOylation, can change the proteins function or localization and regulate downstream cellular events. The SUMO pathway is exploited by viruses to transcribe viral genes and replicate the viral genome. IE2 is an essential gene of human Cytomegalovirus (HCMV), which acts as a transactivator and helps to transcribe other viral proteins required for viral genome replication and viral assembly. SUMOylation of IE2 is necessary for its function. Here, we have uncovered that IE2 functions as a cis-SUMO-E3 ligase, where a SUMO-Interacting Motif (SIM) in IE2 enhances its SUMOylation. Interestingly, phosphorylation of the SIM in IE2 augments its cis-E3 activity to further increase SUMOylation. Moreover, SIM phosphorylation also enhances the interaction between IE2 and SUMOylated binding partners. Thus, we uncover an exciting process, where phosphorylation enhances both covalent and non-covalent interaction of a protein (IE2) and SUMO. We also observe that the cross-talk of phosphorylation and SUMOylation has significant effects on the transactivation function of IE2. Overall, we discover how a viral protein IE2 exploits crosstalk between SUMOylation and Phosphorylation to enhance its activity and in turn, ensure efficient viral replication.

biochemistry↗

bioRxiv2019

Automated design of highly diverse riboswitches

Riboswitches that couple binding of ligands to recruitment of molecular machines offer sensors and control elements for RNA synthetic biology and medical biotechnology. Current approaches to riboswitch design enable significant changes in output activity in the presence vs. absence of input ligands. However, design of these riboswitches has so far required expert intuition and explicit specification of complete target secondary structures, both of which limit the structure-toggling mechanisms that have been explored. We present a fully automated method called RiboLogic for these design tasks and high-throughput experimental tests of 2,875 molecules using RNA-MaP (RNA on a massively parallel array) technology. RiboLogic designs explore an unprecedented diversity of structure-toggling mechanisms validated through experimental tests. These synthetic molecules consistently modulate their affinity to the MS2 bacteriophage coat protein upon binding of flavin mononucleotide, tryptophan, theophylline, and microRNA miR-208a, achieving activation ratios of up to 20 and significantly better performance than control designs. The data enable dissection of features of structure-toggling mechanisms that correlate with higher performance. The diversity of RiboLogic designs and their quantitative experimental characterization provides a rich resource for further improvement of riboswitch models and design methods.

biophysics↗

bioRxiv2019

Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

Nearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features. To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms.\n\nSignificance statementRNA secondary structure prediction algorithms are routinely used to understand, predict and design functional RNA structures in biology and biotechnology. Given the vast number of RNA sequence and structural features, these predictions rely on a series of approximations, and independent tests are needed to quantitatively evaluate the accuracy of predicted RNA structural stabilities. Here we measure the stabilities of over 1000 RNA constructs by using a coupled protein binding assay. Our results reveal substantial deviations from the RNA stabilities predicted by popular algorithms, and identify factors contributing to the observed deviations. We demonstrate the importance of quantitative, experimental tests of computational RNA structure predictions and present an approach that can be used to routinely test and improve the prediction accuracy.

biophysics↗

bioRxiv2018

Spontaneous driving forces give rise to protein-RNA condensates with coexisting phases and complex material properties

Collective phase transitions, including phase separation and gelation of multivalent protein and RNA molecules appears to underlie the biogenesis of biomolecular condensates such as membraneless organelles. In vivo, these condensates encompass hundreds of distinct types of molecules that are often organized into multi-layered structures supporting the differential partitioning of molecules into distinct regions with distinct material properties. The interplay between driven (active) versus spontaneous (passive) processes that are required for enabling the formation of condensates with coexisting layers of distinct material properties remains unclear. Here, we investigate the role of spontaneous driving forces as determinants of protein-RNA condensates with complex morphologies and distinct material properties. Through the use of systematic in vitro experiments and simulations based on coarse-grained models we find that that the collective interactions among the simplest, biologically relevant proteins and archetypal RNA molecules are sufficient for driving the spontaneous emergence of multi-layered condensates with distinct material properties. Our results demonstrate that key properties of protein-RNA condensates such as their overall morphologies, internal dynamics, and the selective partitioning of substrates are governed specific amino acid chemistries as well as RNA sequence and secondary structure. Our findings yield a clear set of heuristics regarding homo- and heterotypic interactions that are likely to be relevant for understanding the interplay between active and passive processes that control the formation of functional biomolecular condensates.

biophysics↗

bioRxiv2018

In Silico Analysis of Non Synonymous Single Nucleotide Polymorphisms (nsSNPs) of SMPX Gene in Hearing Impairment

BackgroundMutations in SMPX gene can disrupt the normal activity of the SMPX protein which is involved in hearing process.\n\nObjectiveIn this study, deleterious non-synonymous single nucleotide polymorphisms were isolated from the neutral variants by using several bioinformatics tools.\n\nMethodFirstly, dbSNP database hosted by NCBI was used to retrieve the SNPs of SMPX gene, secondly, SIFT was used primarily to screen the damaging SNPs. Further, for validation PROVEAN, PredictSNP and PolyPhen 2 were used. I-Mutant 3 was utilized to analyze the protein stability change and MutPred predicted the molecular mechanism of protein stability change. Finally evolutionary conservation was done to study their conservancy by using ConSurf server.\n\nResultsA total of 26 missense (0.6517%) and 3 nonsense variants (0.075%) were retrieved and among them 4 mutations were found deleterious by all the tools of this experiment and are also highly conserved according to ConSurf server. rs772775896, rs759552778, rs200892029 and rs1016314772 are the reference IDs of deleterious mutations where the substitutions are S71L, N19D, A29T and K54N. Loss of Ubiquitination, loss of methylation, loss of glycosylation, and loss of MoRF binding motifs are the root causes of protein stability change.\n\nConclusionThis is the first study regarding nsSNPs of SMPX gene where the most damaging SNPs were screened that are associated with the SMPX gene and can be used for further research to study their effect on protein structure and function, their dynamic behavior and how they actually affect proteins flexibility.

bioinformatics↗

bioRxiv2018

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

ObjectiveTo validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.\n\nSettingThis cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.\n\nParticipantsFor retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included.\n\nResultsAt severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P<001), SOFA (0.74; P<.001), and SIRS (0.62; P<.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.\n\nConclusionsThe MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.\n\nKEY POINTSO_ST_ABSQuestionC_ST_ABSIs a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?\n\nFindingsIn a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset. In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.\n\nMeaningThe accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes.\n\nSTRENGTHS AND LIMITATIONS OF THIS STUDYO_LIA retrospective study of machine learning severe sepsis prediction from a dataset with 510,497 patient encounters demonstrates high accuracy up to 48 hours prior to onset.\nC_LIO_LIA multicenter clinical study of real-world data using this machine learning algorithm for severe sepsis alerts achieved reductions of in-hospital mortality, length of stay, and 30-day readmissions.\nC_LIO_LIThe required presence of an ICD-9 code to classify a patient as severely septic in our retrospective analysis potentially limits our ability to accurately classify all patients.\nC_LIO_LIOnly adults in US hospitals were included in this study.\nC_LIO_LIFor the real-world section of the study, we cannot eliminate the possibility that implementation of a sepsis algorithm raised general awareness of sepsis within a hospital, which may lead to higher recognition of septic patients, independent of algorithm performance.\nC_LI

bioinformatics↗

bioRxiv2018

Multicenter validation of a machine learning algorithm for 48 hour all-cause mortality prediction

PurposeThis study evaluates a machine-learning-based mortality prediction tool.\n\nMaterials and MethodsWe conducted a retrospective study with data drawn from three academic health centers. Inpatients of at least 18 years of age and with at least one observation of each vital sign were included. Predictions were made at 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated on hold-out test data from the same institution and data from the remaining institutions. Predictions were compared to those of qSOFA and MEWS using area under the receiver operating characteristic curve (AUROC).\n\nResultsFor training and testing on data from a single institution, machine learning predictions averaged AUROCs of 0.97, 0.96, and 0.95 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, the algorithm achieved AUROC up to 0.95, 0.93, and 0.91, for 12-, 24-, and 48-hour predictions, respectively. MEWS and qSOFA had average 48-hour AUROCs of 0.86 and 0.82, respectively.\n\nConclusionThis algorithm may help identify patients in need of increased levels of clinical care.

bioinformatics↗

bioRxiv2018

A quantitative and predictive model for RNA binding by human Pumilio proteins

High-throughput methodologies have enabled routine generation of RNA target sets and sequence motifs for RNA-binding proteins (RBPs). Nevertheless, quantitative approaches are needed to capture the landscape of RNA/RBP interactions responsible for cellular regulation. We have used the RNA-MaP platform to directly measure equilibrium binding for thousands of designed RNAs and to construct a predictive model for RNA recognition by the human Pumilio proteins PUM1 and PUM2. Despite prior findings of linear sequence motifs, our measurements revealed widespread residue flipping and instances of positional coupling. Application of our thermodynamic model to published in vivo crosslinking data reveals quantitative agreement between predicted affinities and in vivo occupancies. Our analyses suggest a thermodynamically driven, continuous Pumilio binding landscape that is negligibly affected by RNA structure or kinetic factors, such as displacement by ribosomes. This work provides a quantitative foundation for dissecting the cellular behavior of RBPs and cellular features that impact their occupancies.

biochemistry↗

bioRxiv2018

RNA tertiary structure energetics predicted by an ensemble model of the RNA double helix

Over 50% of residues within functional structured RNAs are base-paired in Watson-Crick helices, but it is not fully understood how these helices geometric preferences and flexibility might influence RNA tertiary structure. Here, we show experimentally and computationally that the ensemble fluctuations of RNA helices substantially impact RNA tertiary structure stability. We updated a model for the conformational ensemble of the RNA helix using crystallographic structures of Watson-Crick base pair steps. To test this model, we made blind predictions of the thermodynamic stability of >1500 tertiary assemblies with differing helical sequences and compared calculations to independent measurements from a high-throughput experimental platform. The blind predictions accounted for thermodynamic effects from changing helix sequence and length with unexpectedly tight accuracies (RMSD of 0.34 and 0.77 kcal/mol, respectively). These comparisons lead to a detailed picture of how RNA base pair steps fluctuate within complex assemblies and suggest a new route toward predicting RNA tertiary structure formation and energetics.

biophysics↗

bioRxiv2018

Sampling native-like structures of RNA-protein complexes through Rosetta folding and docking

RNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe a Rosetta method called RNP-denovo to simultaneously fold and dock RNA to a protein surface. On a benchmark set of structurally diverse RNA-protein complexes that are not solvable with prior strategies, this fold-and-dock method consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges in which previous methods gave poor results: human telomerase, an RNA methyltransferase with a ribosomal RNA domain, and the spliceosome. When coupled with the same sparse FRET, cross-linking, and functional data used in previous work, RNP-denovo gave models with significantly improved accuracy. These results open a route to computationally modeling global folds of RNA-protein complexes from low-resolution data.

biophysics↗

bioRxiv2018

De novo computational RNA modeling into cryoEM maps of large ribonucleoprotein complexes

RNA-protein assemblies carry out many critical biological functions including translation, RNA splicing, and telomere extension. Increasingly, cryo-electron microscopy (cryoEM) is used to determine the structures of these complexes, but nearly all maps determined with this method have regions in which the local resolution does not permit manual coordinate tracing. Because RNA coordinates typically cannot be determined by docking crystal structures of separate components and existing structure prediction algorithms cannot yet model RNA-protein complexes, RNA coordinates are frequently omitted from final models despite their biological importance. To address these omissions, we have developed a new framework for De novo Ribonucleoprotein modeling in Real-space through Assembly of Fragments Together with Electron density in Rosetta (DRRAFTER). We show that DRRAFTER recovers near-native models for a diverse benchmark set of small RNA-protein complexes, as well as for large RNA-protein machines, including the spliceosome, mitochondrial ribosome, and CRISPR-Cas9-sgRNA complexes where the availability of both high and low resolution maps enable rigorous tests. Blind tests on yeast U1 snRNP and spliceosomal P complex maps demonstrate that the method can successfully build RNA coordinates in real-world modeling scenarios. Additionally, to aid in final model interpretation, we present a method for reliable in situ estimation of DRRAFTER model accuracy. Finally, we apply this method to recently determined maps of telomerase, the HIV-1 reverse transcriptase initiation complex, and the packaged MS2 genome, demonstrating that DRRAFTER can be used to accelerate accurate model building in challenging cases.

biophysics↗

bioRxiv2018

Ancient ancestry informative markers for identifying fine-scale ancient population structure in Eurasians

The rapid accumulation of ancient human genomes from various areas and time periods potentially allows the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day populations known to misrepresent the population structure. Past studies addressed these problems using ancestry informative markers (AIMs). However, it is unclear whether AIMs derived from contemporary human genomes can capture ancient population structure and whether AIM finding methods are applicable to ancient DNA (aDNA) provided that the high missingness rates in ancient, oftentimes haploid, DNA can also distort the population structure. Here, we define ancient AIMs (aAIMs) and develop a framework to evaluate established and novel AIM-finding methods in identifying the most informative markers. We show that aAIMs identified by a novel principal component analysis (PCA)-based method outperforms all competing methods in classifying ancient individuals into populations and identifying admixed individuals. In some cases, predictions made using the aAIMs were more accurate than those made with a complete marker set. We discuss the features of the ancient Eurasian population structure and strategies to identify aAIMs. This work informs the design of population microarrays and the interpretation of aDNA results.

genetics↗

bioRxiv2018

EternaBrain: Automated RNA design through move sets from an Internet-scale RNA videogame

Emerging RNA-based approaches to disease detection and gene therapy require RNA sequences that fold into specific base-pairing patterns, but computational algorithms generally remain inadequate for these secondary structure design tasks. The Eterna project has crowdsourced RNA design to human video game players in the form of puzzles that reach extraordinary difficulty. Here, we present an eternamoves-large repository consisting of 1.8 million of player moves on 12 of the most-played Eterna puzzles as well as an eternamoves-select repository of 30,477 moves from the top 72 players on a select set of more advanced puzzles. On eternamoves-select, a multilayer convolutional neural network (CNN) EternaBrain achieves test accuracies of 51% and 34% in base prediction and location prediction, respectively, suggesting that top players moves are partially stereotyped. We then show that while this CNNs move predictions are not enough to solve numerous new puzzles, inclusion of six additional strategies compiled by human players solves 61 out of 100 independent puzzles in the Eterna100 benchmark. This EternaBrain-SAP performance is better than previously published methods and in the middle of the performance range of newer algorithms developed by Eterna participants and other groups. Our study provides useful lessons for efforts to achieve human-competitive performance with automated RNA design algorithms.

bioinformatics↗