bioRxiv Science⌕ Search

Biology subjects

Perez, A.

Publications and source records attributed to Perez, A..

52 records · Page 3Linked to original sources

Structure determination of protein-peptide complexes from NMR chemical shift data using MELD.

Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding upon binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine structures of peptide-protein complexes. Here, we demonstrate this approach for polypeptide complexes formed with the extraterminal (ET) domain of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can form different conformations (e.g., helices and hairpins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding poses, using only backbone chemical shift data, in excellent agreement with experimentally-determined structures. The approach also predicts relative binding affinities of different peptides. This hybrid MELD-NMR approach provides a powerful new tool for structural analysis of protein-polypeptide complexes in the low NMR information content regime, which can be used successfully for flexible systems where one polypeptide binding partner folds upon complex formation.

biophysics↗

Hidden structure in disordered proteins is adaptive to intracellular changes

Intrinsically disordered proteins and protein regions (IDPs) are essential to cellular function in all proteomes. Unlike folded proteins, IDPs exist in an ensemble of rapidly interchanging conformations. IDP sequences encode interactions that create structural biases within the ensemble. Such structural biases determine the three-dimensional shape of IDP ensembles and can affect their activity. However, the plasticity and sensitivity of IDP ensembles means structural biases, often measured in vitro, may differ in the dynamic and heterogeneous intracellular environment. Here we reveal that structural biases found in vitro in well-studied IDPs persist inside human-derived cells. We further show that a subset of IDPs are able to sense changes in cellular physical-chemical composition and modulate their ensemble in response. We propose that IDP ensembles can evolve to sense and respond to intracellular physicochemical changes, or to resist them. This property can be leveraged for biological function, be the underlying cause of IDP-driven pathology, or be leveraged for the design of disorder-based biosensors and actuators.

biophysics↗

Hematopoietic recovery after transplantation is primarily derived from the stochastic contribution of hematopoietic stem cells

Reconstitution after hematopoietic stem cell (HSC) transplantation is assumed to occur in two distinct phases: initial recovery mediated by short-term progenitors and long-term repopulation by multipotent HSCs which do not contribute to hematopoietic reconstitution during the first 6-9 months. We have previously reported the transplantation and exclusive engraftment of the HSC-enriched CD34+CD45RA-CD90+ phenotype in a nonhuman primate model. Here, we closely followed the clonal diversity and kinetics in these animals. Enhanced sampling and high density clonal tracking within the first 3 month revealed that multipotent HSCs actively contributed to the early phases of neutrophil recovery and became the dominant source for blood cells as early as 50 days after transplant. Longitudinal changes in clonal diversity supported a stochastic engraftment of HSCs with the majority of HSCs clones vanishing early during neutrophil recovery and a smaller fraction of HSC clones expanding into bigger pools to support long-term hematopoiesis. In contrast to the bi-phasic model, we propose that hematopoietic recovery after myeloablation and transplantation is primarily derived from HSCs in a stochastic manner rather than in two phases by independent cell populations.

cell biology↗

Improving the analysis of biological ensembles through extended similarity measures

We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically interest. As computer power increases we capture larger ensembles and larger conformational transitions between states. Typically, structural clustering provides the statistical mechanics treatment of the system to identify relevant biological states. The key advantage of our approach is that the newly introduced extended similiarity indices reduce the computational complexity of assessing the similarity of a set of structures from O(N2) to O(N). Here we take advantage of this favorable cost to develop several highly efficient techniques, including a linear-scaling algorithm to determine the medoid of a set (which we effectively use to select the most representative structure of a cluster). Moreover, we use our extended similarity indices as a linkage criterion in a novel hierarchical agglomerative clustering algorithm. We apply these new metrics to analyze the ensembles of several systems of biological interest such as folding and binding of macromolecules (peptide,protein,DNA -protein). In particular, we design a new workflow that is capable of identifying the most important conformations contributing to the protein folding process. We show excellent performance in the resulting clusters (surpassing traditional linkage criteria), along with faster performance and an efficient cost-function to identify when to merge clusters.

biophysics↗

MELD-DNA: A new tool for capturing protein-DNA binding

Herein we present MELD-DNA, a novel computational approach to address the problem of protein-DNA structure prediction. This method addresses well-known issues hampering current computational approaches to bridge the gap between structural and sequence knowledge, such as large conformational changes in DNA and highly charged electrostatic interaction during binding. MELD-DNA is able to: i) sample multiple binding modes, ii) identify the preferred binding mode from the ensembles, and iii) provide qualitative binding preferences between DNA sequences. We expect the results presented herein will have impact in the field of biophysics (through new software development), structural biology (by complementing DNA structural databases) and supramolecular chemistry (by bringing new insights into protein-DNA interactions).

biophysics↗

Modulating glial genes involved in synaptic function mitigates pathogenesis and behavioral deficits in a Drosophila model of Huntington's Disease

Most research on neurodegenerative diseases has focused on neurons, yet glia help form and maintain the synapses whose loss is so prominent in these conditions. To investigate the contributions of glia to Huntingtons disease (HD), we studied transcriptomic changes in HD human, HD mice, and Drosophila expressing human mutant Huntingtin (mHTT) in either glia, neurons or both. A large portion of conserved genes are concordantly dysregulated across the three species; we tested these genes in a high-throughput behavioral assay and found that downregulation of genes involved in synapse assembly mitigated pathogenesis and behavioral deficits. To our surprise, mitigating glial pathogenesis by dNRXN3 knockdown was sufficient to improve the phenotype of flies expressing mHTT in neurons, suggesting that mHTTs toxic effects in glia ramify throughout the brain. This supports a model in which dampening synaptic function is protective because it attenuates the excitotoxicity that characterizes HD.

systems biology↗

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↗

Unilateral vocal nerve resection alters neurogenesis in the avian song system in a region-specific manner

New neurons undergo a critical period soon after migration during which the behavior of the animal may result in the survival or culling of these cells. In the songbird song system, new neurons may be maintained in the song motor pathway with respect to motor progression toward a target song--during juvenile song learning, seasonal song restructuring, and experimentally manipulated song variability. However, it is not known whether the quality of song per se, without progressive improvement, may also influence new neuron survival. To test this idea, we experimentally altered song acoustic structure by unilateral denervation of the syrinx. We found no effect of aberrant song on numbers of new neurons in the HVC of the song motor pathway, a loss of left-side dominance in new neurons in the auditory region caudomedial nidopallium (NCM), and a bilateral decrease in new neurons in the basal ganglia nucleus Area X. We propose new neuron survival may be determined in response to behavioral feedback in accordance with the function of new neurons within a given brain region. Studying the effects of singing behaviors on new neurons across multiple brain regions that subserve singing may give rise to general rules underlying the regulation of new neuron survival across taxa and brain regions more broadly.

neuroscience↗

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts.

bioengineering↗

A machine learning approach predicts essential genes and pharmacological targets in cancer

Loss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated drugs approved for cancer versus those that have not, and can thus be leveraged to identify potential cancer repurposing opportunities. Overall, ECLIPSE allows for a more comprehensive analysis of gene essentiality and drug development; which neither platform can achieve alone.

cancer biology↗

CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics

Cryo-EM is a powerful method for determining protein structures. But it requires computational assistance. Physics-based computations have the power to give low-free-energy structures and ensembles of populations, but have been computationally limited to only small soluble proteins. Here, we introduce CryoFold. By integrating data of varying sparsity from electron density maps of 3-5 [A] resolution with coarse-grained physical knowledge of secondary and tertiary interactions, CryoFold determines ensembles of protein structures directly from sequence. We give six examples showing its broad capabilities, over proteins ranging from 72 to 2000 residues, including membrane and multi-domain proteins, and including results from two EMDB competitions. The ensembles CryoFold predicts starting from the density data of a single known protein conformation encompass multiple low-energy conformations, all of which are experimentally validated and biologically relevant.

biophysics↗

Neuroanatomical Correlates In Bilinguals: The Case Of Children And Elderly

How bilingualism modulates brain areas beyond the language regions is still controversial. Through a comprehensive set of analyses on brain structure, we investigated brain differences between Basque-Spanish bilinguals and monolinguals in children and the elderly, the most sensitive target groups to detect potential brain differences. In particular, we employed Diffusion MRI in combination with T1-MRI, network-based statistics and a graph-theoretical approach to investigate differences between bilinguals and monolinguals in structural connectivity and topological properties of brain networks. Additionally, regional grey and white matter structural differences between groups were examined. The findings suggest that the effects of bilingualism on brain structure are not solid but unstable. However, lifetime experience of active bilingualism may lead to increased neural reserve in ageing, since better global network graph-efficiency has been observed in the elderly lifelong bilinguals compared to monolinguals.

neuroscience↗

Mapping changes in the spatiotemporal distribution of lumpy skin disease virus

Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread across many Eastern European countries as well as Russia and Kazakhstan. This rapid expansion highlights the emergent nature of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread in Eurasia through a) analysis of environmental factors conducive for LSDV and b) estimate of the underlying LSDV risk using a fine spatiotemporally explicit Bayesian hierarchical model on LSDV outbreak occurrence information. We used ecological niche modeling to estimate the potential distribution of LSDV outbreaks for 2014-2016. This analysis resulted in a spatial representation of environmental limits where, if introduced, LSDV is expected to efficiently spread. The Bayesian space-time model incorporated both environmental factors and the changing spatiotemporal distribution of the disease to capture the dynamics of disease spread and predict areas in which there is an increased risk of LSDV occurrence. Variables related to the average temperature, precipitation, wind speed, as well as land cover and host densities were found to be important drivers explaining the observed distribution of LSDV in both modeling approaches. Areas of elevated LSDV risks were identified mainly in Russia, Turkey, Serbia, and Bulgaria. Results suggest that prevailing ecological conditions may be compatible with further spread of LSDV in Eurasia, though models should be continually updated to reflect the current epidemiologic conditions. The results presented here advance our understanding of the ecological requirements of LSDV in temperate regions and may help in the design and implementation of prevention and surveillance strategies in the region.

epidemiology↗

The role of African buffalo in the epidemiology of foot-and-mouth disease in sympatric cattle and buffalo populations in Kenya

Transmission of pathogens at wildlife-livestock interfaces poses a substantial challenge to the control of infectious diseases, including for foot-and-mouth disease virus (FMDV) in African buffalo and cattle. The extent to which buffalo play a role in the epidemiology of this virus in livestock populations remains unresolved in East Africa. Here, we show that FMDV occurs at high seroprevalence (~77%) in Kenyan buffalo. In addition, we recovered 80 FMDV VP1 sequences from buffalo, all of which were serotype SAT1 and SAT2, and seventeen FMDV VP1 sequences from cattle, which included serotypes A, O, SAT1 and SAT2. Notably, six individual buffalo were co-infected with both SAT1 and SAT2 serotypes. Our results suggest that transmission of FMDV between sympatric cattle and buffalo is rare. However, viruses from FMDV outbreaks in cattle elsewhere in Kenya were caused by viruses closely related to SAT1 and SAT2 viruses found in buffalo. We also show that the circulation of FMDV in buffalo is influenced by fine-scale geographic features, such as rivers, and that social segregation amongst sympatric herds may limit between-herd transmission. Our results significantly advance knowledge of the ecology and molecular epidemiology of FMDV at wildlife-livestock interfaces in Eastern Africa, and will help to inform the design of control and surveillance strategies for this disease in the region.

epidemiology↗

High accuracy protein structures from minimal sparse paramagnetic solid-state NMR restraints

There is a pressing need for new computational tools to integrate data from diverse experimental approaches in structural biology. We present a strategy that combines sparse paramagnetic solid-state NMR restraints with physics-based atomistic simulations. Our approach explicitly accounts for uncertainty in the interpretation of experimental data through the use of a semi-quantitative mapping between the data and the restraint energy that is calibrated by extensive simulations. We apply our approach to solid-state NMR data for the model protein GB1 labeled with Cu2+-EDTA at six different sites. We are able to determine the structure to ca. 1 [A] accuracy within a single day of computation on a modest GPU cluster. We further show that in 4 of 6 cases, the data from only a single paramagnetic tag are sufficient to fold the protein to high accuracy.

biochemistry↗

Badnaviruses Of Sweetpotato: Symptomless Co-Inhabitants On A Global Scale

Sweetpotato is among the most important root-crops worldwide, particularly in developing countries, and its production is affected severely by a variety of virus diseases. During the last decade a number of new viruses have been discovered in sweetpotatoes from different continents through next generation sequencing studies, among them belonging to the genus Badnavirus and collectively assigned to the species Sweet potato pakkakuy virus (SPPV). We determined the complete genome sequence of two SPPV isolates and show the ubiquitous presence of similar viruses in germplasm and field material from around the globe. We show SPPV is not integrated into the sweetpotato genome, occurs only at extremely low titers but is nevertheless efficiently transmitted through seeds and cuttings. They are unaffected by virus elimination therapy and lack any discernible symptoms in sweetpotatoes or indicator host plants. Nevertheless, they show considerable variation in their nucleotide sequences and correspond to several genetic lineages. Studies of their interaction with the two most important sweetpotato viruses showed only limited synergistic increase in the titres of one of two SPPV isolates. We contend that these viruses may pose little threat to sweetpotato production and more likely represent a new type of persistent virus in a possibly commensal or mutualistic relationship with sweetpotato.\n\nImportanceNext generation sequencing approaches have in the last few years led to the discovery of many virus like sequences in different crop plants including sweetpotatoes. The significance of such discoveries can sometimes be elusive when they have not been associated with specific symptoms due to mixed infections or have been found in apparently healthy plants. Badnavirus sequences found in sweetpotatoes provide a typical case. Considering they have now been reported globally, it was important to determine how common these viruses are and what their possible impact may be on sweetpotato production. The significance of our research lies in resolving the case of badnaviruses, providing evidence they represent a new type of vertically transmitted persistent and apparently harmless episomal viruses living in a state of commensalism with their host.

plant biology↗