SEARCH · Search bioRxiv

Results for “Molecular Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,747 records · Page 97Linked to original sources

bioRxiv2018

scMetric: An R package of metric learning and visualization for single-cell RNA-seq data

Single cell RNA-seq data provide high-dimensional transcriptome features of each cell and enables the systematic study of cell types and cell states based on molecular characteristics, which are usually regarded as clusters in the high-dimensional space. Usually not all genes are equally informative for the studied biological question and the clusters are embedded in some lower dimensional subspaces.\n\nThere are several popular dimensionality reduction and visualization methods for scRNA-seq data such as PCA and t-SNE1. Different methods may utilize different kinds of distance metrics. Some methods take distance metric as a hyper-parameter for users to choose. Different metrics can result in different data distribution in the low dimensional space2 and therefore result in different final results. But there is no way to know which metric is \"the correct one\" for the particular biological study at hand. A typical practice is to ...

bioinformatics↗

bioRxiv2019

Candidate regulators and target genes of drought stress in needles and roots of Norway spruce

Drought stress impacts on seedling establishment, survival and whole-plant productivity. Drought stress responses have been extensively studied at the physiological and molecular level in angiosperms, particularly in agricultural species and the model Arabidopsis thaliana, with the vast majority of work performed on aboveground tissues. Boreal forests are dominated by coniferous tree species and cover vast areas of the terrestrial surface. These areas are predicted to be particularly influenced by ongoing climate change and will be exposed to more frequent and acute drought. The associated impact at all stages of the forest tree life cycle is expected to have large-scale ecological and economic impacts. To provide a comprehensive understanding of the drought response mechanisms of Picea abies seedlings, we assayed the physiological response of needles and transcriptional responses of roots and needles after exposure to mild and severe drought. Shoots and needles showed extensive reversible plasticity for physiological measures indicative of drought response mechanisms, including stomatal conductance (gs) and shoot water potential. Root and needle transcriptional responses contrasted, with an extensive root-specific down-regulation of growth. When we compared the responses of P. abies with previously-characterised A. thaliana drought response genes, we found that the majority of the genes were conserved across lineages. However, in P. abies, transcription factors (TFs) previously identified as belonging to the ABA-dependent pathway had a more limited role and most differentially expressed genes were specific to the stress response of P. abies. These results highlight the importance of profiling both above- and below-ground tissues and provide a comprehensive framework to advance understanding of the drought response mechanism of P. abies.\n\nOne sentence summaryAnalysis of the drought transcriptome of Norway spruce reveals divergent molecular response pathways in conifers.

plant biology↗

bioRxiv2026

Proteome-wide crosslinking mass spectrometry reveals novel components of essential complexes in Toxoplasma

Protein-protein interactions underpin nearly all cellular processes, yet systematic definition of these networks remains limited outside a few model organisms. As a result, the architectures of essential complexes in many divergent lineages remain poorly characterized. Here we developed a high-coverage crosslinking mass spectrometry framework to map the proteome-wide interactome of the model apicomplexan parasite Toxoplasma gondii. From 29,624 crosslinked peptide pairs, we resolved a network of 2,859 protein-protein interactions that we integrated with structural modeling to resolve interaction interfaces. We identified and validated previously unrecognized components of essential protein complexes, including a structurally distinct ATP synthase subcomplex containing a highly divergent, apicomplexan-specific subunit essential for parasite fitness. Beyond revealing unexpected diversification of core mitochondrial machinery, these findings provide a general strategy to define the molecular architecture of divergent organisms and represent a foundational resource for hypothesis generation, structural inference, and discovery of lineage-specific vulnerabilities in pathogen biology.

microbiology↗

bioRxiv2018

Quantitative Analysis of Interactive Behavior of Mitochondria and Lysosomes Using Structured Illumination Microscopy

Super-resolution optical microscopy has extended the spatial resolution of cell biology from the cellular level to the nanoscale, enabling the observation of the interactive behavior of single mitochondria and lysosomes. Quantitative parametrization of interaction between mitochondria and lysosomes under super-resolution optical microscopy, however, is currently unavailable, which has severely limited our understanding of the molecular machinery underlying mitochondrial functionality. Here, we introduce an M-value to quantitatively investigate mitochondria and lysosome contact (MLC) and mitophagy under structured illumination microscopy. We found that the M-value for an MLC is typically less than 0.4, whereas in mitophagy it ranges from 0.5 to 1.0. This system permits further investigation of the detailed molecular mechanism governing the interactive behavior of mitochondria and lysosomes.

cell biology↗

bioRxiv2016

Network based conditional genome wide association analysis of human metabolomics

BackgroundGenome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics ( omics) including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and, potentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods.\n\nResultsWe propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.\n\nConclusionsWe demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific contra-intuitive pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.\n\nShort abstractWe propose a network-based, conditional approach for genome-wide analysis of multivariate omics phenotypes. Our methods can incorporate prior biological knowledge about biological pathways from external sources. We evaluated our approach using metabolomics data and demonstrated that our approach has bigger power and allows for identification of additional loci. We show that gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.

genomics↗

bioRxiv2018

Stabilized Independent Component Analysis outperforms other methods in finding reproducible signals in tumoral transcriptomes

MotivationMatrix factorization methods are widely exploited in order to reduce dimensionality of transcriptomic datasets to the action of few hidden factors (metagenes). Applying such methods to similar independent datasets should yield reproducible inter-series outputs, though it was never demonstrated yet.\n\nResultsWe systematically test state-of-art methods of matrix factorization on several transcriptomic datasets of the same cancer type. Inspired by concepts of evolutionary bioinformatics, we design a new framework based on Reciprocally Best Hit (RBH) graphs in order to benchmark the methods reproducibility. We show that a particular protocol of application of Independent Component Analysis (ICA), accompanied by a stabilisation procedure, leads to a significant increase in the inter-series output reproducibility. Moreover, we show that the signals detected through this method are systematically more interpretable than those of other state-of-art methods. We developed a user-friendly tool BIODICA for performing the Stabilized ICA-based RBH meta-analysis. We apply this methodology to the study of colorectal cancer (CRC) for which 14 independent publicly available transcriptomic datasets can be collected. The resulting RBH graph maps the landscape of interconnected factors that can be associated to biological processes or to technological artefacts. These factors can be used as clinical biomarkers or robust and tumor-type specific transcriptomic signatures of tumoral cells or tumoral microenvironment. Their intensities in different samples shed light on the mechanistic basis of CRC molecular subtyping.\n\nAvailabilityThe BIODICA tool is available from https://github.com/LabBandSB/BIODICA.\n\nContactlaura.cantini@curie.fr and andrei.zinovyev@curie.fr\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2018

Ribosomal protein imbalance launches a C/EBP-based program to preserve tissue integrity

Genes encoding ribosomal proteins are expressed at rate limiting levels, rendering their biological function highly sensitive to the copy-number variation that results from genomic instability. Cells with a reduced number of ribosomal protein genes (RPGs) are eliminated, when intermingled with wild type cells, via a process known as cell competition. The mechanisms underlying this phenomenon are poorly understood. Here we report the function of a CCAAT-Enhancer-Binding Protein (C/EBP), Xrp1, that is critically required for the elimination of cells with a hemizygous RPG genotype. In such cells, Xrp1 is transcriptionally upregulated by an autoregulatory loop and is able to trigger cell elimination. Since genomic instability is likely to cause the loss of a haploinsufficient RPG, we propose a molecular model of how RPGs, together with a C/EBP-dependent transcriptional program, could preserve the genomic integrity of tissues.

cancer biology↗

bioRxiv2017

A Parallel Processing Model of Drosophila Olfactory Sensory Neurons and Its Biological Validation

In the past two decades, a substantial amount of work characterized the odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized and, the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined.\n\nIdentity and concentration, two key aspects of olfactory coding, originate in the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model identity and concentration in OTP using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the ligand binding/dissociation process. We model the BSG as a Connor-Stevens point neuron.\n\nThe resulting combinatorial encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the combinatorial complexity of the transformation taking place in the antennae. The concentration-dependent combinatorial code determines the complexity of the input space driving olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiology recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

neuroscience↗

bioRxiv2015

Cancer classification in the genomic era: five contemporary problems

Classification is an everyday instinct as well as a full-fledged scientific discipline. Throughout the history of medicine, disease classification is central to how we develop knowledge, make diagnosis, and assign treatment. Here we discuss the classification of cancer, the process of categorizing cancer subtypes based on their observed clinical and biological features. Traditionally, cancer nomenclature is primarily based on organ location, e.g., \"lung cancer\" designates a tumor originating in lung structures. Within each organ-specific major type, finer subgroups can be defined based on patient age, cell type, histological grades, and sometimes molecular markers, e.g., hormonal receptor status in breast cancer, or microsatellite instability in colorectal cancer. In the past 15+ years, high-throughput technologies have generated rich new data regarding somatic variations in DNA, RNA, protein, or epigenomic features for many cancers. These data, collected for increasingly large tumor collections, have provided not only new insights into the biological diversity of human cancers, but also exciting opportunities to discover previously unrecognized cancer subtypes. Meanwhile, the unprecedented volume and complexity of these data pose significant challenges for biostatisticians, cancer biologists, and clinicians alike. Here we review five related issues that represent contemporary problems in cancer taxonomy and interpretation. 1. How many cancer subtypes are there? 2. How can we evaluate the robustness of a new classification system? 3. How are classification systems affected by intratumor heterogeneity and tumor evolution? 4. How should we interpret cancer subtypes? 5. Can multiple classification systems coexist? While related issues have existed for a long time, we will focus on those aspects that have been magnified by the recent influx of complex multi-omics data. Ongoing exploration of these problems is essential for data-driven refinement of cancer classification and the successful application of these concepts in precision medicine.

Genomics↗

bioRxiv2019

Meta-analysis of the human brain transcriptome identifies heterogeneity across human AD coexpression modules robust to sample collection and methodological approach

Alzheimers disease (AD) is a complex and heterogenous brain disease that affects multiple inter-related biological processes. This complexity contributes, in part, to existing difficulties in the identification of successful disease-modifying therapeutic strategies. To address this, systems approaches are being used to characterize AD-related disruption in molecular state. To evaluate the consistency across these molecular models, a consensus atlas of the human brain transcriptome was developed through coexpression meta-analysis across the AMP-AD consortium. Consensus analysis was performed across five coexpression methods used to analyze RNA-seq data collected from 2114 samples across 7 brain regions and 3 research studies. From this analysis, five consensus clusters were identified that described the major sources of AD-related alterations in transcriptional state that were consistent across studies, methods, and samples. AD genetic associations, previously studied AD-related biological processes, and AD targets under active investigation were enriched in only three of these five clusters. The remaining two clusters demonstrated strong heterogeneity between males and females in AD-related expression that was consistently observed across studies. AD transcriptional modules identified by systems analysis of individual AMP-AD teams were all represented in one of these five consensus clusters except ROS/MAP-identified Module 109, which was specific for genes that showed the strongest association with changes in AD-related gene expression across consensus clusters. The other two AMP-AD transcriptional analyses reported modules that were enriched in one of the two sex-specific Consensus Clusters. The fifth cluster has not been previously identified and was enriched for genes related to proteostasis. This study provides an atlas to map across biological inquiries of AD with the goal of supporting an expansion in AD target discovery efforts.

systems biology↗

bioRxiv2013

Cycling Physicochemical Gradients as ‘Evolutionary Drivers’: From Complex Matter to Complex Living States

HighlightsO_LIBiological complexity cannot be reduced to chemistry and physics\nC_LIO_LIComplex living states are: multicomponent, multiphase, crowded, and re-emergent\nC_LIO_LILiving states arise naturally only by the action of cycling physicochemical gradients\nC_LIO_LIBacterial cells can be modeled as viscoelastic capacitors with sol-gel transitions\nC_LIO_LIEvolving living states can be investigated via biotic soup experimentation\nC_LIO_LIDarwinian evolution arises from the process errors of the cell cycle\nC_LIO_LISynthetic biology heralds the transition from unintentional Darwinian evolution to intentional anthropic evolution\nC_LI\n\nAbstractWithin the overlap of physics, chemistry and biology, complex matter becomes more deeply understood when high level mathematics converts regularities of experimental data into scientific laws, theories, and models (Krakauer et al., 2011. The challenges and scope of theoretical biology. J. Theoret. Biol. 276: 269-276). The simplest kinds of complex biological matter are bacterial cells; they appear complex-from a physicochemical standpoint-because they are multicomponent, multiphase, biomacromolecularly crowded, and re-emergent; the property of re-emergence differentiates biological matter from complex chemical and physical matter.\n\nBacterial cells cannot self-reassemble spontaneously from their biomolecules and biomacromolecules (via non-covalent molecular forces) without the action of external drivers; on Earth, such drivers have been diurnal (cycling) physicochemical gradients, i.e. temperature, water activity, etc. brought about by solar radiation striking the Earths rotating surface. About 3.5 billion years ago, these cycling gradients drove complex chemical prebiotic soups toward progenotic living states from which extant bacteria evolved (Spitzer and Poolman, 2009; The role of biomacromolecular crowding, ionic strength and physicochemical gradients in the complexities of lifes emergence. Microbiol. Mol. Biol. Revs. 73:371-388). Thus there is historical non-equilibrium continuity between complex dead chemical matter and complex living states of bacterial cells. This historical continuity becomes accessible to present-day experimentation, when cycling physicochemical gradients act on dead biomacromolecules obtained from (suitably) killed bacterial populations - on a biotic soup of chemicals (Harold, 2005, Molecules into cells: specifying spatial architecture. Microbiol. Mol. Biol. Rev. 69:544-564). The making of biotic soups and recovering living states from them is briefly discussed in terms of novel concepts and experimental possibilities.\n\nIn principle, emergent living states contingently arise and evolve when cycling physicochemical gradients continuously act on complex chemical mass; once living states become dynamically stabilized, the inevitable process errors of primitive cell cycles become the roots of Darwinian evolution.

Biophysics↗

bioRxiv2015

PrediXcan: Trait Mapping Using Human Transcriptome Regulation

Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individuals genetic profile and correlates the \"imputed\" gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple testing burden, more comprehensive annotation of gene function compared to that derived from single variants, and a principled approach to the design of follow-up experiments while also integrating knowledge of regulatory function. Since no actual expression data are used in the analysis of GWAS data - only in silico expression - reverse causality problems are largely avoided. PrediXcan harnesses reference transcriptome data for disease mapping studies. Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

Genomics↗

bioRxiv2016

A mathematical approach for secondary structure analysis can provide an eyehole to the RNA world

The RNA pseudoknot is a conserved secondary structure encountered in a number of ribozymes, which assume a central role in the RNA world hypothesis. However, RNA folding algorithms could not predict pseudoknots until recently. Analytic combinatorics - a newly arisen mathematical field - has introduced a way of enumerating different RNA configurations and quantifying RNA pseudoknot structure robustness and evolvability, two features that drive their molecular evolution. I will present a mathematicians viewpoint of RNA secondary structures, and explain how analytic combinatorics applied on RNA sequence to structure maps can represent a valuable tool for understanding RNA secondary structure evolution. Analytic combinatorics can be implemented for the optimization of RNA secondary structure prediction algorithms, the derivation of molecular evolution mathematical models, as well as in a number of biotechnological applications, such as biosensors, riboswitches etc. Moreover, it showcases how the integration of biology and mathematics can provide a different viewpoint into the RNA world.

Systems Biology↗

bioRxiv2018

Proteomic Analysis of NRROS Interactome Reveals the Presence of Chaperones and Mediators of the ERAD Pathway

Negative regulator of reactive oxygen species (NRROS, previously called LRRC33) is a leucine-rich repeat (LRR) domain containing, ER-resident transmembrane protein expressed primarily in lymphoid organs, especially in myeloid cells. We have previously demonstrated that NRROS regulates reactive oxygen species production by phagocytic cells by mediating degradation of NOX2 (gp91phox), a component of NOX2 complex responsible for the oxidative burst in these cells. Since LRR is the only functional domain in NRROS, it is likely to interact with other proteins for its biological functions. Here, by performing immunoprecipitation of NRROS and mass spectrometric analysis, we describe the NRROS interactome in macrophages and demonstrate that NRROS interacts with molecular chaperones/co-chaperones and mediators of the endoplasmic reticulum associated degradation (ERAD) pathway such as calnexin, suggesting a broader role for NRROS in protein biosynthesis and the ER quality control machinery.

biochemistry↗

bioRxiv2018

Self-assembling Manifolds in Single-cell RNA Sequencing Data

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is a challenging task. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

bioinformatics↗

bioRxiv2018

Microtubules Gate Tau Condensation to Spatially Regulate Microtubule Functions

Tau is an abundant microtubule-associated protein in neurons. Tau aggregation into insoluble fibrils is a hallmark of Alzheimers disease and other dementias, yet the physiological state of tau molecules within cells remains unclear. Using single molecule imaging, we directly observe that the microtubule lattice regulates reversible tau self-association, leading to dynamic condensation of tau molecules on the microtubule surface. Tau condensates form selectively permissible barriers, spatially regulating the activity of MT severing enzymes and the movement of molecular motors through their boundaries. We propose that reversible self-association of tau molecules, controlled by the microtubule, is an important mechanism of taus biological functions, and that oligomerization of tau is a common property shared between the physiological and disease forms of the molecule.\n\nOne Sentence SummaryReversible tau oligomerization regulates microtubule functions.

biophysics↗

bioRxiv2018

Domain topology, stability, and translation speed determine mechanical force generation on the ribosome

The concomitant folding of a nascent protein domain with its synthesis can generate mechanical forces that act on the ribosome and alter translation speed. Such changes in speed can affect the structure and function of the newly synthesized protein as well as cellular phenotype. The domain properties that govern force generation have yet to be identified and understood, and the influence of translation speed is unknown as all reported measurements have been carried out on arrested ribosomes. Here, using coarse-grained molecular simulations and statistical mechanical modeling of protein synthesis, we demonstrate that force generation is determined by a domains stability and topology, as well as translation speed. The statistical mechanical models we create predict how force profiles depend on these properties. These results indicate that force measurements on arrested ribosomes will not always accurately reflect what happens in a cell, especially for slow-folding domains, and suggest the possibility that certain domain properties may be enriched or depleted across the structural proteome of organisms through evolutionary selection pressures to modulate protein synthesis speed and post-translational protein behavior.\n\nSignificance StatementMechanochemistry, the influence of molecular-scale mechanical forces on chemical processes, can occur on actively translating ribosomes through the force-generating actions of motor proteins and the co-translational folding of domains. Such forces are transmitted to the ribosomes catalytic core and alter rates of protein synthesis; representing a form of mechanical allosteric communication. These changes in translation-elongation kinetics are biologically important because they can influence protein structure, function, and localization within a cell. Many fundamental questions are unresolved concerning the properties of protein domains that determine mechanical force generation, the effect of translation speed on this force, and exactly how, at the molecular level, force is generated. In this study we answer these questions using cutting-edge molecular simulations and statistical mechanical modeling.

biophysics↗

bioRxiv2016

Bridging microscopic and macroscopic mechanisms of p53-MDM2 binding using molecular simulations and kinetic network models

Under normal cellular conditions, the tumor suppressor protein p53 is kept at a low levels in part due to ubiquitination by MDM2, a process initiated by binding of MDM2 to the intrinsically disordered transactivation domain (TAD) of p53. Although many experimental and simulation studies suggest that disordered domains such as p53 TAD bind their targets nonspecifically before folding to a tightly-associated conformation, the molecular details are unclear. Toward a detailed prediction of binding mechanism, pathways and rates, we have performed large-scale unbiased all-atom simulations of p53-MDM2 binding. Markov State Models (MSMs) constructed from the trajectory data predict p53 TAD peptide binding pathways and on-rates in good agreement with experiment. The MSM reveals that two key bound intermediates, each with a non-native arrangement of hydrophobic residues in the MDM2 binding cleft, control the overall on-rate. Using microscopic rate information from the MSM, we parameterize a simple four-state kinetic model to (1) determine that induced-fit pathways dominate the binding flux over a large range of concentrations, and (2) predict how modulation of residual p53 helicity affects binding, in good agreement with experiment. These results suggest new ways in which microscopic models of bound-state ensembles can be used to understand biological function on a macroscopic scale.\n\nAUTHOR SUMMARYMany cell signaling pathways involve protein-protein interactions in which an intrinsically disordered peptide folds upon binding its target. Determining the molecular mechanisms that control these binding rates is important for understanding how such systems are regulated. In this paper, we show how extensive all-atom simulations combined with kinetic network models provide a detailed mechanistic understanding of how tumor suppressor protein p53 binds to MDM2, an important target of new cancer therapeutics. A simple four-state model parameterized from the simulations shows a binding-then-folding mechanism, and recapitulates experiments in which residual helicity boosts binding. This work goes beyond previous simulations of small-molecule binding, to achieve pathways and binding rates for a large peptide, in good agreement with experiment.

biophysics↗