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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Centrosome-nuclear envelope tethering and microtubule motor-based pulling forces collaborate in centrosome positioning during mitotic entry

Centrosome positioning relative to the nucleus and cell shape is highly regulated across cell types, during cell migration and during spindle formation in cell division. Across most sexually reproducing animals, centrosomes are provided to the oocyte through fertilization and must be positioned properly to establish the zygotic mitotic spindle. How centrosomes are positioned in space and time through the concerted action of key mitotic entry biochemical regulators including Protein Phosphatase 2A (PP2A-B55/SUR-6), biophysical regulators including Dynein and the nuclear lamina is unclear. Here, we uncover a role for PP2A-B55/SUR-6 in regulating centrosome positioning. Mechanistically, PP2A-B55/SUR-6 regulates nuclear size prior to mitotic entry, in turn affecting nuclear envelope-based Dynein density and motor capacity. Using computational simulations, PP2A-B55/ SUR-6 regulation of nuclear size and nuclear envelope Dynein density were both predicted to be required for proper centrosome positioning. Conversely, compromising nuclear lamina integrity led to centrosome detachment from the nuclear envelope and migration defects. Removal of PP2A-B55/SUR-6 and the nuclear lamina simultaneously further disrupted centrosome positioning, leading to unseparated centrosome pairs dissociated from the nuclear envelope. Taken together, we propose a model in which centrosomes migrate and are positioned through the concerted action of nuclear envelope-based Dynein pulling forces and cen-trosome-nuclear envelope tethering.

cell biology↗

bioRxiv2018

MxB restricts HIV-1 by targeting the tri-hexamer interface of the viral capsid

Myxovirus resistance protein B (MxB) is an interferon-inducible restriction factor of HIV-1 that blocks nuclear import of the viral genome. Evidence suggests that MxB recognizes higher-order interfaces of the HIV capsid lattice, but the mechanistic details of this interaction are not known. Previous studies have mapped the restriction activity of MxB to its N-terminus encompassing a triple arginine motif 11RRR13. Here we demonstrate a direct and specific interaction between the MxB N-terminus and helical assemblies of HIV-1 capsid protein (CA) using highly purified recombinant proteins. We performed thorough mutagenesis to establish the detailed molecular requirements for the CA interaction with MxB. The results map MxB binding to the interface of three CA hexamers, specifically interactions between positively charged MxB N-terminal residues and negatively charged CA residues. Our crystal structures show that the CA mutations affecting MxB interaction and restriction do not alter the conformation of capsid assembly. In addition, 30 microsecond long all-atom molecular dynamics (MD) simulations of the complex between the MxB N-terminus and the HIV CA tri-hexamer interface show persistent MxB binding and identify a MxB-binding pocket surrounded by three CA hexamers. These results establish the molecular details of the binding of a lattice-sensing host factor onto HIV capsid, and provide insight into how MxB recognizes HIV capsid for the restriction of HIV-1 infection.\n\nAuthor summaryThe human antiviral protein MxB is a restriction factor that fights HIV infection. Previous experiments have demonstrated that MxB targets the HIV capsid, a protein shell that protects the viral genome. To make the conical shaped capsid, HIV CA proteins are organized into a lattice composed of hexamer and pentamer building blocks, providing many interfaces for host proteins to recognize. Through extensive biochemical and biophysical studies and molecular dynamics simulations, we show that MxB is targeting the HIV capsid by recognizing the region created at the intersection of three CA hexamers. We are further able to map this interaction to a few CA residues, located in a negatively-charged well at the interface between the three CA hexamers. This work provides detailed residue-level mapping of the targeted capsid interface and how MxB interacts. This information could inspire the development of capsid-targeting therapies for HIV.

biochemistry↗

bioRxiv2018

A Model for Time Interval Learning in The Purkinje Cell

Recent experimental findings indicate that Purkinje cells in the cerebellum represent time intervals by mechanisms other than conventional synaptic weights. This finding adds to the theoretical and experimental observations suggesting the presence of intra-cellular mechanisms for adaptation and processing. To account for these experimental results we developed a biophysical model for time interval learning in a Purkinje cell. The numerical model focuses on a classical delay conditioning task (e.g. eyeblink conditioning) and relies on a few computational steps. In particular, the model posits the activation by the parallel fiber input of a local intra-cellular calcium store which can be modulated by intra-cellular pathways. The reciprocal interaction of the calcium signal with several proteins forming negative and positive feedback loops ensures that the timing of inhibition in the Purkinje cell anticipates the interval between parallel and climbing fiber inputs during training. We show that the model is able to learn along the 150-1000 ms interval range. Finally, we discuss how this model would allow the cerebellum to detect and generate specific spatio-temporal patterns, a classical theory for cerebellar function.\n\nAuthor SummaryThe prevailing view in neurosciences considers synaptic weights between neurons the determinant factor for learning and processing information in the nervous system. Theoretical considerations [1, 2] and experiments [3, 4] examined some potential limitations of this classical paradigm, pointing out that adaptation and computation might also have to rely on other mechanisms besides the learning of synaptic weights. Recent experimental findings [5-7] indicate that Purkinje cells in the cerebellum represent time intervals by mechanisms other than conventional synaptic weights. We propose here a biologically plausible model which complements the modification of synaptic weights for learning one time interval in one synapse of one Purkinje cell. In the model a calcium signal in a small domain keeps track of time. Several molecules read and modify this calcium signal to learn a time interval. We discuss how this model would allow the cerebellum to detect and generate specific patterns in space and time, a classical theory for cerebellar function proposed by Braitenberg [8, 9].

neuroscience↗

bioRxiv2018

Widespread alteration of protein autoinhibition in human cancers

Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins as it prevents spurious pathway activation and primes for signal propagation only under appropriate inputs. Altered functioning of inhibitory allosteric switches underlies the tumorigenic potential of numerous cancer drivers. However, whether protein autoinhibition is altered generically in cancer cells remains elusive. Here, we reveal that cancer-associated missense mutations and fusion breakpoints are found with significant enrichment within inhibitory allosteric switches across all cancer types, which in the case of the fusion breakpoints is specific to cancer and not present in other diseases. Recurrently disrupted or mutated allosteric switches identify established and new cancer drivers. Cancer-specific mutations in allosteric switches are associated with distinct changes in signaling, and suggest molecular mechanisms for altered protein regulation, which in the case of ASK1, DAPK2 and EIF4G1 were supported by biophysical simulations. Our results demonstrate that autoinhibition-modulating genetic alterations are positively selected for by cancer cells, and that their study provides valuable insights into molecular mechanisms of cancer misregulation.

cancer biology↗

bioRxiv2018

Homogenization of capillary flow and oxygenation in deeper cortical layers correlates with increased oxygen extraction

Our understanding of how capillary blood flow and oxygen distribute across cortical layers to meet the local metabolic demand is incomplete. We addressed this question by using two-photon imaging of microvascular oxygen partial pressure (PO2) and flow in the whisker barrel cortex in awake mice at rest. Our measurements in layers I-V show that the capillary red-blood-cell flux and oxygenation heterogeneity, and the intracapillary resistance to oxygen delivery, all decrease with depth, reaching a minimum around layer IV, while the depth-dependent oxygen extraction fraction is increased in layer IV, where oxygen demand is presumably the highest. Our findings suggest that homogenization of physiological observables relevant to oxygen transport to tissue is an important part of the microvascular network adaptation to a local brain metabolism increase. These results will inform the biophysical models of layer-specific cerebral oxygen delivery and consumption and improve our understanding of diseases that affect the cerebral microcirculation.\n\nIMPACT STATEMENTHomogenization of cortical capillary blood flow and oxygenation underpins an important mechanism, by which the microvascular network adapts to an increase in the local brain oxidative metabolism.

neuroscience↗

bioRxiv2018

Dendritic spikes expand the range of well-tolerated population noise structures

The brain operates surprisingly well despite the noisy nature of individual neurons. The central mechanism for noise mitigation in the nervous system is thought to involve averaging over multiple noise-corrupted inputs. Subsequently, there has been considerable interest recently to identify noise structures that can be averaged out to preserve reliable signal encoding. By analyzing realistic synaptic integration in biophysically accurate neuronal models, I report a role for a complementary de-noising approach mediated by focal dendritic spikes. Dendritic spikes might seem unlikely candidates for noise reduction due to their miniscule integration compartments and poor averaging abilities. Nonetheless, the extra thresholding step introduced by dendritic spike generation could increase neuronal tolerance for a broad category of noise structures, some of which cannot be resolved well with averaging. Computations with multiple dendritic spikes compensate for compartment size constraints and expand the repertoire of conditions that can be reliably processed by neuronal populations.

neuroscience↗

bioRxiv2018

Causally investigating cortical dynamics and signal processing by targeting natural system attractors with precisely timed stimulation.

1Electrical stimulation is a promising tool for interacting with neuronal dynamics to identify neural mechanisms that underlie cognitive function. Since effects of a single short stimulation pulse typically vary greatly and depend on the current network state, many experimental paradigms have rather resorted to continuous or periodic stimulation in order to establish and maintain a desired effect. However, such an approach explicitly leads to forced and unnatural brain activity. Further, continuous stimulation can make it hard to parse the recorded activity and separate neural signal from stimulation artifacts. In this study we propose an alternate strategy: by monitoring a system in realtime, we use the existing preferred states or attractors of the network and to apply short and precise pulses in order to switch between its preferred states. When pushed into one of its attractors, one can use the natural tendency of the system to remain in such a state to prolong the effect of a stimulation pulse, opening a larger window of opportunity to observe the consequences on cognitive processing. To elaborate on this idea, we consider flexible information routing in the visual cortex as a prototypical example. When processing a stimulus, neural populations in the visual cortex have been found to engage in synchronized gamma activity. In this context, selective signal routing is achieved by changing the relative phase between oscillatory activity in sending and receiving populations (communication through coherence, CTC). In order to explore how perturbations interact with CTC, we investigate a biophysically realistic network exhibiting similar synchronization and signal routing phenomena. We develop a closed-loop stimulation paradigm based on the phase-response characteristics of the network and demonstrate its ability to establish desired synchronization states. By measuring information content throughout the model, we evaluate the effect of signal contamination caused by the stimulation in relation to the magnitude of the injected pulses and intrinsic noise in the system. Finally, we demonstrate that, up to a critical noise level, precisely timed perturbations can be used to artificially induce the effect of attention by selectively routing visual signals to higher cortical areas.

neuroscience↗

bioRxiv2026

Sequential Molecular Interactions Shape Aβ42 Aggregation, Propagation, and Toxicity

Protein aggregation is a context-dependent process in which the molecular environment can influence the properties of the resulting assemblies. In biological systems, these interactions can occur sequentially, as aggregates formed in one cellular or tissue context may encounter different molecular partners and act as seeds in subsequent aggregation events. Here, we used sequential seeding as a controlled experimental model of this temporal and contextual complexity to investigate how prion-like sequences from the gut microbiome modulate amyloid-{beta} aggregation across successive aggregation cycles. Combining kinetic, biophysical, conformational, and toxicity analyses, we show that early interactions with exogenous peptides modify the properties of first-generation A{beta}40- and A{beta}42-derived seeds, resulting in propagated A{beta}42 assemblies with distinct molecular and functional properties. These findings support an Interaction History model in which exogenous sequences bias the emergence of aggregate populations whose properties and subsequent propagation depend on the molecular contexts experienced during earlier aggregation events. Overall, our results present A{beta} aggregation as a history-dependent process and suggest that single-step assays may fail to capture aggregate diversity that emerges across successive aggregation cycles.

biochemistry↗

bioRxiv2026

The first OpenBind release: An open experimental structure-affinity dataset and benchmark for structure-based AI

High-quality experimental datasets that link protein-ligand structures with binding affinity data are essential for developing and evaluating structure-based machine learning methods. To help address this need, we established OpenBind as an open-science initiative to generate large-scale experimental datasets for structure-based AI and molecular discovery. Here, we describe the first public OpenBind release, which, to the best of our knowledge, is the largest public single-target experimental structure-affinity dataset. The dataset focuses on enteroviral 2A protease, comprising 925 crystallographic binding events from 699 compounds and associated affinity measurements for 601 compounds. It combines structures from an initial fragment screen and follow-on molecules, together with affinity data, linking experimentally determined protein-ligand binding modes to biophysical measurements within a coherent antiviral discovery campaign. We used this dataset to evaluate protein-ligand structure prediction, binding-affinity prediction, and virtual screening using representative structure-based methods, including docking and cofolding. This exposed several challenges that are central to practical structure-based modelling: docking performance depends strongly on binding-pocket conformation, poses are difficult to rank, and structure-based affinity prediction remains challenging. Fine-tuning OpenFold3-p2 on the fragment-screen structures substantially improved pose prediction and virtual screening for related follow-on compounds, demonstrating how early-stage experimental structures can support target-specific model adaptation.

bioinformatics↗

bioRxiv2026

De novo design of CR2 binder as vaccine scaffold

Efficient B cell activation during vaccine-induced humoral immunity relies on both B cell receptor (BCR) antigen recognition and synergistic signaling from co-receptors. Complement receptor 2 (CR2), the primary BCR co-receptor on B cells, lowers the activation threshold and amplifies downstream kinase signaling by orders of magnitude when engaged by complement fragment C3d decorated antigens. Targeting CR2 therefore represents a rational vaccine enhancement strategy, yet native C3d suffers from low affinity, poor stability, and manufacturing challenges. Here, we report the de novo design of a highly stable, high-affinity CR2 binder using deep learning driving protein design methods. Biophysical characterization, high-resolution cryoEM structural determination, and functional assays in vitro and in vivo confirm that the designed binder matches computational design models and specifically engages CR2 to boost B cell activation. When fused to antigen as a vaccine scaffold, the trimeric CR2 binder elicits robust humoral immune responses comparable to nanoparticle vaccines, while retaining the simplicity of single-chain protein production. Our work establishes a modular CR2 targeting vaccine scaffold platform with broad translational potential for next-generation protein vaccines.

immunology↗

bioRxiv2026

Microsecond molecular dynamics of SOD1 variants suggest a structural basis for divergent ALS clinical outcomes

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by progressive motor neuron degeneration. Mutations in the SOD1 gene represent the second most common genetic cause of ALS (ALS), and distinct SOD1 missense variants present with markedly different clinical profiles. A4V leads to an aggressive form of the disease (median survival [~]1y), H46R confers a mild, slowly progressive course and I113T exhibits an intermediate phenotype. The molecular basis by which these mutations produce divergent clinical outcomes remains poorly understood. We performed extensive classical molecular dynamics simulations of wild-type SOD1 and the three ALS-associated variants in the apo monomeric state to attempt to investigate the mechanisms behind such phenotypic differences. Structural stability, global compactness, and conformational flexibility, as well as analysis of collective motions between residues and estimation of free energy, were assessed. The H46R, A4V, and I113T variants exhibited distinct dynamic behaviours, highlighting differences in structural stability, local flexibility, and intramolecular interactions. These findings suggest that specific structural regions may contribute differently to protein dysfunction and could represent key elements for understanding the relationship between molecular dynamic properties and the differing clinical severity associated with these variants. Most strikingly, H46R exhibited exceptional structural stability across every analytical level, the lowest global deviation, most attenuated local flexibility, strongest internal dynamic coordination, and the deepest, most confined free energy basins of any system examined. This convergent multi-layered evidence of structural restraint provides a compelling mechanistic basis for the mild and slowly progressive clinical course of H46R ALS, suggesting that enhanced conformational rigidity, rather than bulk destabilisation, is the defining biophysical feature of this variant, and that its pathogenic mechanism operates through a route fundamentally decoupled from the aggregation-driven toxicity that characterises the more aggressive SOD1-ALS mutations.

genomics↗

bioRxiv2026

Functional plasticity of AIF revealed by dimerization and CHCHD4 interaction states

Apoptosis-inducing factor is a mitochondrial flavoprotein that links redox metabolism to mitochondrial homeostasis through its interaction with the disulfide relay protein CHCHD4. Although NADH-dependent AIF dimerization has been proposed as the activated state mediating CHCHD4 engagement, whether it is strictly required for productive AIF-CHCHD4 function remains unclear. Here, combining cellular, biochemical and biophysical approaches, we show that disruption of the AIF dimer interface compromises oxidative phosphorylation, respiratory-chain organization and CHCHD4-dependent mitochondrial homeostasis, yet preserves partial AIF function. Our data reveal that the AIF-CHCHD4 system operates as a conformational dynamic redox module in which distinct AIF oligomeric and redox states sustain CHCHD4 activity with different efficiencies. Mechanistically, dimerization is coupled to NADH-dependent conformational changes that regulate coenzyme binding, charge-transfer complex stabilization and catalytic efficiency. In turn, CHCHD4 binding remodels AIF conformational and redox properties, partially compensating for defects in dimer stabilization or redox coupling. Consistently, a peptide derived from the CHCHD4 N-terminus partially restores redox function in a pathogenic AIF variant defective in dimer stabilization, supporting partner-assisted allosteric regulation as a potential therapeutic strategy.

biochemistry↗

bioRxiv2026

De novo designed single-domain antibodies protect against lethal cobra venom neurotoxicity in vivo

Generative protein design can now rapidly produce de novo binders with high affinity and functional activity against a wide range of targets, including lethal snake venom toxins. However, so far most reported successes rely on new-to-nature scaffolds with limited therapeutic precedent. Single-domain antibodies (VHHs) offer a clinically validated alternative scaffold that can bind and neutralize long-chain -neurotoxins, which are some of the most lethal components in snake venoms. Here we compare three recently established de novo design models with VHH-design capabilities (Germinal, RFantibody, and BoltzGen) for their ability to generate VHHs against the neurotoxin -cobratoxin from the monocled cobra (Naja kaouthia). Using standardized model inputs and evaluation criteria based on AlphaFold3 interface confidence (ipTM) and RMSD self-consistency, we find that Germinal was the only method to generate designs passing stringent in silico criteria for experimental testing. We therefore performed a larger Germinal design campaign employing three different VHH frameworks and experimentally validated 46 designs in vitro. Of these, 42 expressed as soluble proteins and we identified four binding hits derived from two of the three tested frameworks. Of the four binders, two lead candidates were further characterized and demonstrated high affinity (KDs of 4.1 nM and 10.8 nM), monomeric behavior and low polyreactivity, indicating favorable biophysical and developability properties, as well as functional toxin neutralization in vitro. To assess their therapeutic potential we investigated their ability to protect against -cobratoxin toxicity in vivo. Both candidates fully protected mice after -cobratoxin challenge, with 100% survival compared to a lethal control. One candidate also retained notable neutralization capacity against whole venom of Naja kaouthia with a survival of 56%, while the other protected 22% when tested in a rescue setting. Together, we demonstrate that de novo VHH design can generate high affinity single-domain antibodies with in vivo protection against lethal cobra venom neurotoxicity, and provide practical insights into method- and framework-dependent performance.

bioengineering↗

bioRxiv2026

Structures of LolB bound to LolA or lipoprotein resolve the final steps of bacterial lipoprotein trafficking

In Gram-negative bacteria, lipoproteins are structural elements of the outer membrane and essential components of machineries responsible for its construction and maintenance. The Lol system, responsible for the trafficking of lipoproteins from the site of maturation on the inner membrane to the outer membrane, is therefore crucial to the function of the cell envelope and a key target of efforts to find novel antimicrobials. In the final steps of this process, the outer membrane receptor, LolB accepts triacylated lipoproteins from the periplasmic chaperone LolA before inserting them into the outer membrane. Here we present a structure of LolB in complex with LolA, validated by in vivo and in vitro assays, highlighting how positively charged residues on the convex face of the LolB {beta}-barrel underpin complex formation. A protruding loop of LolB, essential for function, inserts into the LolA cavity in position to initiate the displacement of substrate lipoprotein from LolA to enable transfer to LolB. Structural resolution of a lipoprotein-bound LolB complex in combination with biophysical assays shows how a molecular latch releases the lid of the cavity to accommodate the lipoprotein acyl chains. Modelling of these structures onto computationally predicted orientations for LolB on the outer membrane provides a rationale for LolA release and lipoprotein triacyl group membrane insertion. Taken altogether, our data elucidate atomic resolution of two key intermediates and provide a greater understanding of the terminal steps of lipoprotein trafficking events at the bacterial outer membrane.

microbiology↗

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↗

bioRxiv2013

Aerodynamic characteristics of a feathered dinosaur measured using physical models. Effects of form on static stability and control effectiveness.

We report the effects of posture and morphology on the static aerodynamic stability and control effectiveness of physical models based on the feathered dinosaur, {dagger}Microraptor gui, from the Cretaceous of China. Postures had similar lift and drag coefficients and were broadly similar when simplified metrics of gliding were considered, but they exhibited different stability characteristics depending on the position of the legs and the presence of feathers on the legs and the tail. Both stability and the function of appendages in generating maneuvering forces and torques changed as the glide angle or angle of attack were changed. These are significant because they represent an aerial environment that may have shifted during the evolution of directed aerial descent and other aerial behaviors. Certain movements were particularly effective (symmetric movements of the wings and tail in pitch, asymmetric wing movements, some tail movements). Other appendages altered their function from creating yaws at high angle of attack to rolls at low angle of attack, or reversed their function entirely. While {dagger}M. gui lived after {dagger}Archaeopteryx and likely represents a side experiment with feathered morphology, the general patterns of stability and control effectiveness suggested from the manipulations of forelimb, hindlimb and tail morphology here may help understand the evolution of flight control aerodynamics in vertebrates. Though these results rest on a single specimen, as further fossils with different morphologies tested, the findings here could be applied in a phylogenetic context to reveal biomechanical constraints on extinct flyers arising from the need to maneuver.

Biophysics↗

bioRxiv2013

Parametric inference in the large data limit using maximally informative models

Motivated by data-rich experiments in transcriptional regulation and sensory neuro-science, we consider the following general problem in statistical inference. When exposed to a high-dimensional signal S, a system of interest computes a representation R of that signal which is then observed through a noisy measurement M. From a large number of signals and measurements, we wish to infer the \"filter\" that maps S to R. However, the standard method for solving such problems, likelihood-based inference, requires perfect a priori knowledge of the \"noise function\" mapping R to M. In practice such noise functions are usually known only approximately, if at all, and using an incorrect noise function will typically bias the inferred filter. Here we show that, in the large data limit, this need for a pre-characterized noise function can be circumvented by searching for filters that instead maximize the mutual information I[M; R] between observed measurements and predicted representations. Moreover, if the correct filter lies within the space of filters being explored, maximizing mutual information becomes equivalent to simultaneously maximizing every dependence measure that satisfies the Data Processing Inequality. It is important to note that maximizing mutual information will typically leave a small number of directions in parameter space unconstrained. We term these directions \"diffeomorphic modes\" and present an equation that allows these modes to be derived systematically. The presence of diffeomorphic modes reflects a fundamental and nontrivial substructure within parameter space, one that is obscured by standard likelihood-based inference.

Biophysics↗

bioRxiv2013

RNA Structure Refinement using the ERRASER-Phenix pipeline

The final step of RNA crystallography involves the fitting of coordinates into electron density maps. The large number of backbone atoms in RNA presents a difficult and tedious challenge, particularly when experimental density is poor. The ERRASER-Phenix pipeline can improve an initial set of RNA coordinates automatically based on a physically realistic model of atomic-level RNA interactions. The pipeline couples diffraction-based refinement in Phenix with the Rosetta-based real-space refinement protocol ERRASER (Enumerative Real-Space Refinement ASsisted by Electron density under Rosetta). The combination of ERRASER and Phenix can improve the geometrical quality of RNA crystallographic models while maintaining or improving the fit to the diffraction data (as measured by Rfree). Here we present a complete tutorial for running ERRASER-Phenix through the Phenix GUI, from the command-line, and via an application in the Rosetta On-line Server that Includes Everyone (ROSIE).

Biophysics↗