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bioRxiv2020

Focus Your Screening Library: Rapid Identification of Novel PDE2 Inhibitors with in silico Driven Library Prioritization and MicroScale Thermophoresis

Accelerated development of lead structures is of high interest to the pharmaceutical industry in order to decrease development times and costs. We showcase how an intelligent combination of AI-based drug screening with state-of-the-art biophysics drives the rapid identification of novel inhibitor structures with high chemical diversity for cGMP-dependent 3,5-cyclic phosphodiesterase (PDE2). The starting point was an off-the-shelve chemical library of two million drug-like compounds. In a single in silico reduction step, we short-listed 125 compounds - the focused library - as potential binders to PDE2 and tested their binding behavior in vitro using MicroScale Thermophoresis (MST). Of this focused library, seven compounds indicated binding to PDE2, translating to a hit rate of 6%. Three of these compounds have affinities in the lower micromolar range. The compound with the highest affinity showed a KD of 10 {micro}M and is thus an excellent starting point for further medicinal chemistry optimization. The results show how innovative and structure-driven in silico approaches and biophysics can be used to accelerate drug discovery and to obtain new molecular scaffolds at a fraction of the costs and time - compared with standard high-throughput screening.

bioinformatics↗

bioRxiv2020

Conformational stability and dynamics in crystals recapitulate protein behaviour in solution

A growing body of evidences has established that in many cases proteins may preserve most of their function and flexibility in a crystalline environment, and several techniques are today capable to detect transiently-populated states of macromolecules in tightly packed lattices. Intriguingly, in the case of amyloidogenic precursors, the presence of these conformations (hidden to conventional crystallographic studies) can be correlated to the pathological fate of the native fold. It remains unclear, however, to which extent these minor conformations reflect the protein behaviour that is more commonly studied in solution. Here, we address this question by investigating some biophysical properties of a prototypical amyloidogenic system, {beta}2-microglobulin ({beta}2m) in solution and in microcrystalline state. By combining NMR chemical shifts with Molecular Dynamics (MD) simulations, we confirmed that conformational dynamics of {beta}2m native state in the crystal lattice is in keeping with what observed in solution. A comparative study of protein stability in solution and in crystallo is then carried out, monitoring the change in protein secondary structure at increasing temperature by Fourier transform infrared (FTIR) spectroscopy. The increased structural order of the crystalline state contributes to provide better resolved spectral components compared to those collected in solution and crucially, the crystalline samples display thermal stabilities in good agreement with the trend observed in solution. Overall, this work shows that protein stability and occurrence of pathological hidden states in crystals parallel their solution counterpart, confirming the interest of crystals as a platform for the biophysical characterisation of processes such as unfolding and aggregation.

biochemistry↗

bioRxiv2020

Structure and Evolution of Constitutive Bacterial Promoters

Predicting gene expression levels from any DNA sequence is a major challenge in biology. Using libraries with >25,000 random mutants, we developed a biophysical model that accounts for major features of {sigma}70-binding bacterial promoters to accurately predict constitutive gene expression levels of any sequence. We experimentally and theoretically estimated that 10-20% of random sequences lead to expression and 82% of non-expressing sequences are one point mutation away from a functional promoter. Generating expression from random sequences is pervasive, such that selection acts against {sigma}70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. The pervasiveness of {sigma}70- binding sites, which arises from the structural features of promoters captured by our biophysical model, implies that their emergence is unlikely the limiting step in gene regulatory evolution.

evolutionary biology↗

bioRxiv2020

Comparative performance of mutual information and transfer entropy for analyzing the balance of information flow and energy consumption at synapses

Information theory has become an essential tool of modern neuroscience. It can however be difficult to apply in experimental contexts when acquisition of very large datasets is prohibitive. Here, we compare the relative performance of two information theoretic measures, mutual information and transfer entropy, for the analysis of information flow and energetic consumption at synapses. We show that transfer entropy outperforms mutual information in terms of reliability of estimates for small datasets. However, we also show that a detailed understanding of the underlying neuronal biophysics is essential for properly interpreting the results obtained with transfer entropy. We conclude that when time and experimental conditions permit, mutual information might provide an easier to interpret alternative. Finally, we apply both measures to the study of energetic optimality of information flow at thalamic relay synapses in the visual pathway. We show that both measures recapitulate the experimental finding that these synapses are tuned to optimally balance information flowing through them with the energetic consumption associated with that synaptic and neuronal activity. Our results highlight the importance of conducting systematic computational studies prior to applying information theoretic tools to experimental data. Author summaryInformation theory has become an essential tool of modern neuroscience. It is being routinely used to evaluate how much information flows from external stimuli to various brain regions or individual neurons. It is also used to evaluate how information flows between brain regions, between neurons, across synapses, or in neural networks. Information theory offers multiple measures to do that. Two of the most popular are mutual information and transfer entropy. While these measures are related to each other, they differ in one important aspect: transfer entropy reports a directional flow of information, as mutual information does not. Here, we proceed to a systematic evaluation of their respective performances and trade-offs from the perspective of an experimentalist looking to apply these measures to binarized spike trains. We show that transfer entropy might be a better choice than mutual information when time for experimental data collection is limited, as it appears less affected by systematic biases induced by a relative lack of data. Transmission delays and integration properties of the output neuron can however complicate this picture, and we provide an example of the effect this has on both measures. We conclude that when time and experimental conditions permit, mutual information - especially when estimated using a method referred to as the direct method - might provide an easier to interpret alternative. Finally, we apply both measures in the biophysical context of evaluating the energetic optimality of information flow at thalamic relay synapses in the visual pathway. We show that both measures capture the original experimental finding that those synapses are tuned to optimally balance information flowing through them with the concomitant energetic consumption associated with that synaptic and neuronal activity.

neuroscience↗

bioRxiv2020

Cardiac toxicity predictions: Safety pharmacologists correlate with the CiPA model

There has been a lot of interest and publicity regarding the use of a complex biophysical model within drug development for predicting the TdeP risk of new compounds. Throughout the development of the complex model numerous groups have shown that a simple linear mechanistic model explains the predictive behaviour of complex mechanistic models. That is the input-output relationship is almost linear even when complex kinetic assays are used. We hypothesized that given this linear relationship that scientist would be able to predict the outcome of the biophysical model. The objective of this pilot study was to assess the feasibility of such an analysis but also assess the initial degree of correlation. A set of 15 compounds with diverse ion-channel blocking against 4 ion-channel currents, IKr, ICaL, INa and INaL, was generated. Safety pharmacologists across numerous companies were approached and asked to categorize the TdeP risk of these compounds using only the % block depicted via a bar chart into one of 3 categories: Risk, No-risk or Unsure. 12 scientists participated in the study, of which 11 correlated strongly with the model (11 person ROC AUC range: 0.86-1, 7 scientists had a value >0.9). The combined prediction of all scientists also correlated strongly with the model. These results highlight that the linear input-output relationship can indeed be predicted by the scientist. A future study exploring the degree of correlation with a wider group of scientists and wider set of compounds would be required to get a more precise estimate of the correlation. We hope this initial exploratory study will encourage the community to pursue this idea.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2020

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data

Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series in which we are interested in information flows come in the form of (near) instantaneous events occurring over time, including the spiking of biological neurons, trades on stock markets and posts to social media. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the discrete-time estimator on synthetic examples. We also develop a local permutation scheme for generating null surrogate time series to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another -- signifying the lack of a causal connection under certain weak assumptions. Our approach is capable of detecting conditional independence or otherwise even in the presence of strong pairwise time-directed correlations. The power of this approach is further demonstrated on the inference of the connectivity of biophysical models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed. AUTHOR SUMMARYTransfer Entropy (TE) is an information-theoretic measure commonly used in neuroscience to measure the directed statistical dependence between a source and a target time series, possibly also conditioned on other processes. Along with measuring information flows, it is used for the inference of directed functional and effective networks from time series data. The currently-used technique for estimating TE on neural spike trains first time-discretises the data and then applies a straightforward or "plug-in" information-theoretic estimation procedure. This approach has numerous drawbacks: it is very biased, it cannot capture relationships occurring on both fine and large timescales simultaneously, converges very slowly as more data is obtained, and indeed does not even converge to the correct value. We present a new estimator for TE which operates in continuous time, demonstrating via application to synthetic examples that it addresses these problems, and can reliably differentiate statistically significant flows from (conditionally) independent spike trains. Further, we also apply it to more biologically-realistic spike trains obtained from a biophysical model of the pyloric circuit of the crustacean stomatogastric ganglion; our correct inference of the underlying connection structure here provides an important validation for our approach where similar methods have previously failed

neuroscience↗

bioRxiv2020

Adaptive mechanoproperties characterize glioblastoma fitness for invasion

Glioblastoma are heterogeneous tumors composed of highly invasive and highly proliferative clones. Heterogeneity in invasiveness could emerge from discrete biophysical properties linked to specific molecular expression. We identified clones of patient-derived glioma propagating cells that were either highly proliferative or highly invasive and compared their cellular architecture, migratory and biophysical properties. We discovered that invasiveness was linked to cellular fitness. The most invasive cells were stiffer, developed higher mechanical forces on the substrate and moved stochastically. The mechanochemical-induced expression of the formin FMN1 conferred invasive strength that was confirmed in patient samples. Moreover, FMN1 ectopic expression in less invasive clones increased fitness parameters. Mechanistically, FMN1 acts from the microtubule lattice, counteracting microtubule bundling and promoting a robust cell-body cohesion leading to highly invasive hurdling motility. One Sentence SummaryFMN1 increases cell mechanics and invasiveness.

cell biology↗

bioRxiv2020

Dynamics of a neuronal pacemaker in the weakly electric fish Apteronotus

The precise timing of neuronal activity is critical for normal brain function. In weakly electric fish, the medullary pacemaker network (PN) sets the timing for an oscillating electric organ discharge (EOD) used for electric sensing. This network is the most precise biological oscillator known, with sub-microsecond variation in oscillator period. The PN consists of two principle sets of neurons, pacemaker and relay cells, that are connected by gap junctions and normally fire in synchrony, one-to-one with each EOD cycle. However, the degree of gap junctional connectivity between these cells appears insufficient to provide the population averaging required for the observed temporal precision of the EOD. This has led to the hypothesis that individual cells themselves fire with high precision, but little is known about the oscillatory dynamics of these pacemaker cells. To this end, we have developed a biophysical model of a pacemaker neuron action potential based on experimental recordings. We validated the model by comparing the changes in oscillatory dynamics produced by different experimental manipulations. Our results suggest that a relatively simple model captures the complex dynamics exhibited by pacemaker cells, and that these dynamics may enhance network synchrony and precision. Author summaryMany neural networks in the brain exhibit activity patterns which oscillate regularly in time. These oscillations, like a clock, can provide a precise sense of time, enabling drummers to maintain complex beat patterns and pets to anticipate "feeding time". The exact mechanisms by which brain networks give rise to these biological clocks are not clear. The pacemaker network of weakly electric fish has the highest precision of all known biological clocks. In this study, we develop a detailed biophysical model of neurons in the pacemaker network. We then validate the model against experiments using a nonlinear dynamics approach. Our results show that pacemaker precision is due, at least in part, to how individual pacemaker cells generate their activity. This supports the idea that temporal precision in this network is not solely an emergent property of the network but also relies on the dynamics of individual neurons.

neuroscience↗

bioRxiv2020

Functional solubilisation of the β2-adrenoceptor (β2AR) using Diisobutylene maleic acid (DIBMA).

The β2-adrenoceptor (β2AR) is a well-established target in asthma and a prototypical GPCR for biophysical studies. Solubilisation of membrane proteins has classically involved the use of detergents. However, the detergent environment differs from the native membrane environment and often destabilises membrane proteins. Use of amphiphilic copolymers is a promising strategy to solubilise membrane proteins within their native lipid environment in the complete absence of detergents. Here we show the isolation of the β2AR in the polymer Diisobutylene maleic acid (DIBMA). We demonstrate that β2AR remains functional in the DIBMA lipid particle (DIBMALP) and shows improved thermal stability compared to the n-Dodecyl-β-D-Maltopyranoside (DDM) detergent solubilised β2AR. This unique method of extracting β2AR offers significant advantages over previous methods routinely employed such as the introduction of thermostabilising mutations and the use of detergents, particularly for functional biophysical studies.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2020

Nonlinear effects of intrinsic dynamics on temporal encoding in a model of avian auditory cortex

Neurons exhibit diverse intrinsic dynamics, which govern how they integrate synaptic inputs to produce spikes. Intrinsic dynamics are often plastic during development and learning, but the effects of these changes on stimulus encoding properties are not well known. To examine this relationship, we simulated auditory responses to zebra finch song using a linear-dynamical cascade model, which combines a linear spectrotemporal receptive field with a dynamical, conductance-based neuron model, then used generalized linear models to estimate encoding properties from the resulting spike trains. We focused on the effects of a low-threshold potassium current (KLT) that is present in a subset of cells in the zebra finch caudal mesopallium and is affected by early auditory experience. We found that KLT affects both spike adaptation and the temporal filtering properties of the receptive field. The direction of the effects depended on the temporal modulation tuning of the linear (input) stage of the cascade model, indicating a strongly nonlinear relationship. These results suggest that small changes in intrinsic dynamics in tandem with differences in synaptic connectivity can have dramatic effects on the tuning of auditory neurons. Author summaryExperience-dependent developmental plasticity involves changes not only to synaptic connections, but to voltage-gated currents as well. Using biophysical models, it is straightforward to predict the effects of this intrinsic plasticity on the firing patterns of individual neurons, but it remains difficult to understand the consequences for sensory coding. We investigated this in the context of the zebra finch auditory cortex, where early exposure to a complex acoustic environment causes increased expression of a low-threshold potassium current. We simulated responses to song using a detailed biophysical model and then characterized encoding properties using generalized linear models. This analysis revealed that this potassium current has strong, nonlinear effects on how the model encodes the songs temporal structure, and that the sign of these effects depend on the temporal tuning of the synaptic inputs. This nonlinearity gives intrinsic plasticity broad scope as a mechanism for developmental learning in the auditory system.

neuroscience↗

bioRxiv2020

Site-specific amino acid distributions follow a universal shape

In many applications of evolutionary inference, a model of protein evolution needs to be fitted to the amino acid variation at individual sites in a multiple sequence alignment. Most existing models fall into one of two extremes: Either they provide a coarse-grained description that lacks biophysical realism (e.g. dN/dS models), or they require a large number of parameters to be fitted (e.g. mutation-selection models). Here, we ask whether a middle ground is possible: Can we obtain a realistic description of site-specific amino acid frequencies while severely restricting the number of free parameters in the model? We show that a distribution with a single free parameter can accurately capture the variation in amino acid frequency at most sites in an alignment, as long as we are willing to restrict our analysis to predicting amino acid frequencies by rank rather than by amino acid identity. This result holds equally well both in alignments of empirical protein sequences and of sequences evolved under a biophysically realistic all-atom force field. Our analysis reveals a near universal shape of the frequency distributions of amino acids. This insight has the potential to lead to new models of evolution that have both increased realism and a limited number of free parameters.

evolutionary biology↗

bioRxiv2020

Coincident glutamatergic depolarizations enhance GABAA receptor-dependent Cl- influx in mature and suppress Cl- efflux in immature neurons

The impact of GABAergic transmission on neuronal excitability depends on the Cl--gradient across membranes. However, the Cl--fluxes through GABAA receptors alter the intracellular Cl- concentration ([Cl-]i) and in turn attenuate GABAergic responses, a process termed ionic plasticity. Recently it has been shown that coincident glutamatergic inputs significantly affect ionic plasticity. Yet how the [Cl-]i changes depend on the properties of glutamatergic inputs and their spatiotemporal relation to GABAergic stimuli is unknown. To investigate this issue, we used compartmental biophysical models of Cl- dynamics simulating either a simple ball-and-stick topology or a reconstructed immature CA3 neuron. These computational experiments demonstrated that glutamatergic co-stimulation enhances GABA receptor-mediated Cl- influx at low and attenuates or reverses the Cl- efflux at high initial [Cl-]i. The size of glutamatergic influence on GABAergic Cl--fluxes depends on the conductance, decay kinetics, and localization of glutamatergic inputs. Surprisingly, the glutamatergic shift in GABAergic Cl--fluxes is invariant to latencies between GABAergic and glutamatergic inputs over a substantial interval. In agreement with experimental data, simulations in a reconstructed CA3 pyramidal neuron with physiological patterns of correlated activity revealed that coincident glutamatergic synaptic inputs contribute significantly to the activity-dependent [Cl-]i changes. Whereas the influence of spatial correlation between distributed glutamatergic and GABAergic inputs was negligible, their temporal correlation played a significant role. In summary, our results demonstrate that glutamatergic co-stimulation had a substantial impact on ionic plasticity of GABAergic responses, enhancing the destabilization of GABAergic inhibition in the mature nervous systems, but suppressing GABAergic [Cl-]i changes in the immature brain. Therefore, glutamatergic shift in GABAergic Cl--fluxes should be considered as a relevant factor of short term plasticity. Author SummaryInformation processing in the brain requires that excitation and inhibition are balanced. The main inhibitory neurotransmitter in the brain is gamma-amino-butyric acid (GABA). GABA actions depend on the Cl--gradient, but activation of ionotropic GABA receptors causes Cl--fluxes and thus reduces GABAergic inhibition. Here, we investigated how a coincident membrane depolarization by excitatory, glutamatergic synapses influences GABA-induced Cl--fluxes using a biophysical compartmental model of Cl- dynamics, simulating either simple or realistic neuron topologies. We demonstrate that glutamatergic co-stimulation directly affects GABA-induced Cl--fluxes, with the size of glutamatergic effects depending on the conductance, the decay kinetics, and localization of glutamatergic inputs. We also show that the glutamatergic shift in GABAergic Cl--fluxes is surprisingly stable over a substantial range of latencies between glutamatergic and GABAergic inputs. We conclude from these results that glutamatergic co-stimulation alters GABAergic Cl--fluxes and in turn affects the strength of GABAergic inhibition. These coincidence-dependent ionic changes should be considered as a relevant factor of short term plasticity in the CNS.

neuroscience↗

bioRxiv2020

A robust and versatile nanobody platform for drug delivery

Therapeutic and diagnostic efficacies of numerous small biomolecules and chemical compounds are hampered by the short half-lives. Here we report the development of a repertoire of diverse, high-affinity albumin-nanobodies (NbHSA) to facilitate drug delivery. By integrating biophysics, and hybrid structural biology, we have systematically characterized the NbHSA for albumin binding, mapped the epitopes, and resolved the architecture of a tetrameric Nb-albumin complex. We employed quantitative proteomics for accurate, multiplex Nb pharmacokinetic analysis. Using a humanized albumin mouse model, we found that the NbHSA has outstanding pharmacokinetics; the most stable NbHSA has a 771-fold T1/2 improvement compared with a control Nb. Interestingly, the pharmacokinetics of NbHSA is related to their biophysical and structural properties. To demonstrate the utility of NbHSA, we developed a highly stable NbHSA-hIL-2 cytokine conjugate "Duraleukin" and confirmed its improved anticancer properties than hIL-2 alone. We envision that this high-quality Nb resource will advance research into novel biotherapeutics.

bioengineering↗

bioRxiv2020

Site-specific lipidation enhances IFITM3 membrane interactions and antiviral activity

Interferon-induced transmembrane proteins (IFITMs) are S-palmitoylated proteins in vertebrates that restrict a diverse range of viruses. S-palmitoylated IFITM3 in particular directly engages incoming virus particles, prevents their cytoplasmic entry, and accelerates their lysosomal clearance by host cells. However, the precise molecular mechanisms of action for IFITM-mediated viral restriction are still unclear. To investigate how site-specific S-palmitoylation controls IFITM3 antiviral activity, here we employed computational, chemical, and biophysical approaches to demonstrate that site-specific lipidation of IFITM3 at highly conserved cysteine 72 modulates its conformation and interaction with lipid membranes leading to enhanced antiviral activity of IFITM3 in mammalian cells. Collectively, our results demonstrate that site-specific S-palmitoylation of IFITM3 directly alters its biophysical properties and activity in cells to prevent virus infection.

biochemistry↗

bioRxiv2020

High fidelity reconstitution of stress granules and nucleoli in mammalian cellular lysate

Liquid-liquid phase separation (LLPS) is an important mechanism of intracellular organization that underlies the assembly of a variety of distinct RNP granules. Fundamental biophysical principles governing LLPS during RNP granule assembly have been revealed by simple in vitro systems consisting of several components, but these systems have limitations when studying the biology of complex, multicomponent RNP granules. Visualization of RNP granules in live cells has validated key principles revealed by simple in vitro systems, but this approach presents difficulties for interrogating biophysical features of RNP granules and provides limited ability to manipulate protein, nucleic acid, or small molecule concentrations. Here we introduce a system that builds upon recent insights into the mechanisms underlying RNP granule assembly and permits high fidelity reconstitution of stress granules and the granular component of nucleoli in mammalian cellular lysate. This system fills the gap between simple in vitro systems and live cells, and allows for a wide variety of studies of membraneless organelles.

cell biology↗

bioRxiv2020

A statistical 3D model of the human cortical vasculature to compute the hemodynamic fingerprint of the BOLD fMRI signal

BOLD fMRI is a commonly used technique to map brain activity; nevertheless, BOLD fMRI is an indirect measurement of brain function triggered by neurometabolic and neurovascular coupling. Hence, the origin of the BOLD fMRI signal is quite complex, and the signal formation depends, among others, on the geometry of the cortical vasculature and the associated hemodynamic behavior. To characterize and quantify the hemodynamic contributions to the BOLD signal response in humans, it is necessary to adopt a computational model that resembles the human cortical vascular architecture and mimics realistic hemodynamic changes. To this end, we have developed a statistically defined 3D vascular model that resembles the human cortical vasculature. Using this model, we simulated hemodynamic changes triggered by a neuronal activation and local magnetic field disturbances created by the vascular topology and the blood oxygenation changes. The proposed model considers also the biophysical interactions and the intrinsic magnetic properties of the nearby tissue in order to compute a dynamic BOLD fMRI signal response. This computational pipeline results in an integrated biophysical model that can provide a better insight on the understanding and quantification of the hemodynamic fingerprint of the BOLD fMRI signal evolution.

neuroscience↗

bioRxiv2020

On the complex interplay between spectral harmonicity and different types of cross frequency couplings in non linear oscillators and biologically plausible neural network models

BackgroundCross-frequency coupling (CFC) refers to the non linear interaction between oscillations in different frequency bands, and it is a rather ubiquitous phenomenon that has been observed in a variety of physical and biophysical systems. In particular, the coupling between the phase of slow oscillations and the amplitude of fast oscillations, referred as phase-amplitude coupling (PAC), has been intensively explored in the brain activity recorded from animals and humans. However, the interpretation of these CFC patterns remains challenging since harmonic spectral correlations characterizing non sinusoidal oscillatory dynamics can act as a confounding factor. MethodsSpecialized signal processing techniques are proposed to address the complex interplay between spectral harmonicity and different types of CFC, not restricted only to PAC. For this, we provide an in-depth characterization of the Time Locked Index (TLI) as a novel tool aimed to efficiently quantify the harmonic content of noisy time series. It is shown that the proposed TLI measure is more robust and outperform traditional phase coherence metrics (e.g. Phase Locking Value, Pairwise Phase Consistency) in several aspects. ResultsWe found that a non linear oscillator under the effect of additive noise can produce spurious CFC with low spectral harmonic content. On the other hand, two coupled oscillatory dynamics with independent fundamental frequencies can produce true CFC with high spectral harmonic content via a rectification mechanism or other post-interaction nonlinear processing mechanisms. These results reveal a complex interplay between CFC and harmonicity emerging in the dynamics of biologically plausible neural network models and more generic non linear and parametric oscillators. ConclusionsWe show that, contrary to what is usually assumed in the literature, the high harmonic content observed in non sinusoidal oscillatory dynamics, is neither sufficient nor necessary condition to interpret the associated CFC patterns as epiphenomenal. There is mounting evidence suggesting that the combination of multimodal recordings, specialized signal processing techniques and theoretical modeling is becoming a required step to completely understand CFC patterns observed in oscillatory rich dynamics of physical and biophysical systems. HighlightsO_LITime locked index efficiently quantifies the harmonic content of noisy time series. C_LIO_LIA non linear oscillator under the effect of additive noise can produce spurious cross frequency couplings (CFC) with low spectral harmonic content. C_LIO_LITwo coupled oscillatory dynamics with independent fundamental frequencies can produce true CFC with high spectral harmonic content via rectification mechanisms or other post-interaction nonlinear processing mechanisms. C_LIO_LIA non sinusoidal oscillatory dynamics with high harmonic content is neither sufficient nor necessary condition for spurious CFC. C_LIO_LIA complex interplay between CFC and harmonicity emerges from the dynamics of nonlinear, parametric and biologically plausible oscillators. C_LI

neuroscience↗

bioRxiv2020

Widespread occurrence of the droplet state of proteins in the human proteome

A wide range of proteins have been reported to condensate into a dense liquid phase, forming a reversible droplet state. Failure in the control of the droplet state can lead to the formation of the more stable amyloid state, which is often disease-related. These observations prompt the question of how many proteins can undergo liquid-liquid phase separation. Here, in order to address this problem, we discuss the biophysical principles underlying the droplet state of proteins by analyzing current evidence for droplet-driver and droplet-client proteins. Based on the concept that the droplet state is stabilized by the large conformational entropy associated with non-specific side-chain interactions, we develop the FuzDrop method to predict droplet-promoting regions and proteins, which can spontaneously phase separate. We use this approach to carry out a proteome-level study to rank proteins according to their propensity to form the droplet state, spontaneously or via partner interactions. Our results lead to the conclusion that the droplet state could be, at least transiently, accessible to most proteins under conditions found in the cellular environment. SignificanceLiquid-liquid phase separation of proteins results in biomolecular condensates, which contribute to the organisation of cellular matter into membraneless organelles. It is still unclear, however, whether these condensates represent a common state of proteins. Here, based on biophysical principles driving phase separation, we report a proteome-wide ranking of proteins according to their propensity to condensate into a droplet state. We analyze two mechanisms for droplet formation - driver proteins can spontaneously phase separate, while client proteins require additional components. We conclude that the droplet state, as the native and amyloid states, is a fundamental state of proteins, with most proteins expected to be capable of undergoing liquid-liquid phase separation via either of these two mechanisms.

bioinformatics↗