Data-driven large-scale genomic analysis reveals an intricate phylogenetic and functional landscape in J-domain proteins.

The 70-kD heat shock protein (Hsp70) chaperone system is a central hub of the proteostasis network that helps maintain protein homeostasis in all organisms. The recruitment of Hsp70 to perform different and specific cellular functions is regulated by the J-domain protein (JDP) co-chaperone family carrying the small namesake J-domain, required to interact and drive the ATPase cycle of Hsp70s. Besides the J-domain, prokaryotic and eukaryotic JDPs display a staggering diversity in domain architecture, function, and cellular localization. Very little is known about the overall JDP family, despite their essential role in cellular proteostasis, development, and its link to a broad range of human diseases. In this work, we leverage the exponentially increasing number of JDP gene sequences identified across all kingdoms owing to the advancements in sequencing technology and provide a broad overview of the JDP repertoire. Using an automated classification scheme based on artificial neural networks (ANNs), we demonstrate that the sequences of J-domains carry sufficient discriminatory information to reliably recover the phylogeny, localization, and domain composition of the corresponding full-length JDP. By harnessing the interpretability of the ANNs, we find that many of the discriminatory sequence positions match residues that form the interaction interface between the J-domain and Hsp70. This reveals that key residues within the J-domains have coevolved with their obligatory Hsp70 partners to build chaperone circuits for specific functions in cells.

CERT1 mutations perturb human development by disrupting sphingolipid homeostasis.

Neural differentiation, synaptic transmission, and action potential propagation depend on membrane sphingolipids, whose metabolism is tightly regulated. Mutations in the ceramide transporter CERT (CERT1), which is involved in sphingolipid biosynthesis, are associated with intellectual disability, but the pathogenic mechanism remains obscure. Here, we characterize 31 individuals with de novo missense variants in CERT1. Several variants fall into a previously uncharacterized dimeric helical domain that enables CERT homeostatic inactivation, without which sphingolipid production goes unchecked. The clinical severity reflects the degree to which CERT autoregulation is disrupted, and inhibiting CERT pharmacologically corrects morphological and motor abnormalities in a Drosophila model of the disease, which we call ceramide transporter (CerTra) syndrome. These findings uncover a central role for CERT autoregulation in the control of sphingolipid biosynthetic flux, provide unexpected insight into the structural organization of CERT, and suggest a possible therapeutic approach for patients with CerTra syndrome.

ABC transporters are billion-year-old Maxwell Demons.

ATP-Binding Cassette (ABC) transporters are a broad family of biological machines, found in most prokaryotic and eukaryotic cells, performing the crucial import or export of substrates through both plasma and organellar membranes, and maintaining a steady concentration gradient driven by ATP hydrolysis. Building upon the present biophysical and biochemical characterization of ABC transporters, we propose here a model whose solution reveals that these machines are an exact molecular realization of the autonomous Maxwell Demon, a century-old abstract device that uses an energy source to drive systems away from thermodynamic equilibrium. In particular, the Maxwell Demon does not perform any direct mechanical work on the system, but simply selects which spontaneous processes to allow and which ones to forbid based on information that it collects and processes. In its autonomous version, the measurement device is embedded in the system itself. In the molecular model introduced here, the different operations that characterize Maxwell Demons (measurement, feedback, resetting) are features that emerge from the biochemical and structural properties of ABC transporters, revealing the crucial role of allostery to process information. Our framework allows us to develop an explicit bridge between the molecular-level description and the higher-level language of information theory for ABC transporters.

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules.

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference state associated to the null interaction case. Despite their significant effectiveness, the physical interpretation of knowledge-based potentials has been repeatedly questioned, with no consensus on the choice of the reference state. Here we use the fact that the Flory theorem, originally derived for chains in a dense polymer melt, holds also for chain fragments within the core of globular proteins, if the average over buried fragments collected from different non-redundant native structures is considered. After verifying that the ensuing Gaussian statistics, a hallmark of effectively non-interacting polymer chains, holds for a wide range of fragment lengths, although with significant deviations at short spatial scales, we use it to define a 'bona fide' reference state. Notably, despite the latter does depend on fragment length, deviations from it do not. This allows to estimate an effective interaction potential which is not biased by the presence of correlations due to the connectivity of the protein chain. We show how different sequence-independent effective statistical potentials can be derived using this approach by coarse-graining the protein representation at varying levels. The possibility of defining sequence-dependent potentials is explored.

Relief of ParB autoinhibition by parS DNA catalysis and recycling of ParB by CTP hydrolysis promote bacterial centromere assembly.

Three-component ParABS systems are widely distributed factors for plasmid partitioning and chromosome segregation in bacteria. ParB acts as adaptor protein between the 16­base pair centromeric parS DNA sequences and the DNA segregation proteins ParA and Smc (structural maintenance of chromosomes). Upon cytidine triphosphate (CTP) and parS DNA binding, ParB dimers form DNA clamps that spread onto parS-flanking DNA by sliding, thus assembling the so-called partition complex. We show here that CTP hydrolysis is essential for efficient chromosome segregation by ParABS but largely dispensable for Smc recruitment. Our results suggest that CTP hydrolysis contributes to partition complex assembly via two mechanisms. It promotes ParB unloading from DNA to limit the extent of ParB spreading, and it recycles off-target ParB clamps to allow for parS retargeting, together superconcentrating ParB near parS. We also propose a model for clamp closure involving a steric clash when binding ParB protomers to opposing parS half sites.

Light-Switchable Buffers.

A visible light-switchable buffer system based on a merocyanine photoacid is presented. Para-substitution of the indolium side with a methoxy group affords a compound suitable for making hydrolytically stable aqueous buffers whose pH can be tuned between 7 and 4 using 500 nm light.

Computational pipeline to probe NaV1.7 gain-of-function variants in neuropathic painful syndromes.

Applications of machine learning and graph theory techniques to neuroscience have witnessed an increased interest in the last decade due to the large data availability and unprecedented technology developments. Their employment to investigate the effect of mutational changes in genes encoding for proteins modulating the membrane of excitable cells, whose biological correlates are assessed at electrophysiological level, could provide useful predictive clues. We apply this concept to the analysis of variants in sodium channel NaV1.7 subunit found in patients with chronic painful syndromes, by the implementation of a dedicated computational pipeline empowering different and complementary techniques including homology modeling, network theory, and machine learning. By testing three templates of different origin and sequence identities, we provide an optimal condition for its use. Our findings reveal the usefulness of our computational pipeline in supporting the selection of candidates for cell electrophysiology assay and with potential clinical applications.

Mathematical Modelling of Alternative Pathway of Complement System.

The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.

Thermodynamics and kinetics of protonated merocyanine photoacids in water.

Metastable-state photoacids (mPAHs) are chemical species whose photo-activated state is long-lived enough to allow for proton diffusion. Liao's photoacid (1) represents the archetype of mPAHs, and is being widely used on account of its unique capability to change the acidity of aqueous solutions reversibly. The behavior of 1 in water, however, still remains poorly understood. Herein, we provide in-depth insights on the thermodynamics and kinetics of 1 in water through a series of comparative 1H NMR and UV-Vis studies and relative modelling. Under dark conditions, we quantified a three-component equilibrium system where the dissociation (K a) of the open protonated form (MCH) is followed by isomerization (K c) of the open deprotonated form (MC) to the closed spiropyran form (SP) - i.e., in the absence of light, the ground state acidity can be expressed as K GS a = K a(1 + K c). On the other hand, under powerful and continuous light irradiation we were able to assess, for the first time experimentally, the dissociation constant (K MS a) of the protonated metastable state (cis-MCH). In addition, we found that thermal ring-opening of SP is always rate-determining regardless of pH, whereas hydrolysis is reminiscent of what is found for Schiff bases. The proposed methodology is general, and it was applied to two other compounds bearing a shorter (ethyl, 2) and a longer (butyl, 3) alkyl-1-sulfonate bridge. We found that the pK a remains constant, whereas both pK c and pK MS a linearly increase with the length of the alkyl bridge. Importantly, all results are consistent with a four-component model cycle, which describes perfectly the full dynamics of proton release/uptake of 1-3 in water. The superior hydrolytic stability and water solubility of compound 3, together with its relatively high pK GS a (low K c), allowed us to achieve fully reversible jumps of 2.5 pH units over 18 consecutive cycles (6 hours).

A common root for coevolution and substitution rate variability in protein sequence evolution.

We introduce a simple model that describes the average occurrence of point variations in a generic protein sequence. This model is based on the idea that mutations are more likely to be fixed at sites in contact with others that have mutated in the recent past. Therefore, we extend the usual assumptions made in protein coevolution by introducing a time dumping on the effect of a substitution on its surrounding and makes correlated substitutions happen in avalanches localized in space and time. The model correctly predicts the average correlation of substitutions as a function of their distance along the sequence. At the same time, it predicts an among-site distribution of the number of substitutions per site highly compatible with a negative binomial, consistently with experimental data. The promising outcomes achieved with this model encourage the application of the same ideas in the field of pairwise and multiple sequence alignment.

Self-organization of parS centromeres by the ParB CTP hydrolase.

ParABS systems facilitate chromosome segregation and plasmid partitioning in bacteria and archaea. ParB protein binds centromeric parS DNA sequences and spreads to flanking DNA. We show that ParB is an enzyme that hydrolyzes cytidine triphosphate (CTP) to cytidine diphosphate (CDP). parS DNA stimulates cooperative CTP binding by ParB and CTP hydrolysis. A nucleotide cocrystal structure elucidates the catalytic center of the dimerization-dependent ParB CTPase. Single-molecule imaging and biochemical assays recapitulate features of ParB spreading from parS in the presence but not absence of CTP. These findings suggest that centromeres assemble by self-loading of ParB DNA sliding clamps at parS ParB CTPase is not related to known nucleotide hydrolases and might be a promising target for developing new classes of antibiotics.

Mechanics of Allostery: Contrasting the Induced Fit and Population Shift Scenarios.

In allosteric proteins, binding a ligand can affect function at a distant location, for example, by changing the binding affinity of a substrate at the active site. The induced fit and population shift models, which differ by the assumed number of stable configurations, explain such cooperative binding from a thermodynamic viewpoint. Yet, understanding what mechanical principles constrain these models remains a challenge. Here, we provide an empirical study on 34 proteins supporting the idea that allosteric conformational change generally occurs along a soft elastic mode presenting extended regions of high shear. We argue, based on a detailed analysis of how the energy profile along such a mode depends on binding, that in the induced fit scenario, there is an optimal stiffness ka∗ ∼ 1/N for cooperative binding, where N is the number of residues. We find that the population shift scenario is more robust to mutations affecting stiffness because binding becomes more and more cooperative with stiffness up to the same characteristic value ka∗, beyond which cooperativity saturates instead of decaying. We numerically confirm these findings in a nonlinear mechanical model. Dynamical considerations suggest that a stiffness of order ka∗ is favorable in that scenario as well, supporting that for proper function, proteins must evolve a functional elastic mode that is softer as their size increases. In consistency with this view, we find a fair anticorrelation between the stiffness of the allosteric response and protein size in our data set.

Target engagement and cellular fate of otelixizumab: a repeat dose escalation study of an anti-CD3ε mAb in new-onset type 1 diabetes mellitus patients.

AIMS: This paper describes the pharmacological findings from a study where otelixizumab, an anti-CD3ɛ mAb, was dosed in new onset Type 1 diabetes mellitus (NOT1DM) patients. This is the first time that the full dose-response of an anti-CD3ɛ mAb has been investigated in the clinic. The data have been validated using a previously developed pharmacokinetic/pharmacodynamic (PK/PD) model of otelixizumab to simulate the interplay between drug administration, CD3ɛ target engagement and downmodulation. METHODS: Patients were randomized to control or active treatment with otelixizumab (1:4), administered via infusion over 6 days, in a dose-ascending study consisted of three cohorts (n = 10 per cohort) at doses of 9, 18 or 27 mg respectively. The study allowed quantification of otelixizumab PK, CD3ɛ target engagement and its pharmacodynamic effect (CD3ε/TCR modulation on circulating T lymphocytes). RESULTS: Otelixizumab concentrations increased and averaged to 364.09 (54.3), 1625.55 (72.5) and 2781.35 (28.0) ng ml-1 (Geom.mean, %CV) at the 9, 18 and 27 mg dose respectively. CD3ɛ target engagement was found to be rapid (within the first 30 min), leading to a receptor occupancy of ~60% within 6 h of dosing in all three doses. A dose-response relationship was observed with the two highest doses achieving a ~90% target engagement and consequential CD3ɛ/TCR downmodulation by Day 6. CONCLUSIONS: Data from this study revealed maximum target engagement and CD3ɛ/TCR modulation is achieved at doses of 18, 27 mg of otelixizumab. These findings can be useful in guiding dose selection in clinical trials with anti-CD3ɛ mAbs.

MPBPK-TMDD models for mAbs: alternative models, comparison, and identifiability issues.

The aim of the present study was to evaluate model identifiability when minimal physiologically-based pharmacokinetic (mPBPK) models are integrated with target mediated drug disposition (TMDD) models in the tissue compartment. Three quasi-steady-state (QSS) approximations of TMDD dynamics were explored: on (a) antibody-target complex, (b) free target, and (c) free antibody concentrations in tissue. The effects of the QSS approximations were assessed via simulations, taking as reference the mPBPK-TMDD model with no simplifications. Approximation (a) did not affect model-derived concentrations, while with the inclusion of approximation (b) or (c), target concentration profiles alone, or both drug and target concentration profiles respectively deviated from the reference model profiles. A local sensitivity analysis was performed, highlighting the potential importance of sampling in the terminal pharmacokinetic phase and of collecting target concentration data. The a priori and a posteriori identifiability of the mPBPK-TMDD models were investigated under different experimental scenarios and designs. The reference model and QSS approximation (a) on antibody-target complex were both found to be a priori identifiable in all scenarios, while under the further inclusion of QSS approximation (b) target concentration data were needed for a priori identifiability to be preserved. The property could not be assessed for the model including all three QSS approximations. A posteriori identifiability issues were detected for all models, although improvement was observed when appropriate sampling and dose range were selected. In conclusion, this work provides a theoretical framework for the assessment of key properties of mathematical models before their experimental application. Attention should be paid when applying integrated mPBPK-TMDD models, as identifiability issues do exist, especially when rich study designs are not feasible.

Toward a unified scoring function for native state discrimination and drug-binding pocket recognition.

Protein folding and receptor-ligand recognition are fundamental processes for any living organism. Although folding and ligand recognition are based on the same chemistry, the existing empirical scoring functions target just one problem: predicting the correct fold or the correct binding pose. We here introduce a statistical potential which considers moieties as fundamental units. The scoring function is able to deal with both folding and ligand pocket recognition problems with a performance comparable to the scoring functions specifically tailored for one of the two tasks. We foresee that the capability of the new scoring function to tackle both problems in a unified framework will be a key to deal with the induced fit phenomena, in which a target protein changes significantly its conformation upon binding. Moreover, the new scoring function might be useful in docking protocols towards intrinsically disordered proteins, whose flexibility cannot be handled with the available docking software.

In vivo affinity and target engagement in skin and blood in a first-time-in-human study of an anti-oncostatin M monoclonal antibody.

AIMS: The oncostatin M (OSM) pathway drives fibrosis, inflammation and vasculopathy, and is a potential therapeutic target for inflammatory and fibrotic diseases. The aim of this first-time-in-human experimental medicine study was to assess the safety, tolerability, pharmacokinetics and target engagement of single subcutaneous doses of GSK2330811, an anti-OSM monoclonal antibody, in healthy subjects. METHODS: This was a phase I, randomized, double-blind, placebo-controlled, single-dose escalation, first-time-in-human study of subcutaneously administered GSK2330811 in healthy adults (NCT02386436). Safety and tolerability, GSK2330811 pharmacokinetic profile, OSM levels in blood and skin, and the potential for antidrug antibody formation were assessed. The in vivo affinity of GSK2330811 for OSM and target engagement in serum and skin blister fluid (obtained via a skin suction blister model) were estimated using target-mediated drug disposition (TMDD) models in combination with compartmental and physiology-based pharmacokinetic (PBPK) models. RESULTS: Thirty subjects were randomized to receive GSK2330811 and 10 to placebo in this completed study. GSK2330811 demonstrated a favourable safety profile in healthy subjects; no adverse events were serious or led to withdrawal. There were no clinically relevant trends in change from baseline in laboratory values, with the exception of a reversible dose-dependent reduction in platelet count. GSK2330811 exhibited linear pharmacokinetics over the dose range 0.1-6 mg kg-1 . The estimated in vivo affinity (nM) of GSK2330811 for OSM was 0.568 [95% confidence interval (CI) 0.455, 0.710] in the compartmental with TMDD model and 0.629 (95% CI 0.494, 0.802) using the minimal PBPK with TMDD model. CONCLUSIONS: Single subcutaneous doses of GSK2330811 were well tolerated in healthy subjects. GSK2330811 demonstrated sufficient affinity to achieve target engagement in systemic circulation and target skin tissue, supporting the progression of GSK2330811 clinical development.

Time-to-Event Modeling of Left- or Right-Censored Toxicity Data in Nonclinical Drug Toxicology.

A time-to-event (TTE) model has been developed to characterize a histopathology toxicity that can only be detected at the time of animal sacrifice. The model of choice was a hazard model with a Weibull distribution and dose was a significant covariate. The diagnostic plots showed a satisfactory fit of the data, despite the high degree of left and right censoring. Comparison to a probabilistic logit model shows similar performance in describing the data with a slight underestimation of survival by the Logit model. However, the TTE model was found to be more predictive in extrapolating toxicity risk beyond the observation range of a truncated dataset. The diagnostic and comparison outcomes would suggest using the TTE approach as a first choice for characterizing short and long-term risk from nonclinical toxicity studies. However, further investigations are needed to explore the domain of application of this kind of approach in drug safety assessment.

Analysis of variability in length of sleep state bouts reveals memory-free sleep subcomponents consistent among primary insomnia patients.

The statistical distributions of bout lengths for the different (macro) sleep states (wake, N1, N2, N3, and REM sleep) are essential to understanding whether any memory-free subcomponent ("micro state") is involved in the organization of sleep. Micro state detection can be prevented by the fusion of data including various sources of variability, in particular by the differences in sleep architecture between individuals, along sleep time (or nighttime), or between different nights. In this analysis, a mathematical model of sleep was adopted to disentangle these features and advance the understanding of the dynamics and mechanisms of sleep and its states. The analysis involved 116 primary insomnia patients taking placebo before going to bed and undergoing polysomnography for one night. The individual sequences of macro sleep states had been previously modeled with a mixed-effect nonhomogeneous modified Markov chain model, from which individual conditional probability distributions for the bout durations were derived in this analysis as functions of sleep time. The probability distributions, affected by neither subject, night-time, nor multiple-night pooling, substantially changed at » and ¾ sleep time, had modified exponential shape, and were best described as the sum of one to four exponentials, depending on the sleep state. The time constants and proportions of bouts contributing to each exponential were similar in the different subjects, changing over sleep time. Variability in bout durations thus indicated the presence of multiple memory-free sleep subcomponents whose mean residence times and access probabilities could be identified and shown to be consistent among the studied subjects. NEW & NOTEWORTHY We present a new methodology for deriving, from polysomnography data, the individual conditional probability for the duration of the bouts of wake, N1, N2, N3, and REM sleep. We evaluated the variability of this probability within and between primary insomnia patients and along sleep time. The multiexponential shapes of the probability distributions within the individuals revealed memory-free mechanisms and sleep subcomponents with consistent features in the studied population.

Vibrational entropy estimation can improve binding affinity prediction for non-obligatory protein complexes.

Predicting the binding affinity between protein monomers is of paramount importance for the understanding of thermodynamical and structural factors that guide the formation of a complex. Several numerical techniques have been developed for the calculation of binding affinities with different levels of accuracy. Approaches such as thermodynamic integration and Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodologies which account for well defined physical interactions offer good accuracy but are computationally demanding. Methods based on the statistical analysis of experimental structures are much cheaper but good performances have only been obtained throughout consensus energy functions based on many different molecular descriptors. In this study we investigate the importance of the contribution to the binding free energy of the entropic term due to the fluctuations around the equilibrium structures. This term, which we estimated employing an elastic network model, is usually neglected in most statistical approaches. Our method crucially relies on a novel calibration procedure of the elastic network force constant. The residue mobility profile is fitted to the one obtained through a short all-atom molecular dynamics simulation on a subset of residues only. Our results show how the proper consideration of vibrational entropic contributions can improve the quality of the prediction on a set of non-obligatory protein complexes whose binding affinity is known.