Folding kinetics of an entangled protein.

The possibility of the protein backbone adopting lasso-like entangled motifs has attracted increasing attention. After discovering the surprising abundance of natively entangled protein domain structures, it was shown that misfolded entangled subpopulations might become thermosensitive or escape the homeostasis network just after translation. To investigate the role of entanglement in shaping folding kinetics, we introduce a novel indicator and analyze simulations of a coarse-grained, structure-based model for two small single-domain proteins. The model recapitulates the well-known two-state folding mechanism of a non-entangled SH3 domain. However, despite its small size, a natively entangled antifreeze RD1 protein displays a rich refolding behavior, populating two distinct kinetic intermediates: a short-lived, entangled, near-unfolded state and a longer-lived, non-entangled, near-native state. The former directs refolding along a fast pathway, whereas the latter is a kinetic trap, consistently with known experimental evidence of two different characteristic times. Upon trapping, the natively entangled loop folds without being threaded by the N-terminal residues. After trapping, the native entangled structure emerges by either backtracking to the unfolded state or threading through the already formed but not yet entangled loop. Along the fast pathway, trapping does not occur because the native contacts at the closure of the lasso-like loop fold after those involved in the N-terminal thread, confirming previous predictions. Despite this, entanglement may appear already in unfolded configurations. Remarkably, a longer-lived, near-native intermediate, with non-native entanglement properties, recalls what was observed in cotranslational folding.

Neurobiology and Applications of Inositol in Psychiatry: A Narrative Review.

Inositol is a natural sugar-like compound, commonly present in many plants and foods. It is involved in several biochemical pathways, most of them controlling vital cellular mechanisms, such as cell development, signaling and nuclear processes, metabolic and endocrine modulation, cell growth, signal transduction, etc. In this narrative review, we focused on the role of inositol in human brain physiology and pathology, with the aim of providing an update on both potential applications and current limits in its use in psychiatric disorders. Overall, imaging and biomolecular studies have shown the role of inositol levels in the pathogenesis of mood disorders. However, when administered as monotherapy or in addition to conventional drugs, inositol did not seem to influence clinical outcomes in both mood and psychotic disorders. Conversely, more encouraging results have emerged for the treatment of panic disorders. We concluded that, despite its multifaceted neurobiological activities and some positive findings, to date, data on the efficacy of inositol in the treatment of psychiatric disorders are still controversial, partly due to the heterogeneity of supporting studies. Therefore, systematic use of inositol in routine clinical practice cannot be recommended yet, although further basic and translational research should be encouraged.

Entangled Motifs in Membrane Protein Structures.

Entangled motifs are found in one-third of protein domain structures, a reference set that contains mostly globular proteins. Their properties suggest a connection with co-translational folding. Here, we wish to investigate the presence and properties of entangled motifs in membrane protein structures. From existing databases, we build a non-redundant data set of membrane protein domains, annotated with the monotopic/transmembrane and peripheral/integral labels. We evaluate the presence of entangled motifs using the Gaussian entanglement indicator. We find that entangled motifs appear in one-fifth of transmembrane and one-fourth of monotopic proteins. Surprisingly, the main features of the distribution of the values of the entanglement indicator are similar to the reference case of general proteins. The distribution is conserved across different organisms. Differences with respect to the reference set emerge when considering the chirality of entangled motifs. Although the same chirality bias is found for single-winding motifs in both membrane and reference proteins, the bias is reversed, strikingly, for double-winding motifs only in the reference set. We speculate that these observations can be rationalized in terms of the constraints exerted on the nascent chain by the co-translational bio-genesis machinery, which is different for membrane and globular proteins.

Key interaction patterns in proteins revealed by cluster expansion of the partition function.

The native conformation of structured proteins is stabilized by a complex network of interactions. We analyzed the elementary patterns that constitute such network and ranked them according to their importance in shaping protein sequence design. To achieve this goal, we employed a cluster expansion of the partition function in the space of sequences and evaluated numerically the statistical importance of each cluster. An important feature of this procedure is that it is applied to a dense finite system. We found that patterns that contribute most to the partition function are cycles with even numbers of nodes, while cliques are typically detrimental. Each cluster also gives a contribute to the sequence entropy, which is a measure of the evolutionary designability of a fold. We compared the entropies associated with different interaction patterns to their abundances in the native structures of real proteins.

Folding Rate Optimization Promotes Frustrated Interactions in Entangled Protein Structures.

Many native structures of proteins accomodate complex topological motifs such as knots, lassos, and other geometrical entanglements. How proteins can fold quickly even in the presence of such topological obstacles is a debated question in structural biology. Recently, the hypothesis that energetic frustration might be a mechanism to avoid topological frustration has been put forward based on the empirical observation that loops involved in entanglements are stabilized by weak interactions between amino-acids at their extrema. To verify this idea, we use a toy lattice model for the folding of proteins into two almost identical structures, one entangled and one not. As expected, the folding time is longer when random sequences folds into the entangled structure. This holds also under an evolutionary pressure simulated by optimizing the folding time. It turns out that optmized protein sequences in the entangled structure are in fact characterized by frustrated interactions at the closures of entangled loops. This phenomenon is much less enhanced in the control case where the entanglement is not present. Our findings, which are in agreement with experimental observations, corroborate the idea that an evolutionary pressure shapes the folding funnel to avoid topological and kinetic traps.

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.

PASTA 2.0: an improved server for protein aggregation prediction.

The formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2/.

Native fold and docking pose discrimination by the same residue-based scoring function.

Structure prediction and quality assessment are crucial steps in modeling native protein conformations. Statistical potentials are widely used in related algorithms, with different parametrizations typically developed for different contexts such as folding protein monomers or docking protein complexes. Here, we describe BACH-SixthSense, a single residue-based statistical potential that can be successfully employed in both contexts. BACH-SixthSense shares the same approach as BACH, a knowledge-based potential originally developed to score monomeric protein structures. A term that penalizes steric clashes as well as the distinction between polar and apolar sidechain-sidechain contacts are crucial novel features of BACH-SixthSense. The performance of BACH-SixthSense in discriminating correctly the native structure among a competing set of decoys is significantly higher than other state-of-the-art scoring functions, that were specifically trained for a single context, for both monomeric proteins (QMEAN, Rosetta, RF_CB_SRS_OD, benchmarked on CASP targets) and protein dimers (IRAD, Rosetta, PIE*PISA, HADDOCK, FireDock, benchmarked on 14 CAPRI targets). The performance of BACH-SixthSense in recognizing near-native docking poses within CAPRI decoy sets is good as well.

Sequence-Based Prediction of Protein Phase Separation: The Role of Beta-Pairing Propensity.

The formation of droplets of bio-molecular condensates through liquid-liquid phase separation (LLPS) of their component proteins is a key factor in the maintenance of cellular homeostasis. Different protein properties were shown to be important in LLPS onset, making it possible to develop predictors, which try to discriminate a positive set of proteins involved in LLPS against a negative set of proteins not involved in LLPS. On the other hand, the redundancy and multivalency of the interactions driving LLPS led to the suggestion that the large conformational entropy associated with non specific side-chain interactions is also a key factor in LLPS. In this work we build a LLPS predictor which combines the ability to form pi-pi interactions, with an unrelated feature, the propensity to stabilize the ß-pairing interaction mode. The cross-ß structure is formed in the amyloid aggregates, which are involved in degenerative diseases and may be the final thermodynamically stable state of protein condensates. Our results show that the combination of pi-pi and ß-pairing propensity yields an improved performance. They also suggest that protein sequences are more likely to be involved in phase separation if the main chain conformational entropy of the ß-pairing maintained droplet state is increased. This would stabilize the droplet state against the more ordered amyloid state. Interestingly, the entropic stabilization of the droplet state appears to proceed according to different mechanisms, depending on the fraction of "droplet-driving" proteins present in the positive set.

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.

Survey on COVID-19-related mortality associated with occupational infection during the first phase of the pandemic: A systematic review.

According to the Centre for Disease Control and Prevention in 2020, a cluster of pneumonia cases of unknown etiology caused by the severe acute respiratory syndrome (SARS)-coronavirus 2 was reported in Wuhan, China. The present review examined the literature to reveal the incidence of novel coronavirus-2019 disease (COVID-19) infections, underlying comorbidities, workplace infections and case fatality rates. A review was performed to identify the relevant publications available up to May 15, 2020. Since the early stages of the COVID-19 outbreak, the case fatality rate among healthcare workers (HCWs) has stood at 0.69% worldwide and 0.4% in Italy. Based on the current information, most patients have exhibited good prognoses in terms of after-effects or sequelae and low mortality rate. Patients that became critically ill were primarily in the elderly population or had chronic underlying diseases, including diabetes and hypertension. Among all working sectors, HCWs, since they are front-line caregivers for patients with COVID-19, are considered to be in the high-risk population. Increased age and a number of comorbidity factors have been associated with increased risk of mortality in patients with COVID-19. The most frequent complications of COVID-19 reported that can cause fatality in patients were SARS, cardiac arrest, secondary infections and septic shock, in addition to acute kidney failure and liver failure. Overcoming the COVID-19 pandemic is an ongoing challenge, which poses a threat to global health that requires close surveillance and prompt diagnosis, in coordination with research efforts to understand this pathogen and develop effective countermeasures.

Fibril elongation mechanisms of HET-s prion-forming domain: topological evidence for growth polarity.

The prion-forming C-terminal domain of the fungal prion HET-s forms infectious amyloid fibrils at physiological pH. The conformational switch from the nonprion soluble form to the prion fibrillar form is believed to have a functional role, as HET-s in its prion form participates in a recognition process of different fungal strains. On the basis of the knowledge of the high-resolution structure of the prion forming domain HET-s(218-289) in its fibrillar form, we here present a numerical simulation of the fibril growth process, which emphasizes the role of the topological properties of the fibrillar structure. An accurate thermodynamic analysis of the way an intervening HET-s chain is recruited to the tip of the growing fibril suggests that elongation proceeds through a dock and lock mechanism. First, the chain docks onto the fibril by forming the longest ß-strands. Then, the re-arrangement in the fibrillar form of all the rest of the molecule takes place. Interestingly, we also predict that one side of the HET-s fibril is more suitable for sustaining its growth with respect to the other. The resulting strong polarity of fibril growth is a consequence of the complex topology of HET-s fibrillar structure, as the central loop of the intervening chain plays a crucially different role in favoring or not the attachment of the C-terminus tail to the fibril, depending on the growth side.

Exploring the universe of protein structures beyond the Protein Data Bank.

It is currently believed that the atlas of existing protein structures is faithfully represented in the Protein Data Bank. However, whether this atlas covers the full universe of all possible protein structures is still a highly debated issue. By using a sophisticated numerical approach, we performed an exhaustive exploration of the conformational space of a 60 amino acid polypeptide chain described with an accurate all-atom interaction potential. We generated a database of around 30,000 compact folds with at least of secondary structure corresponding to local minima of the potential energy. This ensemble plausibly represents the universe of protein folds of similar length; indeed, all the known folds are represented in the set with good accuracy. However, we discover that the known folds form a rather small subset, which cannot be reproduced by choosing random structures in the database. Rather, natural and possible folds differ by the contact order, on average significantly smaller in the former. This suggests the presence of an evolutionary bias, possibly related to kinetic accessibility, towards structures with shorter loops between contacting residues. Beside their conceptual relevance, the new structures open a range of practical applications such as the development of accurate structure prediction strategies, the optimization of force fields, and the identification and design of novel folds.

Amyloidogenic potential of transthyretin variants: insights from structural and computational analyses.

Human transthyretin (TTR) is an amyloidogenic protein whose mild amyloidogenicity is enhanced by many point mutations affecting considerably the amyloid disease phenotype. To ascertain whether the high amyloidogenic potential of TTR variants may be explained on the basis of the conformational change hypothesis, an aim of this work was to determine structural alterations for five amyloidogenic TTR variants crystallized under native and/or destabilizing (moderately acidic pH) conditions. While at acidic pH structural changes may be more significant because of a higher local protein flexibility, only limited alterations, possibly representing early events associated with protein destabilization, are generally induced by mutations. This study was also aimed at establishing to what extent wild-type TTR and its amyloidogenic variants are intrinsically prone to beta-aggregation. We report the results of a computational analysis predicting that wild-type TTR possesses a very high intrinsic beta-aggregation propensity which is on average not enhanced by amyloidogenic mutations. However, when located in beta-strands, most of these mutations are predicted to destabilize the native beta-structure. The analysis also shows that rat and murine TTR have a lower intrinsic beta-aggregation propensity and a similar native beta-structure stability compared with human TTR. This result is consistent with the lack of in vitro amyloidogenicity found for both murine and rat TTR. Collectively, the results of this study support the notion that the high amyloidogenic potential of human pathogenic TTR variants is determined by the destabilization of their native structures, rather than by a higher intrinsic beta-aggregation propensity.

REPETITA: detection and discrimination of the periodicity of protein solenoid repeats by discrete Fourier transform.

MOTIVATION: Proteins with solenoid repeats evolve more quickly than non-repetitive ones and their periodicity may be rapidly hidden at sequence level, while still evident in structure. In order to identify these repeats, we propose here a novel method based on a metric characterizing amino-acid properties (polarity, secondary structure, molecular volume, codon diversity, electric charge) using five previously derived numerical functions. RESULTS: The five spectra of the candidate sequences coding for structural repeats, obtained by Discrete Fourier Transform (DFT), show common features allowing determination of repeat periodicity with excellent results. Moreover it is possible to introduce a phase space parameterized by two quantities related to the Fourier spectra which allow for a clear distinction between a non-homologous set of globular proteins and proteins with solenoid repeats. The DFT method is shown to be competitive with other state of the art methods in the detection of solenoid structures, while improving its performance especially in the identification of periodicities, since it is able to recognize the actual repeat length in most cases. Moreover it highlights the relevance of local structural propensities in determining solenoid repeats. AVAILABILITY: A web tool implementing the algorithm presented in the article (REPETITA) is available with additional details on the data sets at the URL: http://protein.bio.unipd.it/repetita/.

A condensation-ordering mechanism in nanoparticle-catalyzed peptide aggregation.

Nanoparticles introduced in living cells are capable of strongly promoting the aggregation of peptides and proteins. We use here molecular dynamics simulations to characterise in detail the process by which nanoparticle surfaces catalyse the self-assembly of peptides into fibrillar structures. The simulation of a system of hundreds of peptides over the millisecond timescale enables us to show that the mechanism of aggregation involves a first phase in which small structurally disordered oligomers assemble onto the nanoparticle and a second phase in which they evolve into highly ordered as their size increases.

Symmetry, shape, and order.

Packing problems have been of great interest in many diverse contexts for many centuries. The optimal packing of identical objects has been often invoked to understand the nature of low-temperature phases of matter. In celebrated work, Kepler conjectured that the densest packing of spheres is realized by stacking variants of the face-centered-cubic lattice and has a packing fraction of pi /(3\square root2)\approximately 0.7405. Much more recently, an unusually high-density packing of approximately 0.770732 was achieved for congruent ellipsoids. Such studies are relevant for understanding the structure of crystals, glasses, the storage and jamming of granular materials, ceramics, and the assembly of viral capsid structures. Here, we carry out analytical studies of the stacking of close-packed planar layers of systems made up of truncated cones possessing uniaxial symmetry. We present examples of high-density packing whose order is characterized by a broken symmetry arising from the shape of the constituent objects. We find a biaxial arrangement of solid cones with a packing fraction of pi/4. For truncated cones, there are two distinct regimes, characterized by different packing arrangements, depending on the ratio c of the base radii of the truncated cones with a transition at c*=\square root2-1.

Sequence and structural patterns detected in entangled proteins reveal the importance of co-translational folding.

Proteins must fold quickly to acquire their biologically functional three-dimensional native structures. Hence, these are mainly stabilized by local contacts, while intricate topologies such as knots are rare. Here, we reveal the existence of specific patterns adopted by protein sequences and structures to deal with backbone self-entanglement. A large scale analysis of the Protein Data Bank shows that loops significantly intertwined with another chain portion are typically closed by weakly bound amino acids. Why is this energetic frustration maintained? A possible picture is that entangled loops are formed only toward the end of the folding process to avoid kinetic traps. Consistently, these loops are more frequently found to be wrapped around a portion of the chain on their N-terminal side, the one translated earlier at the ribosome. Finally, these motifs are less abundant in natural native states than in simulated protein-like structures, yet they appear in 32% of proteins, which in some cases display an amazingly complex intertwining.

Inference of the solvation energy parameters of amino acids using maximum entropy approach.

We present a novel technique, based on the principle of maximum entropy, for deriving the solvation energy parameters of amino acids from the knowledge of the solvent accessible areas in experimentally determined native state structures as well as high quality decoys of proteins. We present the results of detailed studies and analyze the correlations of the solvation energy parameters with the standard hydrophobic scale. We study the ability of the inferred parameters to discriminate between the native state structures of proteins and their decoy conformations.

Emergence of secondary motifs in tubelike polymers in a solvent.

We study the effects of two kinds of interactions in tubelike polymers and demonstrate that they result in the formation of secondary motifs. The first has an entropic origin and is a measure of the effective space available to the solvent. The second arises from solvophobic interactions of the solvent with the polymers and leads to an energy proportional to the contact surface between the tube and solvent particles. The solvent molecules are modeled as hard spheres and the two interactions are considered separately with the solvent density affecting their relative strength. In addition to analytical calculations, we present the results of numerical simulations in order to understand the role played by the finite length of short polymers and the discrete versus continuum descriptions of the system in determining the preferred conformation.