Learning to Identify Physiological and Adventitious Metal-Binding Sites in the Three-Dimensional Structures of Proteins by Following the Hints of a Deep Neural Network.

Thirty-eight percent of protein structures in the Protein Data Bank contain at least one metal ion. However, not all these metal sites are biologically relevant. Cations present as impurities during sample preparation or in the crystallization buffer can cause the formation of protein-metal complexes that do not exist in vivo. We implemented a deep learning approach to build a classifier able to distinguish between physiological and adventitious zinc-binding sites in the 3D structures of metalloproteins. We trained the classifier using manually annotated sites extracted from the MetalPDB database. Using a 10-fold cross validation procedure, the classifier achieved an accuracy of about 90%. The same neural classifier could predict the physiological relevance of non-heme mononuclear iron sites with an accuracy of nearly 80%, suggesting that the rules learned on zinc sites have general relevance. By quantifying the relative importance of the features describing the input zinc sites from the network perspective and by analyzing the characteristics of the MetalPDB datasets, we inferred some common principles. Physiological sites present a low solvent accessibility of the aminoacids forming coordination bonds with the metal ion (the metal ligands), a relatively large number of residues in the metal environment (≥20), and a distinct pattern of conservation of Cys and His residues in the site. Adventitious sites, on the other hand, tend to have a low number of donor atoms from the polypeptide chain (often one or two). These observations support the evaluation of the physiological relevance of novel metal-binding sites in protein structures.

Three-Dimensional Facial Anthropometric Analysis With and Without Landmark Labelling: Is There a Real Difference?

INTRODUCTION: The actual role of landmarks labeling before three-dimensional (3D) facial acquisition is still debated. In this study, several measurements were compared among textured labeled (TL), unlabeled (NL), and untextured (NTL) 3D facial models. MATERIALS AND METHODS: The face of 50 subjects was acquired through stereophotogrammetry. Landmark coordinates were extracted from TL, NL, and NTL facial models, and 33 linear and angular measurements were calculated, together with surface area and volume. Accuracy of measurements among TL, NL, and NTL models was assessed through calculation of relative technical error of measurement (rTEM). The intra- and inter-observer errors for each type of facial model were calculated. RESULTS: Intra- and inter-observer error of measurements increased passing from textured to NTL and NL 3D models. Average rTEMs between TL models, and NTL and NL models were 4.5 ±â€Š2.6% and 4.7 ±â€Š2.8%, respectively, almost all measurements being classified as "very good" or "good." Only for orbital height and its inclination, mandibular ramus length, nasal convexity, alar slope angle, and facial divergence, rTEM was classified as "moderate" or "poor." CONCLUSIONS: Accuracy and precision of measurements decrease when landmarks are not previously labeled; attention must be taken when measurements have a low magnitude or involve landmarks requiring palpation.

Insights into the Dynamics of the Human Zinc Transporter ZnT8 by MD Simulations.

ZnT8 is a human zinc(II) transporter expressed at the membrane of secretory granules where it contributes to insulin storage importing zinc ions from the cytosol. In the human population, the two most common ZnT8 variants carry an arginine (R325) or a tryptophan (W325) in position 325. The former variant has the most efficient kinetics in zinc transport and has been correlated to a higher risk of developing insulin resistance. On the contrary, the W325 variant is less active and protects against type-2-diabetes. Here, we used molecular dynamics (MD) simulations to investigate the main differences between the R325 and W325 variants in the interaction with zinc(II) ions. Our simulations suggested that the position of the metal ion within the transport site was not the same for the two variants, underlying a different rearrangement of the transmembrane (TM) helices in the channel. The W325 variant featured a peculiar zinc environment not detected in the experimental structures. With respect to conformational dynamics, we observed that the R325 variant was significantly more flexible than W325, with the main role played by the transmembrane domain (TMD) and the C-terminal domain (CTD). This dynamics affected the packing of the TM helices and thus the channel accessibility from the cytosol. The dimer interface that keeps the two TM channels in contact became looser in both variants upon zinc binding to the transport site, suggesting that this may be an important step toward the switch from the inward- to the outward-facing state of the protein.

Novel bicistronic lentiviral vectors correct ß-Hexosaminidase deficiency in neural and hematopoietic stem cells and progeny: implications for in vivo and ex vivo gene therapy of GM2 gangliosidosis.

The favorable outcome of in vivo and ex vivo gene therapy approaches in several Lysosomal Storage Diseases suggests that these treatment strategies might equally benefit GM2 gangliosidosis. Tay-Sachs and Sandhoff disease (the main forms of GM2 gangliosidosis) result from mutations in either the HEXA or HEXB genes encoding, respectively, the α- or ß-subunits of the lysosomal ß-Hexosaminidase enzyme. In physiological conditions, α- and ß-subunits combine to generate ß-Hexosaminidase A (HexA, αß) and ß-Hexosaminidase B (HexB, ßß). A major impairment to establishing in vivo or ex vivo gene therapy for GM2 gangliosidosis is the need to synthesize the α- and ß-subunits at high levels and with the correct stoichiometric ratio, and to safely deliver the therapeutic products to all affected tissues/organs. Here, we report the generation and in vitro validation of novel bicistronic lentiviral vectors (LVs) encoding for both the murine and human codon optimized Hexa and Hexb genes. We show that these LVs drive the safe and coordinate expression of the α- and ß-subunits, leading to supranormal levels of ß-Hexosaminidase activity with prevalent formation of a functional HexA in SD murine neurons and glia, murine bone marrow-derived hematopoietic stem/progenitor cells (HSPCs), and human SD fibroblasts. The restoration/overexpression of ß-Hexosaminidase leads to the reduction of intracellular GM2 ganglioside storage in transduced and in cross-corrected SD murine neural progeny, indicating that the transgenic enzyme is secreted and functional. Importantly, bicistronic LVs safely and efficiently transduce human neurons/glia and CD34+ HSPCs, which are target and effector cells, respectively, in prospective in vivo and ex vivo GT approaches. We anticipate that these bicistronic LVs may overcome the current requirement of two vectors co-delivering the α- or ß-subunits genes. Careful assessment of the safety and therapeutic potential of these bicistronic LVs in the SD murine model will pave the way to the clinical development of LV-based gene therapy for GM2 gangliosidosis.

Protein structure prediction assisted with sparse NMR data in CASP13.

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

Speeding Up the Identification of Cystic Fibrosis Transmembrane Conductance Regulator-Targeted Drugs: An Approach Based on Bioinformatics Strategies and Surface Plasmon Resonance.

Cystic fibrosis (CF) is mainly caused by the deletion of Phe 508 (ΔF508) in the cystic fibrosis transmembrane conductance regulator (CFTR) protein that is thus withheld in the endoplasmic reticulum and rapidly degraded by the ubiquitin/proteasome system. New drugs able to rescue ΔF508-CFTR trafficking are eagerly awaited. An integrated bioinformatics and surface plasmon resonance (SPR) approach was here applied to investigate the rescue mechanism(s) of a series of CFTR-ligands including VX809, VX770 and some aminoarylthiazole derivatives (AAT). Computational studies tentatively identified a large binding pocket in the ΔF508-CFTR nucleotide binding domain-1 (NBD1) and predicted all the tested compounds to bind to three sub-regions of this main pocket. Noticeably, the known CFTR chaperone keratin-8 (K8) seems to interact with some residues located in one of these sub-pockets, potentially interfering with the binding of some ligands. SPR results corroborated all these computational findings. Moreover, for all the considered ligands, a statistically significant correlation was determined between their binding capability to ΔF508-NBD1 measured by SPR and the pockets availability measured by computational studies. Taken together, these results demonstrate a strong agreement between the in silico prediction and the SPR-generated binding data, suggesting a path to speed up the identification of new drugs for the treatment of cystic fibrosis.

Investigation of the Iron(II) Release Mechanism of Human H-Ferritin as a Function of pH.

We investigated the kinetics of the release of iron(II) ions from the internal cavity of human H-ferritin as a function of pH. Extensive molecular dynamics simulations of the entire 24-mer ferritin provided atomic-level information on the release mechanism. Double protonation of His residues at pH 4 facilitates the removal of the iron ligands within the C3 channel through the formation of salt bridges, resulting in a significantly lower release energy barrier than pH 9.

A protocol for the refinement of NMR structures using simultaneously pseudocontact shift restraints from multiple lanthanide ions.

The binding of paramagnetic metal ions to proteins produces a number of different effects on the NMR spectra of the system. In particular, when the magnetic susceptibility of the metal ion is anisotropic, pseudocontact shifts (PCSs) arise and can be easily measured. They constitute very useful restraints for the solution structure determination of metal-binding proteins. In this context, there has been great interest in the use of lanthanide(III) ions to induce PCSs in diamagnetic proteins, e.g. through the replacement native calcium(II) ions. By preparing multiple samples in each of which a different ion of the lanthanide series is introduced, it is possible to obtain multiple independent PCS datasets that can be used synergistically to generate protein structure ensembles (typically called bundles). For typical NMR-based determination of protein structure, it is necessary to perform an energetic refinement of such initial bundles to obtain final structures whose geometric quality is suitable for deposition in the PDB. This can be conveniently done by using restrained molecular dynamics simulations (rMD) in explicit solvent. However, there are no available protocols for rMD using multiple PCS datasets as part of the restraints. In this work, we extended the PCS module of the AMBER MD package to handle multiple datasets and tuned a previously developed protocol for NMR structure refinement to achieve consistent convergence with PCS restraints. Test calculations with real experimental data show that this new implementation delivers the expected improvement of protein geometry, resulting in final structures that are of suitable quality for deposition. Furthermore, we observe that also initial structures generated only with traditional restraints can be successfully refined using traditional and PCS restraints simultaneously.

Non-canonical amino acids for site-directed spin labeling of membrane proteins.

Membrane proteins remain challenging targets for conventional structural biology techniques because they need to reside within complex hydrophobic lipid environments to maintain proper structure and function. Magnetic resonance combined with site-directed spin labeling is an alternative method that provides atomic-level structural and dynamical information from effects introduced by an electron- or nuclear-based spin label. With the advent of bioorthogonal click chemistries and genetically engineered non-canonical amino acids (ncAAs), options for linking spin probes to biomolecules have substantially broadened outside the conventional cysteine-based labeling scheme. Here, we highlight current strategies to spin-label membrane proteins through ncAAs for nuclear and electron paramagnetic resonance applications. Such advances are critical for developing bioorthogonal spin labeling schemes to achieve in-cell labeling and in-cell measurements of membrane protein conformational dynamics.

Animal pollination shapes fruits market features, seeds functional traits and modulates their chemistry.

In this study, we experimentally addressed the impact of different pollination treatments on the morphological, reproductive and chemical traits of fruits and seeds of two crop species, the wild strawberry (Fragaria vesca L.) and cowpea (Vigna unguiculata (L.) Walp.). Multiple flowers from each plant were exposed to different pollination treatments: (1) self pollination, (2) hand cross pollination and (3) open pollination. Both crops were positively affected by open pollination in terms of morpho-chemical parameters concerning the marketability (e.g., 35% decrease in sugar/acid ratio in open pollinated strawberries compared to the autogamous ones) and the seed germination rate as a proxy of reproduction efficiency (e.g., the almost complete absence of seed abortion in the open pollination treatment). Remarkably, the pollination treatment also strongly influenced the phytochemical composition. Open-pollinated strawberries exhibited a higher relative concentration of compounds endowed with nutraceutical properties such as anthocyanins, ellagic acid derivatives and flavonoids. At the same time, cowpea seeds displayed higher concentrations of anti-nutrients in the self pollination treatments, such as saponins, compared to the open and hand cross pollinated seeds. This study suggests the presence of a link between the pollination mechanism, market quality, plant reproduction and chemical properties of fruits and seeds, supporting the intricate interplay between pollinators, plants and human nutrition, highlighting the crucial importance of animal pollination in the ecological and dietary contexts.

Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients.

PURPOSE: To evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patients, for the detection of acute appendicular fractures in the pediatric population on conventional X-ray radiography (CXR). MATERIALS AND METHODS: In this retrospective study, anonymized extremities CXRs of pediatric patients (age <17 years), with or without fractures, were included. Six hundred CXRs (maintaining the positive-for-fracture and negative-for-fracture balance) were included, grouping them per body part (shoulder/clavicle, elbow/upper arm, hand/wrist, leg/knee, foot/ankle). Follow-up CXRs and/or second-level imaging were considered as reference standard. A deep learning algorithm interpreted CXRs for fracture detection on a per-patient, per-radiograph, and per-location level, and its diagnostic performance values were compared with the reference standard. AI diagnostic performance was computed by using cross-tables, and 95 % confidence intervals [CIs] were obtained by bootstrapping. RESULTS: The final cohort included 312 male and 288 female with a mean age of 8.9±4.5 years. Three undred CXRs (50 %) were positive for fractures, according to the reference standard. For all fractures, the AI tool showed a per-patient 91.3% (95%CIs = 87.6-94.3) sensitivity, 76.7% (71.5-81.3) specificity, and 84% (82.1-86.0) accuracy. In the per-radiograph analysis the AI tool showed 85% (81.9-87.8) sensitivity, 88.5% (86.3-90.4) specificity, and 87.2% (85.7-89.6) accuracy. In the per-location analysis, the AI tool identified 606 bounding boxes: 472 (77.9 %) were correct, 110 (18.1 %) incorrect, and 24 (4.0 %) were not-overlapping. CONCLUSION: The AI algorithm provides good overall diagnostic performance for detecting appendicular fractures in pediatric patients.

Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties.

Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high-accuracy single-chain modeling. Since then, different customizations have expanded the number of conformational states accessible through AF2. Here, we further expanded AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in an automatic fashion.

Clinical Performance of the Gore Septal Occluder in Patent Foramen Ovale Closure in Different Septal Anatomies: 1-Year Results from a Single-Center Experience.

BACKGROUND: PFO (Patent foramen ovale) is a common defect that affects about 25% of the population. Although its presence is asymptomatic in the majority of the cases, the remaining part becomes overt with different symptoms, including cryptogenic stroke. PFO closure is currently a widely available procedure in complex anatomy, with Amplatzer PFO Occluder (APO) being the most commonly used tool. However, the performance of another device, the GORE Septal Occluder (GSO), has not been completely explored with regard to different septal anatomies. METHODS: From March 2012 to June 2020, 118 consecutive patients with an indication of PFO closure were treated using the GSO system, included in a prospective analysis, and followed. After 12 months, every patient underwent transcranial Doppler ultrasound to evaluate the effectiveness of treatment. RESULTS: Of 111 patients evaluated, 107 showed effective PFO closure (96.4%), and 4 showed a residual shunt (3.6%). To better evaluate the device performance, the overall population was sorted into two clusters based on the echocardiographic characteristics. The main difference between groups was for PFO width (4.85 ± 1.8 vs. 2.9 ± 1 mm, p < 0.001) and PFO tunnel length (12.6 ± 3.8 vs. 7.2 ± 2, p < 0.001), allowing complex and simple anatomies to be identified, respectively. Regardless of the aforementioned cluster, the GSO performance required to reach an effective closure was independent of anatomy type and the chosen device size. CONCLUSION: The GSO device showed a high closure rate at 1-year follow-up in patients, with at least one anatomical factor of complexity of PFO irrespective of the level of complexity itself.

SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography.

Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation.

Optical Coherence Tomography to Assess Proximal Side Optimization Technique in Crush Stenting.

Aim: The aim of this study was to explore the potential intraprocedural benefits of the Proximal Side Optimization (PSO) technique by Optical Coherence Tomography (OCT). Methods: A case series of 10 consecutive true bifurcation lesions, with severe long pathology of long side branch (SB), were randomly assigned to be treated by standard DK Crush procedure (non-PSO group) as compared to DK Crush in PSO modification (PSO group). The data from OCT investigation before crushing of the SB Drug-Eluting Stent (DES), after crushing, after first kissing balloon inflation (KBI), and after final angiography were compared between the two groups (Public trials registry ISRCTN23355755). Results: All 10 cases were successfully treated by the assigned technique. The two groups were similar in terms of indications for the procedure, bifurcation angle, and stent dimensions. As compared to the non-PSO, the PSO group showed larger proximal SB stent areas (5.8 ± 1.8 vs. 4.5 ± 0.5 mm2; p = 0.02), the larger delta between distal and proximal stent areas before crush (1.5 ± 0.7 vs. 0.6 ± 0.5 mm2; p = 0.004), and the larger Space of Optimal Wiring (SOW) after Crush (5.3 ± 1.8 vs. 2.5 ± 1.1 mm2; p = 0.02). The gaps in scaffolding within the ostial segment of the Side Branch DES were found in two patients from the non-PSO group. Conclusion: The DK Crush in PSO modification results in larger SB DES and SOW areas with better apposition to the vessel wall. As result, the SB DES acquires a funnel shape, which reduces the risk of passage outside the SB stent struts during re-wiring, thus, allowing predictable and secure results.

Modeling of protein conformational changes with Rosetta guided by limited experimental data.

Conformational changes are an essential component of functional cycles of many proteins, but their characterization often requires an integrative structural biology approach. Here, we introduce and benchmark ConfChangeMover (CCM), a new method built into the widely used macromolecular modeling suite Rosetta that is tailored to model conformational changes in proteins using sparse experimental data. CCM can rotate and translate secondary structural elements and modify their backbone dihedral angles in regions of interest. We benchmarked CCM on soluble and membrane proteins with simulated Cα-Cα distance restraints and sparse experimental double electron-electron resonance (DEER) restraints, respectively. In both benchmarks, CCM outperformed state-of-the-art Rosetta methods, showing that it can model a diverse array of conformational changes. In addition, the Rosetta framework allows a wide variety of experimental data to be integrated with CCM, thus extending its capability beyond DEER restraints. This method will contribute to the biophysical characterization of protein dynamics.

Sampling alternative conformational states of transporters and receptors with AlphaFold2.

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

Therapeutic advantages of combined gene/cell therapy strategies in a murine model of GM2 gangliosidosis.

Genetic deficiency of ß-N-acetylhexosaminidase (Hex) functionality leads to accumulation of GM2 ganglioside in Tay-Sachs disease and Sandhoff disease (SD), which presently lack approved therapies. Current experimental gene therapy (GT) approaches with adeno-associated viral vectors (AAVs) still pose safety and efficacy issues, supporting the search for alternative therapeutic strategies. Here we leveraged the lentiviral vector (LV)-mediated intracerebral (IC) GT platform to deliver Hex genes to the CNS and combined this strategy with bone marrow transplantation (BMT) to provide a timely, pervasive, and long-lasting source of the Hex enzyme in the CNS and periphery of SD mice. Combined therapy outperformed individual treatments in terms of lifespan extension and normalization of the neuroinflammatory/neurodegenerative phenotypes of SD mice. These benefits correlated with a time-dependent increase in Hex activity and a remarkable reduction in GM2 storage in brain tissues that single treatments failed to achieve. Our results highlight the synergic mode of action of LV-mediated IC GT and BMT, clarify the contribution of treatments to the therapeutic outcome, and inform on the realistic threshold of corrective enzymatic activity. These results have important implications for interpretation of ongoing experimental therapies and for design of more effective treatment strategies for GM2 gangliosidosis.

Multimodality Imaging of Sudden Cardiac Death and Acute Complications in Acute Coronary Syndrome.

Sudden cardiac death (SCD) is a potentially fatal event usually caused by a cardiac arrhythmia, which is often the result of coronary artery disease (CAD). Up to 80% of patients suffering from SCD have concomitant CAD. Arrhythmic complications may occur in patients with acute coronary syndrome (ACS) before admission, during revascularization procedures, and in hospital intensive care monitoring. In addition, about 20% of patients who survive cardiac arrest develop a transmural myocardial infarction (MI). Prevention of ACS can be evaluated in selected patients using cardiac computed tomography angiography (CCTA), while diagnosis can be depicted using electrocardiography (ECG), and complications can be evaluated with cardiac magnetic resonance (CMR) and echocardiography. CCTA can evaluate plaque, burden of disease, stenosis, and adverse plaque characteristics, in patients with chest pain. ECG and echocardiography are the first-line tests for ACS and are affordable and useful for diagnosis. CMR can evaluate function and the presence of complications after ACS, such as development of ventricular thrombus and presence of myocardial tissue characterization abnormalities that can be the substrate of ventricular arrhythmias.

Dynamical Behavior and Conformational Selection Mechanism of the Intrinsically Disordered Sic1 Kinase-Inhibitor Domain.

Intrinsically Disordered Peptides and Proteins (IDPs) in solution can span a broad range of conformations that often are hard to characterize by both experimental and computational methods. However, obtaining a significant representation of the conformational space is important to understand mechanisms underlying protein functions such as partner recognition. In this work, we investigated the behavior of the Sic1 Kinase-Inhibitor Domain (KID) in solution by Molecular Dynamics (MD) simulations. Our results point out that application of common descriptors of molecular shape such as Solvent Accessible Surface (SAS) area can lead to misleading outcomes. Instead, more appropriate molecular descriptors can be used to define 3D structures. In particular, we exploited Weighted Holistic Invariant Molecular (WHIM) descriptors to get a coarse-grained but accurate definition of the variegated Sic1 KID conformational ensemble. We found that Sic1 is able to form a variable amount of folded structures even in absence of partners. Among them, there were some conformations very close to the structure that Sic1 is supposed to assume in the binding with its physiological complexes. Therefore, our results support the hypothesis that this protein relies on the conformational selection mechanism to recognize the correct molecular partners.