Identifying promising druggable binding sites and their flexibility to target the receptor-binding domain of SARS-CoV-2 spike protein.

The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for viral infection. The interaction of its receptor-binding domain (RBD) with the human angiotensin-converting enzyme 2 (ACE2) protein is required for the virus to enter the host cell. We identified RBD binding sites to block its function with inhibitors by combining the protein structural flexibility with machine learning analysis. Molecular dynamics simulations were performed on unbound or ACE2-bound RBD conformations. Pockets estimation, tracking and druggability prediction were performed on a large sample of simulated RBD conformations. Recurrent druggable binding sites and their key residues were identified by clustering pockets based on their residue similarity. This protocol successfully identified three druggable sites and their key residues, aiming to target with inhibitors for preventing ACE2 interaction. One site features key residues for direct ACE2 interaction, highlighted using energetic computations, but can be affected by several mutations of the variants of concern. Two highly druggable sites, located between the spike protein monomers interface are promising. One weakly impacted by only one Omicron mutation, could contribute to stabilizing the spike protein in its closed state. The other, currently not affected by mutations, could avoid the activation of the spike protein trimer.

Combined assessment of DYRK1A, BDNF and homocysteine levels as diagnostic marker for Alzheimer's disease.

Early identification of Alzheimer's disease (AD) risk factors would aid development of interventions to delay the onset of dementia, but current biomarkers are invasive and/or costly to assess. Validated plasma biomarkers would circumvent these challenges. We previously identified the kinase DYRK1A in plasma. To validate DYRK1A as a biomarker for AD diagnosis, we assessed the levels of DYRK1A and the related markers brain-derived neurotrophic factor (BDNF) and homocysteine in two unrelated AD patient cohorts with age-matched controls. Receiver-operating characteristic curves and logistic regression analyses showed that combined assessment of DYRK1A, BDNF and homocysteine has a sensitivity of 0.952, a specificity of 0.889 and an accuracy of 0.933 in testing for AD. The blood levels of these markers provide a diagnosis assessment profile. Combined assessment of these three markers outperforms most of the previous markers and could become a useful substitute to the current panel of AD biomarkers. These results associate a decreased level of DYRK1A with AD and challenge the use of DYRK1A inhibitors in peripheral tissues as treatment. These measures will be useful for diagnosis purposes.

Homocysteine threshold value based on cystathionine beta synthase and paraoxonase 1 activities in mice.

BACKGROUND: Hyperhomocysteinaemia is a metabolic disorder associated with the development of premature atherosclerosis. Among the determinants which predispose to premature thromboembolic and atherothrombotic events, serum activity of paraoxonase 1, mainly synthesized in the liver, has been shown to be a predictor of cardiovascular disease and to be negatively correlated with serum homocysteine levels in human. Even though treatments of hyperhomocysteinaemic patients ongoing cardiovascular complications are commonly used, it still remains unclear above which homocysteine level a preventive therapy should be started. MATERIALS AND METHODS: In order to establish a threshold of plasma homocysteine concentration we have analyzed the hepatic cystathionine beta synthase and paraoxonase 1 activities in a moderate to intermediate murine model of hyperhomocysteinaemia. Using wild type and heterozygous cystathionine beta synthase deficient mice fed a methionine enriched diet or a control diet, we first studied the link between cystathionine beta synthase and paraoxonase 1 activities and plasma homocysteine concentration. RESULTS: Among the animals used in this study, we observed a negative correlation between plasma homocysteine level and cystathionine beta synthase activity (rho=-0.52, P=0.0008) or paraoxonase 1 activity (rho=-0.49, P=0.002). Starting from these results, a homocysteine cut-off value of 15 microm has been found for both cystathionine beta synthase (P=0.0003) and paraoxonase 1 (P=0.0007) activities. CONCLUSIONS: Our results suggest that both cystathionine beta synthase and paraoxonase 1 activities are significantly decreased in mice with a plasma homocysteine value greater than 15 microm. In an attempt to set up preventive treatment for cardiovascular disease our results indicate that treatments should be started from 15 microm of plasma homocysteine.

A reduced amino acid alphabet for understanding and designing protein adaptation to mutation.

Protein sequence world is considerably larger than structure world. In consequence, numerous non-related sequences may adopt similar 3D folds and different kinds of amino acids may thus be found in similar 3D structures. By grouping together the 20 amino acids into a smaller number of representative residues with similar features, sequence world simplification may be achieved. This clustering hence defines a reduced amino acid alphabet (reduced AAA). Numerous works have shown that protein 3D structures are composed of a limited number of building blocks, defining a structural alphabet. We previously identified such an alphabet composed of 16 representative structural motifs (5-residues length) called Protein Blocks (PBs). This alphabet permits to translate the structure (3D) in sequence of PBs (1D). Based on these two concepts, reduced AAA and PBs, we analyzed the distributions of the different kinds of amino acids and their equivalences in the structural context. Different reduced sets were considered. Recurrent amino acid associations were found in all the local structures while other were specific of some local structures (PBs) (e.g Cysteine, Histidine, Threonine and Serine for the alpha-helix Ncap). Some similar associations are found in other reduced AAAs, e.g Ile with Val, or hydrophobic aromatic residues Trp with Phe and Tyr. We put into evidence interesting alternative associations. This highlights the dependence on the information considered (sequence or structure). This approach, equivalent to a substitution matrix, could be useful for designing protein sequence with different features (for instance adaptation to environment) while preserving mainly the 3D fold.

Hidden Markov model-derived structural alphabet for proteins: the learning of protein local shapes captures sequence specificity.

Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence.

SCit: web tools for protein side chain conformation analysis.

SCit is a web server providing services for protein side chain conformation analysis and side chain positioning. Specific services use the dependence of the side chain conformations on the local backbone conformation, which is described using a structural alphabet that describes the conformation of fragments of four-residue length in a limited library of structural prototypes. Based on this concept, SCit uses sets of rotameric conformations dependent on the local backbone conformation of each protein for side chain positioning and the identification of side chains with unlikely conformations. The SCit web server is accessible at http://bioserv.rpbs.jussieu.fr/SCit.

A hidden markov model derived structural alphabet for proteins.

Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have set up a hidden Markov model that discretizes protein backbone conformation as series of overlapping fragments (states) of four residues length. This approach learns simultaneously the geometry of the states and their connections. We obtain, using a statistical criterion, an optimal systematic decomposition of the conformational variability of the protein peptidic chain in 27 states with strong connection logic. This result is stable over different protein sets. Our model fits well the previous knowledge related to protein architecture organisation and seems able to grab some subtle details of protein organisation, such as helix sub-level organisation schemes. Taking into account the dependence between the states results in a description of local protein structure of low complexity. On an average, the model makes use of only 8.3 states among 27 to describe each position of a protein structure. Although we use short fragments, the learning process on entire protein conformations captures the logic of the assembly on a larger scale. Using such a model, the structure of proteins can be reconstructed with an average accuracy close to 1.1A root-mean-square deviation and for a complexity of only 3. Finally, we also observe that sequence specificity increases with the number of states of the structural alphabet. Such models can constitute a very relevant approach to the analysis of protein architecture in particular for protein structure prediction.

A new approach for rapid detection and typing of serum monoclonal components.

When used independently, none of the routine methods to explore serum monoclonal components (MC), including: serum protein electrophoresis (SPE), immunoelectrophoresis (IEP), kappa to lambda ratio (KLR) and immunofixation (IFE), provides a comprehensive quantitative and qualitative identification of the MC. In the past few years the concept of 'protein profile', based on immunonephelometric quantifications of serum proteins, has become widely used. It consists of a qualitative and quantitative graphic representation of numerous serum proteins including immunoglobulins. Aim of study was to develop a multidimensional model based exclusively on protein profiles labeled the protein profile prediction method (PPPM) to improve routine MC detection and typing. Serum samples from 127 hospitalized patients and 99 healthy blood donors were submitted to all of the following: SPE, IFE, KLR and a protein profile (which included IgM, IgA, IgG, kappa and lambda chain detections and quantification). The presence of a MC using IFE was chosen as the gold standard. Healthy donors and patients were randomly divided into two groups defined as testing and validation groups. A logistic model was designed based on the protein profiles of the testing group leading to the determination of a threshold value (called Z(r)) for MC detection. It was then tested to detect MC in the validation group. Using IFE, 73 MC were found in the 127 hospitalized patients. Using the threshold value for MC detection of Z(r)=1.86, the PPPM showed greater sensitivity (94.6%) in detecting a MC compared to either SPE (64.8%) or KLR (89.2%). This result was obtained without diminished specificity (80.8%). The association of SPE or KLR to PPPM did not significantly increase the sensitivity of the PPPM. In the validation group, for samples which had a high predictive probability of a MC using PPPM, the correct MC typing was identified in up to 77% of sera using PPPM only. These results may be interesting in helping to determine when supplementary IFE analysis is required to qualitatively analyze a MC. PPPM allows MC detection with great sensitivity. The immune protein profile dramatically increases the sensitivity of either SPE and/or KLR in detecting MC and may also allow heavy and light chain typing.

The role of percutaneous transluminal coronary angioplasty in heart transplant recipients.

OBJECTIVES: Review the acute and late results of percutaneous transluminal coronary angioplasty (PTCA) in heart transplant recipients and examine the factors predictive of restenosis. BACKGROUND: Coronary graft disease (CGD) is the main factor responsible for late graft loss. Medical treatment, surgical revascularization, or retransplantation gives only suboptimal results in this regard. Therefore, PTCA has been attempted in this situation. METHODS: More than 332 heart transplantations in our institution have been performed since 1992, the date of the first PTCA in our patients. We are currently in charge of 450 patients. All the characteristics, procedure-related information, and clinical outcome of patients needing PTCA were assessed by review of each patient's clinical records. All coronary angiograms were reviewed by an independent cardiologist. RESULTS: Since 1992, 53 coronary sites have been dilated in the course of 39 procedures in 29 patients. Indication for PTCA was asymptomatic angiographic coronary graft disease in 35 sites (64.8%), angina in 9 (16.6%), silent ischemia in 2 (3.7%), acute myocardial infarction in 1 (1.8%), and CHF in 7 (12.9%). Primary success (< 50% residual stenosis) was obtained in 50 (94.3%) of 53 lesions. No periprocedural death occurred. Procedural complications were 1 transient acute renal failure and 1 persistent bleeding at the puncture site. Six months restenosis rate (defined as percent stenosis > 50%) was 32.5% (14/43). Mean follow-up was 1.27 year +/- 1.2 (SD). Five deaths (17. 2%) occurred in follow-up and were all in relation to coronary graft disease. Mean time separating PTCA from death was 0.9 year +/- 1.3 (SD). We also sought to look at factors predictive of restenosis. By multivariate analysis, a positive recipient's serology for cytomegalovirus (CMV) before the graft was the only factor found protective against restenosis (odds ratio 22.4; confidence interval 1.1 to 443.4). CONCLUSION: PTCA in heart transplant recipients allows a high level of primary success with a low periprocedural-complication rate. Restenosis rate seems equivalent to restenosis rate in native coronary arteries. Mortality during follow-up is increased in this population and is the consequence of a high level of coronary events. Recipient positivity for CMV before the graft is associated with a protective effect from restenosis.

Extrahepatic manifestations associated with hepatitis C virus infection. A prospective multicenter study of 321 patients. The GERMIVIC. Groupe d'Etude et de Recherche en Medecine Interne et Maladies Infectieuses sur le Virus de l'Hepatite C.

From January 1996 to January 1997, 321 patients with an average age of 46 +/- 16 years and chronically infected with hepatitis C virus (HCV) were prospectively enrolled in a study designed to determine the prevalence of extrahepatic manifestations associated with HCV infection in a large cohort of HCV patients, to identify associations between clinical and biologic manifestations, and to compare the results obtained in human immunodeficiency virus (HIV)-positive versus HIV-negative subsets. In a cross-sectional study, clinical extrahepatic manifestations, viral coinfections with HIV and/or hepatitis B virus, connective tissue diseases, and a wide panel of autoantibodies were assessed. Thirty-eight percent (122/321) of patients presented at least 1 clinical extrahepatic manifestation including arthralgia (60/321, 19%), skin manifestations (55/321, 17%), xerostomia (40/321, 12%), xerophthalmia (32/321, 10%), and sensory neuropathy (28/321, 9%). Main biologic abnormalities were mixed cryoglobulins (110/196, 56%), thrombocytopenia (50/291, 17%), and the presence of the following autoantibodies: antinuclear (123/302, 41%), rheumatoid factor (107/280, 38%), anticardiolipin (79/298, 27%), antithyroglobulin (36/287, 13%) and antismooth muscle cell (27/288, 9%). At least 1 autoantibody was present in 210/302 (70%) of sera. By multivariate logistic regression analysis, 4 parameters were significantly associated with cryoglobulin positivity: systemic vasculitis (p = 0.01, odds ratio OR[ = 17.3), HIV positivity (p = 0.0006, OR = 10.2), rheumatoid factor positivity (p = 0.01, OR = 2.8), and sicca syndrome (p = 0.03, OR = 0.27). A definite connective tissue disease was noted in 44 patients (14%), mainly symptomatic mixed cryoglobulinemia and systemic vasculitis, HIV coinfection (23%) was associated with 3 parameters: anticardiolipin (p = 0.003, OR = 4.18), thrombocytopenia (p = 0.01, OR = 3.56), and arthralgia or myalgia (p = 0.017, OR = 0.23). HIV-positive patients presented more severe histologic lesions (p = 0.0004). Extrahepatic clinical manifestations in HCV patients involve primarily the skin and joints. The most frequent immunologic abnormalities include mixed cryoglobulins, rheumatoid factor, antinuclear, anticardiolipin, and antithyroglobulin antibodies. Cryoglobulin positivity is associated with systemic vasculitis and rheumatoid factor and HIV positivity. HIV coinfection is associated with arthralgia or myalgia, anticardiolipin antibodies, and thrombocytopenia.

Hidden Markov model approach for identifying the modular framework of the protein backbone.

The hidden Markov model (HMM) was used to identify recurrent short 3D structural building blocks (SBBs) describing protein backbones, independently of any a priori knowledge. Polypeptide chains are decomposed into a series of short segments defined by their inter-alpha-carbon distances. Basically, the model takes into account the sequentiality of the observed segments and assumes that each one corresponds to one of several possible SBBs. Fitting the model to a database of non-redundant proteins allowed us to decode proteins in terms of 12 distinct SBBs with different roles in protein structure. Some SBBs correspond to classical regular secondary structures. Others correspond to a significant subdivision of their bounding regions previously considered to be a single pattern. The major contribution of the HMM is that this model implicitly takes into account the sequential connections between SBBs and thus describes the most probable pathways by which the blocks are connected to form the framework of the protein structures. Validation of the SBBs code was performed by extracting SBB series repeated in recoding proteins and examining their structural similarities. Preliminary results on the sequence specificity of SBBs suggest promising perspectives for the prediction of SBBs or series of SBBs from the protein sequences.

Clinical features and prognostic implications of familial hypertrophic cardiomyopathy related to the cardiac myosin-binding protein C gene.

BACKGROUND: Little information is available on phenotype-genotype correlations in familial hypertrophic cardiomyopathy that are related to the cardiac myosin binding protein C (MYBPC3) gene. The aim of this study was to perform this type of analysis. METHODS AND RESULTS: We studied 76 genetically affected subjects from nine families with seven recently identified mutations (SASint20, SDSint7, SDSint23, branch point int23, Glu542Gln, a deletion in exon 25, and a duplication/deletion in exon 33) in the MYBPC3 gene. Detailed clinical, ECG, and echocardiographic parameters were analyzed. An intergene analysis was performed by comparing the MYBPC3 group to seven mutations in the beta-myosin heavy-chain gene (beta-MHC) group (n=52). There was no significant phenotypic difference among the different mutations in the MYBPC3 gene. However, in the MYBPC3 group compared with the beta-MHC group, (1) prognosis was significantly better (P<0.0001), and no deaths occurred before the age of 40 years; (2) the age at onset of symptoms was delayed (41+/-19 versus 35+/-17 years, P<0.002); and (3) before 30 years of age, the phenotype was particularly mild because penetrance was low (41% versus 62%), maximal wall thicknesses lower (12+/-4 versus 16+/-7 mm, P<0.03), and abnormal T waves less frequent (9% versus 45%, P<0.02). CONCLUSIONS: These results are consistent with specific clinical features related to the MYBPC3 gene: onset of the disease appears delayed and the prognosis is better than that associated with the beta-MHC gene. These findings could be particularly important for the purpose of clinical management and genetic counseling in familial hypertrophic cardiomyopathy.

Heart retransplantation: a 23-year single-center clinical experience.

BACKGROUND: The main causes of allograft failure after cardiac transplantation are primary graft dysfunction, intractable acute rejection, and coronary graft disease. Despite the important progress in the last several years in graft preservation, surgical techniques, immunosuppression, and treatment of coronary graft disease, retransplantation in selected cases is the only way to achieve long-term recipient survival. METHODS: We compare here in a case-control study 24 retransplantations with 47 first transplants in patients matched for date of transplantation. RESULTS: Between 1973 and 1996, 1,063 patients underwent cardiac transplantation in our institution. In this cohort, 22 patients had a total of 24 retransplantations (2 second-time retransplantations). The causes of retransplantations were primary graft failure (n=4), acute rejection (n=7), coronary graft disease (n=11), and miscellaneous (n=2). Survival at 1 and 5 years of patients with retransplantations is 45.5% and 31.2%, and survival of control patients is 59.4% and 38.8% (p=0.07). An interval between first transplantation and retransplantation shorter (n=11) or longer (n=13) than 1 year is associated with a 1-year survival of 27.3% and 61.5% and a 4-year survival of 27.3% and 46%, respectively (not significant). Intervals shorter than 1 year between first transplantation and retransplantation were exclusively secondary to primary graft failure or intractable acute rejection. CONCLUSIONS: In the face of lack of donor grafts, these and other data indicate that retransplantation should be considered cautiously, especially when the interval between the first transplantation and retransplantation is short.

A hidden Markov model approach to neuron firing patterns.

Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.

Stochastic modeling of the hypothalamic pulse generator activity.

Luteinizing hormone (LH) is released by the pituitary in discrete pulses. In the monkey, the appearance of LH pulses in the plasma is invariably associated with sharp increases (i.e, volleys) in the frequency of the hypothalamic pulse generator electrical activity, so that continuous monitoring of this activity by telemetry provides a unique means to study the temporal structure of the mechanism generating the pulses. To assess whether the times of occurrence and durations of previous volleys exert significant influence on the timing of the next volley, we used a class of periodic counting process models that specify the stochastic intensity of the process as the product of two factors: 1) a periodic baseline intensity and 2) a stochastic regression function with covariates representing the influence of the past. This approach allows the characterization of circadian modulation and memory range of the process underlying hypothalamic pulse generator activity, as illustrated by fitting the model to experimental data from two ovariectomized rhesus monkeys.