The endoplasmic reticulum proteostasis network profoundly shapes the protein sequence space accessible to HIV envelope.

The sequence space accessible to evolving proteins can be enhanced by cellular chaperones that assist biophysically defective clients in navigating complex folding landscapes. It is also possible, at least in theory, for proteostasis mechanisms that promote strict quality control to greatly constrain accessible protein sequence space. Unfortunately, most efforts to understand how proteostasis mechanisms influence evolution rely on artificial inhibition or genetic knockdown of specific chaperones. The few experiments that perturb quality control pathways also generally modulate the levels of only individual quality control factors. Here, we use chemical genetic strategies to tune proteostasis networks via natural stress response pathways that regulate the levels of entire suites of chaperones and quality control mechanisms. Specifically, we upregulate the unfolded protein response (UPR) to test the hypothesis that the host endoplasmic reticulum (ER) proteostasis network shapes the sequence space accessible to human immunodeficiency virus-1 (HIV-1) envelope (Env) protein. Elucidating factors that enhance or constrain Env sequence space is critical because Env evolves extremely rapidly, yielding HIV strains with antibody- and drug-escape mutations. We find that UPR-mediated upregulation of ER proteostasis factors, particularly those controlled by the IRE1-XBP1s UPR arm, globally reduces Env mutational tolerance. Conserved, functionally important Env regions exhibit the largest decreases in mutational tolerance upon XBP1s induction. Our data indicate that this phenomenon likely reflects strict quality control endowed by XBP1s-mediated remodeling of the ER proteostasis environment. Intriguingly, and in contrast, specific regions of Env, including regions targeted by broadly neutralizing antibodies, display enhanced mutational tolerance when XBP1s is induced, hinting at a role for host proteostasis network hijacking in potentiating antibody escape. These observations reveal a key function for proteostasis networks in decreasing instead of expanding the sequence space accessible to client proteins, while also demonstrating that the host ER proteostasis network profoundly shapes the mutational tolerance of Env in ways that could have important consequences for HIV adaptation.

α-Synuclein reduces acetylserotonin O-methyltransferase mediated melatonin biosynthesis by microtubule-associated protein 1 light chain 3 beta-related degradation pathway.

Previous studies have demonstrated that α-synuclein (α-SYN) is closely associated with rapid eye movement sleep behavior disorder (RBD) related to several neurodegenerative disorders. However, the exact molecular mechanisms are still rarely investigated. In the present study, we found that in the α-SYNA53T induced RBD-like behavior mouse model, the melatonin level in the plasma and pineal gland were significantly decreased. To elucidate the underlying mechanism of α-SYN-induced melatonin reduction, we investigated the effect of α-SYN in melatonin biosynthesis. Our findings showed that α-SYN reduced the level and activity of melatonin synthesis enzyme acetylserotonin O-methyltransferase (ASMT) in the pineal gland and in the cell cultures. In addition, we found that microtubule-associated protein 1 light chain 3 beta (LC3B) as an important autophagy adapter is involved in the degradation of ASMT. Immunoprecipitation assays revealed that α-SYN increases the binding between LC3B and ASMT, leading to ASMT degradation and a consequent reduction in melatonin biosynthesis. Collectively, our results demonstrate the molecular mechanisms of α-SYN in melatonin biosynthesis, indicating that melatonin is an important molecule involved in the α-SYN-associated RBD-like behaviors, which may provide a potential therapeutic target for RBD of Parkinson's disease.

Comparative genomic analyses of Klebsiella pneumoniae K57 capsule serotypes isolated from bovine mastitis in China.

Klebsiella pneumoniae can cause severe clinical mastitis in dairy cows, with K. pneumoniae type K57 (K57-KP) being the most common capsular serotype. To identify virulence factors and antimicrobial-resistance (AMR) genes of K57-KP with varying virulence, Galleria mellonella (greater wax moth) larvae were infected as a screening model to characterize virulence of 90 K57-KP strains, with 10 and 11 strains defined as virulent or attenuated, respectively, based on larval survival rates. Next, virulence of these 21 isolates was subsequently confirmed in adhesion and lactate dehydrogenase release assays, using bovine mammary epithelial cells cultured in vitro. Finally, genes associated with virulence and AMR were characterize with whole-genome sequencing. These 21 K57-KP strains were designated into 16 sequence types based on multi-locus sequence typing and allocated in phylogenetic analysis based on single nucleotide polymorphisms. We found great genetic diversity among isolates. In addition, adhesion-associated genes (e.g., fimA, sfaA, and focA) aminoglycoside-resistance genes (aph(6)-Id, strAB) were associated with virulence. This study provided new knowledge regarding virulence of K57-KP associated with bovine mastitis, which may inform development of novel diagnostic tools and prevention strategies for bovine mastitis.

ß,ß-Dimethylacrylalkannin, a Natural Naphthoquinone, Inhibits the Growth of Hepatocellular Carcinoma Cells by Modulating Tumor-Associated Macrophages.

Tumor-associated macrophages (TAMs) are pivotal in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC), influencing various stages from initiation to metastasis. Understanding the role of TAMs in HCC is crucial for developing novel therapeutic strategies. Macrophages exhibit plasticity, resulting in M1 and M2 phenotypes, with M1 macrophages displaying antitumor properties and M2 macrophages promoting tumor progression. Targeting TAMs to alter their polarization could offer new avenues for HCC treatment. ß,ß-dimethylacrylalkannin (DMAKN), a natural naphthoquinone, has gained attention for its antitumor properties. However, its impact on TAMs modulation remains unclear. This study investigates DMAKN's modulation of TAMs and its anti-HCC activity. Using an in vitro model with THP-1 cells, we induced M1 macrophages with LPS/IFN-γ and M2 macrophages with IL-4/IL-13, confirming polarization with specific markers. Co-culturing these macrophages with HCC cells showed that M1 cells inhibited HCC growth, while M2 cells promoted it. Screening for non-toxic DMAKN concentrations revealed its ability to induce M1 polarization and enhance LPS/IFN-γ-induced M1 macrophages, both showing anti-HCC effects. Conversely, DMAKN suppressed IL-4/IL-13-induced M2 polarization, inhibiting M2 macrophages' promotion of HCC cell viability. In summary, DMAKN induces and enhances M1 polarization while inhibiting M2 polarization of macrophages, thereby inhibiting HCC cell growth. These findings suggest that DMAKN has the potential to regulate TAMs in HCC, offering promise for future therapeutic development.

Potent and Protease Resistant Azapeptide Agonists of the GLP-1 and GIP Receptors.

The gut-derived peptide hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) play important physiological roles. Stabilized agonists of the GLP-1 receptor (GLP-1R) and the GIP receptor (GIPR) for the management of diabetes and obesity have generated widespread enthusiasm and have become blockbuster drugs. These therapeutics are refractory to the action of dipeptidyl peptidase-4 (DPP4), that catalyzes rapid removal of the two N-terminal residues of the native peptides, in turn severely diminishing their activity profiles.  Here we report that a single atom change from carbon to nitrogen in the backbone of the entire peptide make them refractory to DPP4 action while still retaining full potency and efficacy at their respective receptors.  This was accomplished by use of aza-amino acids, that are bioisosteric replacements for a-amino acids that perturb the structural backbone and local side chain conformations.  Molecular dynamics simulations reveal that aza-amino acid can populate the same conformational space that GLP-1 adopts when bound to the GLP-1R. The insertion of an aza-amino acid at the second position from the N-terminus in semaglutide and in a dual agonist of GLP-1R and GIPR further demonstrates its capability as a viable alternative to current DPP4 resistance strategies while offering additional structural variety.

A Boron-Dependent Antibiotic Derived from a Calcium-Dependent Antibiotic.

The prevalence of drug-resistant bacterial pathogens foreshadows a healthcare crisis. Calcium-dependent antibiotics (CDAs) are promising candidates to combat infectious diseases as many of them show modes of action (MOA) orthogonal to widespread resistance mechanisms. The calcium dependence is nonetheless one of the hurdles toward realizing their full potential. Using laspartomycin C (LspC) as a model, we explored the possibility of reducing, or even eliminating, its calcium dependence. We report herein a synthetic LspC analogue (B1) whose activity no longer depends on calcium and is instead induced by phenylboronic acid (PBA). In LspC, Asp1 and Asp7 coordinate to calcium to anchor it in the active conformation; these residues are replaced by serine in B1 and condense with PBA to form a boronic ester with the same anchoring effect. Using thin-layer chromatography, MS, NMR, and complementation assays, we demonstrate that B1 inhibits bacterial growth via the same MOA as LspC, i.e., sequestering the cell wall biosynthetic intermediate undecaprenyl phosphate. B1 is as potent and effective as LspC against several Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus. Our success in converting a CDA to a boron-dependent antibiotic opens a new avenue in the design and functional control of drug molecules.

Deep learning enables automatic adult age estimation based on CT reconstruction images of the costal cartilage.

OBJECTIVE: Adult age estimation (AAE) is a challenging task. Deep learning (DL) could be a supportive tool. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. METHODS: Chest CT were reconstructed using volume rendering (VR) and maximum intensity projection (MIP) separately. Retrospective data of 2500 patients aged 20.00-69.99 years were obtained. The cohort was split into training (80%) and validation (20%) sets. Additional independent data from 200 patients were used as the test set and external validation set. Different modality DL models were developed accordingly. Comparisons were hierarchically performed by VR versus MIP, single-modality versus multi-modality, and DL versus manual method. Mean absolute error (MAE) was the primary parameter of comparison. RESULTS: A total of 2700 patients (mean age = 45.24 years ± 14.03 [SD]) were evaluated. Of single-modality models, MAEs yielded by VR were lower than MIP. Multi-modality models generally yielded lower MAEs than the optimal single-modality model. The best-performing multi-modality model obtained the lowest MAEs of 3.78 in males and 3.40 in females. On the test set, DL achieved MAEs of 3.78 in males and 3.92 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. For the external validation, MAEs were 6.05 in males and 6.68 in females for DL, and 6.93 and 8.28 for the manual method. CONCLUSIONS: DL demonstrated better performance than the manual method in AAE based on CT reconstruction of the costal cartilage. CLINICAL RELEVANCE STATEMENT: Aging leads to diseases, functional performance deterioration, and both physical and physiological damage over time. Accurate AAE may aid in diagnosing the personalization of aging processes. KEY POINTS: • VR-based DL models outperformed MIP-based models with lower MAEs and higher R2 values. • All multi-modality DL models showed better performance than single-modality models in adult age estimation. • DL models achieved a better performance than expert assessments.

Semi-supervised automatic dental age and sex estimation using a hybrid transformer model.

Teeth-based age and sex estimation is an important task in mass disasters, criminal scenes, and archeology. Although various methods have been proposed, most of them are subjective and influenced by observers' experiences. In this study, we aimed to develop a deep learning model for automatic dental age and sex estimation from orthopantomograms (OPGs) and compare to manual methods. A large dataset of 15,195 OPGs (age range, 16 ~ 50 years; mean age, 29.65 years ± 9.36 [SD]; 10,218 females) was used to train and test a hybrid deep learning model which is a combination of convolutional neural network and transformer model. The final performance of this model was evaluated on additional independent 100 OPGs and compared to the manual method for external validation. In the test of 1413 OPGs, the mean absolute error (MAE) of age estimation was 2.61 years by this model. The accuracy and the area under the receiver operating characteristic curve (AUC) of sex estimation were 95.54% and 0.984. The heatmap indicated that the crown and pulp chamber of premolars and molars contain the most age-related information. In the additional independent 100 OPGs, this model achieved an MAE of 3.28 years for males and 3.79 years for females. The accuracy of this model was much higher than that of the manual models. Therefore, this model has the potential to assist radiologists in automated age and sex estimation.

Forensic age estimation in adults by pubic bone mineral density using multidetector computed tomography.

Radiology plays a crucial role in forensic anthropology for age estimation. However, most studies rely on morphological methods. This study aims to investigate the feasibility of using pubic bone mineral density (BMD) as a new age estimation method in the Chinese population. 468 pubic bone CT scans from living individuals in a Chinese hospital aged 18 to 87 years old were used to measure pubic BMD. The BMD of the bilateral pubic bone was measured using the Mimics software on cross-sectional CT images and the mean BMD of the bilateral pubic bone was also calculated. Regression analysis was performed to assess the correlation between pubic BMD and chronological age and to develop mathematical models for age estimation. We evaluated the accuracy of the best regression model using an independent validation sample by calculating the mean absolute error (MAE). Among all established models, the cubic regression model had the highest R2 value in both genders, with R2 = 0.550 for males and R2 = 0.634 for females. The results of the best model test showed that the MAE for predicting age using pubic BMD was 8.66 years in males and 7.69 years in females. This study highlights the potential of pubic BMD as a useful objective indicator for adult age estimation and could be used as an alternative in forensic practice when other better indicators are lacking.

Computational Prediction of Cyclic Peptide Structural Ensembles and Application to the Design of Keap1 Binders.

The Nrf2 transcription factor is a master regulator of the cellular response to oxidative stress, and Keap1 is its primary negative regulator. Activating Nrf2 by inhibiting the Nrf2-Keap1 protein-protein interaction has shown promise for treating cancer and inflammatory diseases. A loop derived from Nrf2 has been shown to inhibit Keap1 selectively, especially when cyclized, but there are no reliable design methods for predicting an optimal macrocyclization strategy. In this work, we employed all-atom, explicit-solvent molecular dynamics simulations with enhanced sampling methods to predict the relative degree of preorganization for a series of peptides cyclized with a set of bis-thioether "staples". We then correlated these predictions to experimentally measured binding affinities for Keap1 and crystal structures of the cyclic peptides bound to Keap1. This work showcases a computational method for designing cyclic peptides by simulating and comparing their entire solution-phase ensembles, providing key insights into designing cyclic peptides as selective inhibitors of protein-protein interactions.

Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations.

Protein-protein interactions are vital to biological processes, but the shape and size of their interfaces make them hard to target using small molecules. Cyclic peptides have shown promise as protein-protein interaction modulators, as they can bind protein surfaces with high affinity and specificity. Dozens of cyclic peptides are already FDA approved, and many more are in various stages of development as immunosuppressants, antibiotics, antivirals, or anticancer drugs. However, most cyclic peptide drugs so far have been natural products or derivatives thereof, with de novo design having proven challenging. A key obstacle is structural characterization: cyclic peptides frequently adopt multiple conformations in solution, which are difficult to resolve using techniques like NMR spectroscopy. The lack of solution structural information prevents a thorough understanding of cyclic peptides' sequence-structure-function relationship. Here we review recent development and application of molecular dynamics simulations with enhanced sampling to studying the solution structures of cyclic peptides. We describe novel computational methods capable of sampling cyclic peptides' conformational space and provide examples of computational studies that relate peptides' sequence and structure to biological activity. We demonstrate that molecular dynamics simulations have grown from an explanatory technique to a full-fledged tool for systematic studies at the forefront of cyclic peptide therapeutic design.

Prognostic value of neutrophil-lymphocyte ratio in out-of-hospital cardiac arrest patients receiving targeted temperature management: An observational cohort study.

BACKGROUND: Out-hospital cardiac arrest (OHCA) is a major cause of mortality and morbidity worldwide. The magnitude of the post-resuscitation inflammatory response is closely related to the severity of the circulatory dysfunction. Currently, targeted temperature management (TTM) has become an essential part of the post-resuscitation care for unconscious OHCA survivors. Some novel prognostic inflammatory markers may help predict outcomes of OHCA patients after TTM. METHODS: A retrospective observational cohort study of 65 OHCA patients treated with TTM was conducted in a tertiary hospital in Taiwan. The primary outcome measure was in-hospital mortality. Baseline and post-TTM neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte (PLR), and the systemic immune inflammation index (SII) were identified as potential predictors. RESULTS: These patients had a mean age of 62.2 ± 17.0 years. Among the total sample, 53.8% had an initial shockable rhythm and 61.5% had a presumed cardiac etiology. The median resuscitation duration was 20 min (IQR 13.5-28.5) and 60% received subsequent percutaneous coronary intervention. The mean baseline NLR, PLR and SII were 7.5 ± 16.7, 118 ± 207, 1395 ± 3004, and the mean post-TTM NLR, PLR and SII were 15.0 ± 11.6, 206 ± 124, 2369 ± 2569, respectively. Using multiple logistic regression analysis, post-TTM NLR was one of the independent factors which predicted in-hospital mortality (adjusted odds ratio (aOR): 1.249, 95% confidence interval (CI): 1.040-1.501, p = 0.017). CONCLUSION: Post-TTM NLR is a predictor of in-hospital mortality in OHCA patients who underwent TTM.

In Vitro and In Vivo Anti-Cancer Activity of Lasiokaurin in a Triple-Negative Breast Cancer Model.

Due to its intricate heterogeneity, high invasiveness, and poor prognosis, triple-negative breast cancer (TNBC) stands out as the most formidable subtype of breast cancer. At present, chemotherapy remains the prevailing treatment modality for TNBC, primarily due to its lack of estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth receptor 2 (HER2). However, clinical chemotherapy for TNBC is marked by its limited efficacy and a pronounced incidence of adverse effects. Consequently, there is a pressing need for novel drugs to treat TNBC. Given the rich repository of diverse natural compounds in traditional Chinese medicine, identifying potential anti-TNBC agents is a viable strategy. This study investigated lasiokaurin (LAS), a natural diterpenoid abundantly present in Isodon plants, revealing its significant anti-TNBC activity both in vitro and in vivo. Notably, LAS treatment induced cell cycle arrest, apoptosis, and DNA damage in TNBC cells, while concurrently inhibiting cell metastasis. In addition, LAS effectively inhibited the activation of the phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/Akt/mTOR) pathway and signal transducer and activator of transcription 3 (STAT3), thus establishing its potential for multitarget therapy against TNBC. Furthermore, LAS demonstrated its ability to reduce tumor growth in a xenograft mouse model without exerting detrimental effects on the body weight or vital organs, confirming its safe applicability for TNBC treatment. Overall, this study shows that LAS is a potent candidate for treating TNBC.

Advances in NURR1-Regulated Neuroinflammation Associated with Parkinson's Disease.

Neuroinflammation plays a crucial role in the progression of neurodegenerative disorders, particularly Parkinson's disease (PD). Glial cell activation and subsequent adaptive immune involvement are neuroinflammatory features in familial and idiopathic PD, resulting in the death of dopaminergic neuron cells. An oxidative stress response, inflammatory mediator production, and immune cell recruitment and activation are all hallmarks of this activation, leading to chronic neuroinflammation and progressive neurodegeneration. Several studies in PD patients' cerebrospinal fluid and peripheral blood revealed alterations in inflammatory markers and immune cell populations that may lead to or exacerbate neuroinflammation and perpetuate the neurodegenerative process. Most of the genes causing PD are also expressed in astrocytes and microglia, converting their neuroprotective role into a pathogenic one and contributing to disease onset and progression. Nuclear receptor-related transcription factor 1 (NURR1) regulates gene expression linked to dopaminergic neuron genesis and functional maintenance. In addition to playing a key role in developing and maintaining neurotransmitter phenotypes in dopaminergic neurons, NURR1 agonists have been shown to reverse behavioral and histological abnormalities in animal PD models. NURR1 protects dopaminergic neurons from inflammation-induced degeneration, specifically attenuating neuronal death by suppressing the expression of inflammatory genes in microglia and astrocytes. This narrative review highlights the inflammatory changes in PD and the advances in NURR1-regulated neuroinflammation associated with PD. Further, we present new evidence that targeting this inflammation with a variety of potential NURR1 target therapy medications can effectively slow the progression of chronic neuroinflammation-induced PD.

Stapled ß-Hairpins Featuring 4-Mercaptoproline.

Peptides constrained by intramolecular cross-links, especially stapled α-helices, have emerged as versatile scaffolds for drug development. However, there are fewer examples of similarly constrained scaffolds for other secondary structures. Here, we used a novel computational strategy to identify an optimal staple for antiparallel ß-strands, and then we incorporated that staple within a ß-hairpin peptide. The hairpin uses 4-mercaptoproline as a novel staple component, which contributes to a unique, kinked structure. The stapled hairpins show a high degree of structure in aqueous solution, excellent resistance to degradation in cell lysates, and cytosolic penetration at micromolar concentrations. They also overlay with a unique subset of kinked hairpin motifs at protein-protein interaction interfaces. Thus, these scaffolds represent promising starting points for developing inhibitors of cellular protein-protein interactions.

Destabilized adaptive influenza variants critical for innate immune system escape are potentiated by host chaperones.

The threat of viral pandemics demands a comprehensive understanding of evolution at the host-pathogen interface. Here, we show that the accessibility of adaptive mutations in influenza nucleoprotein at fever-like temperatures is mediated by host chaperones. Particularly noteworthy, we observe that the Pro283 nucleoprotein variant, which (1) is conserved across human influenza strains, (2) confers resistance to the Myxovirus resistance protein A (MxA) restriction factor, and (3) critically contributed to adaptation to humans in the 1918 pandemic influenza strain, is rendered unfit by heat shock factor 1 inhibition-mediated host chaperone depletion at febrile temperatures. This fitness loss is due to biophysical defects that chaperones are unavailable to address when heat shock factor 1 is inhibited. Thus, influenza subverts host chaperones to uncouple the biophysically deleterious consequences of viral protein variants from the benefits of immune escape. In summary, host proteostasis plays a central role in shaping influenza adaptation, with implications for the evolution of other viruses, for viral host switching, and for antiviral drug development.

Mycoplasma bovis subverts autophagy to promote intracellular replication in bovine mammary epithelial cells cultured in vitro.

Mycoplasma species are the smallest prokaryotes capable of self-replication. To investigate Mycoplasma induced autophagy in mammalian cells, Mycoplasma bovis (M. bovis) and bovine mammary epithelial cells (bMEC) were used in an in vitro infection model. Initially, intracellular M. bovis was enclosed within a membrane-like structure in bMEC, as viewed with transmission electron microscopy. In infected bMEC, increased LC3II was verified by Western blotting, RT-PCR and laser confocal microscopy, confirming autophagy at 1, 3 and 6 h post-infection (hpi), with a peak at 6 hpi. However, the M. bovis-induced autophagy flux was subsequently blocked. P62 degradation in infected bMEC was inhibited at 3, 6, 12 and 24 hpi, based on Western blotting and RT-PCR. Beclin1 expression decreased at 12 and 24 hpi. Furthermore, autophagosome maturation was subverted by M. bovis. Autophagosome acidification was inhibited by M. bovis infection, based on detection of mCherry-GFP-LC3 labeled autophagosomes; the decreases in protein levels of Lamp-2a indicate that the lysosomes were impaired by infection. In contrast, activation of autophagy (with rapamycin or HBSS) overcame the M. bovis-induced blockade in phagosome maturation by increasing delivery of M. bovis to the lysosome, with a concurrent decrease in intracellular M. bovis replication. In conclusion, although M. bovis infection induced autophagy in bMEC, the autophagy flux was subsequently impaired by inhibiting autophagosome maturation. Therefore, we conclude that M. bovis subverted autophagy to promote its intracellular replication in bMEC. These findings are the impetus for future studies to further characterize interactions between M. bovis and mammalian host cells.

CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting.

Molecular dynamics (MD) simulations are an exceedingly and increasingly potent tool for molecular behavior prediction and analysis. However, the enormous wealth of data generated by these simulations can be difficult to process and render in a human-readable fashion. Cluster analysis is a commonly used way to partition data into structurally distinct states. We present a method that improves on the state of the art by taking advantage of the temporal information of MD trajectories to enable more accurate clustering at a lower memory cost. To date, cluster analysis of MD simulations has generally treated simulation snapshots as a mere collection of independent data points and attempted to separate them into different clusters based on structural similarity. This new method, cluster analysis of trajectories based on segment splitting (CATBOSS), applies density-peak-based clustering to classify trajectory segments learned by change detection. Applying the method to a synthetic toy model as well as four real-life data sets-trajectories of MD simulations of alanine dipeptide and valine dipeptide as well as two fast-folding proteins-we find CATBOSS to be robust and highly performant, yielding natural-looking cluster boundaries and greatly improving clustering resolution. As the classification of points into segments emphasizes density gaps in the data by grouping them close to the state means, CATBOSS applied to the valine dipeptide system is even able to account for a degree of freedom deliberately omitted from the input data set. We also demonstrate the potential utility of CATBOSS in distinguishing metastable states from transition segments as well as promising application to cases where there is little or no advance knowledge of intrinsic coordinates, making for a highly versatile analysis tool.

Cyclic peptides: backbone rigidification and capability of mimicking motifs at protein-protein interfaces.

Cyclization is commonly employed in efforts to improve the target binding affinity of peptide-based probes and therapeutics. Many structural motifs have been identified at protein-protein interfaces and provide promising targets for inhibitor design using cyclic peptides. Cyclized peptides are generally assumed to be rigidified relative to their linear counterparts. This rigidification potentially pre-organizes the molecules to interact properly with their targets. However, the actual impact of cyclization on, for example, peptide configurational entropy, is currently poorly understood in terms of both its magnitude and molecular-level origins. Moreover, even with thousands of desired structural motifs at hand, it is currently not possible to a priori identify the ones that are most promising to mimic using cyclic peptides nor to select the ideal linker length. Instead, labor-intensive chemical synthesis and experimental characterization of various cyclic peptide designs are required, in hopes of finding one with improved target affinity. Herein, using molecular dynamics simulations of polyglycines, we elucidated how head-to-tail cyclization impacts peptide backbone dihedral entropy and developed a simple strategy to rapidly screen for structures that can be reliably mimicked by preorganized cyclic peptides. As expected, cyclization generally led to a reduction in backbone dihedral entropy; notably, however, this effect was minimal when the length of polyglycines was >9 residues. We also found that the reduction in backbone dihedral entropy upon cyclization of small polyglycine peptides does not result from more restricted distributions of the dihedrals; rather, it was the correlations between specific dihedrals that caused the decrease in configurational entropy in the cyclic peptides. Using our comprehensive cyclo-Gn structural ensembles, we obtained a holistic picture of what conformations are accessible to cyclic peptides. Using "hot loops" recently identified at protein-protein interfaces as an example, we provide clear guidelines for choosing the "easiest" hot loops for cyclic peptides to mimic and for identifying appropriate cyclic peptide lengths. In conclusion, our results provide an understanding of the thermodynamics and structures of this interesting class of molecules. This information should prove particularly useful for designing cyclic peptide inhibitors of protein-protein interactions.

Association study between genetic variants and the risk of schizophrenia in the Chinese population based on GWAS-implicated 6p21.3-23.1 human genome region: a case-control study.

BACKGROUND: Schizophrenia is a polygenic disease; however, the specific risk genetic variants of schizophrenia are still largely unknown. Single nucleotide polymorphism (SNP) is important genetic factor for the susceptibility of schizophrenia. Investigating individual candidate gene contributing to disease risk remains important. METHODS: In a case-control study, five SNPs located in 6p21.3-p23.1 including rs2021722 in human leukocyte antigen (HLA) locus and rs107822, rs383711, rs439205 and rs421446 within the upstream of microRNA-219a-1 were genotyped in 454 schizophrenia patients and 445 healthy controls to investigate the possible association between the loci and schizophrenia in a Han Chinese population. RESULTS: Our results showed significant associations between the rs2021722 and schizophrenia in allele (A vs. G: adjusted OR = 1.661, 95%CI = 1.196-2.308), co-dominant (AG vs. GG: OR = 1.760, 95%CI = 1.234-2.510) and dominant genetic model (AG + AA vs. GG: OR = 1.756, 95%CI = 1.237-2.492), respectively. Haplotype analysis showed that TGGT and CAAC were protective factor for schizophrenia compared with TAAC haplotype (OR = 0.324, 95% CI = 0.157-0.672; OR = 0.423, 95% CI = 0.199-0.900). CONCLUSIONS: These findings indicate that rs2021722 in HLA locus might be involved in pathogenesis of schizophrenia and that genotypes AG and allele A of the locus are risk factors for schizophrenia in the Han Chinese population, confirming the association between immune system and schizophrenia.