Improving protein secondary structure prediction based on short subsequences with local structure similarity

<p>Abstract</p> <p>Background</p> <p>When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.</p> <p>Results</p> <p>In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an <it>n-</it>gram pattern of amino acids that reflects the sequence variation in a protein’s evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.</p> <p>On a large non-redundant dataset of 8,297 protein chains (<it>DsspNr-25</it>), the average <it>Q</it>₃ of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (<it>EVA Set_1</it> and <it>EVA_Set2</it>), the average <it>Q</it>₃ of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases.</p> <p>Conclusions</p> <p>Our experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at <url>http://bio-cluster.iis.sinica.edu.tw/SymPred/</url>.</p

Factors for poor prognosis of neonatal bacterial meningitis in a medical center in Northern Taiwan

BackgroundBacterial meningitis has long been a severe infectious disease in neonates, as well as a leading cause of adverse outcomes. We designed this study to know the factors for poor prognosis in neonatal bacterial meningitis.MethodsWe enrolled children aged less than 1 month who were admitted to Mackay Memorial Hospital from 1984 to 2008 and had culture-proven bacterial meningitis. The laboratory data and children’s clinical features were recorded. The patients’ outcomes were divided into four groups: death, having sequelae, complete recovery, and loss to follow-up. Patients with the outcomes of death and having sequelae were regarded as having a poor prognosis. Those who were lost to follow-up were excluded from the analysis of outcome. Multivariate analyses were performed to find the risk factors for poor prognosis.ResultsOne hundred fifty-six neonates fulfilled the inclusion criteria. Among these, 96 were boys (61.5%) and 102 (65.4%) had concomitant bacteremia. Group B streptococci (39.1%) and Escherichia coli (20.1%) were the two leading pathogens. Excluding those who were lost to follow-up (4.5%), 22 of 149 patients (14.8%) died, 36 (24.2%) had sequelae, and 91 (61.1%) recovered completely. Cerebrospinal fluid (CSF) protein more than 500 mg/dL at admission {odds ratio (OR): 171.18 [95% confidence interval (CI): 25.6–1000]}, predisposition to congenital heart disease [OR: 48.96 (95% CI: 6.06–395.64)], hearing impairment found during hospitalization [OR: 23.40 (95% CI: 3.62–151.25)], and seizure at admission or during hospitalization [OR: 10.10 (95% CI: 2.11–48.32)] were the factors predicting poor prognosis.ConclusionIn this 25-year study of newborns with bacterial meningitis, approximately one-seventh of the patients died, while two-fifths had sequelae. Nearly two-thirds of these had concomitant bacteremia. Group B streptococci and E. coli remained the two leading pathogens throughout the study period. Several factors for poor prognosis in newborns with culture-proven bacterial meningitis were found: high CSF protein concentration, congenital heart disease, hearing impairment, and seizure

Structural insights into the gating of DNA passage by the topoisomerase II DNA-gate.

Type IIA topoisomerases (Top2s) manipulate the handedness of DNA crossovers by introducing a transient and protein-linked double-strand break in one DNA duplex, termed the DNA-gate, whose opening allows another DNA segment to be transported through to change the DNA topology. Despite the central importance of this gate-opening event to Top2 function, the DNA-gate in all reported structures of Top2-DNA complexes is in the closed state. Here we present the crystal structure of a human Top2 DNA-gate in an open conformation, which not only reveals structural characteristics of its DNA-conducting path, but also uncovers unexpected yet functionally significant conformational changes associated with gate-opening. This structure further implicates Top2's preference for a left-handed DNA braid and allows the construction of a model representing the initial entry of another DNA duplex into the DNA-gate. Steered molecular dynamics calculations suggests the Top2-catalyzed DNA passage may be achieved by a rocker-switch-type movement of the DNA-gate

Protein subcellular localization prediction of eukaryotes using a knowledge-based approach

<p>Abstract</p> <p>Background</p> <p>The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles.</p> <p>Results</p> <p>In this study, we propose a knowledge based method, called KnowPred_site, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPred_site's performance. The experiment results show that KnowPred_siteachieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPred_siteis 91.7%. For multi-localized proteins, the overall accuracy of KnowPred_siteis 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPred_site.</p> <p>Conclusion</p> <p>KnowPred_sitedemonstrates the power of identifying related sequences in the knowledge base. The experiment results show that even though the sequence similarity is low, the local similarity is effective for prediction. Experiment results show that KnowPred_siteis a highly accurate prediction method for both single- and multi-localized proteins. It is worth-mentioning the prediction process of KnowPred_siteis transparent and biologically interpretable and it shows a set of template sequences to generate the prediction result. The KnowPred_siteprediction server is available at <url>http://bio-cluster.iis.sinica.edu.tw/kbloc/</url>.</p

Analysis of clinical outcomes in pediatric bacterial meningitis focusing on patients without cerebrospinal fluid pleocytosis

BackgroundCerebrospinal fluid (CSF) cell count and biochemical examinations and cultures form the basis for the diagnosis of bacterial meningitis. However, some patients do not have typical findings and are at a higher risk of being missed or having delayed treatment. To better understand the correlation between CSF results and outcomes, we evaluated CSF data focusing on the patients with atypical findings.MethodsThis study enrolled CSF culture-proven bacterial meningitis patients aged from 1 month to 18 years in a medical center. The patients were divided into “normal” and “abnormal” groups for each laboratory result and in combination. The correlations between the laboratory results and the outcomes were analyzed.ResultsA total of 175 children with confirmed bacterial meningitis were enrolled. In CSF examinations, 16.2% of patients had normal white blood cell counts, 29.5% had normal glucose levels, 24.5% had normal protein levels, 10.2% had normal results in two items, and 8.6% had normal results in all three items. In logistic regression analysis, a normal CSF leukocyte count and increased CSF protein level were related to poor outcomes. Patients with meningitis caused by Streptococcus pneumoniae and hyponatremia were at a higher risk of mortality and the development of sequelae.ConclusionsIn children with bacterial meningitis, nontypical CSF findings and, in particular, normal CSF leukocyte count and increased protein level may indicate a worse prognosis

Human Leukocyte Antigen Typing Using a Knowledge Base Coupled with a High-Throughput Oligonucleotide Probe Array Analysis

Human leukocyte antigens (HLA) are important biomarkers because multiple diseases, drug toxicity, and vaccine responses reveal strong HLA associations. Current clinical HLA typing is an elimination process requiring serial testing. We present an alternative in situ synthesized DNA-based microarray method that contains hundreds of thousands of probes representing a complete overlapping set covering 1,610 clinically relevant HLA class I alleles accompanied by computational tools for assigning HLA type to 4-digit resolution. Our proof-of-concept experiment included 21 blood samples, 18 cell lines, and multiple controls. The method is accurate, robust, and amenable to automation. Typing errors were restricted to homozygous samples or those with very closely related alleles from the same locus, but readily resolved by targeted DNA sequencing validation of flagged samples. High-throughput HLA typing technologies that are effective, yet inexpensive, can be used to analyze the world’s populations, benefiting both global public health and personalized health care

Atomic-scale visualization of quasiparticle interference on a type-II Weyl semimetal surface

We combine quasiparticle interference simulation (theory) and atomic resolution scanning tunneling spectro-microscopy (experiment) to visualize the interference patterns on a type-II Weyl semimetal Mo$_{x}$W$_{1-x}$Te$_2$ for the first time. Our simulation based on first-principles band topology theoretically reveals the surface electron scattering behavior. We identify the topological Fermi arc states and reveal the scattering properties of the surface states in Mo$_{0.66}$W$_{0.34}$Te$_2$. In addition, our result reveals an experimental signature of the topology via the interconnectivity of bulk and surface states, which is essential for understanding the unusual nature of this material.Comment: To appear in Phys. Rev. Let

Inhibition of SARS-CoV 3C-like Protease Activity by Theaflavin-3,3′-digallate (TF3)

SARS-CoV is the causative agent of severe acute respiratory syndrome (SARS). The virally encoded 3C-like protease (3CL(Pro)) has been presumed critical for the viral replication of SARS-CoV in infected host cells. In this study, we screened a natural product library consisting of 720 compounds for inhibitory activity against 3CL(Pro). Two compounds in the library were found to be inhibitive: tannic acid (IC(50) = 3 µM) and 3-isotheaflavin-3-gallate (TF2B) (IC(50) = 7 µM). These two compounds belong to a group of natural polyphenols found in tea. We further investigated the 3CL(Pro)-inhibitory activity of extracts from several different types of teas, including green tea, oolong tea, Puer tea and black tea. Our results indicated that extracts from Puer and black tea were more potent than that from green or oolong teas in their inhibitory activities against 3CL(Pro). Several other known compositions in teas were also evaluated for their activities in inhibiting 3CL(Pro). We found that caffeine, (—)-epigallocatechin gallte (EGCg), epicatechin (EC), theophylline (TP), catechin (C), epicatechin gallate (ECg) and epigallocatechin (EGC) did not inhibit 3CL(Pro) activity. Only theaflavin-3,3′-digallate (TF3) was found to be a 3CL(Pro) inhibitor. This study has resulted in the identification of new compounds that are effective 3CL(Pro) inhibitors