Bayesian models and algorithms for protein beta-sheet prediction

Prediction of the three-dimensional structure greatly benefits from the information related to secondary structure, solvent accessibility, and non-local contacts that stabilize a protein's structure. Prediction of such components is vital to our understanding of the structure and function of a protein. In this paper, we address the problem of beta-sheet prediction. We introduce a Bayesian approach for proteins with six or less beta-strands, in which we model the conformational features in a probabilistic framework. To select the optimum architecture, we analyze the space of possible conformations by efficient heuristics. Furthermore, we employ an algorithm that finds the optimum pairwise alignment between beta-strands using dynamic programming. Allowing any number of gaps in an alignment enables us to model beta-bulges more effectively. Though our main focus is proteins with six or less beta-strands, we are also able to perform predictions for proteins with more than six beta-strands by combining the predictions of BetaPro with the gapped alignment algorithm. We evaluated the accuracy of our method and BetaPro. We performed a 10-fold cross validation experiment on the BetaSheet916 set and we obtained significant improvements in the prediction accuracy

Bayesian models and algorithms for protein beta-sheet prediction

Prediction of the three-dimensional structure greatly benefits from the information related to secondary structure, solvent accessibility, and non-local contacts that stabilize a protein's structure. Prediction of such components is vital to our understanding of the structure and function of a protein. In this paper, we address the problem of beta-sheet prediction. We introduce a Bayesian approach for proteins with six or less beta-strands, in which we model the conformational features in a probabilistic framework. To select the optimum architecture, we analyze the space of possible conformations by efficient heuristics. Furthermore, we employ an algorithm that finds the optimum pairwise alignment between beta-strands using dynamic programming. Allowing any number of gaps in an alignment enables us to model beta-bulges more effectively. Though our main focus is proteins with six or less beta-strands, we are also able to perform predictions for proteins with more than six beta-strands by combining the predictions of BetaPro with the gapped alignment algorithm. We evaluated the accuracy of our method and BetaPro. We performed a 10-fold cross validation experiment on the BetaSheet916 set and we obtained significant improvements in the prediction accuracy

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

BACKGROUND: The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. RESULTS: In this paper, we further refine and extend the hidden semi-Markov model (HSMM) initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. CONCLUSIONS: We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable to current similarity search methods

Exchange processes and watermass modifications along the subarctic front in the North Pacific: Oxygen consumption rates and net carbon flux

Exchange processes along the subarctic front and the modification of subpolar water in the North Pacific are investigated using tracer data from World Ocean Circulation Experiment P14N and P17N lines. The North Pacific Current transports water on both sides of the subarctic front from the western to eastern North Pacific. During this transport, subpolar water from the western subpolar gyre becomes warmer and saltier through the main thermocline via isopycnal mixing with subtropical water. It is shown that this modified subpolar water of western origin is the primary source of well-ventilated water to the eastern subpolar gyre. The isopycnal mixing along the subarctic front is quantified with a two end-member linear mixing analysis using potential temperature, which allowed estimates of oxygen consumption and nitrate remineralization on intermediate layers. Based on the oxygen consumption estimates and temporal information from transient tracers, the vertically integrated oxygen consumption rate is calculated to be 2.1 ± 0.4 M m-2y-1 in the 132-706 m depth range. This implies a net carbon flux of approximately 19 ± 4 gC m-2y-1 out of the euphotic zone

Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure

<p>Abstract</p> <p>Background</p> <p>Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic Bayesian networks (DBNs) and support vector machines (SVMs) have been shown to provide state-of-the-art performance in secondary structure prediction. As the size of the protein database grows, it becomes feasible to use a richer model in an effort to capture subtle correlations among the amino acids and the predicted labels. In this context, it is beneficial to derive sparse models that discourage over-fitting and provide biological insight.</p> <p>Results</p> <p>In this paper, we first show that we are able to obtain accurate secondary structure predictions. Our per-residue accuracy on a well established and difficult benchmark (CB513) is 80.3%, which is comparable to the state-of-the-art evaluated on this dataset. We then introduce an algorithm for sparsifying the parameters of a DBN. Using this algorithm, we can automatically remove up to 70-95% of the parameters of a DBN while maintaining the same level of predictive accuracy on the SD576 set. At 90% sparsity, we are able to compute predictions three times faster than a fully dense model evaluated on the SD576 set. We also demonstrate, using simulated data, that the algorithm is able to recover true sparse structures with high accuracy, and using real data, that the sparse model identifies known correlation structure (local and non-local) related to different classes of secondary structure elements.</p> <p>Conclusions</p> <p>We present a secondary structure prediction method that employs dynamic Bayesian networks and support vector machines. We also introduce an algorithm for sparsifying the parameters of the dynamic Bayesian network. The sparsification approach yields a significant speed-up in generating predictions, and we demonstrate that the amino acid correlations identified by the algorithm correspond to several known features of protein secondary structure. Datasets and source code used in this study are available at <url>http://noble.gs.washington.edu/proj/pssp</url>.</p

Yetim proteinlerde ikincil yapı öngörüsü için eğitim kümesi indirgeme yöntemleri = Training set reduction methods for single sequence protein secondary structure prediction

Orphan proteins are characterized by the lack of significant sequence similarity to almost all proteins in the database. To infer the functional properties of the orphans, more elaborate techniques that utilize structural information are required. In this regard, the protein structure prediction gains considerable importance. Secondary structure prediction algorithms designed for orphan proteins (also known as single-sequence algorithms) cannot utilize multiple alignments or aligment profiles, which are derived from similar proteins. This is a limiting factor for the prediction accuracy. One way to improve the performance of a single-sequence algorithm is to perform re-training. In this approach, first, the models used by the algorithm are trained by a representative set of proteins and a secondary structure prediction is computed. Then, using a distance measure, the original training set is refined by removing proteins that are dissimilar to the initial prediction. This step is followed by the re-estimation of the model parameters and the prediction of the secondary structure. In this paper, we compare training set reduction methods that are used to re-train the hidden semi-Markov models employed by the IPSSP algorithm. We found that the composition based reduction method has the highest performance compared to the other reduction methods. In addition, threshold-based reduction performed bettern than the reduction technique that selects the first 80% of the dataset proteins

Synchronous Detection of Hairy Cell Leukemia and HIV-Negative Kaposi's Sarcoma of the Lymph Node: A Diagnostic Challenge and a Rare Coincidence

Hairy cell leukemia (HCL) is an uncommon chronic lymphoproliferative disorder and accounts for around 2% of all forms of leukemias. The association of HCL with other neoplasms, mainly non-Hodgkin's lymphomas, is well known. However, the simultaneous diagnosis of HCL and Kaposi's sarcoma is rare, with only few cases of such an association having been reported. We describe a 42-year-old male patient with a well characterized HCL and in whom HIV-negative Kaposi's sarcoma of the lymph node was detected

Materials characterization of innovative composite materials for solar-driven thermochemical heat storage (THS) suitable for building application

Thermochemical Heat Storage (THS) systems have recently attracted a lot of attention in research and development. One of the main parameters that influence the performance of a THS system is the thermochemical materials. This paper aims to investigate thermochemical materials which are suitable for both short-term and long-term building heat storage application driven by solar energy for an open system. Innovative composite materials using MgCl2-MgSO4, CaCl2-LiCl and MgSO4-CaCl2salts mixtures impregnated into vermiculite, and potassium formate (KCOOH) impregnated into silica gel will be presented in this study. Initial screening and characterization results of the composite THS materials based on the energy density using differential scanning calorimetry analysis, mass loss against temperature using thermogravimetric analysis, and moisture vapor adsorption isotherms testing are discussed. The characterization analysis suggest that the vermiculite with salts mixtures are promising candidates for thermochemical heat storage (THS) systems compared to composite materials with individual salts. Meanwhile the potential of KCOOH-silica gel as THS materials may be further investigated in the future. The performance of the materials may be further optimized in the future by changing the concentration ratio of the mixed salts

Minimal residual disease (MRD) detection with translocations and T-cell receptor and immunoglobulin gene rearrangements in adult acute lymphoblastic leukemia patients: a pilot study

Objective: Monitoring minimal residual disease has become increasingly important in clinical practice of ALL management. Break-point fusion regions of leukaemia related chromosomal aberrations and rearranged immunoglobulin (Ig) and T cell-receptor (TCR) genes are used as leukaemia specific markers in genetic studies of MRD.Material and Methods: A total of 31 consecutive patients with newly diagnosed ALL were screened for eligibility criteria. Of those 26 were included in the study. One patient with partial response following induction therapy and four patients who were lost to follow-up after induction were excluded from the study; thus 21 patients were evaluated for MRD by using polymerase chain reaction (PCR), heteroduplex analysis, sequencing and quantitative real time PCR techniques. Results: Chromosomal aberrations were detected in 5 (24%) of the patients and were used for MRD monitoring. Three patients had t(9;22) translocation, the other 2 had t(4;11) and t(1;19). MRD-based risk stratification of the16 patients analysed for Ig/TCR rearrangements revealed 3 low-risk, 11 intermediate-risk and 2 high-risk patients.Conclusion: MRD monitoring is progressively getting to be a more important predictive factor in adult ALL patients. As reported by others confirmed by our limited data there is a good correlation between MRD status and clinical outcome in patients receiving chemotherapy. The pilot-study presented here is the first that systematically and consecutively performs a molecular MRD monitoring of ALL patients in Turkey

İzmir Körfezi’nde (Ege Denizi) Çok Büyük Bir Granyöz Balığının (Argyrosomus regius) Bulunuşu

On 16 May 2017, a specimen of Argyrosomus regius with a total length (TL) of 1310 mm (21.1 kg) was captured by an experimental trawl from eastern coast of Yassıcaada Island, Urla, İzmir Bay at a depth of 30 m. This ichthyologic note presents a new maximal size record of A. regius for İzmir Bay, Aegean Sea.16 Mayıs 2017 tarihinde, 1310 mm total boyuyla (21,1 kg) bir Argyrosomus regius bireyi Urla (İzmir Körfezi) Yassıcaada’nın doğu kıyısından bir deneysel dip trolüyle 30 m derinlikte yakalanmıştır. Bu ihtiyolojik not İzmir Körfezi (Ege Denizi) için A. regius’un yeni bir maksimum boyut kaydını sunmaktadır