النشر العلمي

  • Effect of shaking velocity on mono-glycosyl-stevioside productivityvia alternansucrase acceptor reaction

In this study, effect of shaking velocity on mono-glycosyl-stevioside production by Leuconostoc citreumSK24.002 alternansucrase acceptor reaction was investigated, with four different level of shaking velocity(75, 100, 125 and 150 rpm) up to 24 h at 25◦C, using sucrose as donor and stevioside as acceptor. Theresults revealed that mono-glycosyl-stevioside yield significantly increased with increase in the reactionshaking velocity and reached maximum yield of 3.78 ± .02 mg/mL at 150 rpm shaking velocity after 6 hof reaction. And an increase in the reaction shaking velocity up to 150 rpm reduced the final reactiontime and increased the productivity of mono-glycosyl-stevioside product such that the high quantityof mono-glycosyl-stevioside product was produced in only 6 h rather than 24 h. The mono-glycosyl-stevioside product was completely separated using macroporous resin AB-8 flowed by semi-preparativeHPLC. Macroporous resin AB-8 showed high selectivity and capacity toward mono-glycosyl-stevioside.The structure of mono-glycosyl-stevioside was characterized, as 13-{[-d-glucopyranosyl-(1→6)--d-glucopyranosyl-(1→2)--d-glucopyranosyl]oxy}kaur-16-en-19-oic acid -d-glucopyranosyl eater, acorroding to extensive 1D and 2D NMR (1H and 13C, COSY, HSQC, HMBC) and mass spectral data.

published in Journal of Molecular Catalysis B: Enzymatic

  • Biotransformation of stevioside by Leuconostoc citreum SK24.002 alternansucrase acceptor reaction

Stevioside (13-O-b-sophorosyl-19-O-b-D-glucosyl-steviol) is a non-cariogenic and low-calorigenic diterpenoid
glycoside. It has a slightly bitter taste and bad aftertaste. Enzymatic modification by alternansucrase
from Leuconostoc citreum SK24.002 was utilised in the biotransformation of stevioside to fully or
partially remove the bitter taste of the stevioside. The effect of the reaction conditions including, time
(1–24 h), temperature (20–40 C), pH (4–7), donor concentration (10–100 mg/ml) and enzyme concentration
(0.5–2.5 U/ml) were investigated in order to maximise the transglucosylation yield. The highest
transglucosylation yield of approximately 43.7% was achieved at 20 C, pH 5.4 for 24 h using sucrose
at 10 mg/ml and alternansucrase at 1 U/ml. LC/MS analysis confirmed that the product was composed
of mono-di- and tri- glucosylated stevioside and their isomers.

published in Food Chemistry

  • Multiple Logistic Regression Model for Beta-Turns Prediction

Beta-turns comprise on average of 25% of the residues in all the protein sequence, their formation
plays an important role in protein folding, protein stability and molecular recognition processes. In
this work we have developed a method based on logistic regression (LR) model that uses multiple
sequence alignment or protein profile data for Beta-turns prediction. Using this method we achieved a
Qtotal of 79.8% on BT426 dataset, which is excellent compared to the available successful methods that
are based on either neural network or support vector machine, which are black box predictors. Our LR
model achieved good performance as measured by the Matthews correlation coefficient (MCC = 0.35).
LR also has th

published in Journal of Convergence Information Technology

  • Predicting 𝛽-Turns in Protein Using Kernel Logistic Regression

A 𝛽-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25%
of amino acids in protein structures are located in 𝛽-turns. It is very important to develope an accurate and efficient method for
𝛽-turns prediction. Most of the current successful 𝛽-turns prediction methods use support vector machines (SVMs) or neural
networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in
many classification problems.However, it is often not found in 𝛽-turns classification,mainly because it is computationally expensive.
In this paper, we used KLR to obtain sparse 𝛽-turns prediction in short evolution time. Secondary structure information and
position-specific scoring matrices (PSSMs) are utilized as input features.We achieved 𝑄total of 80.7% and MCC of 50% on BT426
dataset.These results show that KLRmethod with the right algorithmcan yield performance equivalent to or even better than NNs
and SVMs in 𝛽-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension tomulticlass case.

published in BioMed Research International

  • Kernel Logistic Regression Algorithm for Large- Scale Data Classification

Abstract: Kernel Logistic Regression (KLR) is a powerful classification technique that has been applied successfully in many
classification problems. However, it is often not found in large-scale data classification problems and this is mainly because it
is computationally expensive. In this paper, we present a new KLR algorithm based on Truncated Regularized Iteratively Reweighted
Least Squares(TR-IRLS) algorithm to obtain sparse large-scale data classification in short evolution time. This new
algorithm is called Nystrom Truncated Kernel Logistic Regression (NTR-KLR). The performance achieved using NTR-KLR
algorithm is comparable to that of Support Vector Machines (SVMs) methods. The advantage is NTR-KLR can yield
probabilistic outputs and its extension to the multi class case is well defined. In addition, its computational complexity is lower
than that of SVMs methods and it is easy to implement.

published in The International Arab Journal of Information Technology

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