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    What make malarial adenosine deaminase from PLASMODIUM VIVAX recognise adenosine and 5′-methylthioadenosine: simulation studies
    (2023-01-01)
    Chotpatiwetchkul, Warot
    ;
    Sittiwanichai, Sirin
    ;
    Niramitranon, Jitti
    ;
    Pongprayoon, Prapasiri
    Malaria is a life-threatening disease in humans caused by Plasmodium parasites. Plasmodium vivax (P. vivax) is one of the prevalent species found worldwide. An increase in an anti-malarial drug resistance suggests the urgent need for new drugs. Zn<sup>2+</sup>-containing adenosine deaminase (ADA) is a promising drug target because the ADA inhibition is fatal to the parasite. Malarial ADA accepts both adenosine (ADN) and 5′-methylthioadenosine (MTA) as substrates. The understanding of the substrate binding becomes crucial for an anti-malarial drug development. In this work, ADA from P. vivax (pvADA) is of interest due to its prevalence worldwide. The binding of ADN and MTA are studied here using Molecular Dynamics (MD) simulations. Upon binding, the open and closed states of pvADA are captured. The displacement of (Formula presented.) 7, linking loops of (Formula presented.) 3/ (Formula presented.) 12, (Formula presented.) 4/ (Formula presented.) 13, (Formula presented.) 5/ (Formula presented.) 15, and (Formula presented.) / (Formula presented.) 11 is involved in the cavity closure and opening. Also, the inappropriate substrate orientation induces a failure in a complete cavity closure. Interactions with D46, D172, S280, D310, and D311 are important for ADN binding, whereas only hydrogen bonds with D172 and D311 are sufficient to anchor MTA inside the pocket. No Zn<sup>2+</sup>-coordinated histidine residues is acquired for substrate binding. D172 is found to play a role in ribose moiety recognition, while D311 is crucial for trapping the amine group of an adenine ring towards the Zn<sup>2+</sup> site. Comparing between ADN and MTA, the additional interaction between D310 and an amine nitrogen on ADN supports a tighter fit that may facilitate the deamination. Communicated by Ramaswamy H. Sarma.
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    Counting and classification of malarial parasite from giemsa-stained thin film images
    (2020-01-01)
    Kudisthalert, Wasu
    ;
    Pasupa, Kitsuchart
    ;
    Tongsima, Sissades
    Malaria is a life-threatening disease causing by an infection of the protozoan parasite Plasmodium. Plasmodium falciparum is the deadliest and most common human infected parasites hosted by anopheles mosquito vector. To cure a malaria infected patient and prevent further spreading, malaria diagnosis using microscopy to visualize Giemsa-stained parasites is commonly done. The microscopy diagnosis is somewhat time consuming and requires well-trained malaria experts to interpret what they see under the microscope. To address this limitation, an automated malaria infected diagnosis is needed. This work proposed a computer-aided automated diagnosis system that can perform remote field diagnosis with high accuracy while requiring less computational demands. The proposed framework consists of two main parts that are red blood cell counting and parasite life-cycle stage classification. The counting process is performed by computer vision techniques, namely Hough transform. Different machine learning techniques, i.e., Multilayer Perceptron, Linear Discriminant Analysis, Support Vector Machine, and Weighted Similarity Extreme Learning Machine, are employed in the classification task. We also demonstrated that combining hand-crafted and deep-learned features can enhance the overall performance of the framework. The experimental results showed that the proposed methods could correctly count and classify at 97.94% and 98.12% accuracy, respectively. The overall proposal system can achieve at 96.18% accuracy. This is achieved by WELM in conjunction with deep-learned (AlexNet_FC7) and the hand-crafted (color) features.