Medical Biology - Research Publications

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Type: Conference Paper × Start date: 2008 × End date: 2009 ×

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    Synthesis and antiplasmodial activity of 3-furyl and 3-thienylquinoxaline-2-carbonitrile 1,4-di-N-oxide derivatives.
    Vicente, E ; Charnaud, S ; Bongard, E ; Villar, R ; Burguete, A ; Solano, B ; Ancizu, S ; Pérez-Silanes, S ; Aldana, I ; Vivas, L ; Monge, A (MDPI AG, 2008-01-17)
    The aim of this study was to identify new compounds active against Plasmodium falciparum based on our previous research carried out on 3-phenyl-quinoxaline-2-carbonitrile 1,4-di-N-oxide derivatives. Twelve compounds were synthesized and evaluated for antimalarial activity. Eight of them showed an IC(50) less than 1 microM against the 3D7 strain. Derivative 1 demonstrated high potency (IC(50)= 0.63 microM) and good selectivity (SI=10.35), thereby becoming a new lead-compound.
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    Predicting disordered regions in proteins using the profiles of amino acid indices.
    Han, P ; Zhang, X ; Feng, Z-P (Springer Science and Business Media LLC, 2009-01-30)
    BACKGROUND: Intrinsically unstructured or disordered proteins are common and functionally important. Prediction of disordered regions in proteins can provide useful information for understanding protein function and for high-throughput determination of protein structures. RESULTS: In this paper, algorithms are presented to predict long and short disordered regions in proteins, namely the long disordered region prediction algorithm DRaai-L and the short disordered region prediction algorithm DRaai-S. These algorithms are developed based on the Random Forest machine learning model and the profiles of amino acid indices representing various physiochemical and biochemical properties of the 20 amino acids. CONCLUSION: Experiments on DisProt3.6 and CASP7 demonstrate that some sets of the amino acid indices have strong association with the ordered and disordered status of residues. Our algorithms based on the profiles of these amino acid indices as input features to predict disordered regions in proteins outperform that based on amino acid composition and reduced amino acid composition, and also outperform many existing algorithms. Our studies suggest that the profiles of amino acid indices combined with the Random Forest learning model is an important complementary method for pinpointing disordered regions in proteins.