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Chou K.-C.,Gordon Life Science Institute | Chou K.-C.,King Abdulaziz University
Molecular BioSystems

Many molecular biosystems and biomedical systems belong to the multi-label systems in which each of their constituent molecules possesses one or more than one function or feature, and hence needs one or more than one label to indicate its attribute(s). With the avalanche of biological sequences generated in the post genomic age, it is highly desirable to develop computational methods to timely and reliably identify their various kinds of attributes. Compared with the single-label systems, the multi-label systems are much more complicated and difficult to deal with. The current mini review focuses on the recent progresses in this area from both conceptual aspects and detailed mathematical formulations. © 2013 The Royal Society of Chemistry. Source

Wang J.-F.,Shanghai JiaoTong University | Wang J.-F.,Shanghai Center for Bioinformation and Technology | Chou K.-C.,Gordon Life Science Institute

Human mitochondrial ornithine transporter-1 is reported in coupling with the hyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome, which is a rare autosomal recessive disorder. For in-depth understanding of the molecular mechanism of the disease, it is crucially important to acquire the 3D structure of human mitochondrial ornithine transporter-1. Since no such structure is available in the current protein structure database, we have developed it via computational approaches based on the recent NMR structure of human mitochondrial uncoupling protein (Berardi MJ, Chou JJ, et al. Nature 2011, 476:109-113). Subsequently, we docked the ligand L-ornithine into the computational structure to search for the favorable binding mode. It was observed that the binding interaction for the most favorable binding mode is featured by six remarkable hydrogen bonds between the receptor and ligand, and that the most favorable binding mode shared the same ligand-binding site with most of the homologous mitochondrial carriers from different organisms, implying that the ligand-binding sites are quite conservative in the mitochondrial carriers family although their sequences similarity is very low with 20% or so. Moreover, according to our structural analysis, the relationship between the disease-causing mutations of human mitochondrial ornithine transporter-1 and the HHH syndrome can be classified into the following three categories: (i) the mutation occurs in the pseudo-repeat regions so as to change the region of the protein closer to the mitochondrial matrix; (ii) the mutation is directly affecting the substrate binding pocket so as to reduce the substrate binding affinity; (iii) the mutation is located in the structural region closer to the intermembrane space that can significantly break the salt bridge networks of the protein. These findings may provide useful insights for in-depth understanding of the molecular mechanism of the HHH syndrome and developing effective drugs against the disease. © 2012 Wang, Chou. Source

Chou K.-C.,Gordon Life Science Institute
Current Drug Metabolism

Using graphic rules to deal with kinetic systems is an elegant approach by combining the graph representation (schematic representation) and rigorous mathematical derivation. It bears the following advantages: (1) providing an intuitive picture or illuminative insights; (2) helping grasp the key points from complicated details; (3) greatly simplifying many tedious, laborious, and error-prone calculations; and (4) able to double-check the final results. In this mini review, the non-steady state graphic rule in enzyme-catalyzed kinetics and protein-folding kinetics was extended to cover drugmetabolic systems. As a demonstration, a step-by-step illustration is presented showing how to use the graphic rule to derive the concentrations of the parent drug and its metabolites vs. time for the seliciclib, vildagliptin, and cyclin-dependent kinase inhibitor (AG-024322) metabolic systems, respectively. It can be seen from these paradigms that the graphic rule is particularly useful to analyze complicated drug metabolic systems and ensure the correctness of the derived results. Meanwhile, the intuitive feature of graphic representation may facilitate analyzing and classifying drug metabolic systems; e.g., according to their directed graphs, the metabolism of seliciclib and the metabolism of vildagliptin can be categorized as 0 → 5 mechanism while that of AG-024322 as 0 → 4 → 3 mechanism. © 2010 Bentham Science Publishers Ltd. Source

Chou K.-C.,Gordon Life Science Institute
Journal of Theoretical Biology

With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased explosively. In contrast, the pace is much slower in determining their biological attributes. As a consequence, the gap between sequence-known proteins and attribute-known proteins has become increasingly large. The unbalanced situation, which has critically limited our ability to timely utilize the newly discovered proteins for basic research and drug development, has called for developing computational methods or high-throughput automated tools for fast and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. Actually, during the last two decades or so, many methods in this regard have been established in hope to bridge such a gap. In the course of developing these methods, the following things were often needed to consider: (1) benchmark dataset construction, (2) protein sample formulation, (3) operating algorithm (or engine), (4) anticipated accuracy, and (5) web-server establishment. In this review, we are to discuss each of the five procedures, with a special focus on the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences. © 2010 Elsevier Ltd. Source

Lin H.,University of Electronic Science and Technology of China | Lin H.,Gordon Life Science Institute | Deng E.-Z.,University of Electronic Science and Technology of China | Ding H.,University of Electronic Science and Technology of China | And 4 more authors.
Nucleic Acids Research

The σ54 promoters are unique in prokaryotic genome and responsible for transcripting carbon and nitrogen-related genes. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the σ54 promoters. Here, a predictor called 'iPro54-PseKNC' was developed. In the predictor, the samples of DNA sequences were formulated by a novel feature vector called 'pseudo k-tuple nucleotide composition', which was further optimized by the incremental feature selection procedure. The performance of iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmark data set. As a user-friendly web-server, iPro54-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iPro54-PseKNC. For the convenience of the vast majority of experimental scientists, a step-by-step protocol guide was provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented in this paper just for its integrity. Meanwhile, we also discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the σ54 promoters. © 2014 The Author(s). Source

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