Browsing by Subject "protein function"

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    Dehydron as a Marker For Drug Design
    (2006-07-26T14:23:51Z) Jain, Manojkumar D.; Fernandez, Ariel
    The approach of exploiting highly conserved protein folds and structure in understanding protein function and in designing drugs leads to drugs that are less selective due to association with similar proteins. Over the years an open problem for researchers has been to develop drug design models based on non-conserved features to have higher selectivity. Recently a new structural feature, the dehydron, has been demonstrated to vary across proteins with conserved folds. Dehydrons are backbone hydrogen bonds that are not adequately protected from water. The importance of wrapping dehydrons in ligand binding and non-conservation of dehydrons across similar proteins makes them important candidates for markers in drug design. Investigation on a series of proteins – PDB entries: 1IA8, 1NVQ, 1NVS, 1NVR, 1OKZ, and 1PKD – revealed the potential impact of wrapping on binding affinity of the ligands. Unlike in 1NVS, 1NVR, 1OKZ, and 1PKD, inhibitor UCN in 1NVQ wrapped both the dehydrons in active site region of the checkpoint protein kinase, thereby indicating an increased potency and higher selectivity. On detailed analysis of 193 protein kinases, roughly 70% were found to have two or more dehydrons in the neighborhood of the bound ligand. Also, about 70% of proteins had dehydrons within the active site region. Only around 20% of ligands, however, actually wrapped two or more dehydrons. These statistics clearly illustrate the significance of dehydrons and their potential use as markers for drug design to enhance drug efficacy as well as selectivity, and to reduce side effects in the process.
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    Protein function prediction by integrating sequence, structure and binding affinity information
    (2014-02-03) Zhao, Huiying; Zhou, Yaoqi; Liu, Yunlong; Meroueh, Samy; Janga, Sarath Chandra
    Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.