Prediction of Protein—Ligand Interactions. Docking and Scoring: Successes and Gaps

Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States
Journal of Medicinal Chemistry (Impact Factor: 5.45). 11/2006; 49(20):5851-5. DOI: 10.1021/jm060999m
Source: PubMed


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    ABSTRACT: Compound comparison is an important task for the computational chemistry. By the comparison results, potential inhibitors can be found and then used for the pharmacy experiments. The time complexity of a pairwise compound comparison is O ( n 2 ) , where n is the maximal length of compounds. In general, the length of compounds is tens to hundreds, and the computation time is small. However, more and more compounds have been synthesized and extracted now, even more than tens of millions. Therefore, it still will be time-consuming when comparing with a large amount of compounds (seen as a multiple compound comparison problem, abbreviated to MCC). The intrinsic time complexity of MCC problem is O ( k 2 n 2 ) with k compounds of maximal length n . In this paper, we propose a GPU-based algorithm for MCC problem, called CUDA-MCC, on single- and multi-GPUs. Four LINGO-based load-balancing strategies are considered in CUDA-MCC in order to accelerate the computation speed among thread blocks on GPUs. CUDA-MCC was implemented by C+OpenMP+CUDA. CUDA-MCC achieved 45 times and 391 times faster than its CPU version on a single NVIDIA Tesla K20m GPU card and a dual-NVIDIA Tesla K20m GPU card, respectively, under the experimental results.
    International Journal of Genomics 10/2015; 2015(9-10):1-9. DOI:10.1155/2015/950905 · 0.95 Impact Factor
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    • "In previous works, using this strategy, we were able to show that the position and attribute of the water sites are good predictors of the hydroxyl groups positions in the resulting lectin–carbohydrate complex (Di Lella et al. 2007; Gauto et al. 2009), being able, even to predict the subtle selectivity of lectins between two epimers (Gauto et al. 2011). Given the relevance of the determination of an atomic resolution structure for any given protein–ligand complex (Fadda and Woods 2010), there is a widespread use of in silico strategies for their prediction (i.e., molecular docking methods) (Morris et al. 1998; Taylor et al. 2002; Brooijmans and Kuntz 2003; Friesner et al. 2004; Leach et al. 2006; Abel et al. 2008; Englebienne and Moitessier 2009; Yuriev et al. 2009; Wang et al. 2011). These, however, show a weak performance for lectin–carbohydrate complexes (Kerzmann et al. 2008; Agostino et al. 2009; Mishra et al. 2012; Gauto et al. 2013). "
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    ABSTRACT: Understanding protein–ligand interactions is a fundamental question in basic biochemistry, and the role played by the solvent along this process is not yet fully understood. This fact is particularly relevant in lectins, proteins that mediate a large variety of biological processes through the recognition of specific carbohydrates. In the present work, we have thoroughly analyzed a nonredundant and well-curated set of lectin structures looking for a potential relationship between the structural water properties in the apo-structures and the corresponding protein–ligand complex structures. Our results show that solvent structure adjacent to the binding sites mimics the ligand oxygen structural framework in the resulting protein–ligand complex, allowing us to develop a predictive method using a Naive Bayes classifier. We also show how these properties can be used to improve docking predictions of lectin–carbohydrate complex structures in terms of both accuracy and precision, thus developing a solid strategy for the rational design of glycomimetic drugs. Overall our results not only contribute to the understanding of protein–ligand complexes, but also underscore the role of the water solvent in the ligand recognition process. Finally, we discuss our findings in the context of lectin specificity and ligand recognition properties.
    Glycobiology 09/2014; 25(2). DOI:10.1093/glycob/cwu102 · 3.15 Impact Factor
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    • "In molecular simulation, docking [Perola et al., 2004; Leach et al., 2006] is utilized to predict the conformer of a molecule that can form a stable complex structure with another molecule. The basic idea is consistent with the seminal lock and key approach to pharmacology, where a ligand functions as a key, that needs the correct relative orientation to interact with the protein surface, which functions as a lock [Jorgensen, 1991]. "
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    ABSTRACT: Preclinical ResearchCellular proteins are the mediators of multiple organism functions being involved in physiological mechanisms and disease. By discovering lead compounds that affect the function of target proteins, the target diseases or physiological mechanisms can be modulated. Based on knowledge of the ligand–receptor interaction, the chemical structures of leads can be modified to improve efficacy, selectivity and reduce side effects. One rational drug design technology, which enables drug discovery based on knowledge of target structures, functional properties and mechanisms, is computer-aided drug design (CADD). The application of CADD can be cost-effective using experiments to compare predicted and actual drug activity, the results from which can used iteratively to improve compound properties. The two major CADD-based approaches are structure-based drug design, where protein structures are required, and ligand-based drug design, where ligand and ligand activities can be used to design compounds interacting with the protein structure. Approaches in structure-based drug design include docking, de novo design, fragment-based drug discovery and structure-based pharmacophore modeling. Approaches in ligand-based drug design include quantitative structure–affinity relationship and pharmacophore modeling based on ligand properties. Based on whether the structure of the receptor and its interaction with the ligand are known, different design strategies can be seed. After lead compounds are generated, the rule of five can be used to assess whether these have drug-like properties. Several quality validation methods, such as cost function analysis, Fisher's cross-validation analysis and goodness of hit test, can be used to estimate the metrics of different drug design strategies. To further improve CADD performance, multi-computers and graphics processing units may be applied to reduce costs.
    Drug Development Research 09/2014; 75(6). DOI:10.1002/ddr.21222 · 0.77 Impact Factor
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