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WIREs Syst Biol Med
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From base pair to bedside: molecular simulation and the translation of genomics to personalized medicine

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Abstract Despite the promises made that genomic sequencing would transform therapy by introducing a new era of personalized medicine, relatively few tangible breakthroughs have been made. This has led to the recognition that complex interactions at multiple spatial, temporal, and organizational levels may often combine to produce disease. Understanding this complexity requires that existing and future models are used and interpreted within a framework that incorporates knowledge derived from investigations at multiple levels of biological function. It also requires a computational infrastructure capable of dealing with the vast quantities of data generated by genomic approaches. In this review, we discuss the use of molecular modeling to generate quantitative and qualitative insights at the smallest scales of the systems biology hierarchy, how it can play an important role in the development of a systems understanding of disease and in the application of such knowledge to help discover new therapies and target existing ones on a personal level. WIREs Syst Biol Med 2012, 4:585–598. doi: 10.1002/wsbm.1186 This article is categorized under: Biological Mechanisms > Chemical Biology Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine

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Structures of three enzymes targeted by antiretroviral drugs in HIV treatment showing the binding of inhibitory drugs (pictured in atomic representation in each case). (a) The HIV protease (PR) bound to the protease inhibitor (PRI) lopinavir (PDB: 2Q5U). The catalytic dyad is shown in atomic representation and the flaps are shown in blue. (b) The HIV‐1 reverse transcriptase (RT) bound to DNA and a non‐nucleoside RT inhibitor (NNRTI) nevirapine (PDB: 3V81). The polymerase and RNaseH active site residues are shown as orange spheres. The subdomains of the p66 monomer are colored; fingers—blue, thumb—red, palm—silver, connection—yellow, and RNaseH—green. The main view is along the DNA binding cleft, inset from above it. (c) The proto foamy virus (PFV) integrase bound to the integrase inhibitor (INI) raltegravir (PDB: 3OYA). The large picture shows the detail of the INI binding site, the inset shows this region in the context of the PFV IN dimer (the INI binding region is outlined in purple).

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Structures of nelfinavir, a lead HIV inhibitor, complexed with its designed target HIV‐1 protease (a), and two of its many possible off‐target molecules: protein kinase B (PKB) (b) and epidermal growth factor receptors (EGFR) (c). The nelfinavir‐HIV protease (PDB: 1OHR) uses the same representation as in Figure 1(a). Nelfinavir has been shown to be able to dock at the ATP‐binding site of PKB, EGFR, and other kinases. The complex structures of PKB (PDB: 3QKL) and EGFR (PDB: 2J6M) with nelfinavir are for display only and do not imply the drug's optimized orientations.

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Cartoon representations of multiscale cancer models, from (a) the macroscopic tissue level, through (b) mesoscopic intercellular and (c) intra‐cellular/molecular network levels, down to (d) the microscopic atomistic level.

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(a) The NNRTI TMC120 bound to wild‐type HIV‐1 RT. This conformation is similar to that adopted by other NNRTIs. (b) shows the related drug Etravirine (TMC125) bound to the K103N mutant HIV‐1 which exhibits resistance to most other NNRTIs. The conformation adopted by Etravirine in this crystal structure (closer to residues 100 and 103 with substantially different wing orientations relative to the body) is not seen in other NNRTIs but simulations suggest it can also adopt a conformation similar to that shown in (a). The structure in (c) shows the two torsion angles, rotations around which give Etravirine its conformational flexibility, which in turn allows it to bind to sequences resistant to other NNRTIs.

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Analytical and Computational Methods > Computational Methods
Models of Systems Properties and Processes > Mechanistic Models
Biological Mechanisms > Chemical Biology
Translational, Genomic, and Systems Medicine > Translational Medicine

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