SIMS: A Hybrid Method for Rapid Conformational Analysis

Proteins are at the root of many biological functions, often performing complex tasks as the result of large changes in their structure. Describing the exact details of these conformational changes, however, remains a central challenge for computational biology due the enormous computational requirements of the problem. This has engendered the development of a rich variety of useful methods designed to answer specific questions at different levels of spatial, temporal, and energetic resolution. These methods fall largely into two classes: physically accurate, but computationally demanding methods and fast, approximate methods. We introduce here a new hybrid modeling tool, the Structured Intuitive Move Selector (SIMS), designed to bridge the divide between these two classes, while allowing the benefits of both to be seamlessly integrated into a single framework. This is achieved by applying a modern motion planning algorithm, borrowed from the field of robotics, in tandem with a well-established protein modeling library. SIMS can combine precise energy calculations with approximate or specialized conformational sampling routines to produce rapid, yet accurate, analysis of the large-scale conformational variability of protein systems. Several key advancements are shown, including the abstract use of generically defined moves (conformational sampling methods) and an expansive probabilistic conformational exploration. We present three example problems that SIMS is applied to and demonstrate a rapid solution for each. These include the automatic determination of ﾑﾑactiveﾒﾒ residues for the hinge-based system Cyanovirin-N, exploring conformational changes involving long-range coordinated motion between non-sequential residues in Ribose- Binding Protein, and the rapid discovery of a transient conformational state of Maltose-Binding Protein, previously only determined by Molecular Dynamics. For all cases we provide energetic validations using well-established energy fields, demonstrating this framework as a fast and accurate tool for the analysis of a wide range of protein flexibility problems

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Sunitinib is a tyrosine kinase inhibitor for GIST and advanced renal cell carcinoma. Diclofenac is used in cancer pain management. Coadministration may mediate P450 toxicity. We evaluate their interaction, assessing biomarkers ALT, AST, BUN, creatinine, and histopathological changes in the liver, kidney, heart, brain, and spleen. ICR mice (male, n = 6 per group/dose) were administered saline (group A) or 30 mg/kg diclofenac ip (group B), or sunitinib po at 25, 50, 80, 100, 140 mg/kg (group C) or combination of diclofenac (30 mg/kg, ip) and sunitinib (25, 50, 80, 100, 140 mg/kg po). Diclofenac was administered 15 min before sunitinib, mice were euthanized 4 h post-sunitinib dose, and biomarkers and tissue histopathology were assessed. AST was 92.2 ± 8.0 U/L in group A and 159.7 ± 14.6 U/L in group B (p < 0.05); in group C, it the range was 105.1-152.6 U/L, and in group D, it was 156.0-209.5 U/L (p < 0.05). ALT was 48.9 ± 1.6 U/L (group A), 95.1 ± 4.5 U/L (p < 0.05) in group B, and 50.5-77.5 U/L in group C and 82.3-115.6 U/L after coadministration (p < 0.05). Renal function biomarker BUN was 16.3 ± 0.6 mg/dl (group A) and increased to 29.9 ± 2.6 mg/dl in group B (p < 0.05) and it the range was 19.1-33.3 mg/dl (p < 0.05) and 26.9-40.8 mg/dl in groups C and D, respectively. Creatinine was 5.9 pmol/ml in group A; 6.2 pmol/ml in group B (p < 0.01), and the range was 6.0-6.2 and 6.2-6.4 pmol/ml in groups C and D, respectively (p < 0.05 for D). Histopathological assessment (vascular and inflammation damages) showed toxicity in group B (p < 0.05) and mild toxicity in group C. Damage was significantly lesser in group D than group B (p < 0.05). Spleen only showed toxicity after coadministration. These results suggest vascular and inflammation protective effects of sunitinib, not shown after biomarker analysis