Edited by Norma AllewellP-glycoprotein (P-gp) is a polyspecific ATP-dependent transporter linked to multidrug resistance in cancer; it plays important roles in determining the pharmacokinetics of many drugs. Understanding the structural basis of P-gp, substrate polyspecificity has been hampered by its intrinsic flexibility, which is facilitated by a 75-residue linker that connects the two halves of P-gp. Here we constructed a mutant murine P-gp with a shortened linker to facilitate structural determination. Despite dramatic reduction in rhodamine 123 and calcein-AM transport, the linker-shortened mutant P-gp possesses basal ATPase activity and binds ATP only in its N-terminal nucleotide-binding domain. Nine independently determined structures of wild type, the linker mutant, and a methylated P-gp at up to 3.3 Å resolution display significant movements of individual transmembrane domain helices, which correlated with the opening and closing motion of the two halves of P-gp. The open-andclose motion alters the surface topology of P-gp within the drugbinding pocket, providing a mechanistic explanation for the polyspecificity of P-gp in substrate interactions.The association of multidrug resistance (MDR) 3 in cancer treatment with expression of human ABC transporters on the cell surface has raised the possibility that overcoming MDR might be achieved by inhibiting these transporters (1). One transporter in particular, the human P-glycoprotein (P-gp), has been well characterized; it is able to confer MDR by transporting numerous structurally unrelated drugs at the expense of hydrolyzing ATP (2, 3). In addition, P-gp plays important roles in drug distribution in normal physiology and is an essential component of many physiological barriers (4, 5).Long-standing efforts have been devoted to understanding the mechanism of P-gp function by various experimental approaches. Among them, structural studies of P-gp from various organisms have been reported (6 -8). In particular, a number of structures for the mouse P-gp (mP-gp) were obtained (8 -10). However, the diffraction limits of "near-native," inhibitor-bound, and nanobody-associated mP-gp crystals were relatively low, mostly near 4 Å resolution. The methylated protein gave higher resolution structures (11), but the impact of reductive methylation on the structure and function of P-gp was not addressed. The low resolution diffraction of P-gp crystals has been attributed in part to the intrinsic flexibility of the molecule (12-14).Mouse P-gp is a 1276-residue polypeptide and bears 87% sequence identity to human P-gp; it consists of two homologous halves connected by a flexible linker of ϳ75 residues. Each half is made of a transmembrane domain (TMD) implicated in drug recognition and transport and a nucleotide-binding domain (NBD). Each NBD is able to bind and hydrolyze ATP (15, 16), which is, however, dependent on the other NBD being functional (17,18). The two NBDs of P-gp are highly homologous, each featuring a consensus nucleotide-binding site with full-fledged Walker...

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Real production applications ranging from enterprise applications to large e-commerce sites share a crucial but seldom-noted characteristic: The relative frequencies of transaction types in their workloads are nonstationary, i.e., the transaction mix changes over time. Accurately predicting application-level performance in businesscritical production applications is an increasingly important problem. However, transaction mix nonstationarity casts doubt on the practical usefulness of prediction methods that ignore this phenomenon.This paper demonstrates that transaction mix nonstationarity enables a new approach to predicting application-level performance as a function of transaction mix. We exploit nonstationarity to circumvent the need for invasive instrumentation and controlled benchmarking during model calibration; our approach relies solely on lightweight passive measurements that are routinely collected in today's production environments. We evaluate predictive accuracy on two real business-critical production applications. The accuracy of our response time predictions ranges from 10% to 16% on these applications, and our models generalize well to workloads very different from those used for calibration.We apply our technique to the challenging problem of predicting the impact of application consolidation on transaction response times. We calibrate models of two testbed applications running on dedicated machines, then use the models to predict their performance when they run together on a shared machine and serve very different workloads. Our predictions are accurate to within 4% to 14%. Existing approaches to consolidation decision support predict post-consolidation resource utilizations. Our method allows application-level performance to guide consolidation decisions.

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