The structures of the MAP kinase p38 in complex with docking site peptides containing a phi(A)-X-phi(B) motif, derived from substrate MEF2A and activating enzyme MKK3b, have been solved. The peptides bind to the same site in the C-terminal domain of the kinase, which is both outside the active site and distinct from the "CD" domain previously implicated in docking site interactions. Mutational analysis on the interaction of p38 with the docking sites supports the crystallographic models and has uncovered two novel residues on the docking groove that are critical for binding. The two peptides induce similar large conformational changes local to the peptide binding groove. The peptides also induce unexpected and different conformational changes in the active site, as well as structural disorder in the phosphorylation lip.

This paper reviews the development of agent-based (computational) economics (ACE) from an econometrics viewpoint. The review comprises three stages, characterizing the past, the present, and the future of this development. The first two stages can be interpreted as an attempt to build the econometric foundation of ACE, and, through that, enrich its empirical content. The second stage may then invoke a reverse reflection on the possible agent-based foundation of econometrics. While ACE modeling has been applied to different branches of economics, the one, and probably the only one, which is able to provide evidence of this three-stage development is finance or financial economics. We will, therefore, focus our review only on the literature of agent-based computational finance, or, more specifically, the agent-based modeling of financial markets. 188of those stylized facts based on the literature. Section 3.2 then analyzes how these stylized facts are successfully explained or replicated by various classes of ACF models.The same style of analysis is carried over in addressing the second stage of the development. At this stage, we ask what makes an ACE model econometrically tractable. Alternatively put, what are the necessary restrictions or assumptions that need to be imposed before an ACE model can be estimated? However, we will soon realize that the answer cannot be independent of the estimation method involved. This leads us to distinguish three main estimation methods, namely, maximum likelihood (MLE), least squares (LS), and the method of moments, as well as variations of the three. Generally speaking, the complexity of the objective function associated with the number of the parameters to be estimated will determine which of the estimation methods will be used. As a result, ACE models can also be categorized by the appropriate estimation method, which makes them econometrically tractable.The questions addressed in the third stage are very different from those asked in the first two. The issue is not the empirical relevance or empirical validation of the built ACE models, but concerns using the ACE models as a tool to address the aggregation problem or the analogy principle, which has been extensively discussed in the literature (Blinder, 1983; Barker & Pesaran, 1990;Forni & Lippi, 1997;Gallegati et al., 2006). In light of the Debreu-Mantel-Sonnenschein theorem, there are no grounds to expect macro behavior to be in any way similar or analogous to the behavior of individual agents. ACE models can help us to see how dissimilar the macro and micro behavior can be.With the above description, the rest of the chapter is organized as follows. A taxonomy of ACE models is given in Section 2. The three-stage development of the literature is described in Section 3, Section 4, and Section 5. Concluding remarks are given in Section 6.Agent-based economic models and econometrics

The Drosophila peptidoglycan recognition protein SA (PGRP-SA) is critically involved in sensing bacterial infection and activating the Toll signaling pathway, which induces the expression of specific antimicrobial peptide genes. We have determined the crystal structure of PGRP-SA to 2.2-Å resolution and analyzed its peptidoglycan (PG) recognition and signaling activities. We found an extended surface groove in the structure of PGRP-SA, lined with residues that are highly diverse among different PGRPs. Mutational analysis identified it as a PG docking groove required for Toll signaling and showed that residue Ser158 is essential for both PG binding and Toll activation. Contrary to the general belief that PGRP-SA has lost enzyme function and serves primarily for PG sensing, we found that it possesses an intrinsic L,D-carboxypeptidase activity for diaminopimelic acid-type tetrapeptide PG fragments but not lysine-type PG fragments, and that Ser158 and His42 may participate in the hydrolytic activity. As L,D-configured peptide bonds exist only in prokaryotes, this work reveals a rare enzymatic activity in a eukaryotic protein known for sensing bacteria and provides a possible explanation of how PGRP-SA mediates Toll activation specifically in response to lysine-type PG.

Thioester-containing proteins (TEPs) are a major component of the innate immune response of insects to invasion by bacteria and protozoa. TEPs form a distinct clade of a superfamily that includes the pan-protease inhibitors ␣2-macroglobulins and vertebrate complement factors. The essential feature of these proteins is a sequestered thioester bond that, after cleavage in a proteasesensitive region of the protein, is activated and covalently binds to its target. Recently, TEP1 from the malarial vector Anopheles gambiae was shown to mediate recognition and killing of ookinetes from the malarial parasite Plasmodium berghei, a model for the human malarial parasite Plasmodium falciparum. Here, we present the crystal structure of the TEP1 isoform TEP1r. Although the overall protein fold of TEP1r resembles that of complement factor C3, the TEP1r domains are repositioned to stabilize the inactive conformation of the molecule (containing an intact thioester) in the absence of the anaphylotoxin domain, a central component of complement factors. The structure of TEP1r provides a molecular basis for the differences between TEP1 alleles TEP1r and TEP1s, which correlate with resistance of A. gambiae to infection by P. berghei.Anopheles gambiae ͉ crystal structure ͉ innate immunity ͉ thioester M alaria is a major global health concern, with Ͼ300 million episodes per year (1). The increasing prevalence of malaria in tropical developing nations, the appearance of multidrug-resistant forms, and the lack of an effective vaccine have fueled interest in vector control strategies, including the vector's innate immune response to malarial infection (2). Thioester-containing proteins (TEPs) are a major component of the innate immune response of insects to invasion by bacteria and protozoa (3). TEPs form a distinct clade of a superfamily that includes the pan-protease inhibitors ␣ 2 -macroglobulins and vertebrate complement factors (4). The essential feature of these proteins is a sequestered thioester bond that, after cleavage in a protease-sensitive region of the protein, is activated and covalently binds to its target. The vertebrate complement system is activated by multiple pathways and is an effector of acquired and innate immune responses, including opsonization and direct killing of pathogens by lysis (5).The chief vector for the human malarial parasite Plasmodium falciparum is the mosquito Anopheles gambiae. The sequencing of the A. gambiae genome revealed 19 TEP ORFs, including 4 putative haplotypes (6). Two of these, TEP1 and TEP16, are alleles of a common gene, and were renamed TEP1s and TEP1r, respectively (7). TEP1 protein opsonizes bacteria in a thioesterdependent manner (8). TEP1 is up-regulated in the early stages of infection by P. berghei, a rodent model for P. falciparum (7), and furthermore, the two TEP1 alleles correlate with A. gambiae strains that are susceptible (S strain, TEP1s) or refractory (R strain, TEP1r), respectively, to infection by Plasmodium berghei.It is yet to be determined whether infection by P. fa...