In bee venom phospholipase A2, histidine-34 probably functions as a Brønsted base to deprotonate the attacking water. Aspartate-64 and tyrosine-87 form a hydrogen bonding network with histidine-34. We have prepared mutants at these positions and studied their kinetic properties. The mutant in which histidine-34 is changed to glutamine is catalytically inactive, while the mutants in which aspartate-64 is changed to asparagine or alanine (interfacial turnover numbers are reduced by 50−100-fold) or in which tyrosine-87 is changed to phenylalanine (no change in turnover number) retain good activity. The interfacial Michaelis constants are changed by less than 10-fold for all mutants. Molecular simulations suggest that mutation of aspartate-64 and tyrosine-87 should yield enzymes that retain a native-like structure and support catalysis. The pKa of the histidine-34 imidazole was deduced from the pH-rate profile and from the pH dependence of the rate of histidine-34 alkylation by 2-bromo-4'-nitroacetophenone. The pKa is increased about one-half unit by the tyrosine-87 mutation and reduced about one-half unit by the aspartate-64 to asparagine mutation, while in the aspartate-64 to alanine mutant the pKa is unchanged. These pKas are generally consistent with results of electrostatic calculations and suggest that the hydrogen bond between aspartate-64 and histidine-34 is not unusually strong. The hydrogen bonding network linking tyrosine-87 to aspartate-64 and aspartate-64 to histidine-34 is not critical for catalysis.
Computational methods are increasingly used to streamline and enhance the lead discovery and optimization process. However, accurate prediction of absorption, distribution, metabolism and excretion (ADME) and adverse drug reactions (ADR) is often difficult, due to the complexity of underlying physiological mechanisms. Modeling approaches have been hampered by the lack of large, robust and standardized training datasets. In an extensive effort to build such a dataset, the BioPrint database was constructed by systematic profiling of nearly all drugs available on the market, as well as numerous reference compounds. The database is composed of several large datasets: compound structures and molecular descriptors, in vitro ADME and pharmacology profiles, and complementary clinical data including therapeutic use information, pharmacokinetics profiles and ADR profiles. These data have allowed the development of computational tools designed to integrate a program of computational chemistry into library design and lead development. Models based on chemical structure are strengthened by in vitro results that can be used as additional compound descriptors to predict complex in vivo endpoints. The BioPrint pharmacoinformatics platform represents a systematic effort to accelerate the process of drug discovery, improve quantitative structure-activity relationships and develop in vitro/in vivo associations. In this review, we will discuss the importance of training set size and diversity in model development, the implementation of linear and neighborhood modeling approaches, and the use of in silico methods to predict potential clinical liabilities.
We describe a framework for automatically identifying and visualizing the most differentiating attributes of each cluster in a clustered data set. A dissimilarity function measures the cluster-conditional distinguishing saliency of each attribute with respect to a reference realization of the same attribute. For each cluster, the N attributes that are most dissimilar are presented first to the human expert, along with the overall dissimilarity of the cluster. We discuss the computational benefits of the proposed framework, how it can be implemented with map-reduce, its application to the behavioral analysis of mobile phone users, and it broad applicability to diverse problem domains.
TCR engagement of peptide-MHC class II ligands involves specific contacts between the TCR and residues on both the MHC and peptide molecules. We have used molecular modeling and assays of peptide binding and T cell function to characterize these interactions for a CD4+ Th1 cell clone, ESL4.34, which recognizes a peptide epitope of the herpes simplex type 2 virus virion protein, VP16 393-405, in the context of several HLA-DR alleles. This clone responded to VP16 393-405 in proliferation and cytotoxicity assays when presented by DRB1*0402, DRB1*1102, and DRB1*1301, which share a common amino acid sequence, ILEDE, at residues 67-71 in the alpha-helical portion of the DRbeta polypeptide, but not when presented by other DR4, DR11, and DR13 alleles that are negative for this sequence. Using a panel of APCs expressing DR4 molecules that were mutagenized in vitro at individual residues within this shared epitope and using peptide analogues with single amino acid substitutions of predicted MHC and TCR contact residues, a unit of recognition was identified dependent on DRbeta residues 67-71 and relative position 4 (P4) of the VP16 393-405 peptide. The interactions of this portion of the peptide-DR ligand with the ESL4.34 TCR support a structural model for MHC-biased recognition in some Ag-specific and alloreactive T cell responses and suggest a possible mechanism for autoreactive T cell selection in rheumatoid arthritis.
It is currently unclear whether small molecules dissociate from a protein binding site along a defined pathway or through a collection of dissociation pathways. We report herein a joint crystallographic, computational, and biophysical study that suggests the Asp-128 → Ala (D128A) streptavidin mutant closely mimics an intermediate on a well-defined dissociation pathway. Asp-128 is hydrogen bonded to a ureido nitrogen of biotin and also networks with the important aromatic binding contacts Trp-92 and Trp-108. The Asn-23 hydrogen bond to the ureido oxygen of biotin is lengthened to 3.8 Å in the D128A structure, and a water molecule has moved into the pocket to replace the missing carboxylate interaction. These alterations are accompanied by the coupled movement of biotin, the flexible binding loop containing Ser-45, and the loop containing the Ser-27 hydrogen bonding contact. This structure closely parallels a key intermediate observed in a potential of mean force-simulated dissociation pathway of native streptavidin, where the Asn-23 hydrogen bond breaks first, accompanied by the replacement of the Asp-128 hydrogen bond by an entering water molecule. Furthermore, both biotin and the flexible loop move in a concerted conformational change that closely approximates the D128A structural changes. The activation and thermodynamic parameters for the D128A mutant were measured and are consistent with an intermediate that has traversed the early portion of the dissociation reaction coordinate through endothermic bond breaking and concomitant gain in configurational entropy. These composite results suggest that the D128A mutant provides a structural “snapshot” of an early intermediate on a relatively well-defined dissociation pathway for biotin.
While personalisation has advanced formidably in our digital age, marketers remain challenged by how to deliver truly individualised customer experiences that are optimised for relevance, timeliness and value, and do so at scale. Marketers know that the more targeted a marketing interaction is, the stronger the customer response. Yet as customer behaviour continually changes, more advanced technologies capable of acting on dynamic data and increasing targeting granularity are required to discover and exploit optimal customer experiences at scale. In this paper the author discusses the application of machine learning to marketing personalisation, and how it can be used in conjunction with closed-loop experimentation to learn optimal combinations of targeting audiences, marketing experiences, and execution parameters to boost marketing performance even in highly competitive, dynamic markets.
Small myocardial infarctions (MI) may be life threatening, but cannot be easily detected using standard, body surface electrocardiography. This model study explores the use of an intracavitary probe to detect small MI's. The likelihood ratio is used for detection because it is optimum for most optimality criteria. A new quasi-static electromagnetic model of MI is presented which preserves the essential geometric features, while according relatively fast numerical solutions. A tabulation of simulated results shows that an intracavitary probe can detect infarcts as small as 400 mm/sup 2/ in 1 mV of noise with a detectability index of 0.495. (The detectability index is a measure of detection performance between 0.0 and 0.5, where 0.5 indicates perfect detection.) Simulations are presented for a variety of noise sources, infarction sizes, and probe designs.< >
