4D-QSAR analysis incorporates conformational and alignment freedom into the development of 3D-QSAR models for training sets of structure−activity data by performing ensemble averaging, the fourth “dimension”. The descriptors in 4D-QSAR analysis are the grid cell (spatial) occupancy measures of the atoms composing each molecule in the training set realized from the sampling of conformation and alignment spaces. Grid cell occupancy descriptors can be generated for any atom type, group, and/or model pharmacophore. A single “active” conformation can be postulated for each compound in the training set and combined with the optimal alignment for use in other molecular design applications including other 3D-QSAR methods. The influence of the conformational entropy of each compound on its activity can be estimated. Serial use of partial least-squares, PLS, regression and a genetic algorithm, GA, is used to perform data reduction and identify the manifold of top 3D-QSAR models for a training set. The unique manifold of 3D-QSAR models is arrived at by computing the extent of orthogonality in the residuals of error among the most significant 3D-QSAR models in the general GA population. Receptor independent (RI) 4D-QSAR analysis has been successfully applied to three training sets:  (a) benzylpyrimidine inhibitors of dihydrofolate reductase, (b) prostaglandin PGF2α antinidatory analogs, and, (c) dipyridodiazepinone inhibitors of HIV-1 reverse transcriptase (RT). Two general findings from these applications are that grid cell occupancy descriptors associated with the “constant” chemical structure of an analog series can be significant in the 3D-QSAR models and that there is an enormous data reduction in constructing 3D-QSAR models. The resultant 3D-QSAR models can be graphically represented by plotting the significant 3D-QSAR grid cells in space along with their descriptor attributes.

Nucleotide-binding domain leucine-rich repeat proteins (NLRs) play a key role in immunity and disease through their ability to modulate inflammation in response to pathogen-derived and endogenous danger signals. Here, we identify the requirements for activation of NLRP1, an NLR protein associated with a number of human pathologies, including vitiligo, rheumatoid arthritis, and Crohn disease. We demonstrate that NLRP1 activity is dependent upon ASC, which associates with the C-terminal CARD domain of NLRP1. In addition, we show that NLRP1 activity is dependent upon autolytic cleavage at Ser(1213) within the FIIND. Importantly, this post translational event is dependent upon the highly conserved distal residue His(1186). A disease-associated single nucleotide polymorphism near His(1186) and a naturally occurring mRNA splice variant lacking exon 14 differentially affect this autolytic processing and subsequent NLRP1 activity. These results describe key molecular pathways that regulate NLRP1 activity and offer insight on how small sequence variations in NLR genes may influence human disease pathogenesis.

Neomorphic mutations in isocitrate dehydrogenase 1 (IDH1) are driver mutations in acute myeloid leukemia (AML) and other cancers. We report the development of new allosteric inhibitors of mutant IDH1. Crystallographic and biochemical results demonstrated that compounds of this chemical series bind to an allosteric site and lock the enzyme in a catalytically inactive conformation, thereby enabling inhibition of different clinically relevant IDH1 mutants. Treatment of IDH1 mutant primary AML cells uniformly led to a decrease in intracellular 2-HG, abrogation of the myeloid differentiation block and induction of granulocytic differentiation at the level of leukemic blasts and more immature stem-like cells, in vitro and in vivo. Molecularly, treatment with the inhibitors led to a reversal of the DNA cytosine hypermethylation patterns caused by mutant IDH1 in AML patients’ cells. Our study provides proof-of-concept for the molecular and biological activity of novel allosteric inhibitors for targeting different mutant forms of IDH1 in leukemia.

The human, cytosolic enzyme isocitrate dehydrogenase 1 (IDH1) reversibly converts isocitrate to α-ketoglutarate (αKG). Cancer-associated somatic mutations in IDH1 result in a loss of this normal function but a gain in a new or neomorphic ability to convert αKG to the oncometabolite 2-hydroxyglutarate (2HG). To improve our understanding of the basis for this phenomenon, we have conducted a detailed kinetic study of wild-type IDH1 as well as the known 2HG-producing clinical R132H and G97D mutants and mechanistic Y139D and (newly described) G97N mutants. In the reductive direction of the normal reaction (αKG to isocitrate), dead-end inhibition studies suggest that wild-type IDH1 goes through a random sequential mechanism, similar to previous reports on related mammalian IDH enzymes. However, analogous experiments studying the reductive neomorphic reaction (αKG to 2HG) with the mutant forms of IDH1 are more consistent with an ordered sequential mechanism, with NADPH binding before αKG. This result was further confirmed by primary kinetic isotope effects for which saturating with αKG greatly reduced the observed isotope effect on (D)(V/K)NADPH. For the mutant IDH1 enzyme, the change in mechanism was consistently associated with reduced efficiencies in the use of αKG as a substrate and enhanced efficiencies using NADPH as a substrate. We propose that the sum of these kinetic changes allows the mutant IDH1 enzymes to reductively trap αKG directly into 2HG, rather than allowing it to react with carbon dioxide and form isocitrate, as occurs in the wild-type enzyme.

Heterozygously expressed single-point mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2, respectively) render these dimeric enzymes capable of producing the novel metabolite α-hydroxyglutarate (αHG). Accumulation of αHG is used as a biomarker for a number of cancer types, helping to identify tumors with similar IDH mutations. With IDH1, it has been shown that one role of the mutation is to increase the rate of conversion from αKG to αHG. To improve our understanding of the function of this mutation, we have detailed the kinetics of the normal (isocitrate to αKG) and neomorphic (αKG to αHG) reactions, as well as the coupled conversion of isocitrate to αHG. We find that the mutant IDH1 is very efficient in this coupled reaction, with the ability to form αHG from isocitrate and NADP(+). The wild type/wild type IDH1 is also able to catalyze this conversion, though it is much more sensitive to concentrations of isocitrate. This difference in behavior can be attributed to the competitive binding between isocitrate and αKG, which is made more favorable for αKG by the neomorphic mutation at arginine 132. Thus, each partial reaction in the heterodimer is functionally isolated from the other. To test whether there is a cooperative effect resulting from the two subunits being in a dimer, we selectively inactivated each subunit with a secondary mutation in the NADP/H binding site. We observed that the remaining, active subunit was unaffected in its associated activity, reinforcing the notion of each subunit being functionally independent. This was further demonstrated using a monomeric form of IDH from Azotobacter vinelandii, which can be shown to gain the same neomorphic reaction when a homologous mutation is introduced into that protein.

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.

Authors are urged to introduce these corrections into any reprints they distribute. Secondary (abstract) services are urged to carry notice of these corrections as prominently as they carried the original abstracts.

No abstract