A potent, selective and cell-active allosteric inhibitor of protein arginine methyltransferase†3 (PRMT3).

PRMT3 catalyzes the asymmetric dimethylation of arginine residues of various proteins. It is essential for maturation of ribosomes, may have a role in lipogenesis, and is implicated in several diseases. A potent, selective, and cell-active PRMT3 inhibitor would be a valuable tool for further investigating PRMT3 biology. Here we report the discovery of the first PRMT3 chemical probe, SGC707, by structure-based optimization of the allosteric PRMT3 inhibitors we reported previously, and thorough characterization of this probe in biochemical, biophysical, and cellular assays. SGC707 is a potent PRMT3 inhibitor (IC50 =31±2 nM, KD =53±2 nM) with outstanding selectivity (selective against 31 other methyltransferases and more than 250 non-epigenetic targets). The mechanism of action studies and crystal structure of the PRMT3-SGC707 complex confirm the allosteric inhibition mode. Importantly, SGC707 engages PRMT3 and potently inhibits its methyltransferase activity in cells. It is also bioavailable and suitable for animal studies. This well-characterized chemical probe is an excellent tool to further study the role of PRMT3 in health and disease.

Novel naphthalene-based inhibitors of Trypanosoma brucei RNA editing ligase 1.

BACKGROUND: Neglected tropical diseases, including diseases caused by trypanosomatid parasites such as Trypanosoma brucei, cost tens of millions of disability-adjusted life-years annually. As the current treatments for African trypanosomiasis and other similar infections are limited, new therapeutics are urgently needed. RNA Editing Ligase 1 (REL1), a protein unique to trypanosomes and other kinetoplastids, was identified recently as a potential drug target. METHODOLOGY/PRINCIPAL FINDINGS: Motivated by the urgent need for novel trypanocidal therapeutics, we use an ensemble-based virtual-screening approach to discover new naphthalene-based TbREL1 inhibitors. The predicted binding modes of the active compounds are evaluated within the context of the flexible receptor model and combined with computational fragment mapping to determine the most likely binding mechanisms. Ultimately, four new low-micromolar inhibitors are presented. Three of the four compounds may bind to a newly revealed cleft that represents a putative druggable site not evident in any crystal structure. CONCLUSIONS/SIGNIFICANCE: Pending additional optimization, the compounds presented here may serve as precursors for future novel therapies useful in the fight against several trypanosomatid pathogens, including human African trypanosomiasis, a devastating disease that afflicts the vulnerable patient populations of sub-Saharan Africa.

Detection of ligand binding hot spots on protein surfaces via fragment-based methods: application to DJ-1 and glucocerebrosidase.

The identification of hot spots, i.e., binding regions that contribute substantially to the free energy of ligand binding, is a critical step for structure-based drug design. Here we present the application of two fragment-based methods to the detection of hot spots for DJ-1 and glucocerebrosidase (GCase), targets for the development of therapeutics for Parkinson's and Gaucher's diseases, respectively. While the structures of these two proteins are known, binding information is lacking. In this study we employ the experimental multiple solvent crystal structures (MSCS) method and computational fragment mapping (FTMap) to identify regions suitable for the development of pharmacological chaperones for DJ-1 and GCase. Comparison of data derived via MSCS and FTMap also shows that FTMap, a computational method for the identification of fragment binding hot spots, is an accurate and robust alternative to the performance of expensive and difficult crystallographic experiments.

Fragment-based identification of druggable 'hot spots' of proteins using Fourier domain correlation techniques.

MOTIVATION: The binding sites of proteins generally contain smaller regions that provide major contributions to the binding free energy and hence are the prime targets in drug design. Screening libraries of fragment-sized compounds by NMR or X-ray crystallography demonstrates that such 'hot spot' regions bind a large variety of small organic molecules, and that a relatively high 'hit rate' is predictive of target sites that are likely to bind drug-like ligands with high affinity. Our goal is to determine the 'hot spots' computationally rather than experimentally. RESULTS: We have developed the FTMAP algorithm that performs global search of the entire protein surface for regions that bind a number of small organic probe molecules. The search is based on the extremely efficient fast Fourier transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids, but can use only sums of correlation functions for scoring and hence is generally restricted to very simple energy expressions. The novelty of FTMAP is that we were able to incorporate and represent on grids a detailed energy expression, resulting in a very accurate identification of low-energy probe clusters. Overlapping clusters of different probes are defined as consensus sites (CSs). We show that the largest CS is generally located at the most important subsite of the protein binding site, and the nearby smaller CSs identify other important subsites. Mapping results are presented for elastase whose structure has been solved in aqueous solutions of eight organic solvents, and we show that FTMAP provides very similar information. The second application is to renin, a long-standing pharmaceutical target for the treatment of hypertension, and we show that the major CSs trace out the shape of the first approved renin inhibitor, aliskiren. AVAILABILITY: FTMAP is available as a server at http://ftmap.bu.edu/.

Novel druggable hot spots in avian influenza neuraminidase H5N1 revealed by computational solvent mapping of a reduced and representative receptor ensemble.

The influenza virus subtype H5N1 has raised concerns of a possible human pandemic threat because of its high virulence and mutation rate. Although several approved anti-influenza drugs effectively target the neuraminidase, some strains have already acquired resistance to the currently available anti-influenza drugs. In this study, we present the synergistic application of extended explicit solvent molecular dynamics (MD) and computational solvent mapping (CS-Map) to identify putative 'hot spots' within flexible binding regions of N1 neuraminidase. Using representative conformations of the N1 binding region extracted from a clustering analysis of four concatenated 40-ns MD simulations, CS-Map was utilized to assess the ability of small, solvent-sized molecules to bind within close proximity to the sialic acid binding region. Mapping analyses of the dominant MD conformations reveal the presence of additional hot spot regions in the 150- and 430-loop regions. Our hot spot analysis provides further support for the feasibility of developing high-affinity inhibitors capable of binding these regions, which appear to be unique to the N1 strain.

Identification of hot spots within druggable binding regions by computational solvent mapping of proteins.

Here we apply the computational solvent mapping (CS-Map) algorithm toward the in silico identification of hot spots, that is, regions of protein binding sites that are major contributors to the binding energy and, hence, are prime targets in drug design. The CS-Map algorithm, developed for binding site characterization, moves small organic functional groups around the protein surface and determines their most energetically favorable binding positions. The utility of CS-Map algorithm toward the prediction of hot spot regions in druggable binding pockets is illustrated by three test systems: (1) renin aspartic protease, (2) a set of previously characterized druggable proteins, and (3) E. coli ketopantoate reductase. In each of the three studies, existing literature was used to verify our results. Based on our analyses, we conclude that the information provided by CS-Map can contribute substantially to the identification of hot spots, a necessary predecessor of fragment-based drug discovery efforts.

JEDA: Joint entropy diversity analysis. An information-theoretic method for choosing diverse and representative subsets from combinatorial libraries.

The joint entropy-based diversity analysis (JEDA) program is a new method of selecting representative subsets of compounds from combinatorial libraries. Similar to other cell-based diversity analyses, a set of chemical descriptors is used to partition the chemical space of a library of compounds; however, unlike other metrics for choosing a compound from each partition, a Shannon-entropy based scoring function implemented in a probabilistic search algorithm determines a representative subset of compounds. This approach enables the selection of compounds that are not only diverse but that also represent the densities of chemical space occupied by the original chemical library. Additionally, JEDA permits the user to define the size of the subset that the chemist wishes to create so that restrictions on time and chemical reagents can be considered. Subsets created from a chemical library by JEDA are compared to subsets obtained using other partition-based diversity analyses, namely principal components analysis and median partitioning, on a combinatorial library derived from the Comprehensive Medical Chemistry Dataset.

Clustering of domains of functionally related enzymes in the interaction database PRECISE by the generation of primary sequence patterns.

The PRECISE database was developed by our laboratory to allow for the systematic study of the ligand interactions common to a set of functionally related enzymes, where an interaction site is defined broadly as any residue(s) that interact with a ligand. During the construction of PRECISE, enzyme chains are extracted from the protein data bank (PDB) and clustered according to functional homology as defined by the enzyme commission (EC) nomenclature system. A sequence representative is chosen from each cluster based on the criterion set forth by the non-redundant PDB set, and pair-wise alignments of each cluster member to the representative are performed. Atom-based residue-ligand interactions are calculated for each cluster member, and the summation of ligand interactions for all cluster members at each aligned position is determined. Although we were able to successfully align most clusters using a simple dynamic programming algorithm, several cluster created exhibited poor pair-wise alignments of each cluster member to its sequence representative. We hypothesized that the observed alignment problems were, in most cases, due to the incorrect separation and alignment of different domains in multi-domain proteins, a mistake that frequently causes error proliferation in functional annotation. Here we present the results of generating primary sequence patterns for each poorly aligned cluster in PRECISE to assess the extent to which multi-domain proteins that are incorrectly aligned contributes to poor pair-wise alignments of each cluster member to its representative. This requires the use of an iterative locally optimal pair-wise alignment algorithm to build a hierarchical similarity-based sequence pattern for a set of functionally related enzymes. Our results show that poor alignments in PRECISE are caused most frequently by the misalignment of multi-domain proteins, and that the generation of primary sequence patterns for the assignment of sequence family membership yields better alignments for the functionally related enzyme clusters in PRECISE than our original alignment algorithm.

Computational methods for functional site identification suggest a substrate access channel in transaldolase.

The homozygous deletion of Serine 171 results in the catalytic inactivation of the human transaldolase. Since Ser171 is in an outside loop, whereas the catalytic site is inside of the alpha/beta-barrel of the protein at least 15 A away, the loss of activity is difficult to explain. Two distinct computational methods are used to elucidate the potential origin of inactivation. Computational solvent mapping, which moves small organic molecules as probes around a protein surface and finds favorable binding positions, identifies the region around Ser171 as an important binding site. Three-dimensional cluster analysis, based both on a reference structure and multiple sequence alignment, shows that a patch of functionally important residues extends from Ser171 toward the catalytic site. Based on the findings of these two methods, we propose a novel ligand access path connecting these specific sites to the enzyme's active site. We also suggest that this mechanism may be aided by a significant conformational change involving the separation of two helices, alphaD and alphaG, in order to create an easy-access channel between the Ser171-related site and the active site. Further experimental procedures will be necessary to examine the biological feasibility of this proposed ligand shuttling path.

Ethics and policy perspectives on personalized medicine in the post-genomic era.

The completion of the human genome project and the accompanying biotechnological revolution hold great promise for the creation of pharmaceutical agents to combat not only previously incurable diseases, but also those for which therapeutics exist, yet they either cause severe adverse effects or exhibit no benefit in subsets of the population. In many instances variation in therapy response can be attributed to genetic differences, more particularly single nucleotide polymorphism (SNPs). Detection of the genetic differences which affect drug response, commonly referred to as pharmacogenomics, may result in further classification of diseases, and consequently, the development of 'personalized' therapies. While of potential great benefit, the widespread use of pharmacogenomic data poses social, ethical, and economic risks that need to be addressed by regulatory agencies such as the Food and Drug Administration (FDA). This paper explores some of the common problems associated with the use of pharmacogenomic data including validation of the data, patient confidentiality, social stratification, economic risks faced by pharmaceutical and insurance companies, and offers suggestions for regulatory procedures to ensure the appropriate use of the data in drug development and clinical trials.

PRECISE: a Database of Predicted and Consensus Interaction Sites in Enzymes.

PRECISE (Predicted and Consensus Interaction Sites in Enzymes) is a database of interactions between the amino acid residues of an enzyme and its ligands (substrate and transition state analogs, cofactors, inhibitors and products). It is available online at http://precise.bu.edu/. In the current version, all information on interactions is extracted from the enzyme-ligand complexes in the Protein Data Bank (PDB) by performing the following steps: (i) clustering homologous enzyme chains such that, in each cluster, the proteins have the same EC number and all sequences are similar; (ii) selecting a representative chain for each cluster; (iii) selecting ligand types; (iv) finding non-bonded interactions and hydrogen bonds; and (v) summing the interactions for all chains within the cluster. The output of the search is the color-coded sequence of the representative. The colors indicate the total number of interactions found at each amino acid position in all chains of the cluster. Clicking on a residue displays a detailed list of interactions for that residue. Optional filters allow restricting the output to selected chains in the cluster, to non-bonded or hydrogen bonding interactions, and to selected ligand types. The binding site information is essential for understanding and altering substrate specificity and for the design of enzyme inhibitors.

Telomerase protects cancer-prone human cells from chromosomal instability and spontaneous immortalization.

Studies were conducted to directly test whether the introduction of telomerase protects cancer-prone human mammary epithelial cells from chromosomal instability and spontaneous immortalization. Using a model for Li Fraumeni Syndrome (LFS), infection of human telomerase resulted in maintenance of telomere lengths, extension of in vitro lifespan, and prevention of spontaneous immortalization. In stark contrast to the spontaneously immortalized LFS cells, cells expressing ectopic telomerase displayed a remarkably stable karyotype and even after >150 population doublings, did not express endogenous telomerase. Since the hTERT-infected and spontaneously immortal LFS cells, like the parental cells, exhibit loss of p53 function, our data suggests that telomere shortening is the primary driving force for the genomic instability characteristic of LFS cells, while p53 inactivation is necessary for triggering the spontaneous immortalization event. Collectively, our data indicate that exogenous telomerase prevents chromosomal instability and spontaneous immortalization of LFS cells, suggesting a unique protective role for telomerase in the progression to immortalization.

Retroviral-mediated expression of telomerase in normal human cells provides a selective growth advantage.

Retroviral infection of hTERT, the catalytic component of telomerase, into BJ fibroblasts (population doubling 28) resulted in reconstitution of telomerase activity, telomere maintenance, and extension of in vitro lifespan. The hTERT-infected cells also exhibited increased growth rate and colony forming efficiency relative to controls, while remaining contact-inhibited and maintaining a p53-mediated damage response following gamma-irradiation. All single cell-derived BJ-hTERT clones grew faster than the hTERT mass cultures and maintained telomeres; however, neither telomerase activity levels nor mean telomere length correlated with the growth rate. Introduction of hTERT rescued aged BJ fibroblasts from senescence via a telomere-dependent mechanism and provided renewed proliferative potential. Collectively, our data indicate that both early and late in the cellular lifespan of human cells, ectopic expression of telomerase using a retroviral system provides a growth advantage while maintaining normal cellular characteristics.