Chakrapani Kalyanaraman

Physics-Based Scoring of Protein−Ligand Complexes: Enrichment of Known Inhibitors in Large-Scale Virtual Screening

We demonstrate that using an all-atom molecular mechanics force field combined with an implicit solvent model for scoring protein-ligand complexes is a promising approach for improving inhibitor enrichment in the virtual screening of large compound databases. The rescoring method is evaluated by the extent to which known binders for nine diverse, therapeutically relevant enzymes are enriched against a background of approximately 100,000 drug-like decoys. The improvement in enrichment is most robust and dramatic within the top 1% of the ranked database, that is, the first thousand compounds; below the first few percent of the ranked database, there is little overall improvement. The improved early enrichment is likely due to the more realistic treatment of ligand and receptor desolvation in the rescoring procedure. We also present anecdotal but encouraging results assessing the ability of the rescoring method to predict specificity of inhibitors for structurally related proteins.

Molecular mechanics methods for predicting protein–ligand binding

Ligand binding affinity prediction is one of the most important applications of computational chemistry. However, accurately ranking compounds with respect to their estimated binding affinities to a biomolecular target remains highly challenging. We provide an overview of recent work using molecular mechanics energy functions to address this challenge. We briefly review methods that use molecular dynamics and Monte Carlo simulations to predict absolute and relative ligand binding free energies, as well as our own work in which we have developed a physics-based scoring method that can be applied to hundreds of thousands of compounds by invoking a number of simplifying approximations. In our previous studies, we have demonstrated that our scoring method is a promising approach for improving the discrimination between ligands that are known to bind and those that are presumed not to, in virtual screening of large compound databases. In new results presented here, we explore several improvements to our computational method including modifying the dielectric constant used for the protein and ligand interiors, and empirically scaling energy terms to compensate for deficiencies in the energy model. Future directions for further improving our physics-based scoring method are also discussed.

Discovery of a dipeptide epimerase enzymatic function guided by homology modeling and virtual screening.

We have developed a computational approach to aid the assignment of enzymatic function for uncharacterized proteins that uses homology modeling to predict the structure of the binding site and in silico docking to identify potential substrates. We apply this method to proteins in the functionally diverse enolase superfamily that are homologous to the characterized L-Ala-D/L-Glu epimerase from Bacillus subtilis. In particular, a protein from Thermotoga martima was predicted to have different substrate specificity, which suggests that it has a different, but as yet unknown, biological function. This prediction was experimentally confirmed, resulting in the assignment of epimerase activity for L-Ala-D/L-Phe, L-Ala-D/L-Tyr, and L-Ala-D/L-His, whereas the enzyme is annotated incorrectly in GenBank as muconate cycloisomerase. Subsequently, crystal structures of the enzyme were determined in complex with three substrates, showing close agreement with the computational models and revealing the structural basis for the observed substrate selectivity.

Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily

The rapid advance in genome sequencing presents substantial challenges for protein functional assignment, with half or more of new protein sequences inferred from these genomes having uncertain assignments. The assignment of enzyme function in functionally diverse superfamilies represents a particular challenge, which we address through a combination of computational predictions, enzymology, and structural biology. Here we describe the results of a focused investigation of a group of enzymes in the enolase superfamily that are involved in epimerizing dipeptides. The first members of this group to be functionally characterized were Ala-Glu epimerases in Eschericiha coli and Bacillus subtilis, based on the operon context and enzymological studies; these enzymes are presumed to be involved in peptidoglycan recycling. We have subsequently studied more than 65 related enzymes by computational methods, including homology modeling and metabolite docking, which suggested that many would have divergent specificities;, i.e., they are likely to have different (unknown) biological roles. In addition to the Ala-Phe epimerase specificity reported previously, we describe the prediction and experimental verification of: (i) a new group of presumed Ala-Glu epimerases; (ii) several enzymes with specificity for hydrophobic dipeptides, including one from Cytophaga hutchinsonii that epimerizes D-Ala-D-Ala; and (iii) a small group of enzymes that epimerize cationic dipeptides. Crystal structures for certain of these enzymes further elucidate the structural basis of the specificities. The results highlight the potential of computational methods to guide experimental characterization of enzymes in an automated, large-scale fashion.

Structure-guided discovery of the metabolite carboxy-SAM that modulates tRNA function

Identifying novel metabolites and characterizing their biological functions are major challenges of the post-genomic era. X-ray crystallography can reveal unanticipated ligands which persist through purification and crystallization. These adventitious protein:ligand complexes provide insights into new activities, pathways and regulatory mechanisms. We describe a new metabolite, carboxy-S-adenosylmethionine (Cx-SAM), its biosynthetic pathway and its role in tRNA modification. The structure of CmoA, a member of the SAM-dependent methyltransferase superfamily, revealed a ligand in the catalytic site consistent with Cx-SAM. Mechanistic analyses demonstrated an unprecedented role for prephenate as the carboxyl donor and the involvement of a unique ylide intermediate as the carboxyl acceptor in the CmoA-mediated conversion of SAM to Cx-SAM. A second member of the SAM-dependent methyltransferase superfamily, CmoB, recognizes Cx-SAM and acts as a carboxymethyltransferase to convert 5-hydroxyuridine (ho5U) into 5-oxyacetyl uridine (cmo5U) at the wobble position of multiple tRNAs in Gram negative bacteria1, resulting in expanded codon-recognition properties2,3. CmoA and CmoB represent the first documented synthase and transferase for Cx-SAM. These findings reveal new functional diversity in the SAM-dependent methyltransferase superfamily and expand the metabolic and biological contributions of SAM-based biochemistry. These discoveries highlight the value of structural genomics approaches for identifying ligands in the context of their physiologically relevant macromolecular binding partners and for aiding in functional assignment.The identification of novel metabolites and the characterization of their biological functions are major challenges in biology. X-ray crystallography can reveal unanticipated ligands that persist through purification and crystallization. These adventitious protein–ligand complexes provide insights into new activities, pathways and regulatory mechanisms. We describe a new metabolite, carboxy-S-adenosyl-l-methionine (Cx-SAM), its biosynthetic pathway and its role in transfer RNA modification. The structure of CmoA, a member of the SAM-dependent methyltransferase superfamily, revealed a ligand consistent with Cx-SAM in the catalytic site. Mechanistic analyses showed an unprecedented role for prephenate as the carboxyl donor and the involvement of a unique ylide intermediate as the carboxyl acceptor in the CmoA-mediated conversion of SAM to Cx-SAM. A second member of the SAM-dependent methyltransferase superfamily, CmoB, recognizes Cx-SAM and acts as a carboxymethyltransferase to convert 5-hydroxyuridine into 5-oxyacetyl uridine at the wobble position of multiple tRNAs in Gram-negative bacteria, resulting in expanded codon-recognition properties. CmoA and CmoB represent the first documented synthase and transferase for Cx-SAM. These findings reveal new functional diversity in the SAM-dependent methyltransferase superfamily and expand the metabolic and biological contributions of SAM-based biochemistry. These discoveries highlight the value of structural genomics approaches in identifying ligands within the context of their physiologically relevant macromolecular binding partners, and in revealing their functions.

Computation-facilitated assignment of the function in the enolase superfamily: a regiochemically distinct galactarate dehydratase from Oceanobacillus iheyensis .

The structure of an uncharacterized member of the enolase superfamily from Oceanobacillus iheyensis (GI 23100298, IMG locus tag Ob2843, PDB entry 2OQY ) was determined by the New York SGX Research Center for Structural Genomics (NYSGXRC). The structure contained two Mg(2+) ions located 10.4 A from one another, with one located in the canonical position in the (beta/alpha)(7)beta-barrel domain (although the ligand at the end of the fifth beta-strand is His, unprecedented in structurally characterized members of the superfamily); the second is located in a novel site within the capping domain. In silico docking of a library of mono- and diacid sugars to the active site predicted a diacid sugar as a likely substrate. Activity screening of a physical library of acid sugars identified galactarate as the substrate (k(cat) = 6.8 s(-1), K(M) = 620 microM, k(cat)/K(M) = 1.1 x 10(4) M(-1) s(-1)), allowing functional assignment of Ob2843 as galactarate dehydratase (GalrD-II). The structure of a complex of the catalytically impaired Y90F mutant with Mg(2+) and galactarate allowed identification of a Tyr 164-Arg 162 dyad as the base that initiates the reaction by abstraction of the alpha-proton and Tyr 90 as the acid that facilitates departure of the beta-OH leaving group. The enzyme product is 2-keto-d-threo-4,5-dihydroxyadipate, the enantiomer of the product obtained in the GalrD reaction catalyzed by a previously characterized bifunctional l-talarate/galactarate dehydratase (TalrD/GalrD). On the basis of the different active site structures and different regiochemistries, we recognize that these functions represent an example of apparent, not actual, convergent evolution of function. The structure of GalrD-II and its active site architecture allow identification of the seventh functionally and structurally characterized subgroup in the enolase superfamily. This study provides an additional example in which an integrated sequence- and structure-based strategy employing computational approaches is a viable approach for directing functional assignment of unknown enzymes discovered in genome projects.

Virtual Ligand Screening against Escherichia coli Dihydrofolate Reductase: Improving Docking Enrichment Using Physics-Based Methods:

Motivated by their participation in the McMaster Data-Mining and Docking Competition, the authors developed 2 new computational technologies and applied them to docking against Escherichia coli dihydrofolate reductase: a receptor preparation procedure that incorporates rotamer optimization of side chains and a physics-based rescoring procedure for estimating relative binding affinities of the protein-ligand complexes. Both methods use the same energy function, consisting of the all-atom OPLS-AA force field and a generalized Born solvent model, which treats the protein receptor and small-molecule ligands in a consistent manner. Thus, the energy function is similar to that used in more sophisticated approaches, such as free-energy perturbation and the molecular mechanics Poisson-Boltzmann/surface area, but sampling during the rescoring procedure is limited to simple energy minimization of the ligand. The use of a highly efficient minimization algorithm permitted the authors to apply this rescoring procedure to hundreds of thousands of protein-ligand complexes during the competition, using a modest Linux cluster. To test these methods, they used the 12 competitive inhibitors identified in the training set, plus methotrexate, as positive controls in enrichment studies with both the training and test sets, each containing 50,000 compounds. The key conclusion is that combining the receptor preparation and rescoring methods makes it possible to identify most of the positive controls within the top few tenths of a percent of the rank-ordered training and test set libraries.

A cysteine protease inhibitor rescues mice from a lethal Cryptosporidium parvum infection

ABSTRACT Cryptosporidiosis, caused by the protozoan parasite Cryptosporidium parvum, can stunt infant growth and can be lethal in immunocompromised individuals. The most widely used drugs for treating cryptosporidiosis are nitazoxanide and paromomycin, although both exhibit limited efficacy. To investigate an alternative approach to therapy, we demonstrate that the clan CA cysteine protease inhibitor N-methyl piperazine-Phe-homoPhe-vinylsulfone phenyl (K11777) inhibits C. parvum growth in mammalian cell lines in a concentration-dependent manner. Further, using the C57BL/6 gamma interferon receptor knockout (IFN-γR-KO) mouse model, which is highly susceptible to C. parvum, oral or intraperitoneal treatment with K11777 for 10 days rescued mice from otherwise lethal infections. Histologic examination of untreated mice showed intestinal inflammation, villous blunting, and abundant intracellular parasite stages. In contrast, K11777-treated mice (210 mg/kg of body weight/day) showed only minimal inflammation and no epithelial changes. Three putative protease targets (termed cryptopains 1 to 3, or CpaCATL-1, -2, and -3) were identified in the C. parvum genome, but only two are transcribed in infected mammals. A homology model predicted that K11777 would bind to cryptopain 1. Recombinant enzymatically active cryptopain 1 was successfully targeted by K11777 in a competition assay with a labeled active-site-directed probe. K11777 exhibited no toxicity in vitro and in vivo, and surviving animals remained free of parasites 3 weeks after treatment. The discovery that a cysteine protease inhibitor provides potent anticryptosporidial activity in an animal model of infection encourages the investigation and development of this biocide class as a new, and urgently needed, chemotherapy for cryptosporidiosis.

Leveraging structure for enzyme function prediction: methods, opportunities, and challenges.

The rapid growth of the number of protein sequences that can be inferred from sequenced genomes presents challenges for function assignment, because only a small fraction (currently <1%) has been experimentally characterized. Bioinformatics tools are commonly used to predict functions of uncharacterized proteins. Recently, there has been significant progress in using protein structures as an additional source of information to infer aspects of enzyme function, which is the focus of this review. Successful application of these approaches has led to the identification of novel metabolites, enzyme activities, and biochemical pathways. We discuss opportunities to elucidate systematically protein domains of unknown function, orphan enzyme activities, dead-end metabolites, and pathways in secondary metabolism.

Studying Enzyme−Substrate Specificity in Silico: A Case Study of the Escherichia coli Glycolysis Pathway

In silico protein-ligand docking methods have proven to be useful in drug design and have also shown promise for predicting the substrates of enzymes, an important goal given the number of enzymes with uncertain function. Further testing of this latter approach is critical because (1) metabolites are on average much more polar than druglike compounds and (2) binding is necessary but not sufficient for catalysis. Here, we demonstrate that docking against the enzymes that participate in the 10 major steps of the glycolysis pathway in Escherichia coli succeeds in identifying the substrates among the top 1% of a virtual metabolite library.