Comparison of protein active site structures for functional annotation of proteins and drug design

Synthesis of Novel Nitro-Halogenated Aryl-Himachalene Sesquiterpenes from Atlas Cedar Oil Components: Characterization, DFT Studies, and Molecular Docking Analysis against Various Isolated Smooth Muscles

We report the synthesis of two novel halogenated nitro-arylhimachalene derivatives: 2-bromo-3,5,5,9-tetramethyl-1-nitro-6,7,8,9-tetrahydro-5H-benzo[7]annulene (bromo-nitro-arylhimachalene) and 2-chloro-3,5,5,9-tetramethyl-1,4-dinitro-6,7,8,9-tetrahydro-5H-benzo[7]annulene (chloro-dinitro-arylhimachalene). These compounds were derived from arylhimachalene, an important sesquiterpene component of Atlas cedar essential oil, via a two-step halogenation and nitration process. Characterization was performed using 1H and 13C NMR spectrometry, complemented by X-ray structural analysis. Quantum chemical calculations employing density functional theory (DFT) with the Becke3-Lee-Yang-parr (B3LYP) functional and a 6-31++G(d,p) basis set were conducted. The optimized geometries of the synthesized compounds were consistent with X-ray structure data. Frontier molecular orbitals and molecular electrostatic potential (MEP) profiles were identified and discussed. DFT reactivity indices provided insights into the compounds' behaviors. Moreover, Hirshfeld surface and 2D fingerprint analyses revealed significant intermolecular interactions within the crystal structures, predominantly H-H and H-O contacts. Molecular docking studies demonstrate strong binding affinities of the synthesized compounds to the active site of protein 7B2W, suggesting potential therapeutic applications against various isolated smooth muscles and neurotransmitters.

AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters

Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.

Exploring Reactive Conformations of Coenzyme A during Binding and Unbinding to Pyruvate Formate-Lyase

Pyruvate formate-lyase (PFL) is a glycyl radical enzyme that converts pyruvate and coenzyme A (CoA) into formate and acetyl-CoA in two half-reactions. Recently, we showed that the acetylation of the PFL active site in the first half-reaction induces subtle conformational changes, leading to the opening of a potential channel for CoA entry. Entry of CoA into the active site is crucial for the second half-reaction, involving the acetyl transfer to CoA, and the completion of the catalytic cycle. Using steered molecular dynamics (SMD) simulations, performed on acetylated and nonacetylated monomeric PFL model systems, we first of all investigate the possible entry/exit pathways of CoA with respect to the active site through the previously identified channel. We then perform umbrella sampling simulations on multiple snapshots from SMD trajectories as well as unrestrained molecular dynamics simulations starting from the final structures obtained from entry SMD, with a view to identifying possible bound states of CoA in the near vicinity of the active site. Detailed study of the unrestrained dissociation processes reveals the presence of stable and reactive bound states of CoA close to the active site, one of which is in an ideal position for triggering the second half-reaction. Examination of the spatial distributions associated with the reactive bound states allows us to discuss the free energy barriers. Umbrella sampling, performed on snapshots from unrestrained dynamics confirms the above findings. The significance of the results for the catalysis are discussed for both acetylated and nonacetylated systems.

Functional Evolution of Proteins

The functional evolution of proteins advances through gene duplication followed by functional drift, whereas molecular evolution occurs through random mutational events. Over time, protein active-site structures or functional epitopes remain highly conserved, which enables relationships to be inferred between distant orthologs or paralogs. In this study, we present the first functional clustering and evolutionary analysis of the RCSB Protein Data Bank (RCSB PDB) based on similarities between active-site structures. All of the ligand-bound proteins within the RCSB PDB were scored using our Comparison of Protein Active-site Structures (CPASS) software and database (http://cpass.unl.edu/). Principal component analysis was then used to identify 4431 representative structures to construct a phylogenetic tree based on the CPASS comparative scores (http://itol.embl.de/shared/jcatazaro). The resulting phylogenetic tree identified a sequential, step-wise evolution of protein active-sites and provides novel insights into the emergence of protein function or changes in substrate specificity based on subtle changes in geometry and amino acid composition.

In Silico Target Druggability Assessment: From Structural to Systemic Approaches

This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.

Particle Targeting in Complex Biological Media

Over the past few decades, nanoengineered particles have gained increasing interest for applications in the biomedical realm, including diagnosis, imaging, and therapy. When functionalized with targeting ligands, these particles have the potential to interact with specific cells and tissues, and accumulate at desired target sites, reducing side effects and improve overall efficacy in applications such as vaccination and drug delivery. However, when targeted particles enter a complex biological environment, the adsorption of biomolecules and the formation of a surface coating (e.g., a protein corona) changes the properties of the carriers and can render their behavior unpredictable. For this reason, it is of importance to consider the potential challenges imposed by the biological environment at the early stages of particle design. This review describes parameters that affect the targeting ability of particulate drug carriers, with an emphasis on the effect of the protein corona. We highlight strategies for exploiting the protein corona to improve the targeting ability of particles. Finally, we provide suggestions for complementing current in vitro assays used for the evaluation of targeting and carrier efficacy with new and emerging techniques (e.g., 3D models and flow-based technologies) to advance fundamental understanding in bio-nano science and to accelerate the development of targeted particles for biomedical applications.

Designing novel inhibitors against histone acetyltransferase (HAT: GCN5) of Plasmodium falciparum

During active proliferation phase of intra-erythrocytic cycle, the genome of P. falciparum is regulated epigenetically and evolutionary conserved parasite-specific histone proteins are extensively acetylated. The reversible process of lysine acetylation, causing transcriptional activation and its deacetylation, causing transcriptional repression is regulated by balanced activities of HATs and HDACs. They are also known to regulate antigenic variations and gametocytic conversion in P. falciparum. These histone modifying enzymes have been identified as potential targets for development of anitmalarials in literature. PfGCN5, a HAT family member of P. falciparum is predominantly involved in H3K9 acetylation. In this study, through comparative structure and sequence analysis, we elucidate differences in the catalytic pocket of PfGCN5 which can be exploited to design selective inhibitors. Through virtual screening of known antimalarials from ChEMBL bioassay database, we mapped 10 compounds with better affinity towards PfGCN5. Further, we identified 10 more novel compounds which showed remarkably better affinity towards the Plasmodium target from analogues of mapped inhibitors from ZINC database of commercially available compounds. Comparative molecular dynamics simulation study of one of the compounds (C14) complex with PfGCN5 and HsGCN5 suggested the possible reason for its selectivity. In vitro parasite growth assay in the presence of C14 showed IC50 value at lower nanomolar range (∼ 225 nM). However, no effect in mammalian fibroblast cells was observed for C14 (up to 20 μM). Further, reduced level of HAT activity of recombinant GCN5 and H3K9Ac was observed in the parasites treated with C14. Overall, this study reports 20 potential inhibitors of PfGCN5 and experimental validation of one molecule (C14) with antimalarial activity at low nanomolar range.

Genetically encoded fluorescent sensors reveal dynamic regulation of NADPH metabolism

Reduced nicotinamide adenine dinucleotide phosphate (NADPH) is essential for biosynthetic reactions and antioxidant functions; however, detection of NADPH metabolism in living cells remains technically challenging. We develop and characterize ratiometric, pH-resistant, genetically encoded fluorescent indicators for NADPH (iNap sensors) with various affinities and wide dynamic range. iNap sensors enabled quantification of cytosolic and mitochondrial NADPH pools that are controlled by cytosolic NAD⁺ kinase levels and revealed cellular NADPH dynamics under oxidative stress depending on glucose availability. We found that mammalian cells have a strong tendency to maintain physiological NADPH homeostasis, which is regulated by glucose-6-phosphate dehydrogenase and AMP kinase. Moreover, using the iNap sensors we monitor NADPH fluctuations during the activation of macrophage cells or wound response in vivo. These data demonstrate that the iNap sensors will be valuable tools for monitoring NADPH dynamics in live cells and gaining new insights into cell metabolism.

Design of novel lead molecules against RhoG protein as cancer target - a computational study

Cancer is a class of diseases characterized by uncontrolled cell growth. Every year more than 2 million people are affected by the disease. Rho family proteins are actively involved in cytoskeleton regulation. Over-expression of Rho family proteins show oncogenic activity and promote cancer progression. In the present work RhoG protein is considered as novel target of cancer. It is a member of Rho family and Rac subfamily protein, which plays pivotal role in regulation of microtubule formation, cell migration and contributes in cancer progression. In order to understand the binding interaction between RhoG protein and the DH domain of Ephexin-4 protein, the 3D structure of RhoG was evaluated and Molecular Dynamic Simulations was performed to stabilize the structure. The 3D structure of RhoG protein was validated and active site identified using standard computational protocols. Protein-protein docking of RhoG with Ephexin-4 was done to understand binding interactions and the active site structure. Virtual screening was carried out with ligand databases against the active site of RhoG protein. The efficiency of virtual screening is analysed with enrichment factor and area under curve values. The binding free energy of docked complexes was calculated using prime MM-GBSA module. The SASA, FOSA, FISA, PISA and PSA values of ligands were carried out. New ligands with high docking score, glide energy and acceptable ADME properties were prioritized as potential inhibitors of RhoG protein.

In Silico Analysis of Glutaminase from Different Species of Escherichia and Bacillus

BACKGROUND

Glutaminase (EC 3.5.1.2) catalyzes the hydrolytic degradation of L-glutamine to L-glutamic acid and has been introduced for cancer therapy in recent years. The present study was an in silico analysis of glutaminase to further elucidate its structure and physicochemical properties.

METHODS

Forty glutaminase protein sequences from different species of Escherichia and Bacillus obtained from the UniProt Protein Database were characterized for homology search, physiochemical properties, phylogenetic tree construction, motif, superfamily search, and multiple sequence alignment.

RESULTS

The sequence level homology was obtained among different groups of glutaminase enzymes, which belonged to superfamily serine-dependent β-lactamases and penicillin-binding proteins. The phylogenetic tree constructed indicated 2 main clusters for the glutaminases. The distribution of common β-lactamase motifs was also observed; however, various non-common motifs were also observed.

CONCLUSION

Our results showed that the existence of a conserved motif with a signature amino-acid sequence of β-lactamases could be considered for the genetic engineering of glutaminases in view of their potential application in cancer therapy. Nonetheless, further research is needed to improve the stability of glutaminases and decrease their immunogenicity in both medical and food industrial applications.

Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design

Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.

Spatial proximity statistics suggest a regulatory role of protein phosphorylation on compound binding

Phosphorylation is an important post-translational modification that regulates protein function by the attachment of negatively charged phosphate groups to phosphorylatable amino acid residues. As a mode of action, an influence of phosphorylation on the binding of compounds to proteins has been discussed and described for a number of proteins in the literature. However, a systematic statistical survey probing for enriched phosphorylation sites close to compound binding sites in support of this notion and with properly chosen random reference distributions has not been presented yet. Using high-resolution protein structures from the Protein Data Bank including their co-crystallized non-covalently bound compounds and experimentally determined phosphorylation sites, we analyzed the pairwise distance distributions of phosphorylation and compound binding sites on protein surfaces. We found that phosphorylation sites are indeed located at significantly closer distances to compounds than expected by chance holding true specifically also for the subset of compound binding sites serving as catalytic sites of metabolic reactions. This tendency was particularly evident when treating phosphorylation sites as collective sets supporting the relevance of phosphorylation hotspots. Interestingly, phosphorylation sites were found to be closer to negatively charged than to positively charged compounds suggesting a stronger modulation of the binding of negatively charged compounds in dependence on phosphorylation status than on positively charged compounds. The enrichment of phosphorylation sites near compound binding sites confirms a regulatory role of phosphorylation in compound binding and provides a solid statistical basis for the literature-reported selected events.

Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity

To better understand and ultimately predict both the metabolic activities as well as the signaling functions of metabolites, a detailed understanding of the physical interactions of metabolites with proteins is highly desirable. Focusing in particular on protein binding specificity vs. promiscuity, we performed a comprehensive analysis of the physicochemical properties of compound-protein binding events as reported in the Protein Data Bank (PDB). We compared the molecular and structural characteristics obtained for metabolites to those of the well-studied interactions of drug compounds with proteins. Promiscuously binding metabolites and drugs are characterized by low molecular weight and high structural flexibility. Unlike reported for drug compounds, low rather than high hydrophobicity appears associated, albeit weakly, with promiscuous binding for the metabolite set investigated in this study. Across several physicochemical properties, drug compounds exhibit characteristic binding propensities that are distinguishable from those associated with metabolites. Prediction of target diversity and compound promiscuity using physicochemical properties was possible at modest accuracy levels only, but was consistently better for drugs than for metabolites. Compound properties capturing structural flexibility and hydrogen-bond formation descriptors proved most informative in PLS-based prediction models. With regard to diversity of enzymatic activities of the respective metabolite target enzymes, the metabolites benzylsuccinate, hypoxanthine, trimethylamine N-oxide, oleoylglycerol, and resorcinol showed very narrow process involvement, while glycine, imidazole, tryptophan, succinate, and glutathione were identified to possess broad enzymatic reaction scopes. Promiscuous metabolites were found to mainly serve as general energy currency compounds, but were identified to also be involved in signaling processes and to appear in diverse organismal systems (digestive and nervous system) suggesting specific molecular and physiological roles of promiscuous metabolites.

Statistical removal of background signals from high-throughput (1)H NMR line-broadening ligand-affinity screens

NMR ligand-affinity screens are vital to drug discovery, are routinely used to screen fragment-based libraries, and used to verify chemical leads from high-throughput assays and virtual screens. NMR ligand-affinity screens are also a highly informative first step towards identifying functional epitopes of unknown proteins, as well as elucidating the biochemical functions of protein-ligand interaction at their binding interfaces. While simple one-dimensional (1)H NMR experiments are capable of indicating binding through a change in ligand line shape, they are plagued by broad, ill-defined background signals from protein (1)H resonances. We present an uncomplicated method for subtraction of protein background in high-throughput ligand-based affinity screens, and show that its performance is maximized when phase-scatter correction is applied prior to subtraction.

Identification of Functionally Related Enzymes by Learning-to-Rank Methods

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work, we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes.

Ornithine aminotransferase versus GABA aminotransferase: implications for the design of new anticancer drugs

Ornithine aminotransferase (OAT) and γ-aminobutyric acid aminotransferase (GABA-AT) are classified under the same evolutionary subgroup and share a large portion of structural, functional, and mechanistic features. Therefore, it is not surprising that many molecules that bind to GABA-AT also bind well to OAT. Unlike GABA-AT, OAT had not been viewed as a potential therapeutic target until recently; consequently, the number of therapeutically viable molecules that target OAT is very limited. In this review the two enzymes are compared with respect to their active-site structures, catalytic and inactivation mechanisms, and selective inhibitors. Insight is offered that could aid in the design and development of new selective inhibitors of OAT for the treatment of cancer.

Functional evolution of PLP-dependent enzymes based on active-site structural similarities

Families of distantly related proteins typically have very low sequence identity, which hinders evolutionary analysis and functional annotation. Slowly evolving features of proteins, such as an active site, are therefore valuable for annotating putative and distantly related proteins. To date, a complete evolutionary analysis of the functional relationship of an entire enzyme family based on active-site structural similarities has not yet been undertaken. Pyridoxal-5'-phosphate (PLP) dependent enzymes are primordial enzymes that diversified in the last universal ancestor. Using the comparison of protein active site structures (CPASS) software and database, we show that the active site structures of PLP-dependent enzymes can be used to infer evolutionary relationships based on functional similarity. The enzymes successfully clustered together based on substrate specificity, function, and three-dimensional-fold. This study demonstrates the value of using active site structures for functional evolutionary analysis and the effectiveness of CPASS.

Identification of low-molecular-weight compounds inhibiting growth of corynebacteria: potential lead compounds for antibiotics

The bacterial genus Corynebacteria contains several pathogenic species that cause diseases such as diphtheria in humans and "cheesy gland" in goats and sheep. Thus, identifying new therapeutic targets to treat Corynebacteria infections is both medically and economically important. CG2496, a functionally uncharacterized protein from Corynebacterium glutamicum, was evaluated using an NMR ligand-affinity screen. A total of 11 compounds from a library of 460 biologically active compounds were shown to selectively bind CG2496 in a highly conserved region of the protein. The best binder was identified to be methiothepin (KD =54 ± 19 µM), an FDA-approved serotonin receptor antagonist. Methiothepin was also shown to inhibit the growth of C. glutamicum, but not bacteria that lack CG2496 homologs. Our results suggest that CG2496 is a novel therapeutic target and methiothepin is a potential lead compound or structural scaffold for developing new antibiotics specifically targeting Corynebacteria.

Identification of Similar Binding Sites to Detect Distant Polypharmacology

The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

Structure-based target druggability assessment

The focus of this chapter is on the important concepts behind the in silico techniques that are used today to assess target druggability. The first step of the assessment consists of finding cavity space in the protein using 2D and/or 3D topological concepts. These concepts underlie the geometry and energy-based pocketfinder algorithms. Analysis pursues on the physico-chemical complementarity between the binding site and the drug like molecule. Geometrical and molecular flexibility aspect are also included in this assessment. The presence of hot interaction spots are shown to be particularly important for targeting protein-protein interactions. Finally, binding site promiscuity can be assessed by large scale structural comparison with other targets. Common chemical features amongst protein cavities can predict potential cross-reactivity with unwanted targets.

SiteBinder: an improved approach for comparing multiple protein structural motifs

There is a paramount need to develop new techniques and tools that will extract as much information as possible from the ever growing repository of protein 3D structures. We report here on the development of a software tool for the multiple superimposition of large sets of protein structural motifs. Our superimposition methodology performs a systematic search for the atom pairing that provides the best fit. During this search, the RMSD values for all chemically relevant pairings are calculated by quaternion algebra. The number of evaluated pairings is markedly decreased by using PDB annotations for atoms. This approach guarantees that the best fit will be found and can be applied even when sequence similarity is low or does not exist at all. We have implemented this methodology in the Web application SiteBinder, which is able to process up to thousands of protein structural motifs in a very short time, and which provides an intuitive and user-friendly interface. Our benchmarking analysis has shown the robustness, efficiency, and versatility of our methodology and its implementation by the successful superimposition of 1000 experimentally determined structures for each of 32 eukaryotic linear motifs. We also demonstrate the applicability of SiteBinder using three case studies. We first compared the structures of 61 PA-IIL sugar binding sites containing nine different sugars, and we found that the sugar binding sites of PA-IIL and its mutants have a conserved structure despite their binding different sugars. We then superimposed over 300 zinc finger central motifs and revealed that the molecular structure in the vicinity of the Zn atom is highly conserved. Finally, we superimposed 12 BH3 domains from pro-apoptotic proteins. Our findings come to support the hypothesis that there is a structural basis for the functional segregation of BH3-only proteins into activators and enablers.

Cofactor-binding sites in proteins of deviating sequence: comparative analysis and clustering in torsion angle, cavity, and fold space

Small molecules are recognized in protein-binding pockets through surface-exposed physicochemical properties. To optimize binding, they have to adopt a conformation corresponding to a local energy minimum within the formed protein-ligand complex. However, their conformational flexibility makes them competent to bind not only to homologous proteins of the same family but also to proteins of remote similarity with respect to the shape of the binding pockets and folding pattern. Considering drug action, such observations can give rise to unexpected and undesired cross reactivity. In this study, datasets of six different cofactors (ADP, ATP, NAD(P)(H), FAD, and acetyl CoA, sharing an adenosine diphosphate moiety as common substructure), observed in multiple crystal structures of protein-cofactor complexes exhibiting sequence identity below 25%, have been analyzed for the conformational properties of the bound ligands, the distribution of physicochemical properties in the accommodating protein-binding pockets, and the local folding patterns next to the cofactor-binding site. State-of-the-art clustering techniques have been applied to group the different protein-cofactor complexes in the different spaces. Interestingly, clustering in cavity (Cavbase) and fold space (DALI) reveals virtually the same data structuring. Remarkable relationships can be found among the different spaces. They provide information on how conformations are conserved across the host proteins and which distinct local cavity and fold motifs recognize the different portions of the cofactors. In those cases, where different cofactors are found to be accommodated in a similar fashion to the same fold motifs, only a commonly shared substructure of the cofactors is used for the recognition process.

In silico characterization of alkaline proteases from different species of Aspergillus

A total of 49 protein sequences of alkaline proteases retrieved from GenBank representing different species of Aspergillus have been characterized for various physiochemical properties, homology search, multiple sequence alignment, motif, and super family search and phylogenetic tree construction. The sequence level homology was obtained among different groups of alkaline protease enzymes, viz alkaline serine protease, oryzin, calpain-like protease, serine protease, subtilisin-like alkaline proteases. Multiple sequence alignment of alkaline protease protein sequence of different Aspergillus species revealed a stretch of conserved region for amino acid residues from 69 to 110 and 130-204. The phylogenetic tree constructed indicated several Aspergillus species-specific clusters for alkaline proteases namely Aspergillus fumigatus, Aspergillus niger, Aspergillus oryzae, Aspergillus clavatus. The distributions of ten commonly observed motifs were analyzed among these proteases. Motif 1 with a signature amino acid sequence of 50 amino acids, i.e., ASFSNYGKVVDIFAPGQDILSCWIGSTTATNTISGTSMATPHIVGLSCYL, was uniformly observed in proteases protein sequences indicating its involvement with the structure and enzymatic function. Motif analysis of acidic proteases of Aspergillus and bacterial alkaline proteases has revealed different signature amino acid sequences. The superfamily search for these proteases revealed the presence of subtilases, serine-carboxyl proteinase, calpain large subunit, and thermolysin-like superfamilies with 45 representing the subtilases superfamily.

Research Spotlight: Research in bioanalysis and separations at the University of Nebraska – Lincoln

The Chemistry Department at the University of Nebraska – Lincoln (UNL) is located in Hamilton Hall on the main campus of UNL in Lincoln, NE, USA. This department houses the primary graduate and research program in chemistry in the state of Nebraska. This program includes the traditional fields of analytical chemistry, biochemistry, inorganic chemistry, organic chemistry and physical chemistry. However, this program also contains a great deal of multidisciplinary research in fields that range from bioanalytical and biophysical chemistry to nanomaterials, energy research, catalysis and computational chemistry. Current research in bioanalytical and biophysical chemistry at UNL includes work with separation methods such as HPLC and CE, as well as with techniques such as MS and LC–MS, NMR spectroscopy, electrochemical biosensors, scanning probe microscopy and laser spectroscopy. This article will discuss several of these areas, with an emphasis being placed on research in bioanalytical separations, binding assays and related fields.

Use of 2-aminoprop-1-ene-1,1,3-tricarbonitrile for the synthesis of tetrahydronaphthalene, hexahydroisoquinoline and hexahydrocinnoline derivatives with potential antitumor activities

The aim of the work was to synthesize heterocyclic compounds from 2-aminoprop-1-ene-1,1,3-tricarbonitrile and to study their antitumor activities. The title reagent reacted with cyclohexanone to give the ethylidene derivative 2. The reactivity of the latter product towards different chemical reagents was studied to give tetrahydronaphthalene, hexahydroisoquinoline and hexahydrocinnoline derivatives. The newly synthesized products were screened as antitumor agents on the in vitro growth of three human tumor cell lines representing different tumor types, namely, breast adenocarcinoma (MCF-7), non-small cell lung cancer (NCI-H460) and CNS cancer (SF-268). It was found that some of these compounds showed inhibitory effects on the three cell lines, indicating their potential use in the development of oncology products.

Solution structure and function of YndB, an AHSA1 protein from Bacillus subtilis

The solution structure of the Bacillus subtilis protein YndB has been solved using NMR to investigate proposed biological functions. The YndB structure exhibits the helix-grip fold, which consists of a β-sheet with two small and one long α-helix, forming a hydrophobic cavity that preferentially binds lipid-like molecules. Sequence and structure comparisons with proteins from eukaryotes, prokaryotes, and archaea suggest that YndB is very similar to the eukaryote protein Aha1, which binds to the middle domain of Hsp90 and induces ATPase activity. On the basis of these similarities, YndB has been classified as a member of the activator of Hsp90 ATPase homolog 1-like protein (AHSA1) family with a function that appears to be related to stress response. An in silico screen of a compound library of ∼ 18,500 lipids was used to identify classes of lipids that preferentially bind YndB. The in silico screen identified, in order of affinity, the chalcone/hydroxychalcone, flavanone, and flavone/flavonol classes of lipids, which was further verified by 2D (1) H-(15) N HSQC NMR titration experiments with trans-chalcone, flavanone, flavone, and flavonol. All of these compounds are typically found in plants as precursors to various flavonoid antibiotics and signaling molecules. The sum of the data suggests an involvement of YndB with the stress response of B. subtilis to chalcone-like flavonoids released by plants due to a pathogen infection. The observed binding of chalcone-like molecules by YndB is likely related to the symbiotic relationship between B. subtilis and plants.

Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites

A new binding site comparison algorithm using optimal superposition of the continuous pharmacophoric property distributions is reported. The method demonstrates high sensitivity in discovering both, distantly homologous and convergent binding sites. Good quality of superposition is also observed on multiple examples. Using the new approach, a measure of site similarity is derived and applied to clustering of ligand binding pockets in PDB.

Searching the protein structure database for ligand-binding site similarities using CPASS v.2

Background

A recent analysis of protein sequences deposited in the NCBI RefSeq database indicates that ~8.5 million protein sequences are encoded in prokaryotic and eukaryotic genomes, where ~30% are explicitly annotated as "hypothetical" or "uncharacterized" protein. Our Comparison of Protein Active-Site Structures (CPASS v.2) database and software compares the sequence and structural characteristics of experimentally determined ligand binding sites to infer a functional relationship in the absence of global sequence or structure similarity. CPASS is an important component of our Functional Annotation Screening Technology by NMR (FAST-NMR) protocol and has been successfully applied to aid the annotation of a number of proteins of unknown function.

Findings

We report a major upgrade to our CPASS software and database that significantly improves its broad utility. CPASS v.2 is designed with a layered architecture to increase flexibility and portability that also enables job distribution over the Open Science Grid (OSG) to increase speed. Similarly, the CPASS interface was enhanced to provide more user flexibility in submitting a CPASS query. CPASS v.2 now allows for both automatic and manual definition of ligand-binding sites and permits pair-wise, one versus all, one versus list, or list versus list comparisons. Solvent accessible surface area, ligand root-mean square difference, and Cβ distances have been incorporated into the CPASS similarity function to improve the quality of the results. The CPASS database has also been updated.

Conclusions

CPASS v.2 is more than an order of magnitude faster than the original implementation, and allows for multiple simultaneous job submissions. Similarly, the CPASS database of ligand-defined binding sites has increased in size by ~ 38%, dramatically increasing the likelihood of a positive search result. The modification to the CPASS similarity function is effective in reducing CPASS similarity scores for false positives by ~30%, while leaving true positives unaffected. Importantly, receiver operating characteristics (ROC) curves demonstrate the high correlation between CPASS similarity scores and an accurate functional assignment. As indicated by distribution curves, scores ≥ 30% infer a functional similarity. Software URL: http://cpass.unl.edu.

Application of NMR and molecular docking in structure-based drug discovery

Drug discovery is a complex and costly endeavor, where few drugs that reach the clinical testing phase make it to market. High-throughput screening (HTS) is the primary method used by the pharmaceutical industry to identify initial lead compounds. Unfortunately, HTS has a high failure rate and is not particularly efficient at identifying viable drug leads. These shortcomings have encouraged the development of alternative methods to drive the drug discovery process. Specifically, nuclear magnetic resonance (NMR) spectroscopy and molecular docking are routinely being employed as important components of drug discovery research. Molecular docking provides an extremely rapid way to evaluate likely binders from a large chemical library with minimal cost. NMR ligand-affinity screens can directly detect a protein-ligand interaction, can measure a corresponding dissociation constant, and can reliably identify the ligand binding site and generate a co-structure. Furthermore, NMR ligand affinity screens and molecular docking are perfectly complementary techniques, where the combination of the two has the potential to improve the efficiency and success rate of drug discovery. This review will highlight the use of NMR ligand affinity screens and molecular docking in drug discovery and describe recent examples where the two techniques were combined to identify new and effective therapeutic drugs.

Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins

Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.

Protein pockets: inventory, shape, and comparison

The shape of the protein surface dictates what interactions are possible with other macromolecules, but defining discrete pockets or possible interaction sites remains difficult. First, there is the problem of defining the extent of the pocket. Second, one has to characterize the shape of each pocket. Third, one needs to make quantitative comparisons between pockets on different proteins. An elegant solution to these problems is to sort all surface and solvent points by travel depth and then collect a hierarchical tree of pockets. The connectivity of the tree is determined via the deepest saddle points between each pair of neighboring pockets. The resulting pocket surfaces tessellate the entire protein surface, producing a complete inventory of pockets. This method of identifying pockets also allows one to easily compute important shape metrics, including the problematic pocket volume, surface area, and mouth size. Pockets are also annotated with their lining residue lists and polarity and with other residue-based properties. Using this tree and the various shape metrics pockets can be merged, grouped, or filtered for further analysis. Since this method includes the entire surface, it guarantees that any pocket of interest will be found among the output pockets, unlike all previous methods of pocket identification. The resulting hierarchy of pockets is easy to visualize and aids users in higher level analysis. Comparison of pockets is done by using the shape metrics, avoiding the complex shape alignment problem. Example applications show that the method facilitates pocket comparison along mutational or time-dependent series. Pockets from families of proteins can be examined using multiple pocket tree alignments to see how ligand binding sites or how other pockets have changed with evolution. Our method is called CLIPPERS for complete liberal inventory of protein pockets elucidating and reporting on shape.

PROFESS: a PROtein function, evolution, structure and sequence database

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are approximately 1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of protein-protein interaction networks. Database URL: http://cse.unl.edu/~profess/

Pharmacophore screening of the protein data bank for specific binding site chemistry

A simple computational approach was developed to screen the Protein Data Bank (PDB) for putative pockets possessing a specific binding site chemistry and geometry. The method employs two commonly used 3D screening technologies, namely identification of cavities in protein structures and pharmacophore screening of chemical libraries. For each protein structure, a pocket finding algorithm is used to extract potential binding sites containing the correct types of residues, which are then stored in a large SDF-formatted virtual library; pharmacophore filters describing the desired binding site chemistry and geometry are then applied to screen this virtual library and identify pockets matching the specified structural chemistry. As an example, this approach was used to screen all human protein structures in the PDB and identify sites having chemistry similar to that of known methyl-lysine binding domains that recognize chromatin methylation marks. The selected genes include known readers of the histone code as well as novel binding pockets that may be involved in epigenetic signaling. Putative allosteric sites were identified on the structures of TP53BP1, L3MBTL3, CHEK1, KDM4A, and CREBBP.

Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery

Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.

Alignment-free ultra-high-throughput comparison of druggable protein-ligand binding sites

Inferring the biological function of a protein from its three-dimensional structure as well as explaining why a drug may bind to various targets is of crucial importance to modern drug discovery. Here we present a generic 4833-integer vector describing druggable protein-ligand binding sites that can be applied to any protein and any binding cavity. The fingerprint registers counts of pharmacophoric triplets from the Calpha atomic coordinates of binding-site-lining residues. Starting from a customized data set of diverse protein-ligand binding site pairs, the most appropriate metric and a similarity threshold could be defined for similar binding sites. The method (FuzCav) has been used in various scenarios: (i) screening a collection of 6000 binding sites for similarity to different queries; (ii) classifying protein families (serine endopeptidases, protein kinases) by binding site diversity; (iii) discriminating adenine-binding cavities from decoys. The fingerprint generation and comparison supports ultra-high throughput (ca. 1000 measures/s), does not require prior alignment of protein binding sites, and is able to detect local similarity among subpockets. It is thus particularly well suited to the functional annotation of novel genomic structures with low sequence identity to known X-ray templates.

Structural and functional similarity between the bacterial type III secretion system needle protein PrgI and the eukaryotic apoptosis Bcl-2 proteins

Background

Functional similarity is challenging to identify when global sequence and structure similarity is low. Active-sites or functionally relevant regions are evolutionarily more stable relative to the remainder of a protein structure and provide an alternative means to identify potential functional similarity between proteins. We recently developed the FAST-NMR methodology to discover biochemical functions or functional hypotheses of proteins of unknown function by experimentally identifying ligand binding sites. FAST-NMR utilizes our CPASS software and database to assign a function based on a similarity in the structure and sequence of ligand binding sites between proteins of known and unknown function.

Methodology/Principal Findings

The PrgI protein from Salmonella typhimurium forms the needle complex in the type III secretion system (T3SS). A FAST-NMR screen identified a similarity between the ligand binding sites of PrgI and the Bcl-2 apoptosis protein Bcl-xL. These ligand binding sites correlate with known protein-protein binding interfaces required for oligomerization. Both proteins form membrane pores through this oligomerization to release effector proteins to stimulate cell death. Structural analysis indicates an overlap between the PrgI structure and the pore forming motif of Bcl-xL. A sequence alignment indicates conservation between the PrgI and Bcl-xL ligand binding sites and pore formation regions. This active-site similarity was then used to verify that chelerythrine, a known Bcl-xL inhibitor, also binds PrgI.

Conclusions/Significance

A structural and functional relationship between the bacterial T3SS and eukaryotic apoptosis was identified using our FAST-NMR ligand affinity screen in combination with a bioinformatic analysis based on our CPASS program. A similarity between PrgI and Bcl-xL is not readily apparent using traditional global sequence and structure analysis, but was only identified because of conservation in ligand binding sites. These results demonstrate the unique opportunity that ligand-binding sites provide for the identification of functional relationships when global sequence and structural information is limited.

Advances in Nuclear Magnetic Resonance for Drug Discovery

BACKGROUND: Drug discovery is a complex and unpredictable endeavor with a high failure rate. Current trends in the pharmaceutical industry have exasperated these challenges and are contributing to the dramatic decline in productivity observed over the last decade. The industrialization of science by forcing the drug discovery process to adhere to assembly-line protocols is imposing unnecessary restrictions, such as short project time-lines. Recent advances in nuclear magnetic resonance are responding to these self-imposed limitations and are providing opportunities to increase the success rate of drug discovery. OBJECTIVE/METHOD: A review of recent advancements in NMR technology that have the potential of significantly impacting and benefiting the drug discovery process will be presented. These include fast NMR data collection protocols and high-throughput protein structure determination, rapid protein-ligand co-structure determination, lead discovery using fragment-based NMR affinity screens, NMR metabolomics to monitor in vivo efficacy and toxicity for lead compounds, and the identification of new therapeutic targets through the functional annotation of proteins by FAST-NMR. CONCLUSION: NMR is a critical component of the drug discovery process, where the versatility of the technique enables it to continually expand and evolve its role. NMR is expected to maintain this growth over the next decade with advancements in automation, speed of structure calculation, in-cell imaging techniques, and the expansion of NMR amenable targets.

Structure and function of Pseudomonas aeruginosa protein PA1324 (21-170)

Pseudomonas aeruginosa is the prototypical biofilm-forming gram-negative opportunistic human pathogen. P. aeruginosa is causatively associated with nosocomial infections and with cystic fibrosis. Antibiotic resistance in some strains adds to the inherent difficulties that result from biofilm formation when treating P. aeruginosa infections. Transcriptional profiling studies suggest widespread changes in the proteome during quorum sensing and biofilm development. Many of the proteins found to be upregulated during these processes are poorly characterized from a functional standpoint. Here, we report the solution NMR structure of PA1324, a protein of unknown function identified in these studies, and provide a putative biological functional assignment based on the observed prealbumin-like fold and FAST-NMR ligand screening studies. PA1324 is postulated to be involved in the binding and transport of sugars or polysaccharides associated with the peptidoglycan matrix during biofilm formation.

A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery

Functional relationships between proteins that do not share global structure similarity can be established by detecting their ligand-binding-site similarity. For a large-scale comparison, it is critical to accurately and efficiently assess the statistical significance of this similarity. Here, we report an efficient statistical model that supports local sequence order independent ligand-binding-site similarity searching. Most existing statistical models only take into account the matching vertices between two sites that are defined by a fixed number of points. In reality, the boundary of the binding site is not known or is dependent on the bound ligand making these approaches limited. To address these shortcomings and to perform binding-site mapping on a genome-wide scale, we developed a sequence-order independent profile-profile alignment (SOIPPA) algorithm that is able to detect local similarity between unknown binding sites a priori. The SOIPPA scoring integrates geometric, evolutionary and physical information into a unified framework. However, this imposes a significant challenge in assessing the statistical significance of the similarity because the conventional probability model that is based on fixed-point matching cannot be applied. Here we find that scores for binding-site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version. Efficient statistical analysis makes it possible to apply SOIPPA to genome-based drug discovery. Consequently, we have applied the approach to the structural genome of Mycobacterium tuberculosis to construct a protein-ligand interaction network. The network reveals highly connected proteins, which represent suitable targets for promiscuous drugs.

Prediction of sub-cavity binding preferences using an adaptive physicochemical structure representation

MOTIVATION

The ability to predict binding profiles for an arbitrary protein can significantly improve the areas of drug discovery, lead optimization and protein function prediction. At present, there are no successful algorithms capable of predicting binding profiles for novel proteins. Existing methods typically rely on manually curated templates or entire active site comparison. Consequently, they perform best when analyzing proteins sharing significant structural similarity with known proteins (i.e. proteins resulting from divergent evolution). These methods fall short when used to characterize the binding profile of a novel active site or one for which a template is not available. In contrast to previous approaches, our method characterizes the binding preferences of sub-cavities within the active site by exploiting a large set of known protein-ligand complexes. The uniqueness of our approach lies not only in the consideration of sub-cavities, but also in the more complete structural representation of these sub-cavities, their parametrization and the method by which they are compared. By only requiring local structural similarity, we are able to leverage previously unused structural information and perform binding inference for proteins that do not share significant structural similarity with known systems.

RESULTS

Our algorithm demonstrates the ability to accurately cluster similar sub-cavities and to predict binding patterns across a diverse set of protein-ligand complexes. When applied to two high-profile drug targets, our algorithm successfully generates a binding profile that is consistent with known inhibitors. The results suggest that our algorithm should be useful in structure-based drug discovery and lead optimization.

A survey of available tools and web servers for analysis of protein-protein interactions and interfaces

The unanimous agreement that cellular processes are (largely) governed by interactions between proteins has led to enormous community efforts culminating in overwhelming information relating to these proteins; to the regulation of their interactions, to the way in which they interact and to the function which is determined by these interactions. These data have been organized in databases and servers. However, to make these really useful, it is essential not only to be aware of these, but in particular to have a working knowledge of which tools to use for a given problem; what are the tool advantages and drawbacks; and no less important how to combine these for a particular goal since usually it is not one tool, but some combination of tool-modules that is needed. This is the goal of this review.