BSP-SLIM: a blind low-resolution ligand-protein docking approach using predicted protein structures

Protein-Ligand Blind Docking Using CB-Dock2

Protein-ligand blind docking is a widely used method for studying the binding sites and poses of ligands and receptors in pharmaceutical and biological research. Recently, our new blind docking server named CB-Dock2 has been released and is currently being utilized by researchers worldwide. CB-Dock2 outperforms state-of-the-art methods due to its accuracy in binding site identification and binding pose prediction, which are enabled by its knowledge-based docking engine. This highly automated server offers interactive and intuitive input and output web interfaces, making it an efficient and user-friendly tool for the bioinformatics and cheminformatics communities. This chapter provides a brief overview of the methods, followed by a detailed guide on using the CB-Dock2 server. Additionally, we present a case study that evaluates the performance of protein-ligand blind docking using this tool.

Synthesis of Pyrrolo[3,4-b]pyridin-5-ones via Ugi-Zhu Reaction and In Vitro-In Silico Studies against Breast Carcinoma

An Ugi-Zhu three-component reaction (UZ-3CR) coupled in a one-pot manner to a cascade process (N-acylation/aza Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of pyrrolo[3,4-b]pyridin-5-ones in 20% to 92% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against breast cancer cell lines MDA-MB-231 and MCF-7, finding that compound 1f, at a concentration of 6.25 μM, exhibited a potential cytotoxic effect. Then, to understand the interactions between synthesized compounds and the main proteins related to the cancer cell lines, docking studies were performed on the serine/threonine kinase 1 (AKT1) and Orexetine type 2 receptor (Ox2R), finding moderate to strong binding energies, which matched accurately with the in vitro results. Additionally, molecular dynamics were performed between proteins related to the studied cell lines and the three best ligands.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

EViS: An Enhanced Virtual Screening Approach Based on Pocket-Ligand Similarity

Virtual screening (VS) is a popular technology in drug discovery to identify a new scaffold of actives for a specific drug target, which can be classified into ligand-based and structure-based approaches. As the number of protein-ligand complex structures available in public databases increases, it would be possible to develop a template searching-based VS approach that utilizes such information. In this work, we proposed an enhanced VS approach, which is termed EViS, to integrate ligand docking, protein pocket template searching, and ligand template shape similarity calculation. A novel and simple PL-score to characterize local pocket-ligand template similarity was used to evaluate the screening compounds. Benchmark tests were performed on three datasets including DUDE, LIT-PCBA, and DEKOIS. EViS achieved the average enrichment factors (EFs) of 27.8 and 23.4 at a 1% cutoff for experimental and predicted structures on the widely used DUDE dataset, respectively. Detailed data analysis shows that EViS benefits from obtaining favorable ligand poses from docking and using such ligand geometric information to perform three-dimensional (3D) ligand similarity calculations, and the PL-score is efficient to screen compounds based on template searching in the protein-ligand structure database.

CHARMM-GUI Ligand Designer for Template-Based Virtual Ligand Design in a Binding Site

Rational drug design involves a task of finding ligands that would bind to a specific target protein. This work presents CHARMM-GUI Ligand Designer that is an intuitive and interactive web-based tool to design virtual ligands that match the shape and chemical features of a given protein binding site. Ligand Designer provides ligand modification capabilities with 3D visualization that allow researchers to modify and redesign virtual ligands while viewing how the protein-ligand interactions are affected. Virtual ligands can also be parameterized for further molecular dynamics (MD) simulations and free energy calculations. Using 8 targets from 8 different protein classes in the directory of useful decoys, enhanced (DUD-E) data set, we show that Ligand Designer can produce similar ligands to the known active ligands in the crystal structures. Ligand Designer also produces stable protein-ligand complex structures when tested using short MD simulations. We expect that Ligand Designer can be a useful and user-friendly tool to design small molecules in any given potential ligand binding site on a protein of interest.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Residue-Residue Interaction Prediction via Stacked Meta-Learning

Protein-protein interactions (PPIs) are the basis of most biological functions determined by residue-residue interactions (RRIs). Predicting residue pairs responsible for the interaction is crucial for understanding the cause of a disease and drug design. Computational approaches that considered inexpensive and faster solutions for RRI prediction have been widely used to predict protein interfaces for further analysis. This study presents RRI-Meta, an ensemble meta-learning-based method for RRI prediction. Its hierarchical learning structure comprises four base classifiers and one meta-classifier to integrate predictive strengths from different classifiers. It considers multiple feature types, including sequence-, structure-, and neighbor-based features, for characterizing other properties of a residue interaction environment to better distinguish between noninteracting and interacting residues. We conducted the same experiments using the same data as previously reported in the literature to demonstrate RRI-Meta's performance. Experimental results show that RRI-Meta is superior to several current prediction tools. Additionally, to analyze the factors that affect the performance of RRI-Meta, we conducted a comparative case study using different protein complexes.

Immunoediting role for major vault protein in apoptotic signaling induced by bacterial N-acyl homoserine lactones

The major vault protein (MVP) mediates diverse cellular responses, including cancer cell resistance to chemotherapy and protection against inflammatory responses to Pseudomonas aeruginosa Here, we report the use of photoactive probes to identify MVP as a target of the N-(3-oxo-dodecanoyl) homoserine lactone (C12), a quorum sensing signal of certain proteobacteria including P. aeruginosa. A treatment of normal and cancer cells with C12 or other N-acyl homoserine lactones (AHLs) results in rapid translocation of MVP into lipid raft (LR) membrane fractions. Like AHLs, inflammatory stimuli also induce LR-localization of MVP, but the C12 stimulation reprograms (functionalizes) bioactivity of the plasma membrane by recruiting death receptors, their apoptotic adaptors, and caspase-8 into LR. These functionalized membranes control AHL-induced signaling processes, in that MVP adjusts the protein kinase p38 pathway to attenuate programmed cell death. Since MVP is the structural core of large particles termed vaults, our findings suggest a mechanism in which MVP vaults act as sentinels that fine-tune inflammation-activated processes such as apoptotic signaling mediated by immunosurveillance cytokines including tumor necrosis factor-related apoptosis inducing ligand (TRAIL).

FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening

To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITE^comb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITE^comb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITE^comb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITE^comb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE's predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITE^comb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http:/sites.gatech.edu/cssb/FRAGSITE.

Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.

The hydrogen-bond configuration modulates the energy transfer efficiency in helical protein nanotubes

Energy transport in proteins is critical to a variety of physical, chemical, and biological processes in living organisms. While strenuous efforts have been made to study vibrational energy transport in proteins, thermal transport processes across the most fundamental building blocks of proteins, i.e. helices, are not well understood. This work studies energy transport in a group of "isomer" helices. The π-helix is shown to have the highest thermal conductivity, 110% higher than that of the α-helix and 207% higher than that of the 310-helix. The H-bond connectivity is found to govern thermal transport mechanisms including the phonon spectral energy density, dispersion, mode-specific transport, group velocity, and relaxation time. The energy transport is strongly correlated with the H-bond strength which is also modulated by the H-bond connectivity. These fundamental insights provide a novel perspective for understanding energy transfer in proteins and guiding a rational molecule-level design of novel materials with configurable H-bonds.

A "Drug Sweeping" State of the TriABC Triclosan Efflux Pump from Pseudomonas aeruginosa

The structure of the TriABC inner membrane component of the triclosan/SDS-specific efflux pump from Pseudomonas aeruginosa was determined by cryoelectron microscopy to 4.5 Å resolution. The complete structure of the inner membrane transporter TriC of the resistance-nodulation-division (RND) superfamily was solved, including a partial structure of the fused periplasmic membrane fusion subunits, TriA and TriB. The substrate-free conformation of TriABC represents an intermediate step in efflux complex assembly before the engagement of the outer membrane channel. Structural analysis identified a tunnel network whose constriction impedes substrate efflux, indicating inhibition of TriABC in the unengaged state. Blind docking studies revealed binding to TriC at the same loci by substrates and bulkier non-substrates. Together with functional analyses, we propose that selective substrate translocation involves conformational gating at the tunnel narrowing that, together with conformational ordering of TriA and TriB, creates an engaged state capable of mediating substrate efflux.

Maturation Mechanism of Nitrile Hydratase From Streptomyces canus CGMCC 13662 and Its Structural Character

Nitrile hydratases have received significant interest both in the large-scale industrial production of acrylamide and nicotinamide, and the remediation of environmental contamination with nitrile-containing pollutants. Almost all known nitrile hydratases include an α-subunit (AnhA) and β-subunit (AnhB), and a specific activator protein is crucial for their maturation and catalytic activity. Many studies exist on nitrile hydratase characteristics and applications, but few have reported their metal insertion and post-translational maturation mechanism. In this study, we investigated the cobalt insertion and maturation mechanism of nitrile hydratase from Streptomyces canus CGMCC 13662 (ScNHase) bearing three subunits (AnhD, AnhE, and AnhA). ScNHase subunits were purified, and the cobalt content and nitrile hydratase activity of the ScNHase subunits were detected. We discovered that cobalt could insert into the cobalt-free AnhA of ScNHase in the absence of activator protein under reduction agent DL-dithiothreitol (DTT) environment. AnhD not only performed the function of AnhB of NHase, but also acted as a metal ion chaperone and self-subunit swapping chaperone, while AnhE did not act as similar performance. A cobalt direct-insertion under reduction condition coordinated self-subunit swapping mechanism is responsible for ScNHase post-translational maturation. Molecular docking of ScNHase and substrates suggested that the substrate specificity of ScNHase was correlated with its structure. ScNHase had a weak hydrophobic interaction with IAN through protein-ligand interaction analysis and, therefore, had no affinity with indole-3-acetonitrile (IAN). The post-translational maturation mechanism and structure characteristics of ScNHase could help guide research on the environmental remediation of nitrile-containing waste contamination and three-subunit nitrile hydratase.

Virtual Screening of Human Class-A GPCRs Using Ligand Profiles Built on Multiple Ligand-Receptor Interactions

G protein-coupled receptors (GPCRs) are a large family of integral membrane proteins responsible for cellular signal transductions. Identification of therapeutic compounds to regulate physiological processes is an important first step of drug discovery. We proposed MAGELLAN, a novel hierarchical virtual-screening (VS) pipeline, which starts with low-resolution protein structure prediction and structure-based binding-site identification, followed by homologous GPCR detections through structure and orthosteric binding-site comparisons. Ligand profiles constructed from the homologous ligand-GPCR complexes are then used to thread through compound databases for VS. The pipeline was first tested in a large-scale retrospective screening experiment against 224 human Class A GPCRs, where MAGELLAN achieved a median enrichment factor (EF) of 14.38, significantly higher than that using individual ligand profiles. Next, MAGELLAN was examined on 5 and 20 GPCRs from two public VS databases (DUD-E and GPCR-Bench) and resulted in an average EF of 9.75 and 13.70, respectively, which compare favorably with other state-of-the-art docking- and ligand-based methods, including AutoDock Vina (with EF = 1.48/3.16 in DUD-E and GPCR-Bench), DOCK 6 (2.12/3.47 in DUD-E and GPCR-Bench), PoLi (2.2 in DUD-E), and FINDSITECcomb2.0 (2.90 in DUD-E). Detailed data analyses show that the major advantage of MAGELLAN is attributed to the power of ligand profiling, which integrates complementary methods for ligand-GPCR interaction recognition and thus significantly improves the coverage and sensitivity of VS models. Finally, cases studies on opioid and motilin receptors show that new connections between functionally related GPCRs can be visualized in the minimum spanning tree built on the similarities of predicted ligand-binding ensembles, suggesting a novel use of MAGELLAN for GPCR deorphanization.

EDock: blind protein-ligand docking by replica-exchange monte carlo simulation

Protein–ligand docking is an important approach for virtual screening and protein function annotation. Although many docking methods have been developed, most require a high-resolution crystal structure of the receptor and a user-specified binding site to start. This information is, however, not available for the majority of unknown proteins, including many pharmaceutically important targets. Developing blind docking methods without predefined binding sites and working with low-resolution receptor models from protein structure prediction is thus essential. In this manuscript, we propose a novel Monte Carlo based method, EDock, for blind protein–ligand docking. For a given protein, binding sites are first predicted by sequence-profile and substructure-based comparison searches with initial ligand poses generated by graph matching. Next, replica-exchange Monte Carlo (REMC) simulations are performed for ligand conformation refinement under the guidance of a physical force field coupled with binding-site distance constraints. The method was tested on two large-scale datasets containing 535 protein–ligand pairs. Without specifying binding pockets on the experimental receptor structures, EDock achieves on average a ligand RMSD of 2.03 Å, which compares favorably with state-of-the-art docking methods including DOCK6 (2.68 Å) and AutoDock Vina (3.92 Å). When starting with predicted models from I-TASSER, EDock still generates reasonable docking models, with a success rate 159% and 67% higher than DOCK6 and AutoDock Vina, respectively. Detailed data analyses show that the major advantage of EDock lies in reliable ligand binding site predictions and extensive REMC sampling, which allows for the implementation of multiple van der Waals weightings to accommodate different levels of steric clashes and cavity distortions and therefore enhances the robustness of low-resolution docking with predicted protein structures.

A comprehensive study on identifying the structural and functional SNPs of human neuronal membrane glycoprotein M6A (GPM6A)

Glycoprotein M6A, a stress related gene, plays an important role in synapse and filopodia formation. Filopodia formation is vital for development, immunity, angiogenesis, wound healing and metastasis. In this study, structural and functional analysis of high-risk SNPs associated with Glycoprotein M6-A were evaluated using six different bioinformatics tools. Results classified T210I, T134I, Y153H, I215T, F156L, T160I, I226T, R247W, R178C, W159R, N157S and P151L as deleterious mutants that are crucial for the structure and function of the protein causing malfunction of M6-a and ultimately leads to disease development. The three-dimensional structure of wild-type M6-a and mutant M6-a were also predicted. Furthermore, the effects of high risk substitutions were also analyzed with interaction with valproic acid. Based on structural models obtained, the binding pocket of ligand bound glycoprotein M6-A structure showed few core interacting residues which are different in the mutant models. Among all substitutions, F156L showed complete loss of binding pocket when interacting with valproic acid as compared to the wild type model. Up to the best of our knowledge this is the first comprehensive study where GPM6A mutations were analyzed. The mechanism of action of GPM6A is still not fully defined which limits the understanding of functional details encoding M6-A. Our results may help enlighten some molecular aspects underlying glycoprotein M6-A. Communicated by Ramaswamy H. Sarma.

Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets

The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures of some proteins (the so-called undruggable targets) are known, their targeted drugs are still absent. As increasing crystal/cryogenic electron microscopy structures are deposited in Protein Data Bank, it is much more possible to discover the targeted drugs. Moreover, it is also highly probable to turn previous undruggable targets into druggable ones when we identify their hidden allosteric sites. In this review, we focus on the currently available advanced methods for the discovery of novel compounds targeting proteins without 3D structure and how to turn undruggable targets into druggable ones.

CB-Dock: a web server for cavity detection-guided protein-ligand blind docking

As the number of elucidated protein structures is rapidly increasing, the growing data call for methods to efficiently exploit the structural information for biological and pharmaceutical purposes. Given the three-dimensional (3D) structure of a protein and a ligand, predicting their binding sites and affinity are a key task for computer-aided drug discovery. To address this task, a variety of docking tools have been developed. Most of them focus on docking in the preset binding sites given by users. To automatically predict binding modes without information about binding sites, we developed a user-friendly blind docking web server, named CB-Dock, which predicts binding sites of a given protein and calculates the centers and sizes with a novel curvature-based cavity detection approach, and performs docking with a popular docking program, Autodock Vina. This method was carefully optimized and achieved ~70% success rate for the top-ranking poses whose root mean square deviation (RMSD) were within 2 Å from the X-ray pose, which outperformed the state-of-the-art blind docking tools in our benchmark tests. CB-Dock offers an interactive 3D visualization of results, and is freely available at http://cao.labshare.cn/cb-dock/.

Morelloflavone as a novel inhibitor of mitotic kinesin Eg5

Among 40 plant-derived biflavonoids with inhibitory potential against Eg5, morelloflavone from Garcinia dulcis leaves was selected for further testing based on in silico analysis of binding modes, molecular interactions, binding energies and functional groups that interact with Eg5. Computational models predicted that morelloflavone binds the putative allosteric pocket of Eg5, within the cavity surrounded by amino acid residues of Ile-136, Glu-116, Glu-118, Trp-127, Gly-117, Ala-133, Glu-215, Leu-214 and Tyr-211. Binding energy was -8.4 kcal/mol, with a single hydrogen bond formed between morelloflavone and Tyr-211. The binding configuration was comparable to that of a reference inhibitor, S-trityl-L-cysteine. Subsequent biochemical analysis in vitro confirmed that morelloflavone inhibited both the basal and microtubule-activated ATPase activity of Eg5 in a manner that does not compete with ATP binding. Morelloflavone also suppressed Eg5 gliding along microtubules. These results suggest that morelloflavone binds the allosteric binding site in Eg5 and thereby inhibits ATPase activity and motor function of Eg5.

Novel endo-(1,4)-β-glucanase Bgh12A and xyloglucanase Xgh12B from Aspergillus cervinus belong to GH12 subgroup I and II, respectively

In spite of intensive exploitation of aspergilli for the industrial production of carbohydrases, little is known about hydrolytic enzymes of fungi from the section Cervini. Novel glycoside hydrolases Bgh12A and Xgh12B from Aspergillus cervinus represent examples of divergent activities within one enzyme family and belong to the GH12 phylogenetic subgroup I (endo-(1,4)-β-glucanases) and II (endo-xyloglucanases), respectively. The bgh12A and xgh12B genes were identified in the unsequenced genome of A. cervinus using primers designed for conservative regions of the corresponding subgroups and a genome walking approach. The recombinant enzymes were heterologously produced in Pichia pastoris, purified, and characterized. Bgh12A was an endo-(1,4)-β-glucanase (EC 3.2.1.4) hydrolyzing the unbranched soluble β-(1,4)-glucans and mixed linkage β-(1,3;1,4)-D-glucans. Bgh12A exhibited maximum activity on barley β-glucan (BBG), which amounted to 614 ± 30 U/mg of protein. The final products of BBG and lichenan hydrolysis were glucose, cellobiose, cellotriose, 4-O-β-laminaribiosyl-glucose, and a range of higher mixed-linkage gluco-oligosaccharides. In contrast, the activity of endo-xyloglucanase Xgh12B (EC 3.2.1.151) was restricted to xyloglucan, with 542 ± 39 U/mg protein. The enzyme cleaved the (1,4)-β-glycosidic bonds of the xyloglucan backbone at the unsubstituted glucose residues finally generating cellotetraose-based hepta-, octa, and nona-oligosaccharides. Bgh12A and Xgh12B had maximal activity at 55 °C, pH 5.0. At these conditions, the half-time of Xgh12B inactivation was 158 min, whereas the half-life of Bgh12A was 5 min. Recombinant P. pastoris strains produced up to 10⁶ U/L of the target enzymes with at least 75% of recombinant protein in the total extracellular proteins. The Bgh12A and Xgh12B sequences show 43% identity. Strict differences in substrate specificity of Bgh12A and Xgh12B were in congruence with the presence of subgroup-specific structural loops and substrate-binding aromatic residues in the catalytic cleft of the enzymes. Individual composition of aromatic residues in the catalytic cleft defined variability in substrate selectivity within GH12 subgroups I and II.

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present Structure template-based ab initio ligand design solution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.

Interaction of rs316019 variants of SLC22A2 with metformin and other drugs- an in silico analysis

Metformin is one of the first-line and most widely prescribed drugs to treat type 2 diabetes (T2D). Its clearance from circulation is mostly facilitated by SLC22A2 (OCT2) in the renal cells. SLC22A2 is a polyspecific organic cation transporter and mediate transport of structurally unrelated endogenous and exogenous compounds including many drugs. rs316019 (p.270A > S) is the most common variant of SLC22A2 with a frequency as high as 15% or more in many populations. The 270S form of SLC22A2 clears metformin from circulation at much reduced level compared to the 270A form. If accumulated, metformin increases plasma lactate level in a concentration-dependent manner which can lead to a condition known as metformin-associated lactic acidosis (MALA). MALA is a potentially life-threatening complication with a mortality rate of 30-50%. Pre-existing clinical conditions, such as renal impairment, sepsis, anoxia, etc may make individuals more prone to MALA. In this study, we used computational approaches to investigate the effect of 270A > S change in SLC22A2 on interaction with metformin and other drugs. Based on the structural models, all substrates bind to the same pocket of SLC22A2. The substrates fit better to the binding site of 270A form of SLC22A2. The binding site has a few core interacting residues, among which SER358 appears to be the most important. It is an in silico prediction that the T2D patients, who are under metformin regimen, should be cautious in taking ranitidine (an over-the-counter sold drug) on a regular basis as it may lead to metformin associated lactate accumulation in blood.

FINDSITEcomb2.0: A New Approach for Virtual Ligand Screening of Proteins and Virtual Target Screening of Biomolecules

Computational approaches for predicting protein-ligand interactions can facilitate drug lead discovery and drug target determination. We have previously developed a threading/structural-based approach, FINDSITEcomb, for the virtual ligand screening of proteins that has been extensively experimentally validated. Even when low resolution predicted protein structures are employed, FINDSITEcomb has the advantage of being faster and more accurate than traditional high-resolution structure-based docking methods. It also overcomes the limitations of traditional QSAR methods that require a known set of seed ligands that bind to the given protein target. Here, we further improve FINDSITEcomb by enhancing its template ligand selection from the PDB/DrugBank/ChEMBL libraries of known protein-ligand interactions by (1) parsing the template proteins and their corresponding binding ligands in the DrugBank and ChEMBL libraries into domains so that the ligands with falsely matched domains to the targets will not be selected as template ligands; (2) applying various thresholds to filter out falsely matched template structures in the structure comparison process and thus their corresponding ligands for template ligand selection. With a sequence identity cutoff of 30% of target to templates and modeled target structures, FINDSITEcomb2.0 is shown to significantly improve upon FINDSITEcomb on the DUD-E benchmark set by increasing the 1% enrichment factor from 16.7 to 22.1, with a p-value of 4.3 × 10-3 by the Student t-test. With an 80% sequence identity cutoff of target to templates for the DUD-E set and modeled target structures, FINDSITEcomb2.0, having a 1% ROC enrichment factor of 52.39, also outperforms state-of-the-art methods that employ machine learning such as a deep convolutional neural network, CNN, with an enrichment of 29.65. Thus, FINDSITEcomb2.0 represents a significant improvement in the state-of-the-art. The FINDSITEcomb2.0 web service is freely available for academic users at http://pwp.gatech.edu/cssb/FINDSITE-COMB-2 .

G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures

Recent advances in high-throughput structure determination and computational protein structure prediction have significantly enriched the universe of protein structure. However, there is still a large gap between the number of available protein structures and that of proteins with annotated function in high accuracy. Computational structure-based protein function prediction has emerged to reduce this knowledge gap. The identification of a ligand binding site and its structure is critical to the determination of a protein's molecular function. We present a computational methodology for predicting small molecule ligand binding site and ligand structure using G-LoSA, our protein local structure alignment and similarity measurement tool. All the computational procedures described here can be easily implemented using G-LoSA Toolkit, a package of standalone software programs and preprocessed PDB structure libraries. G-LoSA and G-LoSA Toolkit are freely available to academic users at http://compbio.lehigh.edu/GLoSA . We also illustrate a case study to show the potential of our template-based approach harnessing G-LoSA for protein function prediction.

Genome-Wide Identification, Evolutionary and Expression Analyses of the GALACTINOL SYNTHASE Gene Family in Rapeseed and Tobacco

Galactinol synthase (GolS) is a key enzyme in raffinose family oligosaccharide (RFO) biosynthesis. The finding that GolS accumulates in plants exposed to abiotic stresses indicates RFOs function in environmental adaptation. However, the evolutionary relationships and biological functions of GolS family in rapeseed (Brassica napus) and tobacco (Nicotiana tabacum) remain unclear. In this study, we identified 20 BnGolS and 9 NtGolS genes. Subcellular localization predictions showed that most of the proteins are localized to the cytoplasm. Phylogenetic analysis identified a lost event of an ancient GolS copy in the Solanaceae and an ancient duplication event leading to evolution of GolS4/7 in the Brassicaceae. The three-dimensional structures of two GolS proteins were conserved, with an important DxD motif for binding to UDP-galactose (uridine diphosphate-galactose) and inositol. Expression profile analysis indicated that BnGolS and NtGolS genes were expressed in most tissues and highly expressed in one or two specific tissues. Hormone treatments strongly induced the expression of most BnGolS genes and homologous genes in the same subfamilies exhibited divergent-induced expression. Our study provides a comprehensive evolutionary analysis of GolS genes among the Brassicaceae and Solanaceae as well as an insight into the biological function of GolS genes in hormone response in plants.

Characterization of Staphylococcus epidermidis Polynucleotide phosphorylase and its interactions with ribonucleases RNase J1 and RNase J2

Polynucleotide phosphorylase catalyzes both 3'-5' exoribonuclease and polyadenylation reactions. The crystal structure of Staphylococcus epidermidis PNPase revealed a bound phosphate in the PH2 domain of each protomer coordinated by three adjacent serine residues. Mutational analysis suggests that phosphate coordination by these serine residues is essential to maintain the catalytic center in an active conformation. We note that PNPase forms a complex with RNase J1 and RNase J2 without substantially altering either exo-ribonuclease or polyadenylation activity of this enzyme. This decoupling of catalytic activity from protein-protein interactions suggests that association of these endo- or exo-ribonucleases with PNPase could be more relevant for cellular localization or concerted targeting of structured RNA for recycling.

Novel Organelles with Elements of Bacterial and Eukaryotic Secretion Systems Weaponize Parasites of Drosophila

The evolutionary success of parasitoid wasps, a highly diverse group of insects widely used in biocontrol, depends on a variety of life history strategies in conflict with those of their hosts [1]. Drosophila melanogaster is a natural host of parasitic wasps of the genus Leptopilina. Attack by L. boulardi (Lb), a specialist wasp to flies of the melanogaster group, activates NF-κB-mediated humoral and cellular immunity. Inflammatory blood cells mobilize and encapsulate Lb eggs and embryos [2-5]. L. heterotoma (Lh), a generalist wasp, kills larval blood cells and actively suppresses immune responses. Spiked virus-like particles (VLPs) in wasp venom have clearly been linked to wasps' successful parasitism of Drosophila [6], but the composition of VLPs and their biotic nature have remained mysterious. Our proteomics studies reveal that VLPs lack viral coat proteins but possess a pharmacopoeia of (1) the eukaryotic vesicular transport system, (2) immunity, and (3) previously unknown proteins. These novel proteins distinguish Lh from Lb VLPs; notably, some proteins specific to Lh VLPs possess sequence similarities with bacterial secretion system proteins. Structure-informed analyses of an abundant Lh VLP surface and spike-tip protein, p40, reveal similarities to the needle-tip invasin proteins SipD and IpaD of Gram-negative bacterial type-3 secretion systems that breach immune barriers and deliver virulence factors into mammalian cells. Our studies suggest that Lh VLPs represent a new class of extracellular organelles and share pathways for protein delivery with both eukaryotic microvesicles and bacterial surface secretion systems. Given their mixed prokaryotic and eukaryotic properties, we propose the term mixed-strategy extracellular vesicle (MSEV) to replace VLP.

Arjunolic acid, a peroxisome proliferator-activated receptor α agonist, regresses cardiac fibrosis by inhibiting non-canonical TGF-β signaling

Cardiac hypertrophy and associated heart fibrosis remain a major cause of death worldwide. Phytochemicals have gained attention as alternative therapeutics for managing cardiovascular diseases. These include the extract from the plant Terminalia arjuna, which is a popular cardioprotectant and may prevent or slow progression of pathological hypertrophy to heart failure. Here, we investigated the mode of action of a principal bioactive T. arjuna compound, arjunolic acid (AA), in ameliorating hemodynamic load-induced cardiac fibrosis and identified its intracellular target. Our data revealed that AA significantly represses collagen expression and improves cardiac function during hypertrophy. We found that AA binds to and stabilizes the ligand-binding domain of peroxisome proliferator-activated receptor α (PPARα) and increases its expression during cardiac hypertrophy. PPARα knockdown during AA treatment in hypertrophy samples, including angiotensin II-treated adult cardiac fibroblasts and renal artery-ligated rat heart, suggests that AA-driven cardioprotection primarily arises from PPARα agonism. Moreover, AA-induced PPARα up-regulation leads to repression of TGF-β signaling, specifically by inhibiting TGF-β-activated kinase1 (TAK1) phosphorylation. We observed that PPARα directly interacts with TAK1, predominantly via PPARα N-terminal transactivation domain (AF-1) thereby masking the TAK1 kinase domain. The AA-induced PPARα-bound TAK1 level thereby shows inverse correlation with the phosphorylation level of TAK1 and subsequent reduction in p38 MAPK and NF-κBp65 activation, ultimately culminating in amelioration of excess collagen synthesis in cardiac hypertrophy. In conclusion, our findings unravel the mechanism of AA action in regressing hypertrophy-associated cardiac fibrosis by assigning a role of AA as a PPARα agonist that inactivates non-canonical TGF-β signaling.

COFACTOR: improved protein function prediction by combining structure, sequence and protein-protein interaction information

The COFACTOR web server is a unified platform for structure-based multiple-level protein function predictions. By structurally threading low-resolution structural models through the BioLiP library, the COFACTOR server infers three categories of protein functions including gene ontology, enzyme commission and ligand-binding sites from various analogous and homologous function templates. Here, we report recent improvements of the COFACTOR server in the development of new pipelines to infer functional insights from sequence profile alignments and protein-protein interaction networks. Large-scale benchmark tests show that the new hybrid COFACTOR approach significantly improves the function annotation accuracy of the former structure-based pipeline and other state-of-the-art functional annotation methods, particularly for targets that have no close homology templates. The updated COFACTOR server and the template libraries are available at http://zhanglab.ccmb.med.umich.edu/COFACTOR/.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Modulation of prion polymerization and toxicity by rationally designed peptidomimetics

Misfolding and aggregation of cellular prion protein is associated with a large array of neurological disorders commonly called the transmissible spongiform encephalopathies. Designing inhibitors against prions has remained a daunting task owing to limited information about mechanism(s) of their pathogenic self-assembly. Here, we explore the anti-prion properties of a combinatorial library of bispidine-based peptidomimetics (BPMs) that conjugate amino acids with hydrophobic and aromatic side chains. Keeping the bispidine unit unaltered, a series of structurally diverse BPMs were synthesized and tested for their prion-modulating properties. Administration of Leu- and Trp-BPMs delayed and completely inhibited the amyloidogenic conversion of human prion protein (HuPrP), respectively. We found that each BPM induced the HuPrP to form unique oligomeric nanostructures differing in their biophysical properties, cellular toxicities and response to conformation-specific antibodies. While Leu-BPMs were found to stabilize the oligomers, Trp-BPMs effected transient oligomerization, resulting in the formation of non-toxic, non-fibrillar aggregates. Yet another aromatic residue, Phe, however, accelerated the aggregation process in HuPrP. Molecular insights obtained through MD (molecular dynamics) simulations suggested that each BPM differently engages a conserved Tyr 169 residue at the α2–β2 loop of HuPrP and affects the stability of α2 and α3 helices. Our results demonstrate that this new class of molecules having chemical scaffolds conjugating hydrophobic/aromatic residues could effectively modulate prion aggregation and toxicity.

Repurposing FDA-approved drugs for anti-aging therapies

There is great interest in drugs that are capable of modulating multiple aging pathways, thereby delaying the onset and progression of aging. Effective strategies for drug development include the repurposing of existing drugs already approved by the FDA for human therapy. FDA approved drugs have known mechanisms of action and have been thoroughly screened for safety. Although there has been extensive scientific activity in repurposing drugs for disease therapy, there has been little testing of these drugs for their effects on aging. The pool of FDA approved drugs therefore represents a large reservoir of drug candidates with substantial potential for anti-aging therapy. In this paper we employ FINDSITEcomb, a powerful ligand homology modeling program, to identify binding partners for proteins produced by temperature sensing genes that have been implicated in aging. This list of drugs with potential to modulate aging rates was then tested experimentally for lifespan and healthspan extension using a small invertebrate model. Three protein targets of the rotifer Brachionus manjavacas corresponding to products of the transient receptor potential gene 7, ribosomal protein S6 polypeptide 2 gene, or forkhead box C gene, were screened against a compound library consisting of DrugBank drugs including 1347 FDA approved, non-nutraceutical molecules. Twenty nine drugs ranked in the top 1 % for binding to each target were subsequently included in our experimental analysis. Continuous exposure of rotifers to 1 µM naproxen significantly extended rotifer mean lifespan by 14 %. We used three endpoints to estimate rotifer health: swimming speed (mobility proxy), reproduction (overall vitality), and mitochondria activity (cellular senescence proxy). The natural decline in swimming speed with aging was more gradual when rotifers were exposed to three drugs, so that on day 6, mean swimming speed of females was 1.19 mm/s for naproxen (P = 0.038), 1.20 for fludarabine (P = 0.040), 1.35 for hydralazine (P = 0.038), as compared to 0.88 mm/s in the control. The average reproduction of control females in the second half of their reproductive lifespan was 1.08 per day. In contrast, females treated with 1 µM naproxen produced 1.4 offspring per day (P = 0.027) and females treated with 10 µM fludarabine or 1 µM hydralazine produced 1.72 (P = <0.001) and 1.66 (P = 0.001) offspring per day, respectively. Mitochondrial activity naturally declines with rotifer aging, but B. manjavacas treated with 1 µM hydralazine or 10 µM fludarabine retained 49 % (P = 0.038) and 89 % (P = 0.002) greater mitochondria activity, respectively, than untreated controls. Our results demonstrate that coupling computation to experimentation can quickly identify new drug candidates with anti-aging potential. Screening drugs for anti-aging effects using a rotifer bioassay is a powerful first step in identifying compounds worthy of follow-up in vertebrate models. Even if lifespan extension is not observed, certain drugs could improve healthspan, slowing age-dependent losses in mobility and vitality.

A novel approach using C. elegans DNA damage-induced apoptosis to characterize the dynamics of uptake transporters for therapeutic drug discoveries

Organic cation transporter (OCT) function is critical for cellular homeostasis. C. elegans lacking OCT-1 displays a shortened lifespan and increased susceptibility to oxidative stress. We show that these phenotypes can be rescued by downregulating the OCT-1 paralogue, OCT-2. Herein, we delineate a biochemical pathway in C. elegans where uptake of genotoxic chemotherapeutics such as doxorubicin and cisplatin, and subsequent DNA damage-induced apoptosis of germ cells, are dependent exclusively upon OCT-2. We characterized OCT-2 as the main uptake transporter for doxorubicin, as well as a number of other therapeutic agents and chemical compounds, some identified through ligand-protein docking analyses. We provide insights into the conserved features of the structure and function and gene regulation of oct-1 and oct-2 in distinct tissues of C. elegans. Importantly, our innovative approach of exploiting C. elegans uptake transporters in combination with defective DNA repair pathways will have broad applications in medicinal chemistry.

Ectopic expression and functional characterization of type III polyketide synthase mutants from Emblica officinalis Gaertn

KEY MESSAGE

Functional characterization and ectopic expression studies of chalcone synthase mutants implicate the role of phenylalanine in tailoring the substrate specificity of type III polyketide synthase. Chalcone synthase (CHS) is a plant-specific type III polyketide synthase that catalyzes the synthesis of flavonoids. Native CHS enzyme does not possess any functional activity on N-methylanthraniloyl-CoA, which is the substrate for acridione/quinolone alkaloid biosynthesis. Here, we report the functional transformation of chalcone synthase protein from Emblica officinalis (EoCHS) to quinolone and acridone synthase (ACS) with single amino acid substitutions. A cDNA of 1173 bp encoding chalcone synthase was isolated from E. officinalis and mutants (F215S and F265V) were generated by site-directed mutagenesis. Molecular modeling studies of EoCHS did not show any active binding with N-methylanthraniloyl-CoA, but the mutants of EoCHS showed strong affinity to the same. As revealed by the modeling studies, functional analysis of CHS mutants showed that they could utilize p-coumaroyl-CoA as well as N-methylanthraniloyl-CoA as substrates and yield active products such as naringenin, 4-hydroxy 1-methyl 2(H) quinolone and 1,3-dihydroxy-n-methyl acridone. Exchange of a single amino acid in EoCHS (F215S and F265V) resulted in functionally active mutants that preferred N-methylanthraniloyl-CoA over p-coumaroyl-CoA. This can be attributed to the increase in the relative volume of active sites in mutants by mutation. Moreover, metabolomic and MS analyses of tobacco leaves transiently expressing mutant genes showed high levels of naringenin, acridones and quinolone derivatives compared to wild-type CHS. This is the first report demonstrating the functional activity of EoCHS mutants with N-methylanthraniloyl-CoA and these results indicate the role of phenylalanine in altering the substrate specificity and in the evolution of type III PKSs.

Type III polyketide synthase repertoire in Zingiberaceae: computational insights into the sequence, structure and evolution

Zingiberaceae or 'ginger family' is the largest family in the order 'Zingiberales' with more than 1300 species in 52 genera, which are mostly distributed throughout Asia, tropical Africa and the native regions of America with their maximum diversity in Southeast Asia. Many of the members are important spice, medicinal or ornamental plants including ginger, turmeric, cardamom and kaempferia. These plants are distinguished for the highly valuable metabolic products, which are synthesised through phenylpropanoid pathway, where type III polyketide synthase is the key enzyme. In our present study, we used sequence, structural and evolutionary approaches to scrutinise the type III polyketide synthase (PKS) repertoire encoded in the Zingiberaceae family. Highly conserved amino acid residues in the sequence alignment and phylogram suggested strong relationships between the type III PKS members of Zingiberaceae. Sequence and structural level investigation of type III PKSs showed a small number of variations in the substrate binding pocket, leading to functional divergence among these PKS members. Molecular evolutionary studies indicate that type III PKSs within Zingiberaceae evolved under strong purifying selection pressure, and positive selections were rarely detected in the family. Structural modelling and protein-small molecule interaction studies on Zingiber officinale PKS 'a representative from Zingiberaceae' suggested that the protein is comparatively stable without much disorder and exhibited wide substrate acceptance.

G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design

Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/.

GPCR-I-TASSER: A Hybrid Approach to G Protein-Coupled Receptor Structure Modeling and the Application to the Human Genome

Experimental structure determination remains difficult for G protein-coupled receptors (GPCRs). We propose a new hybrid protocol to construct GPCR structure models that integrates experimental mutagenesis data with ab initio transmembrane (TM) helix assembly simulations. The method was tested on 24 known GPCRs where the ab initio TM-helix assembly procedure constructed the correct fold for 20 cases. When combined with weak homology and sparse mutagenesis restraints, the method generated correct folds for all the tested cases with an average Cα root-mean-square deviation 2.4 Å in the TM regions. The new hybrid protocol was applied to model all 1,026 GPCRs in the human genome, where 923 have a high confidence score and are expected to have correct folds; these contain many pharmaceutically important families with no previously solved structures, including Trace amine, Prostanoids, Releasing hormones, Melanocortins, Vasopressin, and Neuropeptide Y receptors. The results demonstrate new progress on genome-wide structure modeling of TM proteins.

Prediction of protein targets of kinetin using in silico and in vitro methods: a case study on spinach seed germination mechanism

Kinetin, a cytokinin which promotes seed germination by inhibiting the action of abscisic acid, is an important molecule known to trigger various molecular mechanisms by interacting with an array of proteins shown from experimental observations in various model organisms. We report here the prediction of most probable protein targets of kinetin from spinach proteome using in silico approaches. Inverse docking and ligand-based similarity search was performed using kinetin as molecule. The former method prioritized six spinach proteins, whereas the latter method provided a list of protein targets retrieved from several model organisms. The most probable protein targets were selected by comparing the rank list of docking and ligand similarity methods. Both of these methods prioritized chitinase as the most probable protein target (ΔG pred = 5.064 kcal/mol) supported by the experimental structure of yeast chitinase 1 complex with kinetin (PDB: 2UY5) and Gliocladium roseum chitinase complex with 3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione (caffeine; 3G6M) which bears a 3D similarity of 0.43 with kinetin. An in vitro study to evaluate the effect of kinetin on spinach seed germination indicated that a very low concentration of kinetin (0.5 mg/l) did not show a significant effect compared to control in inducing seed germination process. Further, higher levels of kinetin (>0.5 mg/l) constituted an antagonist effect on spinach seed germination. It is anticipated that kinetin may have a molecular interaction with prioritized protein targets synthesized during the seed germination process and reduces growth. Thus, it appears that kinetin may not be a suitable hormone for enhancing spinach seed germination in vitro.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Systems pharmacology and molecular docking strategies prioritize natural molecules as cardioprotective agents

Multidimensional prioritization of cardioprotective natural compounds.

Lead discovery and in silico 3D structure modeling of tumorigenic FAM72A (p17)

FAM72A (p17) is a novel neuronal protein that has been linked to tumorigenic effects in non-neuronal tissue. Using state of the art in silico physicochemical analyses (e.g., I-TASSER, RaptorX, and Modeller), we determined the three-dimensional (3D) protein structure of FAM72A and further identified potential ligand-protein interactions. Our data indicate a Zn(2+)/Fe(3+)-containing 3D protein structure, based on a 3GA3_A model template, which potentially interacts with the organic molecule RSM ((2s)-2-(acetylamino)-N-methyl-4-[(R)-methylsulfinyl] butanamide). The discovery of RSM may serve as potential lead for further anti-FAM72A drug screening tests in the pharmaceutical industry because interference with FAM72A's activities via RSM-related molecules might be a novel option to influence the tumor suppressor protein p53 signaling pathways for the treatment of various types of cancers.

Methyl-methoxylpyrrolinone and flavinium nucleus binding signatures on falcipain-2 active site

Following the increasing reports of human toxicity and plasmodium resistance to artemisinin and its derivatives, falcipain-2 (FP-2) is now emerging as the choice antimalarial drug target. Coincidentally, FP-2 is the in vivo target of naturally occurring, therapeutically safe flavonoids (stenopalustroside, myricetin, and fisetin) and symplostatin (symplostatin 4) compounds known to exhibit potent in vitro and in vivo antiplasmodial actions. Here, the structural bases for their inhibitory actions have been studied using molecular dynamics simulation. Myricetin and fisetin act as proton transfer tunnel breakers by inserting between His174 and Cys42, which are key active site residues of FP-2, stenopalustroside inhibits the polarization of His174 by Asn173; a major preparatory step for Cys42/His174 proton transfer process. The roles of flavonoids are favored by T-shaped pi-pi interactions with His174. Symplostatin 4 inserts its methyl-methoxylpyrrolinone moiety into the active site where its proton acceptor function prepares Cys42 for nucleophilic attack on the Michael α,β-unsaturated bonds on its 4(S)-amino-2(E)-pentenoate moiety. Further analyses of the structures identified a unique bridge formed on FP-2 active site groove by stenopalustroside and symplostatin 4 during interaction with the sub-site I of FP-2, whereas fisetin preferentially interacts with sub-site II and myricetin interacts with sub-site III residues. Ultimately, symplostatin-4, myricetin, and fisetin were better than stenopalustroside at trapping FP-2 in its inactive state as revealed by comparative RSMD plots with X-ray structures of FP-2 co-crystallized with inhibitors. Comparative estimates of free energy of binding using the Molecular Mechanics-Poisson Boltzmann Surface Area (MMPBSA) method suggested that His174 protonation may further enhance stenopalustroside-FP-2 interaction. The unique binding signatures of the ligands within the FP-2 active site groove and its sub-sites may explain the subtle differences in their IC50 values and their mechanism of inhibition.

Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges

Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems.

Alzheimer's disease mutations in APP but not γ-secretase modulators affect epsilon-cleavage-dependent AICD production

Pathological amino-acid substitutions in the amyloid precursor protein (APP) and chemical γ-secretase modulators affect the processing of APP by the γ-secretase complex and the production of the amyloid-beta peptide Aβ42, the accumulation of which is considered causative of Alzheimer's disease. Here we demonstrate that mutations in the transmembrane domain of APP causing aggressive early-onset familial Alzheimer's disease affect both γ- and ε-cleavage sites, by raising the Aβ42/40 ratio and inhibiting the production of AICD50-99, one of the two physiological APP intracellular domains (ICDs). This is in sharp contrast to γ-secretase modulators, which shift Aβ42 production towards the shorter Aβ38, but unequivocally spare the ε-site and APP- and Notch-ICDs production. Molecular simulations suggest that familial Alzheimer's disease mutations modulate the flexibility of the APP transmembrane domain and the presentation of its γ-site, modifying at the same time, the solvation of the ε-site.