Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment

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.

A new supervised over-sampling algorithm with application to protein-nucleotide binding residue prediction

Protein-nucleotide interactions are ubiquitous in a wide variety of biological processes. Accurately identifying interaction residues solely from protein sequences is useful for both protein function annotation and drug design, especially in the post-genomic era, as large volumes of protein data have not been functionally annotated. Protein-nucleotide binding residue prediction is a typical imbalanced learning problem, where binding residues are extremely fewer in number than non-binding residues. Alleviating the severity of class imbalance has been demonstrated to be a promising means of improving the prediction performance of a machine-learning-based predictor for class imbalance problems. However, little attention has been paid to the negative impact of class imbalance on protein-nucleotide binding residue prediction. In this study, we propose a new supervised over-sampling algorithm that synthesizes additional minority class samples to address class imbalance. The experimental results from protein-nucleotide interaction datasets demonstrate that the proposed supervised over-sampling algorithm can relieve the severity of class imbalance and help to improve prediction performance. Based on the proposed over-sampling algorithm, a predictor, called TargetSOS, is implemented for protein-nucleotide binding residue prediction. Cross-validation tests and independent validation tests demonstrate the effectiveness of TargetSOS. The web-server and datasets used in this study are freely available at http://www.csbio.sjtu.edu.cn/bioinf/TargetSOS/.

Identification of 9α-hydroxy-17-oxo-1,2,3,4,10,19-hexanorandrostan-5-oic acid in steroid degradation by Comamonas testosteroni TA441 and its conversion to the corresponding 6-en-5-oyl coenzyme A (CoA) involving open reading frame 28 (ORF28)- and ORF30-encoded acyl-CoA dehydrogenases

Comamonas testosteroni TA441 degrades steroids via aromatization and meta-cleavage of the A ring, followed by hydrolysis, and produces 9,17-dioxo-1,2,3,4,10,19-hexanorandrostan-5-oic acid as an intermediate compound. Herein, we identify a new intermediate compound, 9α-hydroxy-17-oxo-1,2,3,4,10,19-hexanorandrostan-5-oic acid. Open reading frame 28 (ORF28)- and ORF30-encoded acyl coenzyme A (acyl-CoA) dehydrogenase was shown to convert the CoA ester of 9α-hydroxy-17-oxo-1,2,3,4,10,19-hexanorandrostan-5-oic acid to the CoA ester of 9α-hydroxy-17-oxo-1,2,3,4,10,19-hexanorandrost-6-en-5-oic acid. A homology search of the deduced amino acid sequences suggested that the ORF30-encoded protein is a member of the acyl-CoA dehydrogenase_fadE6_17_26 family, whereas the deduced amino acid sequence of ORF28 showed no significant similarity to specific acyl-CoA dehydrogenase family proteins. Possible steroid degradation gene clusters similar to the cluster of TA441 appear in bacterial genome analysis data. In these clusters, ORFs similar to ORFs 28 and 30 are often found side by side and ordered in the same manner as ORFs 28 and 30.

NLRP7 inter-domain interactions: the NACHT-associated domain is the physical mediator for oligomeric assembly

Mutations in NLRP7 (NOD-like-receptor family, pyrin domain containing 7) are responsible for a type of recurrent pregnancy loss known as recurrent hydatidiform mole (HYDM1). This condition is characterized by abnormal growth of the placenta, a lack of proper embryonic development and abnormal methylation patterns at multiple imprinted loci in diploid biparental molar tissues. The role of NLRP7 protein in the disease manifestation is currently not clear. In order to better understand how the effects of HYDM1 are associated with mutations on the structure of NLRP7, we performed an inter-domain interaction screen using a yeast two-hybrid system. Additionally, we generated in silico structural models of NLRP7 in its non-activated and activated forms. Our observations from the yeast two-hybrid screen and modeling suggest that the NACHT-associated domain (NAD) of the NLRP7 protein is central to its oligomeric assembly. Upon activation, the NAD and a small part of the leucine rich repeat (LRR) of one molecule emerged out of the protective LRR domain and interact with the NACHT domain of the second molecule to form an oligomer. Furthermore, we investigated the molecular basis for the pathophysiological effect of four missense mutations, three HYDM1-causing and one rare non-synonymous variant, on the protein using confocal microscopy of transiently transfected NLRP7 in HEK293T cells and in silico structural analysis. We found that with the two clinically severe missense mutations, L398R and R693W, the normal molecule to molecule interaction was apparently affected thus decreasing their oligomerization potential while aggresome formation was increased; these changes could disturb the normal downstream functions of NLRP7 and therefore be a possible molecular effect underlying their pathophysiological impact.

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.

The structure and dynamics of BmR1 protein from Brugia malayi: in silico approaches

Brugia malayi is a filarial nematode, which causes lymphatic filariasis in humans. In 1995, the disease has been identified by the World Health Organization (WHO) as one of the second leading causes of permanent and long-term disability and thus it is targeted for elimination by year 2020. Therefore, accurate filariasis diagnosis is important for management and elimination programs. A recombinant antigen (BmR1) from the Bm17DIII gene product was used for antibody-based filariasis diagnosis in "Brugia Rapid". However, the structure and dynamics of BmR1 protein is yet to be elucidated. Here we study the three dimensional structure and dynamics of BmR1 protein using comparative modeling, threading and ab initio protein structure prediction. The best predicted structure obtained via an ab initio method (Rosetta) was further refined and minimized. A total of 5 ns molecular dynamics simulation were performed to investigate the packing of the protein. Here we also identified three epitopes as potential antibody binding sites from the molecular dynamics average structure. The structure and epitopes obtained from this study can be used to design a binder specific against BmR1, thus aiding future development of antigen-based filariasis diagnostics to complement the current diagnostics.

ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites

The ProBiS-ligands web server predicts binding of ligands to a protein structure. Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at http://probis.cmm.ki.si/ligands.

Beta-caryophyllene modulates expression of stress response genes and mediates longevity in Caenorhabditis elegans

Beta-caryophyllene (BCP) is a natural bicyclic sesquiterpene and is a FDA approved food additive, found as an active ingredient in essential oils of numerous edible plants. It possesses a wide range of biological activities including anti-oxidant, anti-inflammatory, anti-cancerous and local anesthetic actions. We used the well established Caenorhabditis elegans model system to elucidate the stress modulatory and lifespan prolonging action of BCP. The present study for the first time reports the lifespan extension and stress modulation potential of BCP in C. elegans. Upon evaluation, it was found that 50μM dose of BCP increased the lifespan of C. elegans by over 22% (P≤0.0001) and significantly reduced intracellular free radical levels, maintaining cellular redox homeostasis. Moreover, the results suggest that BCP modulates feeding behavior, pharyngeal pumping and body size effectively. Further, this compound also exhibited significant reduction in intestinal lipofuscin levels. In the present investigation, we have predicted possible biological molecular targets for BCP using molecular docking approaches and BCP was found to have interaction with SIR-2.1, SKN-1 and DAF-16. The prediction was further validated in vivo using mutants and transgenic strains unraveling underlying genetic mechanism. It was observed that BCP increased lifespan of mev-1 and daf-16 but failed to augment lifespan in eat-2, sir-2.1 and skn-1 mutants. Relative quantification of mRNA demonstrated that several genes regulating oxidative stress, xenobiotic detoxification and longevity were modulated by BCP treatment. The study unravels the involvement of multiple signaling pathways in BCP mediated lifespan extension.

GalaxySite: ligand-binding-site prediction by using molecular docking

Knowledge of ligand-binding sites of proteins provides invaluable information for functional studies, drug design and protein design. Recent progress in ligand-binding-site prediction methods has demonstrated that using information from similar proteins of known structures can improve predictions. The GalaxySite web server, freely accessible at http://galaxy.seoklab.org/site, combines such information with molecular docking for more precise binding-site prediction for non-metal ligands. According to the recent critical assessments of structure prediction methods held in 2010 and 2012, this server was found to be superior or comparable to other state-of-the-art programs in the category of ligand-binding-site prediction. A strong merit of the GalaxySite program is that it provides additional predictions on binding ligands and their binding poses in terms of the optimized 3D coordinates of the protein–ligand complexes, whereas other methods predict only identities of binding-site residues or copy binding geometry from similar proteins. The additional information on the specific binding geometry would be very useful for applications in functional studies and computer-aided drug discovery.