Natural products and natural product-derived compounds have been widely used for pharmaceuticals for many years, and the search for new natural products that may have interesting activity is on going. Abyssomicins are natural product molecules that have antibiotic activity via inhibition of the folate synthesis pathway in microbiota. These compounds also appear to undergo a required [4+2] cycloaddition in their biosynthetic pathway. Here we report the structure of an FAD-dependent reductase, AbsH3, from the biosynthetic gene cluster of novel abyssomicins found in Streptomyces sp. LC-6-2.
The focal adhesion kinase (FAK) and the proline-rich tyrosine kinase 2-beta (PYK2) are implicated in cancer progression and metastasis and represent promising biomarkers and targets for cancer therapy. FAK and PYK2 are recruited to Focal Adhesions (Fas) via interactions between their Focal Adhesion Targeting (FAT) domains and conserved segments (LD motifs) on the proteins Paxillin, Leupaxin and Hic-5. A promising new approach for the inhibition of FAK and PYK2 targets interactions of the FAK domains with proteins that promote localization at Focal Adhesions. Advances toward this goal include the development of surface plasmon resonance, HSQC-NMR and fluorescence polarization assays for the identification of fragments or compounds interfering with the FAK-Paxillin interaction. We have recently validated this strategy, showing that Paxillin mimicking polypeptides with 2-3 LD motifs displace FAK from FAs and block kinase-dependent and independent functions of FAK, including downstream integrin signalling and FA localization of the protein p130Cas. In the present work we study by all-atom molecular dynamics simulations the recognition of peptides with the Paxillin and Leupaxin LD motifs by the FAK-FAT and PYK2-FAT domains. Our simulations and free-energy analysis interpret experimental data on binding of Paxillin and Leupaxin LD motifs at FAK-FAT and PYK2-FAT binding sites, and assess the roles of consensus LD regions and flanking residues. Our results can assist in the design of effective inhibitory peptides of the FAK-FAT:Paxillin and PYK2-FAT:Leupaxin complexes and the construction of pharmacophore models for the discovery of potential small-molecule inhibitors of the FAK-FAT and PYK2-FAT focal adhesion based functions.
Isoflavonoid is one of the groups of flavonoids that play pivotal roles in the survival of land plants. Chalcone synthase (CHS), the first enzyme of the isoflavonoid biosynthetic pathway, catalyzes the formation of a common isoflavonoid precursor. We have previously reported that an isozyme of soybean CHS (termed GmCHS1) is a key component of the isoflavonoid metabolon, a protein complex to enhance efficiency of isoflavonoid production. Here, we determined the crystal structure of GmCHS1 as a first step of understanding the metabolon structure, as well as to better understand the catalytic mechanism of GmCHS1.
This paper reports on the results of research aimed to translate biometric 3D face recognition concepts and algorithms into the field of protein biophysics in order to precisely and rapidly classify morphological features of protein surfaces. Both human faces and protein surfaces are free-forms and some descriptors used in differential geometry can be used to describe them applying the principles of feature extraction developed for computer vision and pattern recognition. The first part of this study focused on building the protein dataset using a simulation tool and performing feature extraction using novel geometrical descriptors. The second part tested the method on two examples, first involved a classification of tubulin isotypes and the second compared tubulin with the FtSZ protein, which is its bacterial analogue. An additional test involved several unrelated proteins. Different classification methodologies have been used: a classic approach with a Support Vector Machine (SVM) classifier and an unsupervised learning with a k-means approach. The best result was obtained with SVM and the radial basis function (RBF) kernel. The results are significant and competitive with the state-of-the-art protein classification methods. This opens a new area for protein structure analysis.
Structural characterization of alternatively folded and partially disordered protein conformations remains challenging. Outer surface protein A (OspA) is a pivotal protein in Borrelia infection, which is the etiological agent of Lyme disease. OspA exists in equilibrium with intermediate conformations, in which the central and the C-terminal regions of the protein have lower stabilities than the N-terminal. Here, we characterize pressure- and temperature-stabilized intermediates of OspA by nuclear magnetic resonance spectroscopy combined with paramagnetic relaxation enhancement (PRE). We found that the C-terminal region of the intermediate was partially disordered; however, it retains weak specific contact with the N-terminal region, owing to a twist of the central β-sheet and increased flexibility in the polypeptide chain. The disordered C-terminal region of the pressure-stabilized intermediate was more compact than that of the temperature-stabilized form. Further, molecular dynamics simulation demonstrated that temperature-induced disordering of the β-sheet was initiated at the C-terminal region and continued through to the central region. An ensemble of simulation snapshots qualitatively described the PRE data from the intermediate and indicated that the intermediate structures of OspA may expose tick receptor-binding sites more readily than does the basic folded conformation.
The M42 aminopeptidases are a family of dinuclear aminopeptidases widely distributed in Prokaryotes. They are potentially associated to the proteasome, achieving complete peptide destruction. Their most peculiar characteristic is their quaternary structure, a tetrahedron-shaped particle made of twelve subunits. The catalytic site of M42 aminopeptidases is defined by seven conserved residues. Five of them are involved in metal ion binding which is important to maintain both the activity and the oligomeric state. The sixth conserved residue, a glutamate, is the catalytic base deprotonating the water molecule during peptide bond hydrolysis. The seventh residue is an aspartate whose function remains poorly understood. This aspartate residue, however, must have a critical role as it is strictly conserved in all MH clan enzymes. It forms some kind of catalytic triad with the histidine residue and the metal ion of the M2 binding site. We assess its role in TmPep1050, an M42 aminopeptidase of Thermotoga maritima, through a mutational approach. Asp-62 was substituted with alanine, asparagine, or glutamate residue. The three Asp-62 substitutions completely abolished TmPep1050 activity and impeded dodecamer formation. They also interfered with metal ion binding as only one cobalt ion is bound per subunit instead of two. The structural data showed that the Asp62Ala substitution has an impact on the active site folds becoming similar to TmPep1050 dimer. We propose a structural role for Asp-62, helping to stabilize a crucial loop in the active site and to position correctly the catalytic base and a metal ion ligand of the M1 site.
Protein-protein interactions (PPIs) are ubiquitous and functionally of great importance in biological systems. Hence, the ac-curate prediction of PPIs by protein-protein docking and scoring tools is highly desirable in order to characterize their structure and biological function. Ab initio docking protocols are divided into the sampling of docking poses to produce at least one near-native structure, then to evaluate the vast candidate structures by scoring. Concurrent development in both sampling and scoring is crucial for the deployment of protein-protein docking software. In the present work, we apply a machine learning model on pairwise potentials to refine the task of protein quaternary structure native structure detection among decoys. A decoy set was featurized using the Knowledge and Empirical Combined Scoring Algorithm 2 (KECSA2) pairwise potential. The highly unbalanced decoy set was then balanced using a comparison concept between native and decoy structures. The resultant comparison descriptors were used to train a logistic regression (LR) classifier. The LR model yielded the optimal performance for native detection among decoys compared to conventional scoring functions, while exhibiting lesser performance for the detection of low root mean square deviation (RMSD) decoy structures. Its deployment on an independent benchmark set confirms that the scoring function performs competitively relative to other scoring functions. All data and scripts used are available at: https://github.com/TanemuraKiyoto/PPI-native-detection-via-LR .