How proteins bind macrocycles

The potential utility of synthetic macrocycles (MCs) as drugs, particularly against low-druggability targets such as protein-protein interactions, has been widely discussed. There is little information, however, to guide the design of MCs for good target protein-binding activity or bioavailability. To address this knowledge gap, we analyze the binding modes of a representative set of MC-protein complexes. The results, combined with consideration of the physicochemical properties of approved macrocyclic drugs, allow us to propose specific guidelines for the design of synthetic MC libraries with structural and physicochemical features likely to favor strong binding to protein targets as well as good bioavailability. We additionally provide evidence that large, natural product-derived MCs can bind targets that are not druggable by conventional, drug-like compounds, supporting the notion that natural product-inspired synthetic MCs can expand the number of proteins that are druggable by synthetic small molecules.R01 GM094551 - NIGMS NIH HHS; GM064700 - NIGMS NIH HHS; GM094551 - NIGMS NIH HHS; R01 GM064700 - NIGMS NIH HHS; GM094551-01S1 - NIGMS NIH HH

Recovering refined surface normals for relighting clothing in dynamic scenes

In this paper we present a method to relight captured 3D video sequences of non-rigid, dynamic scenes, such as clothing of real actors, reconstructed from multiple view video. A view-dependent approach is introduced to refine an initial coarse surface reconstruction using shape-from-shading to estimate detailed surface normals. The prior surface approximation is used to constrain the simultaneous estimation of surface normals and scene illumination, under the assumption of Lambertian surface reflectance. This approach enables detailed surface normals of a moving non-rigid object to be estimated from a single image frame. Refined normal estimates from multiple views are integrated into a single surface normal map. This approach allows highly non-rigid surfaces, such as creases in clothing, to be relit whilst preserving the detailed dynamics observed in video

Cross section measurement of the astrophysically important 17O(p,gamma)18F reaction in a wide energy range

The 17O(p,g)18F reaction plays an important role in hydrogen burning processes in different stages of stellar evolution. The rate of this reaction must therefore be known with high accuracy in order to provide the necessary input for astrophysical models. The cross section of 17O(p,g)18F is characterized by a complicated resonance structure at low energies. Experimental data, however, is scarce in a wide energy range which increases the uncertainty of the low energy extrapolations. The purpose of the present work is therefore to provide consistent and precise cross section values in a wide energy range. The cross section is measured using the activation method which provides directly the total cross section. With this technique some typical systematic uncertainties encountered in in-beam gamma-spectroscopy experiments can be avoided. The cross section was measured between 500 keV and 1.8 MeV proton energies with a total uncertainty of typically 10%. The results are compared with earlier measurements and it is found that the gross features of the 17O(p,g)18F excitation function is relatively well reproduced by the present data. Deviation of roughly a factor of 1.5 is found in the case of the total cross section when compared with the only one high energy dataset. At the lowest measured energy our result is in agreement with two recent datasets within one standard deviation and deviates by roughly two standard deviations from a third one. An R-matrix analysis of the present and previous data strengthen the reliability of the extrapolated zero energy astrophysical S-factor. Using an independent experimental technique, the literature cross section data of 17O(p,g)18F is confirmed in the energy region of the resonances while lower direct capture cross section is recommended at higher energies. The present dataset provides a constraint for the theoretical cross sections.Comment: Accepted for publication in Phys. Rev. C. Abstract shortened in order to comply with arxiv rule

Towards Feature Learning for HMM-based Offline Handwriting Recognition

Statistical modelling techniques for automatic reading systems substantially rely on the availability of compact and meaningful feature representations. State-of-the-art feature extraction for offline handwriting recognition is usually based on heuristic approaches that describe either basic geometric properties or statistical distributions of raw pixel values. Working well on average, still fundamental insights into the nature of handwriting are desired. In this paper we present a novel approach for the automatic extraction of appearance-based representations of offline handwriting data. Given the framework of deep belief networks -- Restricted Boltzmann Machines -- a two-stage method for feature learning and optimization is developed. Given two standard corpora of both Arabic and Roman handwriting data it is demonstrated across script boundaries, that automatically learned features achieve recognition results comparable to state-of-the-art handcrafted features. Given these promising results the potential of feature learning for future reading systems is discussed

Analysis of Binding Site Hot Spots on the Surface of Ras GTPase

We have recently discovered an allosteric switch in Ras, bringing an additional level of complexity to this GTPase whose mutants are involved in nearly 30% of cancers. Upon activation of the allosteric switch, there is a shift in helix 3/loop 7 associated with a disorder to order transition in the active site. Here, we use a combination of multiple solvent crystal structures and computational solvent mapping (FTMap) to determine binding site hot spots in the “off” and “on” allosteric states of the GTP-bound form of H-Ras. Thirteen sites are revealed, expanding possible target sites for ligand binding well beyond the active site. Comparison of FTMaps for the H and K isoforms reveals essentially identical hot spots. Furthermore, using NMR measurements of spin relaxation, we determined that K-Ras exhibits global conformational dynamics very similar to those we previously reported for H-Ras. We thus hypothesize that the global conformational rearrangement serves as a mechanism for allosteric coupling between the effector interface and remote hot spots in all Ras isoforms. At least with respect to the binding sites involving the G domain, H-Ras is an excellent model for K-Ras and probably N-Ras as well. Ras has so far been elusive as a target for drug design. The present work identifies various unexplored hot spots throughout the entire surface of Ras, extending the focus from the disordered active site to well-ordered locations that should be easier to target