Real value prediction of protein solvent accessibility using enhanced PSSM features

<p>Abstract</p> <p>Background</p> <p>Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the real value ASA based on evolutionary information such as position specific scoring matrix (PSSM).</p> <p>Results</p> <p>This study enhances the PSSM-based features for real value ASA prediction by considering the physicochemical properties and solvent propensities of amino acid types. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The amino acid columns in the PSSM profile that belong to a certain residue group are merged to generate novel features. Finally, support vector regression (SVR) is adopted to construct a real value ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction.</p> <p>Conclusion</p> <p>Experimental results based on a widely used benchmark reveal that the proposed method performs best among several of existing packages for performing ASA prediction. Furthermore, the feature selection mechanism incorporated in this study can be applied to other regression problems using the PSSM. The program and data are available from the authors upon request.</p

Use of an asparaginyl endopeptidase for chemo-enzymatic peptide and protein labeling

Asparaginyl endopeptidases (AEPs) are ideal for peptide and protein labeling. However, because of the reaction reversibility, a large excess of labels or backbone modified substrates are needed. In turn, simple and cheap reagents can be used to label N-terminal cysteine, but its availability inherently limits the potential applications. Aiming to address these issues, we have created a chemo-enzymatic labeling system that exploits the substrate promiscuity of AEP with the facile chemical reaction between N-terminal cysteine and 2-formyl phenylboronic acid (FPBA). In this approach, AEP is used to ligate polypeptides with a Asn–Cys–Leu recognition sequence with counterparts possessing an N-terminal Gly–Leu. Instead of being a labeling reagent, the commercially available FPBA serves as a scavenger converting the byproduct Cys–Leu into an inert thiazolidine derivative. This consequently drives the AEP labeling reaction forward to product formation with a lower ratio of label to protein substrate. By carefully screening the reaction conditions for optimal compatibility and minimal hydrolysis, conversion to the ligated product in the model reaction resulted in excellent yields. The versatility of this AEP-ligation/FPBA-coupling system was further demonstrated by site-specifically labeling the N- or C-termini of various proteins

Effects of a Chinese Herbal Medicine, Guan-Jen-Huang (Aeginetia indica Linn.), on Renal Cancer Cell Growth and Metastasis

Aeginetia indica Linn. (Guan-Jen-Huang, GJH), a traditional Chinese herb, has the potential to be an immunomodulatory agent. The purpose of this study was to explore the effect of GJH in the treatment of renal cancer. Concentration-effect curves for the influence of GJH on cellular proliferation showed a biphasic shape. Besides, GJH had a synergistic effect on cytotoxicity when combined with 5-fluorouracil (5-FU)which may be due to the alternation of the chemotherapeutic agent resistance-related genes and due to the synergistic effects on apoptosis. In addition, treatment with GJH extract markedly reduced 786-O cell adherence to human umbilical vein endothelial cells (HUVECs) and decreased 786-O cell migration and invasion. In a xenograft animal model, GJH extract had an inhibitory effect on tumor cell-induced metastasis. Moreover, western blot analysis showed that the expression of intercellular adhesion molecule-1 (ICAM-1) in 786-O cells was significantly decreased by treatment with GJH extract through inactivation of nuclear factor-κB (NF–κB). These results suggest that GJH extract has a synergistic effect on apoptosis induced by chemotherapeutic agents and an inhibitory effect on cell adhesion, migration, and invasion, providing evidence for the use of water-based extracts of GJH as novel alternative therapeutic agents in the treatment of human renal cancer

Magic3D: High-Resolution Text-to-3D Content Creation

DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: (a) extremely slow optimization of NeRF and (b) low-resolution image space supervision on NeRF, leading to low-quality 3D models with a long processing time. In this paper, we address these limitations by utilizing a two-stage optimization framework. First, we obtain a coarse model using a low-resolution diffusion prior and accelerate with a sparse 3D hash grid structure. Using the coarse representation as the initialization, we further optimize a textured 3D mesh model with an efficient differentiable renderer interacting with a high-resolution latent diffusion model. Our method, dubbed Magic3D, can create high quality 3D mesh models in 40 minutes, which is 2x faster than DreamFusion (reportedly taking 1.5 hours on average), while also achieving higher resolution. User studies show 61.7% raters to prefer our approach over DreamFusion. Together with the image-conditioned generation capabilities, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications.Comment: Accepted to CVPR 2023 as highlight. Project website: https://research.nvidia.com/labs/dir/magic3