RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality.

RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

RNA 3D Structure Prediction: Progress and Perspective.

Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures.

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions.

BACKGROUND: Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. RESULTS: Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. CONCLUSIONS: While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .

ProTSAV: A protein tertiary structure analysis and validation server.

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp.

Identification and structural elucidation of an oxidation product generated during stability studies of Cabergoline drug product.

Cabergoline is a dopamine agonist with applications as anti-Parkinson drug and prolactin inhibitor. The cabergoline drug product Laktostop® 50 µg/mL is used in veterinary medicine for lactation suppression in cats and dogs e.g. during false pregnancy. Recently, during ongoing HPLC stability testing of Laktostop® 50 µg/mL a new oxidation product of Cabergoline was identified. A synthesis starting from Cabergoline was developed, followed by full characterization of the unknown impurity. Preliminary HPLC and LC-MS analyses indicated the unknown impurity as mono-oxygenated product of Cabergoline (Cabergoline N-oxide) that is presumably formed with oxygen by a radical mechanism. Thus, Cabergoline was treated with oxidizing agents such as m-chloroperoxybenzoic acid to afford the desired Cabergoline-N-oxide as a byproduct. After isolation by column chromatography, NMR and LC-MS-MS studies provided evidence that oxidation occurred at the N-allyl nitrogen of Cabergoline to form Cabergoline-N-oxide. © 1905 Elsevier Science. All rights reserved.

Prediction of Behaviour of Thin-Walled DED-Processed Structure: Experimental-Numerical Approach.

Additive manufacturing (AM) becomes a more and more standard process in different fields of industry. There is still only limited knowledge of the relationship between measured material data and the overall behaviour of directed energy deposition (DED)-processed complex structures. The understanding of the structural performance, including flow curves and local damage properties of additively manufactured parts by DED, becomes increasingly important. DED can be used for creating functional surfaces, component repairing using multiple powder feeders, and creating a heterogeneous structure with defined chemical composition. For thin parts that are used with the as-deposited surface, this evaluation is even highly crucial. The main goal of the study was to predict the behaviour of thin-walled structures manufactured by the DED process under static loading by finite element analysis (FEA). Moreover, in this study, the mechanical performance of partly machined and fully machined miniaturized samples produced from the structure was compared. The structure studied in this research resembles a honeycomb shape made of austenitic stainless steel AISI 316L, which is characterized by high strength and ductility. The uncoupled damage models based on a hybrid experimental-numerical approach were used. The microstructure and hardness were examined to comprehend the structural behaviour.

RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures.

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

All-Atom Knowledge-Based Potential for RNA Structure Discrimination Based on the Distance-Scaled Finite Ideal-Gas Reference State.

Noncoding RNAs are increasingly found to play a wide variety of roles in living organisms. Yet, their functional mechanisms are poorly understood because their structures are difficult to determine experimentally. As a result, developing more effective computational techniques to predict RNA structures becomes increasingly an urgent task. One key challenge in RNA structure prediction is the lack of an accurate free energy function to guide RNA folding and discriminate native and near-native structures from decoy conformations. In this study, we developed an all-atom distance-dependent knowledge-based energy function for RNA that is based on a reference state (distance-scaled finite ideal-gas reference state, DFIRE) proven successful for protein structure discrimination. Using four separate benchmarks including RNA puzzles, we found that this DFIRE-based RNA statistical energy function is able to discriminate native and near-native structures against decoys with performance comparable with or better than several existing scoring functions compared. The energy function is expected to be useful for improving the detection of RNA near-native structures.

Novel Bovine Serum Albumin Protein Backbone Reassembly Study: Strongly Twisted ß-Sheet Structure Promotion upon Interaction with GO-PAMAM.

This study investigates the formation of a graphene oxide-polyamidoamine dendrimer complex (GO-PAMAM) and its association and interaction with bovine serum albumin (BSA). Fourier-transform infrared spectrometry and X-ray photoelectron spectrometry indicated the formation of covalent linkage between the GO surface and PAMAM with 7.22% nitrogen content in the GO-PAMAM sample, and various interactions between BSA and GO-PAMAM, including π-π* interactions at 291.5 eV for the binding energy value. Thermogravimetric analysis highlighted the increasing thermal stability throughout the modification process, from 151 to 192 °C for the 10% weight loss temperature. Raman spectrometry and X-ray diffraction analysis were used in order to examine the complexes' assembly, showing a prominent (0 0 2) lattice in GO-PAMAM. Dynamic light scattering tests proved the formation of stable graphenic and graphenic-protein aggregates. The secondary structure rearrangement of BSA after interaction with GO-PAMAM was investigated using circular dichroism spectroscopy. We have observed a shift from 10.9% ß-sheet composition in native BSA to 64.9% ß-sheet composition after the interaction with GO-PAMAM. This interaction promoted the rearrangement of the protein backbone, leading to strongly twisted ß-sheet secondary structure architecture.

Two new steroidal saponins from Maianthemum henryi and their cytotoxic activity against human HepG2 tumor cells.

Two new steroidal saponins (1 and 2), along with four known ones (3 ∼ 6) were obtained from the roots and rhizomes of Maianthemum henryi, their structures were determined to be spirost-5, 25(27)-diene-lß, 3ß-diol 1-O-α-L-rhamnopyranosyl-(1 → 2)-4-O-acetyl-ß-D-fucopyranoside (1), (23S, 24S)-spirost-5, 25(27)-diene-lß, 3ß, 23, 24-tetrol 1-O-α-L-rhamnopyranosyl-(1 → 2)-4-O-acetyl-ß-D-fucopyranoside (2), spirost-5, 25(27)-diene-lß, 3ß-diol 1-O-α-L-rhamnopyranosyl-(1 → 2)-ß-D-fucopyranoside (3), spirost-5, 25(27)-diene-lß, 3ß-diol 1-O-α-L-rhamnopyranosyl-(1 → 2)-ß-D-xylopyranoside (4), Henryioside A (5)、Henryioside B (6), by physicochemical properties and spectroscopic methods. In addition, their cytotoxic activity against human HepG2 tumor cells was evaluated by MTT method and all of the compounds exhibited cytotoxicity with the cells.

Characteristics of Instrumental Methods to Describe and Assess the Recrystallization Process in Ice Cream Systems.

Methods of testing and describing the recrystallization process in ice cream systems were characterized. The scope of this study included a description of the recrystallization process and a description and comparison of the following methods: microscopy and image analysis, focused beam reflectance measurement (FBRM), oscillation thermo-rheometry (OTR), nuclear magnetic resonance (NMR), splat-cooling assay, and X-ray microtomography (micro-CT). All the methods presented were suitable for characterization of the recrystallization process, although they provide different types of information, and they should be individually matched to the characteristics of the tested product.

Using 3dRNA for RNA 3-D Structure Prediction and Evaluation.

This unit describes how to use 3dRNA to predict RNA 3-D structures from their sequences and secondary (2-D) structures, and how to use 3dRNAscore to evaluate the predicted structures. The predicted RNA 3-D structures can be used to predict or understand their functions and can also be used to find the interactions between the RNA and other molecules. © 2017 by John Wiley & Sons, Inc.