Computational modeling of RNA 3D structures and interactions

RNA molecules with novel functions have revived interest in the accurate prediction of RNA three-dimensional (3D) structure and folding dynamics. However, existing methods are inefficient in automated 3D structure prediction. Here, we report a robust computational approach for rapid folding of RNA molecules. We develop a simplified RNA model for discrete molecular dynamics (DMD) simulations, incorporating base-pairing and base-stacking interactions. We demonstrate correct folding of 150 structurally diverse RNA sequences. The majority of DMD-predicted 3D structures have <4 A deviations from experimental structures. The secondary structures corresponding to the predicted 3D structures consist of 94% native base-pair interactions. Folding thermodynamics and kinetics of tRNA(Phe), pseudoknots, and mRNA fragments in DMD simulations are in agreement with previous experimental findings. Folding of RNA molecules features transient, non-native conformations, suggesting non-hierarchical RNA folding. Our method allows rapid conformational sampling of RNA folding, with computational time increasing linearly with RNA length. We envision this approach as a promising tool for RNA structural and functional analyses.

Ribonucleic acid is vital in numerous stages of protein synthesis; it also possesses important functional and structural roles within the cell. The function of an RNA molecule within a particular organic system is principally determined by its structure. The current physical methods available for structure determination are time-consuming and expensive. Hence, computational methods for structure prediction are sought after. The energies involved by the formation of secondary structure elements are significantly greater than those of tertiary elements. Therefore, RNA structure prediction focuses on secondary structure. We present P-RnaPredict, a parallel evolutionary algorithm for RNA secondary structure prediction. The speedup provided by parallelization is investigated with five sequences, and a dramatic improvement in speedup is demonstrated, especially with longer sequences. An evaluation of the performance of P-RnaPredict in terms of prediction accuracy is made through comparison with 10 individual known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA and 16S rRNA) and the mfold dynamic programming algorithm. P-RnaPredict is able to predict structures with higher true positive base pair counts and lower false positives than mfold on certain sequences. P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).

A force field for (η3-allyl)palladium complexes with phosphorus and/or nitrogen ligands has been developed, based upon both X-ray and quantum chemical reference data. The quality of the force field in structure prediction is checked by comparison to X-ray structures, including several not used in the parametrization. The force field is also compared to alternative computational methods for structure prediction, including earlier force fields and two quantum chemical methods (PM3(tm) and B3LYP/LANL2DZ).

The analysis, measuring, and recording of anatomical structures by means of dissection and other destructive techniques introduces inaccuracies due to the risk of damaging the original organization of a body's tissues. More so than other tissues, thin connective tissues are especially susceptible to breakage and distortions. MRI is the method of choice for visualizing and identifying connective tissue structures in 3D without the need for contrast staining, which dehydrates and shrinks soft tissues. The visualization of MRI data, however, comes with their own challenges because optimizing image resolution as well as contrast resolution in a particular data set generally results in non‐cubic voxels. Optimizing image resolution requires minimizing pixel size in the viewing plane, whereas optimizing contrast resolution among pixels in the viewing plane requires maximizing the number of hydrogen atoms and, hence, the voxel volume for a more accurate statistical summation of relaxing hydrogen atoms. The latter would create a non‐cubic voxel with a greater depth than the side length of the pixel in the viewing plane. Non‐cubic voxels, however, present a special challenge for building realistic 3D models based on MRI data, in particular for complexly configured thin connective tissue structures that are thinner than a voxel and, therefore, may not be discriminated from the surrounding tissue along their entire course. Recursive segmentation is a technique that allows the reconstruction of such structures in their entirety by using more than one MRI scan of the same specimen in the same position. While most segmentation can usually be done from 3D MRI scans, some portions of the structure may not have enough contrast resolution to be identified because of the almost cubic voxels. For such a portion, its segmentation from a 2D MRI scan with greater contrast resolution in the viewing plane, although lower image resolution in the planes perpendicular to the viewing plane, can serve as a scaffold for guiding the segmentation of the portion that is invisible in the 3D MRI scan. This technique allows the reconstruction of a realistic (“realitätsgetreues”) 3D model of the entire structure. The recursive technique requires skill and anatomical knowledge of the structures to be segmented by the operator and, therefore, needed to be tested as to its efficacy, reliability, and reproducibility even when used by different operators, some of whom may be less familiar with the anatomy of the structures and the segmentation software. By having a novice operator who is unfamiliar with the anatomy of lampreys execute the segmentation of myosepta with and without the help of the recursive technique, we were able to demonstrate that recursive segmentation using both 2D and 3D MRI data sets is essential to creating accurate and realistic 3D models of thin connective tissue structures. Realistic 3D models of myosepta of lampreys and other piscine vertebrates are necessary prerequisites for an elucidation of the biomechanics underlying undulatory swimming behavior.Support or Funding InformationLSU FoundationThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Open-framework metal phosphates occur as one-dimensional (1D) chains or ladders, two-dimensional (2D) layers, and complex three-dimensional (3D) structures. Zero-dimensional monomers have also been isolated recently. These materials are traditionally prepared by hydrothermal means, in the presence of organic amines, but the reactions of amine phosphates with metal ions provide a facile route for the synthesis, and also throw some light on the mode of formation of these fascinating architectures. Careful studies of the transformations of monophasic zinc phosphates of well-characterized structures show that the 1D structures transform to 2D and 3D structures, while the 2D structures transform to 3D structures. The zero-dimensional monomers transform to 1D, 2D, and 3D structures. There is reason to believe that the 0D monomers, comprising four-membered rings, are the most basic structural units of the open-framework phosphates and that after an optimal precursor state, such as the ladder structure, is formed, further building may occur spontaneously. Evidence for the occurrence of self-assembly in the formation of complex structures is provided by the presence of the structural features of the one-dimensional starting material in the final products. These observations constitute the beginning of our understanding of the building-up principle of such complex structures.

BackgroundProtein phosphorylation catalyzed by kinases plays crucial regulatory roles in cellular processes. Given the high-throughput mass spectrometry-based experiments, the desire to annotate the catalytic kinases for in vivo phosphorylation sites has motivated. Thus, a variety of computational methods have been developed for performing a large-scale prediction of kinase-specific phosphorylation sites. However, most of the proposed methods solely rely on the local amino acid sequences surrounding the phosphorylation sites. An increasing number of three-dimensional structures make it possible to physically investigate the structural environment of phosphorylation sites.ResultsIn this work, all of the experimental phosphorylation sites are mapped to the protein entries of Protein Data Bank by sequence identity. It resulted in a total of 4508 phosphorylation sites containing the protein three-dimensional (3D) structures. To identify phosphorylation sites on protein 3D structures, this work incorporates support vector machines (SVMs) with the information of linear motifs and spatial amino acid composition, which is determined for each kinase group by calculating the relative frequencies of 20 amino acid types within a specific radial distance from central phosphorylated amino acid residue. After the cross-validation evaluation, most of the kinase-specific models trained with the consideration of structural information outperform the models considering only the sequence information. Furthermore, the independent testing set which is not included in training set has demonstrated that the proposed method could provide a comparable performance to other popular tools.ConclusionThe proposed method is shown to be capable of predicting kinase-specific phosphorylation sites on 3D structures and has been implemented as a web server which is freely accessible at http://csb.cse.yzu.edu.tw/PhosK3D/. Due to the difficulty of identifying the kinase-specific phosphorylation sites with similar sequenced motifs, this work also integrates the 3D structural information to improve the cross classifying specificity.

It is already more than 10 years after the completion of Human Genome Project. However, the mechanism about how genomic information guides the gene expression in a particular space and time remains to be elucidated. Structure determines function is a consensus that we understand the laws of nature. In biology, this consensus is not only applicable to RNA, protein and other small-scale organic molecules; the same should also apply to macromolecules such as the entire DNA composition of the whole genome. Recent studies have shown that three-dimensional (3D) structure of the genome has an important impact on the regulation of gene transcription and other nuclear functions. Therefore, the study of the 3D structure and function of the whole genome (referred to as 3D genomics) has become a new trend of development in genomics. With the consideration of the genome sequence, gene structure and regulatory elements at the same time, 3D genomics studies the 3D structure of the genome in the nucleus, and its impact on gene transcription, replication, repair, regulation and others biological processes. With the progress of large-scale genome sequencing technology, we already have the relevant technologies such as ChIA-PET and Hi-C for the study of 3D genomics. Preliminary results showed the influence of genome 3D structures to genome functions, which provides the basis for further study of genome structure and function in the spatial nuclear space. This paper summarizes the emergence of 3D genomics, lists the issues and the current progress in this new field, and discusses possible applications of 3D genomics to human health and agriculture researches. We anticipate that 3D genomics will provide novel insights into the genome regulatory functions and provide new opportunities of improving human life qualities.

Efforts to use computers in predicting the secondary structure of proteins based only on primary structure information started over a quarter century ago [1-3]. Although the results were encouraging initially, the accuracy of the pioneering methods generally did not attain the level required for using predictions of secondary structures reliably in modelling the three-dimensional topology of proteins. During the last decade, however, the introduction of new computational techniques as well as the use of multiple sequence information has lead to a dramatic increase in the success rate of prediction methods, such that successful 3D modelling based on predicted secondary structure has become feasible [e.g., Ref 4]. This review is aimed at presenting an overview of the scale of the secondary structure prediction problem and associated pitfalls, as well as the history of the development of computational prediction methods. As recent successful strategies for secondary structure prediction all rely on multiple sequence information, some methods for accurate protein multiple sequence alignments will also be described. While the main focus is on prediction methods for globular proteins, also the prediction of trans-membrane segments within membrane proteins will be briefly summarised. Finally, an integrated iterative approach tying secondary structure prediction and multiple alignment will be introduced [5].

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.

Metal-organic frameworks (MOFs) have emerged as highly versatile materials with applications in gas storage and separation, solar light energy harvesting and photocatalysis. The design of new MOFs, however, has been hampered by the lack of computational methods for ab initio MOF structure prediction, which could be used to inspire and direct experimental synthesis. Here, we report the first ab initio method for the prediction of MOF structures and test it against a diverse set of known MOFs that were chosen for their differences in topology, metal coordination geometry, and ligand binding sites. In all cases, our calculations produced structures that match experiment using only the target composition and ligand molecular structure, proving the versatility of our procedure. The herein presented methodology utilizes the point group symmetry of ligands to enable, for the first time, prediction of MOF structures from first principles, without having to resort to empirical guidelines based on rigid connectivity of nodes and linkers, or to previously determined crystal structures and topologies of known MOFs. This advance provides the first tool to change MOF design from an empirically based process that is based on chemistʼs intuition rooted in literature- or database-established knowledge of node-and-linker connectivity to a more general and theory-driven materials development. This ab initio MOF structure prediction approach, which is here validated on a range of known MOF classes, provides a unique opportunity to explore the phase landscape of MOFs computationally and enables MOF research and development even in case of limited access to laboratory resources, as for example in case of a global pandemic.

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200 Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7 % (for Ls), Specificity=87.6 % (non-active proteins), and Accuracy=92.5 % (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design.

ConoMode is a database for complex three-dimensional (3D) structures of conopeptides binding with their target proteins. Conopeptides, a large family of peptides from the venom of marine snails of the Conus genus, have exceptionally diverse sequences, and their high specificity to block ion channels makes them crucial as drug leads and tools for physiological studies. ConoMode is a specialized archive for the collection of 3D coordinate data for the conopeptides and their binding target proteins from published literature and the Protein Data Bank. These 3D structures can be determined using experimental methods such as X-ray crystallography and electron microscopy and computational methods including docking, homology modeling and molecular dynamics simulations. The binding modes for the conopeptides determined using computational modeling must be validated based on experimental data. The 3D coordinate data from ConoMode can be searched, visualized, downloaded and uploaded. Currently, ConoMode manages 19 conopeptide sequences (from 10 Conus species), 15 protein sequences and 37 3D structures. ConoMode utilizes a modern technical framework to provide a good user experience on mobile devices with touch interaction features. Furthermore, the database is fully optimized for unstructured data and flexible data models.Database URL: http://conomode.qnlm.ac/conomode/conomode/index

Some insects possess complex three-dimensional (3D) structures that develop under the old cuticle prior to the last imaginal molt. Adult treehoppers (Insecta: Hemiptera: Auchenorrhyncha: Membracidae) have one such complex 3D structure, known as a helmet, on their dorsal side. The adult helmet likely forms inside the nymphal pronotum during the final instar nymphal stage.Previous morphological studies have reported that the adult helmet is a large, bi-layered, plywood-like structure, whereas the nymphal pronotum is a monolayer, sheath-like structure. The adult helmet is much larger than nymphal helmet. Thus, the emergence of the adult helmet involves two structural transitions: a transition from a monolayer, sheath-like pronotum to a bi-layer, plywood-like helmet, and a transition in size from small to large. However, when, how, and in what order these transitions occur within the nymphal cuticle is largely unknown.To determine how adult helmet development occurs under the nymphal cuticle, in the present study we describe the morphology of the final adult helmet and investigate developmental trajectories of the helmet during the final instar nymphal stage. We used micro-CT, scanning electron microscope and paraffin sections for morphological observations, and used Antianthe expansa as a model species.We found that the structural transition (from monolayer, sheath-like structure to bi-layer, roof-like structure) occurs through the formation of a “miniature” of the adult helmet during the middle stage of development and that subsequently, extensive folding and furrows form, which account for the increase in size. We suggest that the making of a “miniature” is the key developmental step for the formation of various 3D structures of treehopper helmets.

Structure of native oligomeric Sprouty2 by electron microscopy and its property of electroconductivity

Protein complex prediction based on maximum matching with domain–domain interaction