LaRA 2: parallel and vectorized program for sequence–structure alignment of RNA sequences

Background The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software. Results We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version. Conclusions With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases.

PDBspheres - a method for finding 3D similarities in local regions in proteins

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions (spheres) adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. Currently, PDBspheres library contains more than 2 million spheres, organized to facilitate searches by sequence and/or structure similarity of protein-ligand binding sites or interfaces between interacting molecules. PDBspheres uses the LGA structure alignment algorithm as the main engine for detecting structure similarities between the protein of interest and library spheres. An all-atom structure similarity metric ensures that sidechain placement is taken into account in the PDBspheres primary assessment of confidence in structural matches. In this paper, we (1) describe the PDBspheres method, (2) demonstrate how PDBspheres can be used to detect and characterize binding sites in protein structures, (3) compare PDBspheres use for binding site prediction with seven other binding site prediction methods using a curated dataset of 2,528 ligand-bound and ligand-free crystal structures, and (4) use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of the 4,876 structures in the refined set of PDBbind 2019 dataset. The PDBspheres library is made publicly available for download at https://proteinmodel.org/AS2TS/PDBspheres

Spatio-temporal modeling of the wind field over an urban lake subject to wind sheltering

The wind field over an urban lake may exhibit considerable variability due to wind shielding effects from surrounding structures. Field measurements at an urban reservoir in Singapore were augmented by computational fluid dynamics (CFD) model results to develop a wind model over the reservoir surface via a data assimilation approach. The field measurements identified, depending on structure alignment with the prevailing wind direction, wind shielding that impacted wind direction and velocity over the reservoir surface. The wind model integrated the temporal response of the measurements and spatial distribution produced by the CFD modelling. The wind model was used to predict the spatio-temporal pattern of the wind field over the reservoir surface for a full year. The modeling results showed good agreement with measured wind data at three measurement locations on the reservoir surface. The wind model has been incorporated with a hydrodynamics and water quality model to provide the spatio-temporal wind forcing over the reservoir surface.

Computational Saturation Mutagenesis of SARS-CoV-1 Spike Glycoprotein: Stability, Binding Affinity, and Comparison With SARS-CoV-2

Severe Acute respiratory syndrome coronavirus (SARS-CoV-1) attaches to the host cell surface to initiate the interaction between the receptor-binding domain (RBD) of its spike glycoprotein (S) and the human Angiotensin-converting enzyme (hACE2) receptor. SARS-CoV-1 mutates frequently because of its RNA genome, which challenges the antiviral development. Here, we per-formed computational saturation mutagenesis of the S protein of SARS-CoV-1 to identify the residues crucial for its functions. We used the structure-based energy calculations to analyze the effects of the missense mutations on the SARS-CoV-1 S stability and the binding affinity with hACE2. The sequence and structure alignment showed similarities between the S proteins of SARS-CoV-1 and SARS-CoV-2. Interestingly, we found that target mutations of S protein amino acids generate similar effects on their stabilities between SARS-CoV-1 and SARS-CoV-2. For example, G839W of SARS-CoV-1 corresponds to G857W of SARS-CoV-2, which decrease the stability of their S glycoproteins. The viral mutation analysis of the two different SARS-CoV-1 isolates showed that mutations, T487S and L472P, weakened the S-hACE2 binding of the 2003–2004 SARS-CoV-1 isolate. In addition, the mutations of L472P and F360S destabilized the 2003–2004 viral isolate. We further predicted that many mutations on N-linked glycosylation sites would increase the stability of the S glycoprotein. Our results can be of therapeutic importance in the design of antivirals or vaccines against SARS-CoV-1 and SARS-CoV-2.

Tree Diet: Reducing the Treewidth to Unlock FPT Algorithms in RNA Bioinformatics

Hard graph problems are ubiquitous in Bioinformatics, inspiring the design of specialized Fixed-Parameter Tractable algorithms, many of which rely on a combination of tree-decomposition and dynamic programming. The time/space complexities of such approaches hinge critically on low values for the treewidth tw of the input graph. In order to extend their scope of applicability, we introduce the Tree-Diet problem, i.e. the removal of a minimal set of edges such that a given tree-decomposition can be slimmed down to a prescribed treewidth tw. Our rationale is that the time gained thanks to a smaller treewidth in a parameterized algorithm compensates the extra post-processing needed to take deleted edges into account. Our core result is an FPT dynamic programming algorithm for Tree-Diet, using 2^O(tw)n time and space. We complement this result with parameterized complexity lower-bounds for stronger variants (e.g., NP-hardness when tw or tw − tw is constant). We propose a prototype implementation for our approach which we apply on difficult instances of selected RNA-based problems: RNA design, sequence-structure alignment, and search of pseudoknotted RNAs in genomes, revealing very encouraging results. This work paves the way for a wider adoption of tree-decomposition-based algorithms in Bioinformatics.

Pose Estimation of GIS Pipeline Based on Spatial Transformation Network

The manual flange structure alignment between GIS pipelines in the power system is inefficient and difficult to accurately align. To solve this problem, combined with the research results in the field of deep learning named spatial transformation network, a new pose estimation method based on single camera is proposed. In view of the high similarity between the moving flange and the static flange at the pixel level, the spatial transformation network is used to find the pixel mapping relationship of the two flange images. Thereby establishing the mapping relationship between the pixel coordinates of the two flange images and then using multiple points. In the perspective method, the pixel coordinates are mapped to the world coordinates to obtain the estimation of the position of the key point in the flange, and then the direction vector of the flange is calculated according to the position of the key point. Since there is a pixel coordinate transformation relationship between the static flange and the movable flange. Only the position of the key point in the static flange can be inversely solved by measuring the position of the key point in the static flange. Experiments show that, compared to the traditional method of measuring flange pose based on instrument measurement and linear regression, the method proposed in this paper can accurately measure the pose of the flange structure. And it can rely as little as possible on the measurement of the key points of the moving flange by the instrument.

Modeling hypotactic structure for Chinese-English neural machine translation of complex sentences

The hypotactic structural relation between clauses plays an important role in improving the discourse coherence of document-level translation. However, the standard neural machine translation (NMT) models do not explicitly model the hypotactic relationship between clauses, which usually leads to structurally incorrect translations of long and complex sentences. This problem is particularly noticeable on Chinese-to-English translation task of complex sentences due to the grammatical form distinction between English and Chinese. English is rich in grammatical form (e.g. verb morphological changes and subordinating conjunctions) while Chinese is poor in grammatical form. These linguistic phenomena make it a challenge for NMT to learn the hypotactic structure knowledge from Chinese as well as the structure alignment between Chinese and English. To address these issues, we propose to model the hypotactic structure for Chinese-to-English complex sentence translation by introducing hypotactic structure knowledge. Specifically, we annotate and build a hypotactic structure aligned parallel corpus that provides rich hypotactic structure knowledge for NMT. Moreover, we further propose a structure-infused neural framework to combine the hypotactic structure knowledge with the NMT model through two integrating strategies. In particular, we introduce a specific structure-aware loss to encourage the NMT model to better learn the structure knowledge. Experimental results on WMT17, WMT18 and WMT19 Chinese-to-English translation tasks demonstrate the effectiveness of the proposed methods.

PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction

Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a deep residual inception-inside-inception neural network with convolutional block attention modules, predicting 1 of 16 PB classes from evolutionary information combined to physicochemical properties of individual amino acids. PYTHIA clearly outperforms the LOCUSTRA reference method for all PB classes and demonstrates great performance for PB prediction on particularly challenging proteins from the CASP14 free modelling category.