Prediction and analysis of functional RNA structures within the integrative genomics viewer

ABSTRACT In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results—in conjunction with probing data and other annotations—in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast ‘omics’ resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

Prediction of Protein Secondary Structure Based on WS-BiLSTM Model

Protein secondary structure prediction is an important topic in bioinformatics. This paper proposed a novel model named WS-BiLSTM, which combined the wavelet scattering convolutional network and the long-short-term memory network for the first time to predict protein secondary structure. This model captures nonlocal interactions between amino acid sequences and remembers long-range interactions between amino acids. In our WS-BiLSTM model, the wavelet scattering convolutional network is used to extract protein features from the PSSM sliding window; the extracted features are combined with the original PSSM data as the input features of the long-short-term memory network to predict protein secondary structure. It is worth noting that the wavelet scattering convolutional network is asymmetric as a member of the continuous wavelet family. The Q3 accuracy on the test set CASP9, CASP10, CASP11, CASP12, CB513, and PDB25 reached 85.26%, 85.84%, 84.91%, 85.13%, 86.10%, and 85.52%, which were higher 2.15%, 2.16%, 3.5%, 3.19%, 4.22%, and 2.75%, respectively, than using the long-short-term memory network alone. Comparing our results with the state-of-art methods shows that our proposed model achieved better results on the CB513 and CASP12 data sets. The experimental results show that the features extracted from the wavelet scattering convolutional network can effectively improve the accuracy of protein secondary structure prediction.

In Silico Study of Secondary Structure of Hemoglobin Protein

Protein structure prediction is one of the important goals in the area of bioinformatics and biotechnology. Prediction methods include structure prediction of both secondary and tertiary structures of protein. Protein secondary structure prediction infers knowledge related to presence of helixes, sheets and coils in a polypeptide chain whereas protein tertiary structure prediction infers knowledge related to three dimensional structures of proteins. Protein secondary structures represent the possible motifs or regular expressions represented as patterns that are predicted from primary protein sequence in the form of alpha helix, betastr and and coils. The secondary structure prediction is useful as it infers information related to the structure and function of unknown protein sequence. There are various secondary structure prediction methods used to predict about helixes, sheets and coils. Based on these methods there are various prediction tools under study. This study includes prediction of hemoglobin using various tools. The results produced inferred knowledge with reference to percentage of amino acids participating to produce helices, sheets and coils. PHD and DSC produced the best of the results out of all the tools used.

Novel MicroRNAs and Their Functional Targets from Phytophthora infestans and Phytophthora cinnamomi

Background: Even though miRNAs play viral roles in developmental biology by regulating the translation of mRNAs, they are poorly studied in oomycetes, especially in plant pathogen Phytophthora. Objective: The study was aimed to predict and identify the putative miRNAs and their targets in Phytophthora infestans and Phytophthora cinnamomi. Methods: Homology based comparative method was used to identify the unique miRNA sequences in P. infestans and P. cinnamomi with 148,689 EST and TSA sequences of these species. Secondary structure prediction of sRNAs for the 76 resultant sequences has been performed with MFOLD tool and their targets were predicted using psRNAtarget. Result: Novel miRNAs, miR-8210 and miR-4968 were predicted from P. infestans and P. cinnamomi, respectively along with their structural features. The newly identified miRNAs were identified to play important roles in gene regulation, with few of their target genes predicted as transcription factors, tumor suppressor genes, stress responsive genes, DNA repairing genes etc. Conclusion: The miRNAs and their targets identified have opened new interference and editing targets for the development of Phytophthora resistant crop varieties.

Caveats to deep learning approaches to RNA secondary structure prediction

Machine learning (ML) and in particular deep learning techniques have gained popularity for predicting structures from biopolymer sequences. An interesting case is the prediction of RNA secondary structures, where well established biophysics based methods exist. These methods even yield exact solutions under certain simplifying assumptions. Nevertheless, the accuracy of these classical methods is limited and has seen little improvement over the last decade. This makes it an attractive target for machine learning and consequently several deep learning models have been proposed in recent years. In this contribution we discuss limitations of current approaches, in particular due to biases in the training data. Furthermore, we propose to study capabilities and limitations of ML models by first applying them on synthetic data that can not only be generated in arbitrary amounts, but are also guaranteed to be free of biases. We apply this idea by testing several ML models of varying complexity. Finally, we show that the best models are capable of capturing many, but not all, properties of RNA secondary structures. Most severely, the number of predicted base pairs scales quadratically with sequence length, even though a secondary structure can only accommodate a linear number of pairs.

In-Silico Screening of Prime PCOS Biomarkers for the Identification of a Potential Model Organism

Polycystic ovarian syndrome (PCOS) is a multigenic endocrine disorder observed in women of reproductive age. Although the condition is characterized by the presence of polycystic ovaries and excess production of androgens, the exact aetiology has not been well deciphered due to the unavailability of a suitable model organism. Defects in the two prime biomarkers namely CYP11A and CYP19A1, have been found to play a role in disease progression. The objective of this study was to carry out an in-silico assessment of these two genes to identify a potential model organism for the efficacious study of PCOS. Bioinformatics tools such as BLAST and EMBOSS were used for local and global alignment respectively, to find sequence homology and thereby, establish a model organism. Sequence comparison was followed by phylogenetic analysis and secondary structure prediction of the enzymes encoded by the respective genes. Our in-silico study revealed Gorilla gorilla to be an ideal candidate for the study of PCOS owing to its high sequence and structural similarities with the human gene counterparts. Future prospects of the research include in-vitro analysis of the biomarkers on Gorilla gorilla ovarian theca cell line to pave the way for therapy.

The influence of new SARS-CoV-2 variant Omicron (B.1.1.529) on vaccine efficacy, its correlation to Delta Variants: a computational approach

The newly discovered COVID variant B.1.1.529 in Botswana has more than 30 mutations in spike and many other in non-spike proteins, far more than any other SARS-CoV-2 variant accepted as a variant of concern by the WHO and officially named Omicron, and has sparked concern among scientists and the general public. Our findings provide insights into structural modification caused by the mutations in the Omicrons receptor-binding domain and look into the effects on interaction with the hosts neutralising antibodies CR3022, B38, CB6, P2B-2F6, and REGN, as well as ACE2R using an in silico approach. We have employed secondary structure prediction, structural superimposition, protein disorderness, molecular docking, and MD simulation to investigate host-pathogen interactions, immune evasion, and transmissibility caused by mutations in the RBD region of the spike protein of the Omicron variant and compared it to the Delta variants (AY.1, AY.2, & AY.3) and wild type. Computational analysis revealed that the Omicron variant has a higher binding affinity for the human ACE2 receptor than the wild and Delta (AY.1 and AY.2 strains), but lower than the Delta AY.3 strain. MD simulation and docking analysis suggest that the omicron and Delta AY.3 were found to have relatively unstable and compact RBD structures and hampered interactions with antibodies more than wild and Delta (AY.1 and AY.2), which may lead to relatively more pathogenicity and antibody escape. In addition, we observed lower binding affinity of Omicron for human monoclonal antibodies (CR3022, B38, CB6, and P2B2F6) when compared to wild and Delta (AY.1 & AY.2). However, the binding affinity of Omicron RBD variants for CR3022, B38, and P2B2F6 antibodies is lower as compared to Delta AY.3, which might promote immune evasion and reinfection and needs further experimental investigation.