Computational Prediction of Protein Secondary Structure from Sequence

Immunoinformatics method to design universal multi-epitope nanoparticle vaccine for TGEV S protein

Porcine transmissible enteritis virus (TGEV) is a fatal pathogen affecting newborn piglets, presenting a significant challenge to global intensive pig farming biosecurity due to its ongoing mutation. There is still a lack of effective vaccines to combat this virus, Vaccination has long been considered the most effective way to overcome infectious diseases, however, traditional vaccines cannot be brought to market quickly enough to deal with rapid mutations and emerging viruses. Therefore, this study addresses this gap by using immunoinformatics methods and ferritin nanoparticle delivery system to build a platform for rapid research and development of porcine coronavirus vaccine, designing a candidate nanoparticle vaccine that targets the TGEV S protein. To this end, multiple servers and strict screening criteria were used to analyze the S protein, and 3 CTL dominant epitopes, 3 Th dominant epitopes, and 6 B cell dominant epitopes were obtained. The candidate nanoparticle vaccine was constructed by incorporating ferritin sequences through the C-terminus after they were tandemly linked in a certain order using a flexible linker. Further experimental analyses showed that the designed candidate nanoparticle vaccine possessed relatively high antigenicity, immunogenicity, non-allergenicity, non-transmembrane proteins, suitable physicochemical properties, and high solubility upon overexpression. Tertiary structure modeling and disulfide engineering ensured conformational similarity to natural proteins and high stability. Additionally, the model predicted 6 Linear Epitopes and 6 Discontinuous Epitopes for B-cell conformational epitopes. Docking with TLR-3 and TLR-4 molecules shows a large number of interacting hydrogen-bonded amino acid residues and hydrophobically interacting amino acid residues. Immunomimetic assays show high levels of immunoglobulin, T-lymphocyte and IFN-γ secretion and may elicit specific immune responses. Through computerized cloning, the candidate nanoparticle vaccine can be efficiently expressed in the E. coli K12 expression system, aligning with future large-scale industrial production strategies. Overall, the results indicate that the constructed candidate nanoparticle vaccine can be effectively expressed and may be able to induce a strong immune response, which is expected to be an ideal candidate vaccine against TGEV.

Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

The secondary structures (SSs) and supersecondary structures (SSSs) underlie the three-dimensional structure of proteins. Prediction of the SSs and SSSs from protein sequences enjoys high levels of use and finds numerous applications in the development of a broad range of other bioinformatics tools. Numerous sequence-based predictors of SS and SSS were developed and published in recent years. We survey and analyze 45 SS predictors that were released since 2018, focusing on their inputs, predictive models, scope of their prediction, and availability. We also review 32 sequence-based SSS predictors, which primarily focus on predicting coiled coils and beta-hairpins and which include five methods that were published since 2018. Substantial majority of these predictive tools rely on machine learning models, including a variety of deep neural network architectures. They also frequently use evolutionary sequence profiles. We discuss details of several modern SS and SSS predictors that are currently available to the users and which were published in higher impact venues.

A drug delivery system constructed by a fusion peptide capturing exosomes targets to titanium implants accurately resulting the enhancement of osseointegration peri-implant

BACKGROUND

Exosomes derived from bone marrow mesenchymal stem cells (BMSC-exos) have been shown triggering osteogenic differentiation and mineralization of MSCs, but exosomes administered via bolus injections are rapidly sequestered and cleared. Therefore, we considered the implant as a new organ of patient's body and expected to find a method to treat implant with BMSC-exos in vivo directly.

METHODS

A fusion peptide (PEP), as a drug delivery system (DDS) which contained a titanium-binding peptide (TBP) possessing the ability to selectively bind to the titanium surface and another peptide CP05 being able to capture exosomes expertly, is constructed to modify the titanium surface.

RESULTS

Both in vitro and in vivo experiments prove PEP retains the ability to bind titanium and exosome simultaneously, and the DDS gain the ability to target exosomes to titanium implants surface following enhancing osseointegration post-implantation. Moreover, the DDS constructed by exosomes of diverse origins shows the similar combination rate and efficiency of therapy.

CONCLUSION

This drug delivery system demonstrates the concept that EXO-PEP system can offer an accurate and efficient therapy for treating implants with long-term effect.

DescribePROT: database of amino acid-level protein structure and function predictions

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Deep learning methods in protein structure prediction

Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the '60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail. In this review, we briefly introduce the problem of protein structure prediction and essential elements of Deep Learning (such as Convolutional Neural Networks, Recurrent Neural Networks and basic feed-forward Neural Networks they are founded on), after which we discuss the evolution of predictive methods for one-dimensional and two-dimensional Protein Structure Annotations, from the simple statistical methods of the early days, to the computationally intensive highly-sophisticated Deep Learning algorithms of the last decade. In the process, we review the growth of the databases these algorithms are based on, and how this has impacted our ability to leverage knowledge about evolution and co-evolution to achieve improved predictions. We conclude this review outlining the current role of Deep Learning techniques within the wider pipelines to predict protein structures and trying to anticipate what challenges and opportunities may arise next.

Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

Many new methods for the sequence-based prediction of the secondary and supersecondary structures have been developed over the last several years. These and older sequence-based predictors are widely applied for the characterization and prediction of protein structure and function. These efforts have produced countless accurate predictors, many of which rely on state-of-the-art machine learning models and evolutionary information generated from multiple sequence alignments. We describe and motivate both types of predictions. We introduce concepts related to the annotation and computational prediction of the three-state and eight-state secondary structure as well as several types of supersecondary structures, such as β hairpins, coiled coils, and α-turn-α motifs. We review 34 predictors focusing on recent tools and provide detailed information for a selected set of 14 secondary structure and 3 supersecondary structure predictors. We conclude with several practical notes for the end users of these predictive methods.

Sequence Similarity Searching

Sequence similarity searching has become an important part of the daily routine of molecular biologists, bioinformaticians and biophysicists. With the rapidly growing sequence databanks, this computational approach is commonly applied to determine functions and structures of unannotated sequences, to investigate relationships between sequences, and to construct phylogenetic trees. We introduce arguably the most popular BLAST-based family of the sequence similarity search tools. We explain basic concepts related to the sequence alignment and demonstrate how to search the current databanks using Web site versions of BLASTP, PSI-BLAST and BLASTN. We also describe the standalone BLAST+ tool. Moreover, this unit discusses the inputs, parameter settings and outputs of these tools. Lastly, we cover recent advances in the sequence similarity searching, focusing on the fast MMseqs2 method. © 2018 by John Wiley & Sons, Inc.