Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

B.1.1.7 (Alpha) variant is the most antigenic compared to Wuhan strain, B.1.351, B.1.1.28/triple mutant and B.1.429 variants

The rapid spread of the SARS-CoV-2 virus and its variants has created a catastrophic impact worldwide. Several variants have emerged, including B.1.351 (Beta), B.1.1.28/triple mutant (P.1), B.1.1.7 (Alpha), and B.1.429 (Epsilon). We performed comparative and comprehensive antigenicity mapping of the total S-glycoprotein using the Wuhan strain and the other variants and identified 9-mer, 15-mer, and 20-mer CTL epitopes through in silico analysis. The study found that 9-mer CTL epitope regions in the B.1.1.7 variant had the highest antigenicity and an average of the three epitope types. Cluster analysis of the 9-mer CTL epitopes depicted one significant cluster at the 70% level with two nodes (KGFNCYFPL and EGFNCYFPL). The phage-displayed peptides showed mimic 9-mer CTL epitopes with three clusters. CD spectra analysis showed the same band pattern of S-glycoprotein of Wuhan strain and all variants other than B.1.429. The developed 3D model of the superantigen (SAg)-like regions found an interaction pattern with the human TCR, indicating that the SAg-like component might interact with the TCR beta chain. The present study identified another partial SAg-like region (ANQFNSAIGKI) from the S-glycoprotein. Future research should examine the molecular mechanism of antigen processing for CD8⁺ T cells, especially all the variants’ antigens of S-glycoprotein.

Bioengineering of Novel Non-Replicating mRNA (NRM) and Self-Amplifying mRNA (SAM) Vaccine Candidates Against SARS-CoV-2 Using Immunoinformatics Approach

Presently, the world needs safe and effective vaccines to overcome the COVID-19 pandemic. Our work has focused on formulating two types of mRNA vaccines that differ in capacity to copy themselves inside the cell. These are non-amplifying mRNA (NRM) and self-amplifying mRNA (SAM) vaccines. Both the vaccine candidates encode an engineered viral replicon which can provoke an immune response. Hence we predicted and screened twelve epitopes from the spike glycoprotein of SARS-CoV-2. We used five CTL, four HTL, and three B-cell-activating epitopes to formulate each mRNA vaccine. Molecular docking revealed that these epitopes could combine with HLA molecules that are important for boosting immunogenicity. The B-cell epitopes were adjoined with GPGPG linkers, while CTL and HTL epitopes were linked with KK linkers. The entire protein chain was reverse translated to develop a specific NRM-based vaccine. We incorporate gene encoding replicase in the upstream region of CDS encoding antigen to design the SAM vaccine. Subsequently, signal sequences were added to human mRNA to formulate vaccines. Both vaccine formulations translated to produce the epitopes in host cells, initiate a protective immune cascade, and generate immunogenic memory, which can counter future SARS-CoV-2 viral exposures before the onset of infection.

TN strain proteome mediated therapeutic target mapping and multi-epitopic peptide-based vaccine development for Mycobacterium leprae

Leprosy is a significant universal health problem that is remarkably still a concern in developing countries due to infection frequency. New therapeutic molecules and next-generation vaccines are urgently needed to accelerate the leprosy-free world. In this direction, the present study was performed using two routes: proteome-mediated therapeutic target identification and mapping as well as multi-epitopic peptide-based novel vaccine development using state of the art of computational biology for the TN strain of M. leprae. The TN strain was selected from 65 Mycobacterium strains, and TN strain proteome mediated 83 therapeutic protein targets were mapped and characterized according to subcellular localization. Also, drug molecules were mapped with respect to protein targets localization. The Druggability potential of proteins was also evaluated. For multi-epitope peptide-based vaccine development, the four common types of B and T cell epitopes were identified (SLFQSHNRK, VVGIGQHAA, MMHRSPRTR, LGVDQTQPV) and combined with the suitable peptide linker. The vaccine component had an acceptable protective antigenic score (0.9751). The molecular docking of vaccine components with TLR4/MD2 complex exhibited a low ACE value (-244.12) which signifies the proper binding between the two molecules. The estimated free Gibbs binding energy ensured accurate protein-protein interactions (-112.46 kcal/mol). The vaccine was evaluated through adaptive immunity stimulation as well as immune interactions. The molecular dynamic simulation was carried out by using CHARMM topology-based parameters to minimize the docked complex. Subsequently, the Normal Mode Analysis in the internal coordinates showed a low eigen-value (1.3982892e-05), which also signifies the stability of molecular docking. Finally, the vaccine components were adopted for reverse transcription and codon optimization in E. coli strain K12 for the pGEX-4T1 vector, which supports in silico cloning of the vaccine components against the pathogen. The study directs the experimental study for therapeutics molecules discovery and vaccine candidate development with higher reliability.

In silico and in vivo approaches to recombinant multi-epitope immunogen of GroEL provides efficient cross protection against S. Typhimurium, S. flexneri, and S. dysenteriae

OBJECTIVES

Stress or Heat Shock Proteins (HSPs) have been included in various operations like protein folding, autophagy, and apoptosis. HSP families recognize as protective antigens in a wide range of bacteria because they have been conserved through evolution. Due to their homology as well as antigenicity they are competent for applying in cross-protection against bacterial diseases.

METHODS

In the present study, bioinformatics approaches utilized to design epitope-based construction of Hsp60 (or GroEL) protein. In this regard, potential B-cell and T-cell epitopes except for allergenic sequences were selected by immunoinformatic tools. The structural and functional aspects of the DNA, RNA, and protein levels were assessed by bioinformatics software. Following in silico investigations, recombinant GroEL multi-epitope of Salmonella typhi was expressed, purified, and validated. Mouse groups were immunized with recombinant protein and humoral immune response was measured by enzyme linked immunosorbent assay (ELISA). Animal challenge against Salmonella Typhimurium, Shigella flexneri, and Shigella dysenteriae was evaluated.

RESULTS

recombinant protein expression and purification with 14.3 kilodaltons (kDa) was confirmed by SDS-PAGE and western blotting. After animal administration, the immunoglobulins evaluated increase after each immunization. Immunized antisera exhibited 80%, 40%, and 40% protection against the lethal dose infection by S. Typhimurium, S. flexneri, and S. dysenteriae respectively. Passive immunization conferred 50%, 30%, and 30% protection in mice against S. Typhimurium, S. flexneri and S. dysentery respectively. In addition, bacterial organ load had exhibited a significant decrease in colony forming unit (CFU) in the liver and spleen of the immunized mice compared to the control.

CONCLUSION

Our study demonstrates the efficacy of S. Typhi recombinant GroEL multi-epitope to consider as a universal immunogen candidate versus multiple bacterial pathogens.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

Evaluation and Designing of Epitopic-Peptide Vaccine Against Bunyamwera orthobunyavirus Using M-Polyprotein Target Sequences

Bunyamwera orthobunyavirus and its serogroup can cause several diseases in humans, cattle, ruminants, and birds. The viral M-polyprotein helps the virus to enter the host body. Therefore, this protein might serve as a potential vaccine target against Bunyamwera orthobunyavirus. The present study applied the immunoinformatics technique to design an epitopic vaccine component that could protect against Bunyamwera infection. Phylogenetic analysis revealed the presence of conserved patterns of M-polyprotein within the viral serogroup. Three epitopes common for both B-cell and T-cell were identified, i.e., YQPTELTRS, YKAHDKEET, and ILGTGTPKF merged with a specific linker peptide to construct an active vaccine component. The low atomic contact energy value of docking complex between human TLR4 (TLR4/MD2 complex) and vaccine construct confirms the elevated protein–protein binding interaction. Molecular dynamic simulation and normal mode analysis illustrate the docking complex’s stability, especially by the higher Eigenvalue. In silico cloning of the vaccine construct was applied to amplify the desired vaccine component. Structural allocation of both the vaccine and epitopes also show the efficacy of the developed vaccine. Hence, the computational research design outcomes support that the peptide-based vaccine construction is a crucial drive target to limit the infection of Bunyamwera orthobunyavirus to an extent.

Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

Brewery: deep learning and deeper profiles for the prediction of 1D protein structure annotations

MOTIVATION

Protein structural annotations (PSAs) are essential abstractions to deal with the prediction of protein structures. Many increasingly sophisticated PSAs have been devised in the last few decades. However, the need for annotations that are easy to compute, process and predict has not diminished. This is especially true for protein structures that are hardest to predict, such as novel folds.

RESULTS

We propose Brewery, a suite of ab initio predictors of 1D PSAs. Brewery uses multiple sources of evolutionary information to achieve state-of-the-art predictions of secondary structure, structural motifs, relative solvent accessibility and contact density.

AVAILABILITY AND IMPLEMENTATION

The web server, standalone program, Docker image and training sets of Brewery are available at http://distilldeep.ucd.ie/brewery/.

CONTACT

gianluca.pollastri@ucd.ie.

Immunoinformatics approach to understand molecular interaction between multi-epitopic regions of SARS-CoV-2 spike-protein with TLR4/MD-2 complex

BACKGROUND

The coronavirus (CoV) spike (S) protein is critical for receptor binding, membrane fusion and internalization of the virus into the human cells. We have tried to search the epitopic component of the S-protein that might be served as crucial targets for the vaccine development and also tried to understand the molecular mechanism of epitopes and TLR4/MD-2 complex for adaptive immunity.

MATERIAL AND METHODS

Here we identified the antigenicity and the epitopic divergence of S-protein via immunoinformatics approach. The study was performed to identify the epitopes, composition of amino acids and its distribution in epitopic regions, composition of amino acid between the identified epitopes, secondary structure architecture of epitopes, physicochemical and biochemical parameters and molecular interaction between the identified epitope and TLR4/MD-2 complex. The SARS-CoV-2 can be possibly recognised by TLR4 of host immune cells that are responsible for the adaptive immune response.

RESULTS

We identified four SARS-CoV-2 S-protein 9mer antigenic epitopes and observed that they bind with the TLR4/MD-2 complex by varied stable molecular bonding interactions. Molecular interaction between these characterized epitopes with TLR4/MD-2 complex might be indicated the binding affinity and downstream signalling of adaptive immune response. Different physicochemical and biochemical parameters such as O-glycosylation and N-glycosylation, Hydrophobicity, GRAVY were identified within epitopic regions of S-protein. These parameters help to understand the protein-protein interaction between epitopes and TLR4/MD-2 complex. The study also revealed different epitopic binding pockets of TLR4/MD-2 complex.

CONCLUSIONS

The identified epitopes impart suitable prospects for the development of novel peptide-based epitopic vaccine for the control of COVID-19 infection.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

BACKGROUND

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts.

OBJECTIVE

A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools.

METHODS

Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data.

RESULTS

Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields.

CONCLUSION

This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Computer aided novel antigenic epitopes selection from the outer membrane protein sequences of Aeromonas hydrophila and its analyses

OBJECTIVES

Gram-negative bacteria are among the causative microorganisms for zoonotic diseases in humans and teleosts. Outer membrane proteins (Omps) of Aeromonas hydrophila, a gram-negative bacterium, are critical for the subcellular integration to eukaryotic cell that can modulate the functions of macrophages. Hence Omps are recognized as immune markers for the vaccine development.

METHODS

In the present study, a 3-D model of Omps was identified using in silico technique and recognized through the Swiss model web-server and confirmed with Procheck and ProSA server.. The B-cell binding sites of the protein were selected from sequence alignment.. Further, the identification of B-cell epitope was carried out using modules of BCpred server (i.e., BCPred and Amino Acid Pairs). The identified antigenic amino acid sequences for B-cells were used to determine the T-cell epitope (both MHC I & II allelic binding sequences) using ProPred 1 and ProPred servers.

RESULTS

The epitopic regions (9 mer: LAGKTTNES and GFDGSQYGK) in the Omps that are bound together with the MHC molecules (MHC-I & II), and have maximum possible numbers of MHC alleles are recognized. It was observed that Omps of A. hydrophila are conserved across the serotypes and are immunogenic. These epitopes can stimulate significant immune responses and can be advantageous while preparing peptide-based vaccines against A. hydrophila infections. Thus, suggesting the use of Omps in the development of vaccines and immunotherapeutics against the bacterial diseases in humans and teleosts.

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.

Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction

Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction accuracy (88–90%), while only a few predict more than the 3 traditional Helix, Strand and Coil classes. In this study we present tests on different models trained both on single sequence and evolutionary profile-based inputs and develop a new state-of-the-art system with Porter 5. Porter 5 is composed of ensembles of cascaded Bidirectional Recurrent Neural Networks and Convolutional Neural Networks, incorporates new input encoding techniques and is trained on a large set of protein structures. Porter 5 achieves 84% accuracy (81% SOV) when tested on 3 classes and 73% accuracy (70% SOV) on 8 classes on a large independent set. In our tests Porter 5 is 2% more accurate than its previous version and outperforms or matches the most recent predictors of secondary structure we tested. When Porter 5 is retrained on SCOPe based sets that eliminate homology between training/testing samples we obtain similar results. Porter is available as a web server and standalone program at http://distilldeep.ucd.ie/porter/ alongside all the datasets and alignments.

Epitope-Based Vaccine Designing of Nocardia asteroides Targeting the Virulence Factor Mce-Family Protein by Immunoinformatics Approach

Nocardia asteroides is the main causative agent responsible for nocardiosis disease in immunocompromised patient viz. Acquired Immunodeficiency Syndrome (AIDS), malignancy, diabetic, organ recipient and genetic disorders. The virulence factor and outer membrane protein pertains immense contribution towards the designing of epitopic vaccine and limiting the robust outbreak of diseases. While epitopic based vaccine element carrying B and T cell epitope along with adjuvant is highly immunoprophylactic in nature. Present research equips immunoinformatics to figure out the suitable epitopes for effective vaccine designing. The selected epitopes VLGSSVQTA, VNIELKPEF and VVPSNLFAV amino acids sequence are identified by HLA-DRB alleles of both MHC class (MHC-I and II) molecules. Simultaneously, these also accessible to B-cell, confirmed through the ABCPred server. Antigenic property expression is validated by the Vaxijen antigenic prediction web portal. Molecular docking between the epitopes and T cell receptor delta chain authenticate the accurate interaction between epitope and receptor with significantly low binding energy. Easy access of epitopes to immune system also be concluded as transmembrane nature of the protein verified by using of TMHMM server. Appropriate structural identity of the virulence factor Mce-family protein generated through Phyre2 server and subsequently validated by ProSA and PROCHECK program suite. The structural configuration of theses epitopes also shaped using DISTILL web server. Both the structure of epitopes and protein will contribute a significant step in designing of epitopic vaccine against N. asteroides. Therefore, such immunoinformatics based computational drive definitely provides a conspicuous impel towards the development of epitopic vaccine as a promising remedy of nocardiosis.

rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments

In the last decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed taking advantage of these new architectures. However, most methods are still heavily based pre-processing of the input data, as well as extraction and integration of multiple hand-picked, and manually designed features. Multiple Sequence Alignments (MSA) are the most common source of information in de novo prediction methods. Deep Networks that automatically refine the MSA and extract useful features from it would be immensely powerful. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering pre-calculated features such as sequence profiles and other features calculated from MSA obsolete. We showcased the rawMSA methodology on three different prediction problems: secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and benchmarked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on par with methods using more pre-calculated features in the inter-residue contact map prediction category in CASP12 and CASP13. Clearly demonstrating that rawMSA represents a promising development that can pave the way for improved methods using rawMSA instead of sequence profiles to represent evolutionary information in the coming years. Availability: datasets, dataset generation code, evaluation code and models are available at: https://bitbucket.org/clami66/rawmsa.

Computational characterization of epitopic region within the outer membrane protein candidate in Flavobacterium columnare for vaccine development

Gram-negative bacteria is the main causative agents for columnaris disease outbreak to finfishes. The outer membrane proteins (OMPs) candidate of Flavobacterium columnare bacterial cell served a critical component for cellular invasion targeted to the eukaryotic cell and survival inside the macrophages. Therefore, OMPs considered as the supreme element for the development of promising vaccine against F. columnare. Implies advanced in silico approaches, the predicted 3-D model of targeted OMPs were characterized by the Swiss model server and validated through Procheck programs and Protein Structure Analysis (ProSA) web server. The protein sequences having B-cell binding sites were preferred from sequence alignment; afterwards the B cell epitopes prediction was prepared using the BCPred and amino acid pairs (AAP) prediction algorithms modules of BCPreds. Consequently, the selected antigenic amino acids sequences (B-cell epitopic regions) were analyzed for T-cell epitopes determination (MHC I and MHC II alleles binding sequence) performing the ProPred 1 and ProPred server respectively. The epitopes (9 mer: IKKYEPAPV, YGPNYKWKF and YRGLNVGTS) within the OMPs binds to both of the MHC classes (MHC I and MHC II) and covered highest number of MHC alleles are characterized. OMPs of F. columnare being conserved across serotypes and highly immunogenic for their exposed epitopes on the cell surface as a potent candidate focus to vaccine development for combating the disease problems in commercial aquaculture. The portrayed epitopes might be beneficial for practical designing of abundant peptide-based vaccine development against the columnaris through boosting up the advantageous immune responses.Communicated by Ramaswamy H. Sarma.

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.

The cinnamyl alcohol dehydrogenase family in flax: Differentiation during plant growth and under stress conditions

Cinnamyl alcohol dehydrogenase (CAD), which catalyzes the reduction of cinnamaldehydes to their alcohol derivatives, is represented by a large family of proteins. The aim of the study was to identify the CAD isoforms in flax (Linum usitatissimum L.) - LuCADs - and to determine their specificity to enhance knowledge of the mechanisms controlling cell wall lignification in flax under environmental stresses. On the basis of genome-wide analysis, we identified 15 isoforms (one in two copies) belonging to three major classes of the CAD protein family. Their specificity was determined at the transcriptomic level in different tissues/organs, under Fusarium infection and abiotic stresses. Considering the function of particular LuCADs, it was established that LuCAD1 and 2 belong to Class I and they take part in the lignification of maturing stem and in the response to cold and drought stress. The Class II members LuCAD3, LuCAD4, LuCAD5 and LuCAD6 play various roles in flax being putatively responsible for lignin synthesis in different organs or under certain conditions. The obtained results indicate that within Class II, LuCAD6 was the most abundant in seedlings and maturing stems, LuCAD3 in leaves, and LuCAD4 in stems. Comparative analysis showed that expression of LuCAD genes in roots after F. oxysporum infection had the greatest contribution to differentiation of LuCAD expression patterns. Surprisingly, most of the analyzed LuCAD isoforms had reduced expression after pathogen infection. The decrease in mRNA level was primarily observed for LuCAD6 and LuCAD4, but also LuCAD1 and 8. However, the induction of LuCAD expression was mostly characteristic for Class I LuCAD1 and 2 in leaves. For cold stress, a clear correlation with phylogenic class membership was observed. Low temperatures caused induction of CAD isoforms belonging to Class I and repression of LuCADs from Class III.

Automatic analysis and 3D-modelling of Hi-C data using TADbit reveals structural features of the fly chromatin colors

The sequence of a genome is insufficient to understand all genomic processes carried out in the cell nucleus. To achieve this, the knowledge of its three-dimensional architecture is necessary. Advances in genomic technologies and the development of new analytical methods, such as Chromosome Conformation Capture (3C) and its derivatives, provide unprecedented insights in the spatial organization of genomes. Here we present TADbit, a computational framework to analyze and model the chromatin fiber in three dimensions. Our package takes as input the sequencing reads of 3C-based experiments and performs the following main tasks: (i) pre-process the reads, (ii) map the reads to a reference genome, (iii) filter and normalize the interaction data, (iv) analyze the resulting interaction matrices, (v) build 3D models of selected genomic domains, and (vi) analyze the resulting models to characterize their structural properties. To illustrate the use of TADbit, we automatically modeled 50 genomic domains from the fly genome revealing differential structural features of the previously defined chromatin colors, establishing a link between the conformation of the genome and the local chromatin composition. TADbit provides three-dimensional models built from 3C-based experiments, which are ready for visualization and for characterizing their relation to gene expression and epigenetic states. TADbit is an open-source Python library available for download from https://github.com/3DGenomes/tadbit.

Comparative phylogenomic analysis provides insights into TCP gene functions in Sorghum

Sorghum is a highly efficient C4 crop with potential to mitigate challenges associated with food, feed and fuel. TCP proteins are of particular interest for crop improvement programs due to their well-demonstrated roles in crop domestication and shaping plant architecture thereby, affecting agronomic traits. We identified 20 TCP genes from Sorghum. Except SbTCP8, all are either intronless or contain introns in the untranslated regions. Comparative phylogenetic analysis of Arabidopsis, rice, Brachypodium and Sorghum TCP proteins revealed two distinct classes categorized into ten sub-clades. Sub-clade F is dicot-specific, whereas A2, G1 and I1 groups only contained genes from grasses. Sub-clade B was missing in Sorghum, whereas group A1 was missing in rice indicating species-specific divergence of TCP proteins. TCP proteins of Sorghum are enriched in disorder promoting residues with class I containing higher percent disorder than class II proteins. Seven pairs of paralogous TCP genes were identified from Sorghum, five of which seem to predate Rice-Sorghum divergence. All of them have diverged in their expression. Based on the expression and orthology analysis, five Sorghum genes have been shortlisted for further investigation for their roles in regulating plant morphology. Whereas, three genes have been identified as candidates for engineering abiotic stress tolerance.

Transcriptomic and proteomic insights into innate immunity and adaptations to a symbiotic lifestyle in the gutless marine worm Olavius algarvensis

Background

The gutless marine worm Olavius algarvensis has a completely reduced digestive and excretory system, and lives in an obligate nutritional symbiosis with bacterial symbionts. While considerable knowledge has been gained of the symbionts, the host has remained largely unstudied. Here, we generated transcriptomes and proteomes of O. algarvensis to better understand how this annelid worm gains nutrition from its symbionts, how it adapted physiologically to a symbiotic lifestyle, and how its innate immune system recognizes and responds to its symbiotic microbiota.

Results

Key adaptations to the symbiosis include (i) the expression of gut-specific digestive enzymes despite the absence of a gut, most likely for the digestion of symbionts in the host's epidermal cells; (ii) a modified hemoglobin that may bind hydrogen sulfide produced by two of the worm’s symbionts; and (iii) the expression of a very abundant protein for oxygen storage, hemerythrin, that could provide oxygen to the symbionts and the host under anoxic conditions. Additionally, we identified a large repertoire of proteins involved in interactions between the worm's innate immune system and its symbiotic microbiota, such as peptidoglycan recognition proteins, lectins, fibrinogen-related proteins, Toll and scavenger receptors, and antimicrobial proteins.

Conclusions

We show how this worm, over the course of evolutionary time, has modified widely-used proteins and changed their expression patterns in adaptation to its symbiotic lifestyle and describe expressed components of the innate immune system in a marine oligochaete. Our results provide further support for the recent realization that animals have evolved within the context of their associations with microbes and that their adaptive responses to symbiotic microbiota have led to biological innovations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3293-y) contains supplementary material, which is available to authorized users.

A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling

Generating tertiary structural models for a target protein from the known structure of its homologous template proteins and their pairwise sequence alignment is a key step in protein comparative modeling. Here, we developed a new stochastic point cloud sampling method, called MTMG, for multi-template protein model generation. The method first superposes the backbones of template structures, and the Cα atoms of the superposed templates form a point cloud for each position of a target protein, which are represented by a three-dimensional multivariate normal distribution. MTMG stochastically resamples the positions for Cα atoms of the residues whose positions are uncertain from the distribution, and accepts or rejects new position according to a simulated annealing protocol, which effectively removes atomic clashes commonly encountered in multi-template comparative modeling. We benchmarked MTMG on 1,033 sequence alignments generated for CASP9, CASP10 and CASP11 targets, respectively. Using multiple templates with MTMG improves the GDT-TS score and TM-score of structural models by 2.96–6.37% and 2.42–5.19% on the three datasets over using single templates. MTMG’s performance was comparable to Modeller in terms of GDT-TS score, TM-score, and GDT-HA score, while the average RMSD was improved by a new sampling approach. The MTMG software is freely available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/mtmg.html.

A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11

Background

With more and more protein sequences produced in the genomic era, predicting protein structures from sequences becomes very important for elucidating the molecular details and functions of these proteins for biomedical research. Traditional template-based protein structure prediction methods tend to focus on identifying the best templates, generating the best alignments, and applying the best energy function to rank models, which often cannot achieve the best performance because of the difficulty of obtaining best templates, alignments, and models.

Methods

We developed a large-scale conformation sampling and evaluation method and its servers to improve the reliability and robustness of protein structure prediction. In the first step, our method used a variety of alignment methods to sample relevant and complementary templates and to generate alternative and diverse target-template alignments, used a template and alignment combination protocol to combine alignments, and used template-based and template-free modeling methods to generate a pool of conformations for a target protein. In the second step, it used a large number of protein model quality assessment methods to evaluate and rank the models in the protein model pool, in conjunction with an exception handling strategy to deal with any additional failure in model ranking.

Results

The method was implemented as two protein structure prediction servers: MULTICOM-CONSTRUCT and MULTICOM-CLUSTER that participated in the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014. The two servers were ranked among the best 10 server predictors.

Conclusions

The good performance of our servers in CASP11 demonstrates the effectiveness and robustness of the large-scale conformation sampling and evaluation. The MULTICOM server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0775-x) contains supplementary material, which is available to authorized users.

In silico prediction of B- and T- cell epitope on Lassa virus proteins for peptide based subunit vaccine design

BACKGROUND

Lassa fever is a severe, often-fatal and one of the most virulent disease in primates. However, the mechanism of escape of virus from the T-cell mediated immune response of the host cell is not explained in any studies yet. In our studies we had aimed to predict B- and T- cell epitope of Lassa virus protein, for impaling the futuristic approach of developing preventive measures against this disease, further we can also study its presumed viral- host mechanism.

MATERIALS AND METHODS

Peptide based subunit vaccine was developed from all four protein against Lassa virus. We adopted sequence, 3D structure and fold level in silico analysis to predict B-cell and T-cell epitopes. The 3-D structure was determined for all protein by homology modeling and the modeled structure validated.

RESULTS

One T-cell epitope from Glycoprotein (WDCIMTSYQ) and one from Nucleoprotein (WPYIASRTS) binds to maximum no of MHC class I and MHC class II alleles. They also specially bind to HLA alleles namely, A*0201, A*2705, DRB*0101 and DRB*0401.

CONCLUSIONS

Taken together, the results indicate the Glycoprotein and nucleoprotein are most suitable vaccine candidates against Lassa virus.

Insights into the structure and inhibition of Giardia intestinalis arginine deiminase: homology modeling, docking, and molecular dynamics studies

Giardia intestinalis arginine deiminase (GiADI) is an important metabolic enzyme involved in the energy production and defense of this protozoan parasite. The lack of this enzyme in the human host makes GiADI an attractive target for drug design against G. intestinalis. One approach in the design of inhibitors of GiADI could be computer-assisted studies of its crystal structure, such as docking; however, the required crystallographic structure of the enzyme still remains unresolved. Because of its relevance, in this work, we present a three-dimensional structure of GiADI obtained from its amino acid sequence using the homology modeling approximation. Furthermore, we present an approximation of the most stable dimeric structure of GiADI identified through molecular dynamics simulation studies. An in silico analysis of druggability using the structure of GiADI was carried out in order to know if it is a good target for design and optimization of selective inhibitors. Potential GiADI inhibitors were identified by docking of a set of 3196 commercial and 19 in-house benzimidazole derivatives, and molecular dynamics simulation studies were used to evaluate the stability of the ligand-enzyme complexes.

A biophysical study on molecular physiology of the uncoupling proteins of the central nervous system

Mitochondrial inner membrane uncoupling proteins (UCPs) facilitate transmembrane (TM) proton flux and consequently reduce the membrane potential and ATP production. It has been proposed that the three neuronal human UCPs (UCP2, UCP4 and UCP5) in the central nervous system (CNS) play significant roles in reducing cellular oxidative stress. However, the structure and ion transport mechanism of these proteins remain relatively unexplored. Recently, we reported a novel expression system for obtaining functionally folded UCP1 in bacterial membranes and applied this system to obtain highly pure neuronal UCPs in high yields. In the present study, we report on the structure and function of the three neuronal UCP homologues. Reconstituted neuronal UCPs were dominantly helical in lipid membranes and transported protons in the presence of physiologically-relevant fatty acid (FA) activators. Under similar conditions, all neuronal UCPs also exhibited chloride transport activities that were partially inhibited by FAs. CD, fluorescence and MS measurements and semi-native gel electrophoresis collectively suggest that the reconstituted proteins self-associate in the lipid membranes. Based on SDS titration experiments and other evidence, a general molecular model for the monomeric, dimeric and tetrameric functional forms of UCPs in lipid membranes is proposed. In addition to their shared structural and ion transport features, neuronal UCPs differ in their conformations and proton transport activities (and possibly mechanism) in the presence of different FA activators. The differences in FA-activated UCP-mediated proton transport could serve as an essential factor in understanding and differentiating the physiological roles of UCP homologues in the CNS.

Evaluation of transmembrane helix predictions in 2014

Experimental structure determination continues to be challenging for membrane proteins. Computational prediction methods are therefore needed and widely used to supplement experimental data. Here, we re-examined the state of the art in transmembrane helix prediction based on a nonredundant dataset with 190 high-resolution structures. Analyzing 12 widely-used and well-known methods using a stringent performance measure, we largely confirmed the expected high level of performance. On the other hand, all methods performed worse for proteins that could not have been used for development. A few results stood out: First, all methods predicted proteins in eukaryotes better than those in bacteria. Second, methods worked less well for proteins with many transmembrane helices. Third, most methods correctly discriminated between soluble and transmembrane proteins. However, several older methods often mistook signal peptides for transmembrane helices. Some newer methods have overcome this shortcoming. In our hands, PolyPhobius and MEMSAT-SVM outperformed other methods.

Folding and self-association of atTic20 in lipid membranes: implications for understanding protein transport across the inner envelope membrane of chloroplasts

Background

The Arabidopsis thaliana protein atTic20 is a key component of the protein import machinery at the inner envelope membrane of chloroplasts. As a component of the TIC complex, it is believed to form a preprotein-conducting channel across the inner membrane.

Results

We report a method for producing large amounts of recombinant atTic20 using a codon-optimized strain of E. coli coupled with an autoinduction method of protein expression. This method resulted in the recombinant protein being directed to the bacterial membrane without the addition of a bacterial targeting sequence. Using biochemical and biophysical approaches, we were able to demonstrate that atTic20 homo-oligomerizes in vitro when solubilized in detergents or reconstituted into liposomes. Furthermore, we present evidence that the extramembranous N-terminus of the mature protein displays characteristics that are consistent with it being an intrinsically disordered protein domain.

Conclusion

Our work strengthens the hypothesis that atTic20 functions similarly to other small α-helical integral membrane proteins, such as Tim23, that are involved in protein transport across membranes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12858-014-0029-y) contains supplementary material, which is available to authorized users.

Computational survey of peptides derived from disulphide-bonded protein loops that may serve as mediators of protein-protein interactions

Background

Bioactive cyclic peptides derived from natural sources are well studied, particularly those derived from non-ribosomal synthetases in fungi or bacteria. Ribosomally synthesised bioactive disulphide-bonded loops represent a large, naturally enriched library of potential bioactive compounds, worthy of systematic investigation.

Results

We examined the distribution of short cyclic loops on the surface of a large number of proteins, especially membrane or extracellular proteins. Available three-dimensional structures highlighted a number of disulphide-bonded loops responsible for the majority of the likely binding interactions in a variety of protein complexes, due to their location at protein-protein interfaces. We find that disulphide-bonded loops at protein-protein interfaces may, but do not necessarily, show biological activity independent of their parent protein. Examining the conservation of short disulphide bonded loops in proteins, we find a small but significant increase in conservation inside these loops compared to surrounding residues. We identify a subset of these loops that exhibit a high relative conservation, particularly among peptide hormones.

Conclusions

We conclude that short disulphide-bonded loops are found in a wide variety of biological interactions. They may retain biological activity outside their parent proteins. Such structurally independent peptides may be useful as biologically active templates for the development of novel modulators of protein-protein interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-305) contains supplementary material, which is available to authorized users.

Mutations in γ adducin are associated with inherited cerebral palsy

OBJECTIVE

Cerebral palsy is estimated to affect nearly 1 in 500 children, and although prenatal and perinatal contributors have been well characterized, at least 20% of cases are believed to be inherited. Previous studies have identified mutations in the actin-capping protein KANK1 and the adaptor protein-4 complex in forms of inherited cerebral palsy, suggesting a role for components of the dynamic cytoskeleton in the genesis of the disease.

METHODS

We studied a multiplex consanguineous Jordanian family by homozygosity mapping and exome sequencing, then used patient-derived fibroblasts to examine functional consequences of the mutation we identified in vitro. We subsequently studied the effects of adducin loss of function in Drosophila.

RESULTS

We identified a homozygous c.1100G>A (p.G367D) mutation in ADD3, encoding gamma adducin in all affected members of the index family. Follow-up experiments in patient fibroblasts found that the p.G367D mutation, which occurs within the putative oligomerization critical region, impairs the ability of gamma adducin to associate with the alpha subunit. This mutation impairs the normal actin-capping function of adducin, leading to both abnormal proliferation and migration in cultured patient fibroblasts. Loss of function studies of the Drosophila adducin ortholog hts confirmed a critical role for adducin in locomotion.

INTERPRETATION

Although likely a rare cause of cerebral palsy, our findings indicate a critical role for adducins in regulating the activity of the actin cytoskeleton, suggesting that impaired adducin function may lead to neuromotor impairment and further implicating abnormalities of the dynamic cytoskeleton as a pathogenic mechanism contributing to cerebral palsy.

[Unique structural organization of ATP-dependent LonA proteases]

Homooligomeric LonA proteases are the key components of the protein quality control system in bacteria and eukaryotes. Domain organization of the common pool of LonA proteases is determined by comparative analysis of primary and secondary structures of a number of bacterial and eukaryotic enzymes. The similarity of individual enzyme domains was estimated, domain-domain linker areas were revealed, regions that are capable to include intercalated peptide fragments were identified. LonA proteases were shown to be unique AAA+ proteins, because in addition to the classic AAA+ module they contain a part of another AAA+ module, namely the alpha-helical domain including a coiled-coil region, which is similar to the alpha-helical domain of the AAA(+)-1 module of the chaperone-disagregases ClpB/Hsp104.

Reconstructing protein structures by neural network pairwise interaction fields and iterative decoy set construction

Predicting the fold of a protein from its amino acid sequence is one of the grand problems in computational biology. While there has been progress towards a solution, especially when a protein can be modelled based on one or more known structures (templates), in the absence of templates, even the best predictions are generally much less reliable. In this paper, we present an approach for predicting the three-dimensional structure of a protein from the sequence alone, when templates of known structure are not available. This approach relies on a simple reconstruction procedure guided by a novel knowledge-based evaluation function implemented as a class of artificial neural networks that we have designed: Neural Network Pairwise Interaction Fields (NNPIF). This evaluation function takes into account the contextual information for each residue and is trained to identify native-like conformations from non-native-like ones by using large sets of decoys as a training set. The training set is generated and then iteratively expanded during successive folding simulations. As NNPIF are fast at evaluating conformations, thousands of models can be processed in a short amount of time, and clustering techniques can be adopted for model selection. Although the results we present here are very preliminary, we consider them to be promising, with predictions being generated at state-of-the-art levels in some of the cases.

Joint probabilistic-logical refinement of multiple protein feature predictors

Background

Computational methods for the prediction of protein features from sequence are a long-standing focus of bioinformatics. A key observation is that several protein features are closely inter-related, that is, they are conditioned on each other. Researchers invested a lot of effort into designing predictors that exploit this fact. Most existing methods leverage inter-feature constraints by including known (or predicted) correlated features as inputs to the predictor, thus conditioning the result.

Results

By including correlated features as inputs, existing methods only rely on one side of the relation: the output feature is conditioned on the known input features. Here we show how to jointly improve the outputs of multiple correlated predictors by means of a probabilistic-logical consistency layer. The logical layer enforces a set of weighted first-order rules encoding biological constraints between the features, and improves the raw predictions so that they least violate the constraints. In particular, we show how to integrate three stand-alone predictors of correlated features: subcellular localization (Loctree [J Mol Biol 348:85–100, 2005]), disulfide bonding state (Disulfind [Nucleic Acids Res 34:W177–W181, 2006]), and metal bonding state (MetalDetector [Bioinformatics 24:2094–2095, 2008]), in a way that takes into account the respective strengths and weaknesses, and does not require any change to the predictors themselves. We also compare our methodology against two alternative refinement pipelines based on state-of-the-art sequential prediction methods.

Conclusions

The proposed framework is able to improve the performance of the underlying predictors by removing rule violations. We show that different predictors offer complementary advantages, and our method is able to integrate them using non-trivial constraints, generating more consistent predictions. In addition, our framework is fully general, and could in principle be applied to a vast array of heterogeneous predictions without requiring any change to the underlying software. On the other hand, the alternative strategies are more specific and tend to favor one task at the expense of the others, as shown by our experimental evaluation. The ultimate goal of our framework is to seamlessly integrate full prediction suites, such as Distill [BMC Bioinformatics 7:402, 2006] and PredictProtein [Nucleic Acids Res 32:W321–W326, 2004].

Predicting protein contact map using evolutionary and physical constraints by integer programming

Motivation: Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains challenging to predict contact map using only sequence information. Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole-contact map. A couple of recent methods predict contact map by using mutual information, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods demand for a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically infeasible.

Results: This article presents a novel method PhyCMAP for contact map prediction, integrating both evolutionary and physical restraints by machine learning and integer linear programming. The evolutionary restraints are much more informative than mutual information, and the physical restraints specify more concrete relationship among contacts than the sparsity restraint. As such, our method greatly reduces the solution space of the contact map matrix and, thus, significantly improves prediction accuracy. Experimental results confirm that PhyCMAP outperforms currently popular methods no matter how many sequence homologs are available for the protein under consideration.

Availability:http://raptorx.uchicago.edu.

Contact:jinboxu@gmail.com

C19orf12 mutation leads to a pallido-pyramidal syndrome

Pallido-pyramidal syndromes combine dystonia with or without parkinsonism and spasticity as part of a mixed neurodegenerative disorder. Several causative genes have been shown to lead to pallido-pyramidal syndromes, including FBXO7, ATP13A2, PLA2G6, PRKN and SPG11. Among these, ATP13A2 and PLA2G6 are inconsistently associated with brain iron deposition. Using homozygosity mapping and direct sequencing in a multiplex consanguineous Saudi Arabian family with a pallido-pyramidal syndrome, iron deposition and cerebellar atrophy, we identified a homozygous p.G53R mutation in C19orf12. Our findings add to the phenotypic spectrum associated with C19orf12 mutations.

PSI: a comprehensive and integrative approach for accurate plant subcellular localization prediction

Predicting the subcellular localization of proteins conquers the major drawbacks of high-throughput localization experiments that are costly and time-consuming. However, current subcellular localization predictors are limited in scope and accuracy. In particular, most predictors perform well on certain locations or with certain data sets while poorly on others. Here, we present PSI, a novel high accuracy web server for plant subcellular localization prediction. PSI derives the wisdom of multiple specialized predictors via a joint-approach of group decision making strategy and machine learning methods to give an integrated best result. The overall accuracy obtained (up to 93.4%) was higher than best individual (CELLO) by ∼10.7%. The precision of each predicable subcellular location (more than 80%) far exceeds that of the individual predictors. It can also deal with multi-localization proteins. PSI is expected to be a powerful tool in protein location engineering as well as in plant sciences, while the strategy employed could be applied to other integrative problems. A user-friendly web server, PSI, has been developed for free access at http://bis.zju.edu.cn/psi/.

SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks

Abstract

The prediction of protein subcellular localization is a important step towards the prediction of protein function, and considerable effort has gone over the last decade into the development of computational predictors of protein localization. In this article we design a new predictor of protein subcellular localization, based on a Machine Learning model (N-to-1 Neural Networks) which we have recently developed. This system, in three versions specialised, respectively, on Plants, Fungi and Animals, has a rich output which incorporates the class “organelle” alongside cytoplasm, nucleus, mitochondria and extracellular, and, additionally, chloroplast in the case of Plants. We investigate the information gain of introducing additional inputs, including predicted secondary structure, and localization information from homologous sequences. To accommodate the latter we design a new algorithm which we present here for the first time. While we do not observe any improvement when including predicted secondary structure, we measure significant overall gains when adding homology information. The final predictor including homology information correctly predicts 74%, 79% and 60% of all proteins in the case of Fungi, Animals and Plants, respectively, and outperforms our previous, state-of-the-art predictor SCLpred, and the popular predictor BaCelLo. We also observe that the contribution of homology information becomes dominant over sequence information for sequence identity values exceeding 50% for Animals and Fungi, and 60% for Plants, confirming that subcellular localization is less conserved than structure.

SCLpredT is publicly available at http://distillf.ucd.ie/sclpredt/. Sequence- or template-based predictions can be obtained, and up to 32kbytes of input can be processed in a single submission.

Predicting binding within disordered protein regions to structurally characterised peptide-binding domains

Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif) containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58).Next, we trained a bidirectional recurrent neural network (BRNN) using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72) showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods) clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.

Accurate prediction of protein enzymatic class by N-to-1 Neural Networks

We present a novel ab initio predictor of protein enzymatic class. The predictor can classify proteins, solely based on their sequences, into one of six classes extracted from the enzyme commission (EC) classification scheme and is trained on a large, curated database of over 6,000 non-redundant proteins which we have assembled in this work. The predictor is powered by an ensemble of N-to-1 Neural Network, a novel architecture which we have recently developed. N-to-1 Neural Networks operate on the full sequence and not on predefined features. All motifs of a predefined length (31 residues in this work) are considered and are compressed by an N-to-1 Neural Network into a feature vector which is automatically determined during training. We test our predictor in 10-fold cross-validation and obtain state of the art results, with a 96% correct classification and 86% generalized correlation. All six classes are predicted with a specificity of at least 80% and false positive rates never exceeding 7%. We are currently investigating enhanced input encoding schemes which include structural information, and are analyzing trained networks to mine motifs that are most informative for the prediction, hence, likely, functionally relevant.

The MULTICOM toolbox for protein structure prediction

Background

As genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques. The gap between protein sequence and structure continues to enlarge rapidly as the throughput of genome sequencing techniques is much higher than that of protein structure determination techniques. Computational software tools for predicting protein structure and structural features from protein sequences are crucial to make use of this vast repository of protein resources.

Results

To meet the need, we have developed a comprehensive MULTICOM toolbox consisting of a set of protein structure and structural feature prediction tools. These tools include secondary structure prediction, solvent accessibility prediction, disorder region prediction, domain boundary prediction, contact map prediction, disulfide bond prediction, beta-sheet topology prediction, fold recognition, multiple template combination and alignment, template-based tertiary structure modeling, protein model quality assessment, and mutation stability prediction.

Conclusions

These tools have been rigorously tested by many users in the last several years and/or during the last three rounds of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7-9) from 2006 to 2010, achieving state-of-the-art or near performance. In order to facilitate bioinformatics research and technological development in the field, we have made the MULTICOM toolbox freely available as web services and/or software packages for academic use and scientific research. It is available at http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Molecular variation and recombination in RNA segment 10 of rice black-streaked dwarf virus isolated from China during 2007-2010

Rice black-streaked dwarf virus (RBSDV) is the causal agent of rice black-streaked dwarf and maize rough dwarf diseases in China. The only open reading frame encoding the viral outer capsid protein on S10 RNA of 21 RBSDV isolates was sequenced for phylogenetic and recombination analysis. Four natural recombinants were detected, and the recombinant breakpoints were identified. In addition, the distribution of synonymous and non-synonymous nucleotide changes revealed that the virus had been evolving under purifying selection.

Computational prediction of secondary and supersecondary structures

The sequence-based prediction of the secondary and supersecondary structures enjoys strong interest and finds applications in numerous areas related to the characterization and prediction of protein structure and function. Substantial efforts in these areas over the last three decades resulted in the development of accurate predictors, which take advantage of modern machine learning models and availability of evolutionary information extracted from multiple sequence alignment. In this chapter, we first introduce and motivate both prediction areas and introduce basic concepts related to the annotation and prediction of the secondary and supersecondary structures, focusing on the β hairpin, coiled coil, and α-turn-α motifs. Next, we overview state-of-the-art prediction methods, and we provide details for 12 modern secondary structure predictors and 4 representative supersecondary structure predictors. Finally, we provide several practical notes for the users of these prediction tools.

Prediction of short linear protein binding regions

Short linear motifs in proteins (typically 3-12 residues in length) play key roles in protein-protein interactions by frequently binding specifically to peptide binding domains within interacting proteins. Their tendency to be found in disordered segments of proteins has meant that they have often been overlooked. Here we present SLiMPred (short linear motif predictor), the first general de novo method designed to computationally predict such regions in protein primary sequences independent of experimentally defined homologs and interactors. The method applies machine learning techniques to predict new motifs based on annotated instances from the Eukaryotic Linear Motif database, as well as structural, biophysical, and biochemical features derived from the protein primary sequence. We have integrated these data sources and benchmarked the predictive accuracy of the method, and found that it performs equivalently to a predictor of protein binding regions in disordered regions, in addition to having predictive power for other classes of motif sites such as polyproline II helix motifs and short linear motifs lying in ordered regions. It will be useful in predicting peptides involved in potential protein associations and will aid in the functional characterization of proteins, especially of proteins lacking experimental information on structures and interactions. We conclude that, despite the diversity of motif sequences and structures, SLiMPred is a valuable tool for prioritizing potential interaction motifs in proteins.

Protein structural modularity and robustness are associated with evolvability

Theory suggests that biological modularity and robustness allow for maintenance of fitness under mutational change, and when this change is adaptive, for evolvability. Empirical demonstrations that these traits promote evolvability in nature remain scant however. This is in part because modularity, robustness, and evolvability are difficult to define and measure in real biological systems. Here, we address whether structural modularity and/or robustness confer evolvability at the level of proteins by looking for associations between indices of protein structural modularity, structural robustness, and evolvability. We propose a novel index for protein structural modularity: the number of regular secondary structure elements (helices and strands) divided by the number of residues in the structure. We index protein evolvability as the proportion of sites with evidence of being under positive selection multiplied by the average rate of adaptive evolution at these sites, and we measure this as an average over a phylogeny of 25 mammalian species. We use contact density as an index of protein designability, and thus, structural robustness. We find that protein evolvability is positively associated with structural modularity as well as structural robustness and that the effect of structural modularity on evolvability is independent of the structural robustness index. We interpret these associations to be the result of reduced constraints on amino acid substitutions in highly modular and robust protein structures, which results in faster adaptation through natural selection.