Microevolutionary, macroevolutionary, ecological and taxonomical implications of punctuational theories of adaptive evolution

A novel approach to design chimeric multi epitope vaccine against Leishmania exploiting infected host cell proteome

Leishmaniasis is a major infectious disease having high mortality which could be attributed to lack of a suitable vaccine candidate. We propose a novel approach to design multiepitope vaccine to leishmaniasis exploiting specific membrane proteome from infected macrophage from host. The MHC-I, MHC-II and BC epitopes predicted for unique proteins from the infected macrophages and Leishmania and a MEV designed in various combinations (1a-1m). The epitope arrangements 1a, 1k, 1l, and 1 m showed a strong antigenicity profile and immune response. The molecular dynamics simulation indicate the 1k, 1l, and 1 m constructs have strong affinity toward TLR-2, TLR-3, and TLR-4. Overall the structural and immunogenicity profile suggests 1k is top candidate. Further, a computational model system with TLR-2, TLR-3, TLR-4, BCR, MHC-I and MHC-II was generated for 1k construct to understand the MEV interactions with immune components. Dihedral distribution and distance was enumerated to understand the movement of immune components towards 1k. The results indicate 1k has strong affinity for the immune response molecules especially TLR-3, BCR and MHC-II are coming in close contact with the MEV through the simulation. The study suggests that designed multi-epitope vaccine 1k has potential to induce proper immune response but warrants further studies.

Experimental trials of predicted CD4+ and CD8+ T-cell epitopes of respiratory syncytial virus

Background

Respiratory syncytial virus (RSV) is the most common cause of viral lower respiratory tract infections (LRTIs) in young children around the world and an important cause of LRTI in the elderly. The available treatments and FDA-approved vaccines for RSV only lessen the severity of the infection and are recommended for infants and elderly people.

Methods

We focused on developing a broad-spectrum vaccine that activates the immune system to directly combat RSV. The objective of this study is to identify CD4+ and CD8+ T-cell epitopes using an immunoinformatics approach to develop RSV vaccines. The efficacy of these peptides was validated through in-vitro and in-vivo studies involving healthy and diseased animal models.

Results

For each major histocompatibility complex (MHC) class-I and II, we found three epitopes of RSV proteins including F, G, and SH with an antigenic score of >0.5 and a projected SVM score of <5. Experimental validation of these peptides on female BALB/c mice was conducted before and after infection with the RSV A2 line 19f. We found that the 3RVMHCI (CD8+) epitope of the F protein showed significant results of white blood cells (19.72 × 103 cells/μl), neutrophils (6.01 × 103 cells/μl), lymphocytes (12.98 × 103 cells/μl), IgG antibodies (36.9 µg/ml), IFN-γ (86.96 ng/L), and granzyme B (691.35 pg/ml) compared to control at the second booster dose of 10 µg. Similarly, 4RVMHCII (CD4+) of the F protein substantially induced white blood cells (27.08 × 103 cells/μl), neutrophils (6.58 × 103 cells/μl), lymphocytes (16.64 × 103 cells/μl), IgG antibodies (46.13 µg/ml), IFN-γ (96.45 ng/L), and granzyme B (675.09 pg/ml). In-vitro studies showed that 4RVMHCII produced a significant level of antibodies in sera on day 45 comparable to mice infected with the virus. 4RVMHCII also induced high IFN-γ and IL-2 secretions on the fourth day of the challenge compared to the preinfectional stage.

Conclusion

In conclusion, epitopes of the F protein showed considerable immune response and are suitable for further validation.

Self-replicating artificial neural networks give rise to universal evolutionary dynamics

In evolutionary models, mutations are exogenously introduced by the modeler, rather than endogenously introduced by the replicator itself. We present a new deep-learning based computational model, the self-replicating artificial neural network (SeRANN). We train it to (i) copy its own genotype, like a biological organism, which introduces endogenous spontaneous mutations; and (ii) simultaneously perform a classification task that determines its fertility. Evolving 1,000 SeRANNs for 6,000 generations, we observed various evolutionary phenomena such as adaptation, clonal interference, epistasis, and evolution of both the mutation rate and the distribution of fitness effects of new mutations. Our results demonstrate that universal evolutionary phenomena can naturally emerge in a self-replicator model when both selection and mutation are implicit and endogenous. We therefore suggest that SeRANN can be applied to explore and test various evolutionary dynamics and hypotheses.

Reverse vaccinology and immunoinformatics approach to design a chimeric epitope vaccine against Orientia tsutsugamushi

Scrub typhus is a vector-borne infectious disease caused by Orientia tsutsugamushi and it is reportedly associated with up to 20 % of hospitalized cases of febrile illnesses. The major challenge of vaccine development is the lack of identified antigens that can induce both heterotypic and homotypic immunity including the production of antibodies, cytotoxic T lymphocyte, and helper T lymphocytes. We employed a comprehensive immunoinformatic prediction algorithm to identify immunogenic epitopes of the 56-kDa type-specific cell membrane surface antigen and surface cell antigen A of O. tsutsugamushi to select potential candidates for developing vaccines and diagnostic assays. We identified 35 linear and 29 continuous immunogenic B-cell epitopes and 51 and 27 strong-binding T-cell epitopes of major histocompatibility complex class I and class II molecules, respectively, in the conserved and variable regions of the 56-kDa type-specific surface antigen. The predicted B- and T-cell epitopes were used to develop immunogenic multi-epitope candidate vaccines and showed to elicit a broad-range of immune protection. A stable interactions between the multi-epitope vaccines and the host fibronectin protein were observed using docking and simulation methods. Molecular dynamics simulation studies demonstrated that the multi-epitope vaccine constructs and fibronectin docked models were stable during simulation time. Furthermore, the multi-epitope vaccine exhibited properties such as antigenicity, non-allergenicity and ability to induce interferon gamma production and had strong associations with their respective human leukocyte antigen alleles of world-wide population coverage. A correlation of immune simulations and the in-silico predicted immunogenic potential of multi-epitope vaccines implicate for further investigations to accelerate designing of epitope-based vaccine candidates and chimeric antigens for development of serological diagnostic assays for scrub typhus.

RecA-NT homology motif in ImuB is essential for mycobacterial ImuA'-ImuB protein interaction and mutasome function

The mycobacterial mutasome - minimally comprising ImuA', ImuB, and DnaE2 proteins - has been implicated in DNA damage-induced mutagenesis in Mycobacterium tuberculosis. ImuB, predicted to enable mutasome function via its interaction with the β clamp, is a catalytically inactive member of the Y-family of DNA polymerases. Like other members of the Y family, ImuB features a recently identified amino acid motif with homology to the RecA-N-terminus (RecA-NT). In RecA, the motif mediates oligomerization of RecA monomers into RecA filaments. Given the role of ImuB in the mycobacterial mutasome, we hypothesized that the ImuB RecA-NT motif might mediate its interaction with ImuA', a RecA homolog of unknown function. To investigate this possibility, we constructed a panel of imuB alleles in which RecA-NT was removed, or mutated. Results from microbiological and biochemical assays indicate that RecA-NT is critical for the interaction of ImuB with ImuA'. A region downstream of RecA-NT (ImuB-C) also appears to stabilize the ImuB-ImuA' interaction, but its removal does not prevent complex formation. In contrast, replacing two key hydrophobic residues of RecA-NT, L378 and V383, is sufficient to disrupt ImuA'-ImuB interaction. To our knowledge, this constitutes the first experimental evidence showing the role of the RecA-NT motif in mediating the interaction between a Y-family member and a RecA homolog.

Computational Modeling and Evaluation of Potential mRNA and Peptide-Based Vaccine against Marburg Virus (MARV) to Provide Immune Protection against Hemorrhagic Fever

A hemorrhagic fever caused by the Marburg virus (MARV) belongs to the Filoviridae family and has been classified as a risk group 4 pathogen. To this day, there are no approved effective vaccinations or medications available to prevent or treat MARV infections. Reverse vaccinology-based approach was formulated to prioritize B and T cell epitopes utilizing a numerous immunoinformatics tools. Potential epitopes were systematically screened based on various parameters needed for an ideal vaccine such as allergenicity, solubility, and toxicity. The most suitable epitopes capable of inducing immune response were shortlisted. Epitopes with population coverage of 100% and fulfilling set parameters were selected for docking with human leukocyte antigen molecules, and binding affinity of each peptide was analyzed. Finally, 4 CTL and HTL each while 6 B cell 16-mers were used for designing multiepitope subunit (MSV) and mRNA vaccine joined via suitable linkers. Immune simulations were used to validate the constructed vaccine's capacity to induce a robust immune response whereas molecular dynamics simulations were used to confirm epitope-HLA complex stability. Based on these parameter's studies, both the vaccines constructed in this study offer a promising choice against MARV but require further experimental verification. This study provides a rationale point to begin with the development of an efficient vaccine against Marburg virus; however, the findings need further experimental validation to confirm the computational finding of this study.

Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications

Tuberculosis is an airborne infectious disease caused by Mycobacterium tuberculosis. BCG is the only approved vaccine. However, it has limited global efficacy. Pathogens could affect the transcription of host genes, especially the ones related to the immune system, by inducing epigenetic modifications. Many proteins of M. tuberculosis were found to affect the host's epigenome. Nine proteins were exploited in this study to predict epitopes to develop an mRNA vaccine against tuberculosis. Many immunoinformatics tools were employed to construct this vaccine to elicit cellular and humoral immunity. We performed molecular docking between selected epitopes and their corresponding MHC alleles. Thirty epitopes, an adjuvant TLR4 agonist RpfE, constructs for subcellular trafficking, secretion booster, and specific linkers were combined to develop the vaccine. This proposed construct was tested to cover 99.38% of the population. Moreover, it was tested to be effective and safe. An in silico immune simulation of the vaccine was also performed to validate our hypothesis. It also underwent codon optimization to ensure mRNA's efficient translation once it reaches the cytosol of a human host. Furthermore, secondary and tertiary structures of the vaccine peptide were predicted and docked against TLR-4 and TLR-3.Molecular dynamics simulation was performed to validate the stability of the binding complex. It was found that this proposed construction can be a promising vaccine against tuberculosis. Hence, our proposed construct is ready for wet-lab experiments to approve its efficacy.

Exploring Klebsiella pneumoniae capsule polysaccharide proteins to design multiepitope subunit vaccine to fight against pneumonia

BACKGROUND

Klebsiella pneumoniae is an emerging human pathogen causing neonatal lung disease, catheter-associated infections, and nosocomial outbreaks with high fatality rates. Capsular polysaccharide (CPS) protein plays a major determinant in virulence and is considered as a promising target for vaccine development.

RESEARCH DESIGN AND METHODS

In this study, we used immunoinformatic approaches to design a multi-peptide vaccine against K. pneumonia. The epitopes were selected through several immune filters, such as antigenicity, conservancy, nontoxicity, non-allergenicity, binding affinity to HLA alleles, overlapping epitopes, and peptides having common epitopes.

RESULTS

Finally, a construct comprising 2 B-Cell, 8 CTL, 2 HTL epitopes, along with adjuvant, linkers was designed. Peptide-HLA interaction analysis showed strong binding of these epitopes with several common HLA molecules. The in silico immune simulation and population coverage analysis of the vaccine showed its potential to evoke strong immune responses.. Further, the interaction between vaccine and immune was evaluated by docking and simulation, revealing high affinity and complex stability. Codon adaptation and in silico cloning revealed higher expression of vaccine in E. coli K12 expression system.

CONCLUSIONS

Conclusively, the findings of the present study suggest that the designed novel multi-epitopic vaccine holds potential for further experimental validation against the pathogen.

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Despite the wide perception that microbes have rapid growth rates, many environments like seawater and soil are often dominated by microorganisms that can only grow very slowly. Our knowledge about growth is necessarily biased toward easily culturable organisms, which tend to be those that grow fast, because microbial growth rates have traditionally been measured using laboratory growth experiments. However, how are potential growth rates distributed in nature? Using genomic data, we predicted the growth rates of over 200,000 organisms, including many as yet uncultivated species. These data reveal how current culture collections are strongly biased toward fast-growing organisms. Finally, we noticed a bimodal distribution of maximal growth rates, suggesting a natural division of microbial growth strategies into two classes.

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.

In Silico Design and Immunological Studies of Two Novel Multiepitope DNA-Based Vaccine Candidates Against High-Risk Human Papillomaviruses

Human papillomaviruses (HPV)-16 and 18 are the most prevalent types associated with cervical cancer. HPV L1 and L2 capsid proteins and E7 oncoprotein play crucial roles in HPV-related diseases. Hence, these proteins were proposed as target antigens for preventive and therapeutic vaccines. In this study, two multiepitope DNA-based HPV vaccine candidates were designed using in silico analysis including the immunogenic and conserved epitopes of HPV16/18 L1, L2 and E7 proteins (the L1-L2-E7 fusion DNA), and of heat shock protein 70 (HSP70) linked to the L1-L2-E7 DNA construct (the HSP70-L1-L2-E7 fusion DNA). Next, the expression of the L1-L2-E7 and HSP70-L1-L2-E7 multiepitope DNA constructs was evaluated in a mammalian cell line. Finally, immunological responses and antitumor effects of the DNA constructs were investigated in C57BL/6 mice. Our data indicated high expression rates of the designed multiepitope L1-L2-E7 DNA (~ 56.16%) and HSP70-L1-L2-E7 DNA (~ 80.45%) constructs in vitro. The linkage of HSP70 epitopes to the L1-L2-E7 DNA construct significantly increased the gene expression. Moreover, the HSP70-L1-L2-E7 DNA construct could significantly increase immune responses toward Th1 response and CTL activity, and induce stronger antitumor effects in mouse model. Thus, the designed HSP70-L1-L2-E7 DNA construct represents promising results for development of HPV DNA vaccine candidates.

Screening and characterization of hypothetical proteins of Plasmodium falciparum as novel vaccine candidates in the fight against malaria using reverse vaccinology

BACKGROUND

Plasmodium falciparum is the most deadly and leading cause of morbidity and mortality in Africa. About 90% of all malaria deaths in the world today occur in Sub-Saharan Africa especially in children aged < 5 years. In 2018, it was reported that there were 228 million malaria cases that resulted in 405,000 deaths from 91 countries. Currently, a fully effective and long-lasting preventive malaria vaccine is still elusive therefore more effort is needed to identify better effective vaccine candidates. The aim of this study was to identify and characterize hypothetical proteins as vaccine candidates derived from Plasmodium falciparum 3D7 genome by reverse vaccinology.

RESULTS

Of the 23 selected hypothetical proteins, 5 were predicted on the extracellular localization by WoLFPSORTv.2.0 program and all the 5 had less than 2 transmembrane regions that were predicted by TMHMMv2.0 and HMMTOP programs at default settings. Two out of the five proteins lacked secretory signal peptides as predicted by SignalP program. Among the 5 extracellular proteins, 3 were predicted to be antigenic by VaxiJen (score ≥ 0.5) and had negative GRAVY values ranging from - 1.156 to - 0.440. B cell epitope prediction by ABCpred and BCpred programs revealed a total of 15 antigenic epitopes. A total of 13 cytotoxic T cells were predicted from the 3 proteins using CTLPred online server. Only 2 out of the 13 CTL were antigenic, immunogenic, non-allergenic, and non-toxic using VaxiJen, IEDB, AllergenFp, and Toxinpred servers respectively in that order. Five HTL peptides from XP_001351030.1 protein are predicted inducers of all the three cytokines. STRING protein-protein network analysis of HPs revealed XP_001350955.1 closely interacts with nucleoside diphosphate kinase (PF13-0349) at 0.704, XP_001351030.1 interacts with male development protein1 (Mdv-1) at 0.645, and XP_001351047.1 with an uncharacterized protein (MAL8P1.53) at 0.400.

CONCLUSION

Reverse vaccinology is a promising strategy for the screening and identification of antigenic antigens with potential capacity to elicit cellular and humoral immune responses against P. falciparum infection. In this study, potential vaccine candidates of Plasmodium falciparum were identified and screened using standard bioinformatics tools. The vaccine candidates contained antigenic and immunogenic epitopes which could be considered for novel and effective vaccine targets. However, we strongly recommend in vivo and in vitro experiments to validate their immunogenicity and protective efficacy to completely decipher the vaccine targets against malaria.

Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches

Cervical cancer, caused by human papillomavirus (HPV), is the fourth most common type of cancer among women worldwide. While HPV prophylactic vaccines are available, they have no therapeutic effects and do not clear up existing infections. This study aims to design a therapeutic vaccine against cervical cancer using reverse vaccinology. In this study, the E6 and E7 oncoproteins from HPV16 were chosen as the target antigens for epitope prediction. Cytotoxic T lymphocytes (CTL) and helper T lymphocytes (HTL) epitopes were predicted, and the best epitopes were selected based on antigenicity, allergenicity, and toxicity. The final vaccine construct was composed of the selected epitopes, along with the appropriate adjuvant and linkers. The multi-epitope vaccine was evaluated in terms of physicochemical properties, antigenicity, and allergenicity. The tertiary structure of the vaccine construct was predicted. Furthermore, several analyses were also carried out, including molecular docking, molecular dynamics (MD) simulation, and in silico cloning of the vaccine construct. The results showed that the final proposed vaccine could be considered an effective therapeutic vaccine for HPV; however, in vitro and in vivo experiments are required to validate the efficacy of this vaccine candidate.

Appendicular skeletal morphology of North American Martes reflect independent modes of evolution in conjunction with Pleistocene glacial cycles

Pleistocene glacial cycles are thought to have driven ecological niche shifts, including novel niche formation. North American pine martens, Martes americana and M. caurina, are exemplar taxa thought to have diverged molecularly and morphologically during Pleistocene glaciation. Previous research found correlations between Martes limb morphology with biome and climate, suggesting that appendicular evolution may have occurred via adaptation to selective pressures imposed by novel and shifting habitats. Such variation can also be achieved through non-adaptive means such as genetic drift. Here, we evaluate whether regional genetic differences reflect limb morphology differences among populations of M. americana and M. caurina by analyzing evolutionary tempo and mode of six limb elements. Our comparative phylogenetic models indicate that genetic structure predicts limb shape better than size. Marten limb size has low phylogenetic signal, and the best supported model of evolution is punctuational (kappa), with morphological and genetic divergence occurring simultaneously. Disparity through time analysis suggests that the tempo of limb evolution in Martes tracks Pleistocene glacial cycles, such that limb size may be responding to shifting climates rather than population genetic structure. Contrarily, we find that limb shape is strongly tied to genetic relationships, with high phylogenetic signal and a lambda mode of evolution. Overall, this pattern of limb size and shape variation may be the result of geographic isolation during Pleistocene glacial advance, while declines in disparity suggest hybridization during interglacial periods. Future inclusion of extinct populations of Martes, which were more morphologically and ecologically diverse, may further clarify Martes phenotypic evolution.

SWPhylo - A Novel Tool for Phylogenomic Inferences by Comparison of Oligonucleotide Patterns and Integration of Genome-Based and Gene-Based Phylogenetic Trees

Modern phylogenetic studies may benefit from the analysis of complete genome sequences of various microorganisms. Evolutionary inferences based on genome-scale analysis are believed to be more accurate than the gene-based alternative. However, the computational complexity of current phylogenomic procedures, inappropriateness of standard phylogenetic tools to process genome-wide data, and lack of reliable substitution models which correlates with alignment-free phylogenomic approaches deter microbiologists from using these opportunities. For example, the super-matrix and super-tree approaches of phylogenomics use multiple integrated genomic loci or individual gene-based trees to infer an overall consensus tree. However, these approaches potentially multiply errors of gene annotation and sequence alignment not mentioning the computational complexity and laboriousness of the methods. In this article, we demonstrate that the annotation- and alignment-free comparison of genome-wide tetranucleotide frequencies, termed oligonucleotide usage patterns (OUPs), allowed a fast and reliable inference of phylogenetic trees. These were congruent to the corresponding whole genome super-matrix trees in terms of tree topology when compared with other known approaches including 16S ribosomal RNA and GyrA protein sequence comparison, complete genome-based MAUVE, and CVTree methods. A Web-based program to perform the alignment-free OUP-based phylogenomic inferences was implemented at http://swphylo.bi.up.ac.za/. Applicability of the tool was tested on different taxa from subspecies to intergeneric levels. Distinguishing between closely related taxonomic units may be enforced by providing the program with alignments of marker protein sequences, eg, GyrA.

General environmental heterogeneity as the explanation of sexuality? Comparative study shows that ancient asexual taxa are associated with both biotically and abiotically homogeneous environments

Ecological theories of sexual reproduction assume that sexuality is advantageous in certain conditions, for example, in biotically or abiotically more heterogeneous environments. Such theories thus could be tested by comparative studies. However, the published results of these studies are rather unconvincing. Here, we present the results of a new comparative study based exclusively on the ancient asexual clades. The association with biotically or abiotically homogeneous environments in these asexual clades was compared with the same association in their sister, or closely related, sexual clades. Using the conservative definition of ancient asexuals (i.e., age >1 million years), we found eight pairs of taxa of sexual and asexual species, six differing in the heterogeneity of their inhabited environment on the basis of available data. The difference between the environmental type associated with the sexual and asexual species was then compared in an exact binomial test. The results showed that the majority of ancient asexual clades tend to be associated with biotically, abiotically, or both biotically and abiotically more homogeneous environments than their sexual controls. In the exploratory part of the study, we found that the ancient asexuals often have durable resting stages, enabling life in subjectively homogeneous environments, live in the absence of intense biotic interactions, and are very often sedentary, inhabiting benthos, and soil. The consequences of these findings for the ecological theories of sexual reproduction are discussed.

Stability-based sorting: The forgotten process behind (not only) biological evolution

Natural selection is considered to be the main process that drives biological evolution. It requires selected entities to originate dependent upon one another by the means of reproduction or copying, and for the progeny to inherit the qualities of their ancestors. However, natural selection is a manifestation of a more general persistence principle, whose temporal consequences we propose to name "stability-based sorting" (SBS). Sorting based on static stability, i.e., SBS in its strict sense and usual conception, favours characters that increase the persistence of their holders and act on all material and immaterial entities. Sorted entities could originate independently from each other, are not required to propagate and need not exhibit heredity. Natural selection is a specific form of SBS-sorting based on dynamic stability. It requires some form of heredity and is based on competition for the largest difference between the speed of generating its own copies and their expiration. SBS in its strict sense and selection thus have markedly different evolutionary consequences that are stressed in this paper. In contrast to selection, which is opportunistic, SBS is able to accumulate even momentarily detrimental characters that are advantageous for the long-term persistence of sorted entities. However, it lacks the amplification effect based on the preferential propagation of holders of advantageous characters. Thus, it works slower than selection and normally is unable to create complex adaptations. From a long-term perspective, SBS is a decisive force in evolution-especially macroevolution. SBS offers a new explanation for numerous evolutionary phenomena, including broad distribution and persistence of sexuality, altruistic behaviour, horizontal gene transfer, patterns of evolutionary stasis, planetary homeostasis, increasing ecosystem resistance to disturbances, and the universal decline of disparity in the evolution of metazoan lineages. SBS acts on all levels in all biotic and abiotic systems. It could be the only truly universal evolutionary process, and an explanatory framework based on SBS could provide new insight into the evolution of complex abiotic and biotic systems.

Prediction of anti-inflammatory proteins/peptides: an insilico approach

Background

The current therapy for inflammatory and autoimmune disorders involves the use of nonspecific anti-inflammatory drugs and other immunosuppressant, which are often accompanied with potential side effects. As an alternative therapy, anti-inflammatory peptides are recently being exploited as anti-inflammatory agents for treatment of various inflammatory diseases such as Alzheimer’s disease and rheumatoid arthritis. Thus, understanding the correlation between amino acid sequence and its potential anti-inflammatory property is of great importance for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics.

Methods

In this study, we have developed a prediction tool for the classification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. The training was performed using experimentally validated epitopes obtained from Immune epitope database and analysis resource database. Different sequence-based features and their hybrids with motif information were employed for development of support vector machine-based machine learning models. Similarly, machine learning models were also constructed using random forest.

Results

The composition and terminal residue conservation analysis of peptides revealed the dominance of leucine, serine, tyrosine and arginine residues in anti-inflammatory epitopes as compared to non anti-inflammatory epitopes. Similarly, the anti-inflammatory epitopes specific motifs were found to be rich in hydrophobic and polar residues. The hybrid of tripeptide composition-based support vector machine model and motif yielded the best performance on 10-fold cross validation with an accuracy of 78.1% and MCC of 0.58. The same displayed an accuracy of 72% and MCC of 0.45 on validation dataset, rejecting any possibility of over-fitting. The tripeptide composition-based random forest model displayed an accuracy of 0.8 and MCC of 0.59 on 10-fold cross validation, however, the accuracy (0.68) and MCC (0.31) was lower as compared to support vector machine model on validation dataset. Thus, the support vector machine model is implemented as the default model and an additional option of using the random forest model is provided.

Conclusion

The prediction models along with tools for epitope mapping and similarity search have been provided as a web server which is freely accessible at http://metagenomics.iiserb.ac.in/antiinflam/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-1103-6) contains supplementary material, which is available to authorized users.

Stochastic effects in adaptive reconstruction of body damage: implied the creativity of natural selection

After an injury occurs, mechanical/biochemical loads on muscles influence the composition and structure of recovering muscles; this effect likely occurs in other tissues, cells and biological molecules as well owing to the similarity, interassociation and interaction among biochemical reactions and molecules. The 'damage and reconstruction' model provides an explanation for how an ideal cytoarchitecture is created by reducing components not suitable for bearing loads; in this model, adaptive changes are induced by promoting the stochasticity of biochemical reactions. Biochemical and mechanical loads can direct the stochasticity of biochemical reactions, which can in turn induce cellular changes. Thus, mechanical and biochemical loads, under natural selection pressure, modify the direction of cell- and tissue-level changes and guide the formation of new structures and traits, thereby influencing microevolution. In summary, the 'damage and reconstruction' model accounts for the role of natural selection in the formation of new organisms, helps explain punctuated equilibrium, and illustrates how macroevolution arises from microevolution.

A 'rule of 0.5' for the metabolite-likeness of approved pharmaceutical drugs

We exploit the recent availability of a community reconstruction of the human metabolic network ('Recon2') to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) 'nearest' human metabolite. This suggests a 'rule of 0.5' mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This 'rule of 0.5' may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.