The emergence of Variants of Concern in infectious diseases, particularly in the context of viruses like SARS-CoV-2, has highlighted the critical importance of continuous prediction and monitoring, showcasing the pivotal role of computational biology in addressing the challenges posed by these emerging infectious diseases. This study advocates for implementing a computational approach able to predict the next SARS-CoV-2 variant of concern (VOC). To that end, inspired by natural selection principles, we used the Genetic Algorithm (GA) as it offers a potent framework for optimizing complex problems. We initiated our investigation with the Wuhan spike protein sequence since it is critical target for variant surveillance and used as reference input. Subsequently, we systematically introduced specific mutations into this sequence to make the initial population. Computational modeling generated three-dimensional structures of the mutated spike within the SARS-CoV-2 ACE2 to evaluate the best candidate of each generation. These were later evaluated by predicting their Gibbs free energy (ΔG values) to evaluate the stability and interactions of these mutants, providing insights into their potential effects on viral behavior and the emergence of VOC. Our analysis demonstrates that the ΔG of our predicted variant closely compares to the delta variant, indicating a similar thermodynamic profile in their interactions. Moreover, our finding indicates that the transmission potential of the new variant is nearly on par with that of the delta variants. Additional factors will be taken into account to evaluate the overall importance of our predicted variant, and we will undertake further research and analysis to comprehend its real-world consequences and potential advantages or drawbacks.

The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.

MOTIVATION: In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway. However, an important number of proteins lacking a signal peptide can be secreted through unconventional routes, including that mediated by exosomes. Currently, no method is available to predict protein secretion via exosomes. RESULTS: Here, we first assembled a dataset including the sequences of 2992 proteins secreted by exosomes and 2961 proteins that are not secreted by exosomes. Subsequently, we trained different random forests models on feature vectors derived from the sequences in this dataset. In tenfold cross-validation, the best model was trained on dipeptide composition, reaching an accuracy of 69.88% ± 2.08 and an area under the curve (AUC) of 0.76 ± 0.03. In an independent dataset, this model reached an accuracy of 75.73% and an AUC of 0.840. After these results, we developed ExoPred, a web-based tool that uses random forests to predict protein secretion by exosomes. CONCLUSION: ExoPred is available for free public use at http://imath.med.ucm.es/exopred/ . Datasets are available at http://imath.med.ucm.es/exopred/datasets/ .

Abstract Background We previously introduced PCPS (Proteasome Cleavage Prediction Server), a web-based tool to predict proteasome cleavage sites using n-grams. Here, we evaluated the ability of PCPS immunoproteasome cleavage model to discriminate CD8+ T cell epitopes. Results We first assembled an epitope dataset consisting of 844 unique virus-specific CD8+ T cell epitopes and their source proteins. We then analyzed cleavage predictions by PCPS immunoproteasome cleavage model on this dataset and compared them with those provided by a related method implemented by NetChop web server. PCPS was clearly superior to NetChop in term of sensitivity (0.89 vs. 0.79) but somewhat inferior with regard to specificity (0.55 vs. 0.60). Judging by the Mathew’s Correlation Coefficient, PCPS predictions were overall superior to those provided by NetChop (0.46 vs. 0.39). We next analyzed the power of C-terminal cleavage predictions provided by the same PCPS model to discriminate CD8+ T cell epitopes, finding that they could be discriminated from random peptides with an accuracy of 0.74. Following these results, we tuned the PCPS web server to predict CD8+ T cell epitopes and predicted the entire SARS-CoV-2 epitope space. Conclusions We report an improved version of PCPS named iPCPS for predicting proteasome cleavage sites and peptides with CD8+ T cell epitope features. iPCPS is available for free public use at https://imed.med.ucm.es/Tools/pcps/ .

The 3rd edition of the computational methods for the immune system function workshop has been held in San Diego, CA, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) from November 18 to 21, 2019. The workshop has continued its growing tendency, with a total of 18 accepted papers that have been presented in a full day workshop. Among these, the best 10 papers have been selected and extended for presentation in this special issue. The covered topics range from computer-aided identification of T cell epitopes to the prediction of heart rate variability to prevent brain injuries, from In Silico modeling of Tuberculosis and generation of digital patients to machine learning applied to predict type-2 diabetes risk.

After publication of the original article [1], we were notified that legends of Fig. 1 and Fig. 2 have been swapped.

The proteasome complex is the main responsible of proteolytic degradation of cytosolic proteins, generating the C-terminus of MHC I-restricted peptide ligands and CD8 T cell epitopes. Prediction of proteasomal cleavage sites is thus relevant for predicting CD8 T cell epitopes. We previously reported an n-gram based method to predict proteasomal cleavage sites named PCPS, which is implemented for free public use online at http://imed.med.ucm.es/pcps/. Here, we used a set of 59 Hepatitis C Virus (HCV) CD8 T-cell epitopes to analyze cleavage predictions by PCPS and compared them with those provided by a related method implemented in NetChop web server. PCPS clearly outperformed NetChop in sensitivity (0.88 and 0.78, respectively); it identified much better than NetChop that the C-terminus of tested CD8 T cell epitopes likely resulted from proteasomal cleavage. However, PCPS identified additional preferential cleavage sites within the tested epitopes more often than NetChop, which resulted in lower specificity (0.57 vs 0.66, respectively). Proteasomal cleavage site predictions are used to enhance CD8 T cell epitopes by discarding peptides resulting from peptide-MHC I binding models that do not have a C-terminus compatible with proteasome production. Thereby, we have now implemented proteasomal cleavage site predictions provided by PCPS n-grams in RANKPEP, a tool to identify peptides presented by MHC molecules, allowing to filter out peptides predicted to bind to MHC I molecules that are unlikely to result from proteasomal cleavage. RANKPEP is available for free public use online at http://imed.med.ucm.es/Tools/rankpep_new.html.

The 2nd Computational Methods for the Immune System function Workshop has been held in Madrid in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, from December 3 to 6, 2018. The workshop has been obtained 100% more submissions in respect to the first edition, highlighting a growing interest for the treated topics. The best papers (9) have been selected for extension in this special issue, with themes about immune system and disease simulation, computer-aided design of novel candidate vaccines, methods for the analysis of immune system involved diseases based on statistical methods, meta-heuristics and game theory, and modelling strategies for improving the simulation of the immune system dynamics.

BACKGROUND: Human Cytomegalovirus (HCMV) is a ubiquitous herpesvirus affecting approximately 90% of the world population. HCMV causes disease in immunologically naive and immunosuppressed patients. The prevention, diagnosis and therapy of HCMV infection are thus crucial to public health. The availability of effective prophylactic and therapeutic treatments remain a significant challenge and no vaccine is currently available. Here, we sought to define an epitope-based vaccine against HCMV, eliciting B and T cell responses, from experimentally defined HCMV-specific epitopes. RESULTS: We selected 398 and 790 experimentally validated HCMV-specific B and T cell epitopes, respectively, from available epitope resources and apply a knowledge-based approach in combination with immunoinformatic predictions to ensemble a universal vaccine against HCMV. The T cell component consists of 6 CD8 and 6 CD4 T cell epitopes that are conserved among HCMV strains. All CD8 T cell epitopes were reported to induce cytotoxic activity, are derived from early expressed genes and are predicted to provide population protection coverage over 97%. The CD4 T cell epitopes are derived from HCMV structural proteins and provide a population protection coverage over 92%. The B cell component consists of just 3 B cell epitopes from the ectodomain of glycoproteins L and H that are highly flexible and exposed to the solvent. CONCLUSIONS: We have defined a multiantigenic epitope vaccine ensemble against the HCMV that should elicit T and B cell responses in the entire population. Importantly, although we arrived to this epitope ensemble with the help of computational predictions, the actual epitopes are not predicted but are known to be immunogenic.

Vaccines represent nowadays one of the most efficient weapons against foreign pathogens. To be effective, vaccines need a proper administration strategy that requires multiple administrations in order to ensure the acquisition of immunological memory. Vaccination schedules are usually based on past experience, and economical, ethical and time constraints have limited the research for better combinations of timing and dosage. We present here a computational approach based on the use of stochastic optimization techniques and validated “in silico” models to study and optimize vaccination protocols. We use Simulated Annealing in conjunction with a validated agent based model to suggest some interventions that may improve the efficacy of a candidate vaccine composed by influenza-A virosome and natural citrus-derived adjuvants to prevent influenza-A infection.

Immune system processes can be simulated using both system dynamics (SD) models based on differential equations and Agent-based (AB) models. The two approaches are intrinsically different but some methodologies have been developed to convert SD models into AB models with a variable degree of success. However, until now none of such methods have considered the use of scale factors in SD to AB model conversion. In this work, we revisited a well know SD model describing the interaction between effector T cells and tumor cells that was previously shown unsuitable for AB modeling. We introduced non-dimensional scaling factors in AB modeling and compared AB and AD simulations through a sensitivity analysis. Under this scenario, we obtained AB models that could successfully reproduce SD simulations with a reasonable number of agents and a stochastic behavior that did not compromise computer resources. In general, our results justify the introduction of non-dimensional scaling factors to reproduce SD simulations with AB models.

MOTIVATION: Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes. RESULTS: To exemplify our approach we designed two epitope ensemble vaccines comprising highly conserved and experimentally verified immunogenic influenza A epitopes as putative non-seasonal influenza vaccines; one specifically targets the US population and the other is a universal vaccine. The USA-specific vaccine comprised 6 CD8+ T cell epitopes (GILGFVFTL, FMYSDFHFI, GMDPRMCSL, SVKEKDMTK, FYIQMCTEL, DTVNRTHQY) and 3 CD4+ epitopes (KGILGFVFTLTVPSE, EYIMKGVYINTALLN, ILGFVFTLTVPSERG). The universal vaccine comprised 8 CD8+ epitopes: (FMYSDFHFI, GILGFVFTL, ILRGSVAHK, FYIQMCTEL, ILKGKFQTA, YYLEKANKI, VSDGGPNLY, YSHGTGTGY) and the same 3 CD4+ epitopes. Our USA-specific vaccine has a population protection coverage (portion of the population potentially responsive to one or more component epitopes of the vaccine, PPC) of over 96 and 95% coverage of observed influenza subtypes. The universal vaccine has a PPC value of over 97 and 88% coverage of observed subtypes. AVAILABILITY AND IMPLEMENTATION: http://imed.med.ucm.es/Tools/episopt.html CONTACT: [email protected].

BACKGROUND: Proteasomes play a central role in the major histocompatibility class I (MHCI) antigen processing pathway. They conduct the proteolytic degradation of proteins in the cytosol, generating the C-terminus of CD8 T cell epitopes and MHCI-peptide ligands (P1 residue of cleavage site). There are two types of proteasomes, the constitutive form, expressed in most cell types, and the immunoproteasome, which is constitutively expressed in mature dendritic cells. Protective CD8 T cell epitopes are likely generated by the immunoproteasome and the constitutive proteasome, and here we have modeled and analyzed the cleavage by these two proteases. RESULTS: We have modeled the immunoproteasome and proteasome cleavage sites upon two non-overlapping sets of peptides consisting of 553 CD8 T cell epitopes, naturally processed and restricted by human MHCI molecules, and 382 peptides eluted from human MHCI molecules, respectively, using N-grams. Cleavage models were generated considering different epitope and MHCI-eluted fragment lengths and the same number of C-terminal flanking residues. Models were evaluated in 5-fold cross-validation. Judging by the Mathew's Correlation Coefficient (MCC), optimal cleavage models for the proteasome (MCC = 0.43 ± 0.07) and the immunoproteasome (MCC = 0.36 ± 0.06) were obtained from 12-residue peptide fragments. Using an independent dataset consisting of 137 HIV1-specific CD8 T cell epitopes, the immunoproteasome and proteasome cleavage models achieved MCC values of 0.30 and 0.18, respectively, comparatively better than those achieved by related methods. Using ROC analyses, we have also shown that, combined with MHCI-peptide binding predictions, cleavage predictions by the immunoproteasome and proteasome models significantly increase the discovery rate of CD8 T cell epitopes restricted by different MHCI molecules, including A*0201, A*0301, A*2402, B*0702, B*2705. CONCLUSIONS: We have developed models that are specific to predict cleavage by the proteasome and the immunoproteasome. These models ought to be instrumental to identify protective CD8 T cell epitopes and are readily available for free public use at http://imed.med.ucm.es/Tools/PCPS/.

SUMMARY: EPIMHC is a relational database of MHC-binding peptides and T cell epitopes that are observed in real proteins. Currently, the database contains 4867 distinct peptide sequences from various sources, including 84 tumor-associated antigens. The EPIMHC database is accessible through a web server that has been designed to facilitate research in computational vaccinology. Importantly, peptides resulting from a query can be selected to derive specific motif-matrices. Subsequently, these motif-matrices can be used in combination with a dynamic algorithm for predicting MHC-binding peptides from user-provided protein queries. AVAILABILITY: The EPIMHC database server is hosted by the Dana-Farber Cancer Institute at the site http://immunax.dfci.harvard.edu/bioinformatics/epimhc/