Proteochemometrics-Based Prediction of Peptide Binding to HLA-DP Proteins

HLA-DPB1 and DPA1 ~ DPB1 linkage mismatch affects the survival of recipients receiving HLA-14/14 matched unrelated donor HSCT

To analyse the effect of HLA-DPA1 and HLA-DPB1 allelic mismatches on the outcomes of unrelated donor haematopoietic stem cell transplantation (URD-HSCT), we collected 258 recipients with haematological disease who underwent HLA-10/10 matched URD-HSCT. HLA-A, -B, -C, -DRB1, -DQB1, -DRB3/4/5, -DQA1, -DPA1 and -DPB1 typing was performed for the donors and recipients using next-generation sequencing (NGS) technology. After excluding 8 cases with DQA1 or DRB3/4/5 mismatches, we included 250 cases with HLA-14/14 matching for further analysis. Our results showed that the proportion of matched DPA1 and DPB1 alleles was only 10.4% (26/250). The remaining 89.6% of donors and recipients demonstrated DPA1 or DPB1 mismatch. In the DPA1 matched and DPB1 mismatched group, accounting for 18.8% (47/250) of the cohort, DPB1*02:01/DPB1*03:01 allelic mismatches were associated with decreased 2-year OS and increased NRM. DPB1*02:02/DPB1*05:01 and DPB1*02:01/DPB1*05:01 mismatches showed no impact on outcomes. Moreover, the specific allelic mismatches observed were consistent with the DPB1 T-cell epitope (TCE) classification as permissive and non-permissive. We innovatively established an analysis method for DPA1 ~ DPB1 linkage mismatch for cases with both DPA1 and DPB1 mismatched, accounting for 70% (175/250) of the total. DPA1*02:02 ~ DPB1*05:01/DPA1*02:01 ~ DPB1*17:01 linkage mismatches were associated with lower 2-year OS, especially among AML/MDS recipients. DPA1*02:02 ~ DPB1*05:01/DPA1*01:03 ~ DPB1*02:01 linkage mismatches showed no impact on outcomes. In conclusion, applying the DPA1 ~ DPB1 linkage mismatch analysis approach can identify different types of mismatches affecting transplant outcomes and provide valuable insight for selecting optimal donors for AML/MDS and ALL recipients.

A novel vaccine candidate against A. baumannii based on a new OmpW family protein (OmpW2); structural characterization, antigenicity and epitope investigation, and in-vivo analysis

A. baumannii is an MDR pathogen whose SARS-CoV-2 has recently increased its mortality rate in hospitalized patients. So, the virulence factors investigation and design of a vaccine against this bacterium seem to be critical. In this regard, the OmpW2 protein was structurally characterized by this study, and its B-T cell epitopes were mapped by bioinformatic tools. In-vivo analyses were employed to verify the immunogenicity of this protein and its selected epitopes. The results indicated that OmpW2 is a conserved virulent antigen, not toxic for the host, and not similar to the human or mouse proteome. A putative interaction between OmpW2 and a Fe-S-cluster redox enzyme was detected. Based on the results, OmpW2 belongs to the OmpW superfamily and eight beta sheets have been predicted in its tight beta-barrel structure. Several exposed epitopes were detected in the OmpW2 sequence and structure, and a sub-unit potential vaccine was generated based on the epitopes. The ELISA results indicated that after the second booster vaccination of BALB/c mice with the whole OmpW2 protein or its sub-unit fragment, the IgG titer significantly raised (p < 0.05). The mortality rate and the bacterial burden in the lung, liver, kidney, and spleen in both passive and active immunized mice were significantly decreased (p ≤ 0.001). In-vivo experiments confirmed that the OmpW2 whole protein and its sub-unit fragment induce the host immune system and can be applied to design a commercial vaccine or diagnostic kit.

Proteochemometric Method for pIC50 Prediction of Flaviviridae

Viruses remain an area of concern despite constant development of antiviral drugs and therapies. One of the contributors is the Flaviviridae family of viruses causing diseases that need attention. Among other anitviral methods, antiviral peptides are being studied as viable candidates. Although antiviral peptides (AVPs) are emerging as potential therapeutics, it is important to assess the efficacy of a given peptide in terms of its bioactivity. Experimental identification of the bioactivity of each potential peptide is an expensive and time consuming task. Computational methods like proteochemometric modeling (PCM) is a promising method for prediction of bioactivity (pIC50) based on peptide and target sequence pair. In this study, we propose a prediction of pIC50 of AVP against the Flaviviridae family that may help make the decision to choose a peptide with desired efficacy. The peptides data was collected from a public database and target sequences were manually curated from literature. Features are calculated using peptide and target sequence PCM descriptors which consist of individual and cross-term features of peptide and respective target. The resultant R² and MAPE values are 0.85 and 8.44%, respectively, for prediction of pIC50 value of AVPs.

In Silico Identification of the B-Cell and T-Cell Epitopes of the Antigenic Proteins of Staphylococcus aureus for Potential Vaccines

Staphylococcus aureus is a leading cause of community-acquired, healthcare-associated, and hospital-acquired infections. S. aureus bacteremia is a common and serious infection with significant morbidity and mortality in older patients. The rise of antibiotic-resistant strains of S. aureus has resulted in substantial loss and effective treatment in hospitalized patients. Thus, there is a need in the development of a vaccine that would provide protection against S. aureus. The antigens of our interest include proteins that are essential for bacterial attachment and colonization (ClfA and ClfB), dermonecrosis-driven toxin (Hla), antigens that are essential for abscess formation (EsxA and EsxB), and antigens that are essential for nutrient acquisition and resistance to phagocytes killing induced by reactive oxygen species (FhuD2 and MntC). Development of a structure-based vaccine based on the antigenic protein epitopes is a novel strategy to provide protection against S. aureus. Using bioinformatic tools, we have determined the B-cell and T-cell epitopes of the antigenic proteins of S. aureus. This chapter reports identification of B-cell and T-cell epitopes of the antigenic protein that could be used in the development of effective structure-based vaccines to protect against S. aureus.

DcaP porin and its epitope-based subunit promise effective vaccines against Acinetobacter baumannii; in-silico and in-vivo approaches

A. baumannii is a multi-drug resistant pathogen with a relatively high mortality rate. To date, no vaccine has been approved against this bacterium. DcaP is a high abundance porin during infection that its structure has been recently determined, but no information about its immunogenic properties has been reported yet. So, in this study DcaP properties were analyzed and its vaccine potential was evaluated. The results showed this porin is an extremely conserved antigen with no allergenicity and toxicity that bears no resemblance to human proteins. Six potential immunogen areas in the DcaP sequence were detected based on in-silico B and T-cell epitope mapping and other approaches. A multiple-epitope potential vaccine was designed based on the predicted linear epitopes and amplified by overlap extension PCR technique. In-vivo results indicated that active and passive immunization of mice with the DcaP protein or its designed subunit vaccine raises the antibody titers and decreases the mortality rate of the immunized mice infected with A. baumannii. Based on the results, DcaP and its indicated immunogen regions can be considered as a peptide or subunit vaccine. The immunogen regions could also be applied in multivalent subunit vaccine candidates against A. baumannii and other bacteria.

Physicochemical and structural characterization, epitope mapping and vaccine potential investigation of a new protein containing Tetratrico Peptide Repeats of Acinetobacter baumannii: An in-silico and in-vivo approach

Acinetobacter baumannii is an opportunistic multidrug-resistant pathogen that causes a significant mortality rate. The proteins containing Tetratrico Peptide Repeats (TPRs) are involved in the pathogenicity and virulence of bacteria and have different roles such as transfer of bacterial virulence factors to host cells, binding to the host cells and inhibition of phagolysosomal maturation. So, in this study, physicochemical properties of a new protein containing TPRs in A. baumannii which was named PcTPRs1 by this study were characterized and its 3D structure was predicted by in-silico tools. The protein B and T cell epitopes were mapped and its vaccine potential was in-silico and in-vivo investigated. Domain analysis indicated that the protein contains the Flp pilus assembly protein TadD domain which has three TPRs. The helix is dominant in the protein structure, and this protein is an outer membrane antigen which, is extremely conserved among A. baumannii strains; thus, has good properties to be applied as a recombinant vaccine. The best-predicted and refined model was applied in ligand-binding sites and conformational epitopes prediction. Based on epitope mapping results, several epitopes were characterized which could stimulate both immune systems. BLAST results showed the introduced epitopes are completely conserved among A. baumannii strains. The in-vivo analysis indicates that a 101 amino acid fragment of the protein which contains the best selected epitope, can produce a good protectivity against A. baumannii as well as the whole TPR protein and thus could be investigated as an effective subunit and potential vaccines.

Two Faces of Milk Proteins Peptides with Both Allergenic and Multidimensional Health Beneficial Impact- Integrated In Vitro/In Silico Approach

The main food-origin antigens that the infant's body is in contact with are cow's milk proteins (CMP). Still, CMP are one of the main sources of beneficial biologically active peptides that play a role in treatment of non-communicable diseases. Safe methods to quickly predict the sensitizing potential of food proteins among their range of health-promoting properties are essential. The aim of study was to adapt an integrated approach combining several in silico (IS) studies and in vitro (IV) assays to screen the multifunctionality of CMP-derived peptides. Major histocompatability complex type II MHC II-binders, interleukin-4 and -10 inducers, interferon γ -inducers and immunobioactivity tools were used to predict the peptide-power of inducing allergies or tolerance. A comparison of the peptide profiless revealed the presence of one identical and one overlapping sequence in IS and IV hydrolysate. By IS analysis, four of 24 peptides were found to have high affinity and stimulate IL-4 expression, and by IV, one of seven peptides had this potential (Bos d9 peptide DIPNPIGSENSEK (195-208)). Three IV peptides may induce IL-10 expression. The IV/IS assessment seems promising agents for peptides' potential determination dedicated only to preliminary screening of peptides. The IV verification is still crucial in further steps of studies.

Peptide Combination Generator: a Tool for Generating Peptide Combinations

Peptides are used as reagents both for basic research and diagnostic purposes. Therefore, there is a need for novel methods for the design of peptide molecules with a particular specific physiochemical profile. The properties of the peptides are governed by the nature of amino acids constituting the peptide. There is a lack of a web server or tools which could predict all the possible combinations of the peptides generated because of the combinations of amino acids based on the physiochemical properties. We have developed a peptide combination generator (PepCoGen), a web server for generating all the possible combinations of peptides by varying the amino acids having similar physiochemical properties at a particular position. It also predicts other properties of the peptides including molecular weight, charge, solubility, hydrophobic plot, and isoelectric point, and random three-dimensional models for each generated combination.

Proteochemometrics - recent developments in bioactivity and selectivity modeling

Proteochemometrics is a machine learning based modeling approach relying on a combination of ligand and protein descriptors. With ongoing developments in machine learning and increases in public data the technique is more frequently applied in early drug discovery, typically in ligand-target binding prediction. Common applications include improvements to single target quantitative structure-activity relationship models, protein selectivity and promiscuity modeling, and large-scale deep learning approaches. The increase in predictive power using proteochemometrics is observed in multi-target bioactivity modeling, opening the door to more extensive studies covering whole protein families. On top of that, with deep learning fueling more complex and larger scale models, proteochemometrics allows faster and higher quality computational models supporting the design, make, test cycle.

Application of fourier transform and proteochemometrics principles to protein engineering

Background

Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.

Results

We show that the digitalized representation of the protein sequence in the form of a Fourier spectrum can be used as an efficient descriptor to model the sequence-activity relationship of proteins. The iSAR methodology that we have developed identifies high fitness mutants from mutant libraries relying on physicochemical properties of the amino acids, digital signal processing and regression techniques. iSAR correlates variations caused by mutations in spectra with biological activity/fitness. It takes into account the impact of mutations on the whole spectrum and does not focus on local fitness alone. The utility of the method is illustrated on 4 datasets: cytochrome P450 for thermostability, TNF-alpha for binding affinity, GLP-2 for potency and enterotoxins for thermostability. The choice of the datasets has been made such as to illustrate the ability of the method to perform when limited training data is available and also when novel mutations appear in the test set, that have not been featured in the training set.

Conclusion

The combination of Fast Fourier Transform and Partial Least Squares regression is efficient in capturing the effects of mutations on the function of the protein. iSAR is a fast algorithm which can be implemented with limited computational resources and can make effective predictions even if the training set is limited in size.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2407-8) contains supplementary material, which is available to authorized users.