Computational comparison of two new fusion proteins for multiple sclerosis

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model demonstrates remarkable performance, achieving an AUC of 0.856 and 0.823 on the PepBCL Set_1 and Set_2 datasets, respectively. Additionally, it attains a Precision of 0.564 in PepBCL Set 1 and 0.527 in PepBCL Set 2, surpassing the performance of previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells, and other fields. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development.

Computational design of newly engineered DARPins as HER2 receptor inhibitors for breast cancer treatment

Background and purpose

Human epidermal growth factor receptor 2 (HER2) is overexpressed in approximately 25% of breast cancer patients; therefore, its inhibition is a therapeutic target in cancer treatment.

Experimental approach

In this study, two new variants of designed ankyrin repeat proteins (DARPins), designated EG3-1 and EG3-2, were designed to increase their affinity for HER2 receptors. To this end, DARPin G3 was selected as a template, and six-point mutations comprising Q26E, I32V, T49A, L53H, K101R, and G124V were created on its structure. Furthermore, the 3D structures were formed through homology modeling and evaluated using molecular dynamic simulation. Then, both structures were docked to the HER2 receptor using the HADDOCK web tool, followed by 100 ns of molecular dynamics simulation for both DARPins / HER2 complexes.

Findings/Results

The theoretical result confirmed both structures' stability. Molecular dynamics simulations reveal that the applied mutations on DARPin EG3-2 significantly improve the receptor binding affinity of DARPin.

Conclusion and implications

The computationally engineered DARPin EG3-2 in this study could provide a hit compound for the design of promising anticancer agents targeting HER2 receptors.

Immunoinformatics based design and prediction of proteome-wide killer cell epitopes of Leishmania donovani: Potential application in vaccine development

Despite several extensive and exhaustive efforts, search for potential therapy against leishmaniasis has not made much progress. In the present work, we have employed mining strategy to screen Leishmania donovani proteome for identification of promising vaccine candidate. We have screened 21 potential antigenic proteins from 7960 total protein of L. donovani, based on the presence of signal peptide, GPI anchor, antigenicity prediction and substractive proteomic approach. Secondly, we have also performed comprehensive immunogenic epitope prediction from the screened 21 proteins, using IEDB-AR tools. Out of the 21 antigenic proteins, we obtained 11 immunogenic epitopes from 9 proteins. The final results revealed that four predicted epitopes namely; YPAFAALVF, VAVAATVAY, AAAPTEAAL and MYPLVAVVF, have significantly better binding potential with respective alleles and could elicits immune responses. Docking analysis using PATCHDOCK server and molecular dynamic simulation using GROMACS revealed the potential of the sequences as immunogenic epitopes. In silico studies also suggested that the epitopes occupied almost same binding cleft with the respective alleles, when compared with the reference peptides. It is also suggested from the molecular dynamic simulation data that the peptides were intact in the pocket for longer periods of time. Our study was designed to select MHC class I restricted epitopes for the activation of CD8 T cells using immunoinformatics for the prediction of probable vaccine candidate against L. donovani parasites. Communicated by Ramaswamy H. Sarma.

Design and molecular dynamic simulation of a new double-epitope tolerogenic protein as a potential vaccine for multiple sclerosis disease

One of the debilitating diseases affecting the central nervous system is multiple sclerosis (MS). As there is no definitive treatment for MS, researchers have mainly consented with optimization of strategies which slows down the progression of the disease such as specific auto-antigens tolerance induction. In this regard, the aim of this study was design of a new double-epitope protective vaccine based on interleukin (IL)-16-neuroantigens fusion proteins. First, we selected highly antigenic epitopes of myelin basic protein (MBP) (aa 84-104) and myelin oligodendrocyte glycoprotein (MOG) (aa 99-107) from available literature and our bioinformatics analysis. The correct cleavage of our constructs and major histocompatibility complex class II binding affinities of cleaved epitopes were checked and evaluated using Pepcleave and IEDB servers, respectively. Then, different combination of MOG and MBP epitopes with or without fusion to C-terminal active part of IL-16 were designed as constructs. Afterward, Modeller and Gromacs softwares used for the investigation of the MBP, and MOG epitopes antigenicity in these constructs. The results of molecular dynamics simulations showed that IL-16 in MOG + linker + MBP + IL-16 construct does not interfere with final epitopes antigenicity of MOG + linker + MBP construct. To sum up, the construct with IL-16 is suggested as a new double-epitope tolerogenic vaccine for prevention and amelioration of MS in human.