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The human transmembrane protease serine 2 (TMPRSS2) protein plays an important role in prostate cancer progression. It also facilitates viral entry into target cells by proteolytically cleaving and activating the S protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the current study, we used different available tools like SIFT, PolyPhen2.0, PROVEAN, SNAP2, PMut, MutPred2, I-Mutant Suite, MUpro, iStable, ConSurf, ModPred, SwissModel, PROCHECK, Verify3D, and TM-align to identify the most deleterious variants and to explore possible effects on the TMPRSS2 stability, structure, and function. The six missense variants tested were evaluated to have deleterious effects on the protein by SIFT, PolyPhen2.0, PROVEAN, SNAP2, and PMut. Additionally, V160M, G181R, R240C, P335L, G432A, and D435Y variants showed a decrease in stability by at least 2 servers; G181R, G432A, and D435Y are highly conserved and identified posttranslational modifications sites (PTMs) for proteolytic cleavage and ADP-ribosylation using ConSurf and ModPred servers. The 3D structure of TMPRSS2 native and mutants was generated using 7 meq as a template from the SwissModeller group, refined by ModRefiner, and validated using the Ramachandran plot. Hence, this paper can be advantageous to understand the association between these missense variants rs12329760, rs781089181, rs762108701, rs1185182900, rs570454392, and rs867186402 and susceptibility to SARS-CoV-2.

It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove thatp2,p1,p1′, andp2′are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.

Diabetes mellitus (DM) is one of the major metabolic disorders that is currently threatening the world. DM is seen associated with obesity and diabetic retinopathy (DR). In the present paper we tried to evaluate the relationship between the three aliments at the gene level and further performed the molecular docking to identify the common drug for all the three diseases. We have adopted several software programs such as Phenopedia, VennViewer, and CDOCKER to accomplish the objective. Our results revealed six genes that commonly associated and are involved in the signalling pathway. Furthermore, evaluation of common gene association from the selected set of genes projected the presence of SIRT1 in all the three aliments. Therefore, we targeted protein 4KXQ which was produced from the gene SIRT1 and challenged it with eight phytochemicals, adopting the CDOCKER. C1 compound has displayed highest -CDOCKER energy and -CDOCKER interaction energy of 43.6905 and 43.3953, respectively. Therefore, this compound is regarded as the most potential lead molecule.

Aptamers are an interesting alternative to antibodies in pharmaceutics and biosensorics, because they are able to bind to a multitude of possible target molecules with high affinity. Therefore the process of finding such aptamers, which is commonly a SELEX screening process, becomes crucial. The standard SELEX procedure schedules the validation of certain found aptamers via binding experiments, which is not leading to any detailed specification of the aptamer enrichment during the screening. For the purpose of advanced analysis of the accrued enrichment within the SELEX library we used sequence information gathered by next generation sequencing techniques in addition to the standard SELEX procedure. As sequence motifs are one possibility of enrichment description, the need of finding those recurring sequence motifs corresponding to substructures within the aptamers, which are characteristically fitted to specific binding sites of the target, arises. In this paper a motif search algorithm is presented, which helps to describe the aptamers enrichment in more detail. The extensive characterization of target and binding aptamers may later reveal a functional connection between these molecules, which can be modeled and used to optimize future SELEX runs in case of the generation of target-specific starting libraries.

MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameterαin the weight formula. By changingαvalue, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.

Since proteins carry out their functions through interactions with other molecules, accurately identifying the protein-ligand binding site plays an important role in protein functional annotation and rational drug discovery. In the past two decades, a lot of algorithms were present to predict the protein-ligand binding site. In this paper, we introduce statistical depth function to define negative samples and propose an SVM-based method which integrates sequence and structural information to predict binding site. The results show that the present method performs better than the existent ones. The accuracy, sensitivity, and specificity on training set are 77.55%, 56.15%, and 87.96%, respectively; on the independent test set, the accuracy, sensitivity, and specificity are 80.36%, 53.53%, and 92.38%, respectively.

Antigen-presenting cells (APCs) sense the microenvironment through several types of receptors that recognize pathogen-associated molecular patterns. In particular, C-type lectins receptors (CLRs), which are expressed by distinct subsets of dendritic cells (DCs) and macrophages (MØs), recognize and internalize specific carbohydrate antigens in a Ca2+-dependent manner. The targeting of these receptors is becoming an efficient strategy for parasite recognition. However, relatively little is known about how CLRs are involved in both pathogen recognition and the internalization of parasites. The role of CLRs in parasite infections is an area of considerable interest because this research will impact our understanding of the initiation of innate immune responses, which influences the outcome of specific immune responses. This paper attempts to summarize our understanding of the effects of parasites’ interactions with CLRs.