The purpose of exploring protein interactions between human adenovirus and heat shock protein 70 is to exploit a potentially synergistic interaction to enhance anti-tumoral efficacy and decrease toxicity in cancer treatment. However, the protein interaction of Hsp70 with E1A32 kDa of human adenovirus serotype 5 remains to be elucidated. In this study, two residues of ATPase domain of human heat shock 70 kDa protein 1 (PDB: 1 HJO) were mutated. 3D mutant models (K71L and T204V) using PyMol software were then constructed. The structures were evaluated by PROCHECK, ProQ, ERRAT, Verify 3D and ProSA modules. All evidence suggests that all protein models are acceptable and of good quality. The E1A32 kDa motif was retrieved from UniProt (P03255), as well as subjected to docking interaction with NBD, K71L and T204V, using the Autodock 4.2 program. The best lowest binding energy value of -9.09 kcal/mol was selected for novel T204V. Moreover, the protein-ligand complex structures were validated by RMSD, RMSF, hydrogen bonds and salt bridge analysis. This revealed that the T204V-E1A32 kDa motif complex was the most stable among all three complex structures. This study provides information about the interaction between Hsp70 and the E1A32 kDa motif, which emphasizes future perspectives to design rational drugs and vaccines in cancer therapy.
Currently, protein interaction of Homo sapiens nucleotide binding domain (NBD) of heat shock 70 kDa protein (PDB: 1HJO) with p53 motif remains to be elucidated. The NBD-p53 motif complex enhances the p53 stabilization, thereby increasing the tumor suppression activity in cancer treatment. Therefore, we identified the interaction between NBD and p53 using STRING version 9.1 program. Then, we modeled the three-dimensional structure of p53 motif through homology modeling and determined the binding affinity and stability of NBD-p53 motif complex structure via molecular docking and dynamics (MD) simulation. Human DNA binding domain of p53 motif (SCMGGMNR) retrieved from UniProt (UniProtKB: P04637) was docked with the NBD protein, using the Autodock version 4.2 program. The binding energy and intermolecular energy for the NBD-p53 motif complex were -0.44 Kcal/mol and -9.90 Kcal/mol, respectively. Moreover, RMSD, RMSF, hydrogen bonds, salt bridge, and secondary structure analyses revealed that the NBD protein had a strong bond with p53 motif and the protein-ligand complex was stable. Thus, the current data would be highly encouraging for designing Hsp70 structure based drug in cancer therapy.
Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.
The first layer of defense that plants deploy to ward off a microbial invasion comes in the form of pattern-triggered immunity (PTI), which is initiated when the pattern-recognition receptors (PRRs) bind with the pathogen-associated molecular patterns (PAMPs) and co-receptor proteins, and transmit a defense signal. Although several plant PRRs have been discovered, very few of them have been fully characterized, and their functional parameters assessed. In this study, the 3D-model prediction of an entire plant PRR protein, Xa21, was done by implementing multiple in silico modeling techniques. Subsequently, the PAMP RaxX21-sY (sulphated RaxX21) and leucine-rich repeat (LRR) domain of the co-receptor OsSERK2 were docked with the LRR domain of Xa21. The docked complex of these three proteins formed a heterodimer that closely resembles the other crystallographic PTI complexes available. Molecular dynamics simulations and MM/PBSA calculations were applied for an in-depth analysis of the interactions between Xa21 LRR, RaxX21-sY, and OsSERK2 LRR. Arg230 and Arg185 from Xa21 LRR, Val2 and Lys15 from RaxX21-sY and Lys164 from OsSERK2 LRR were found to be the prominent residues which might contribute significantly in the formation of a heterodimer during the PTI process mediated by Xa21. Additionally, RaxX21-sY interacted much more favorably with Xa21 LRR in the presence of OsSERK2 LRR in the complex, which substantiates the necessity of the co-receptor in Xa21 mediated PTI to recognize the PAMP RaxX21-sY. However, the free energy binding calculation reveals the favorability of a heterodimer formation of PRR Xa21 and co-receptor OsSERK2 without the presence of PAMP RaxX21-sY, which validate the previous lab result.