Current Proteomics - Volume 19, Issue 5, 2022

Although treatable, resistant form of tuberculosis (TB) has posed a major impediment to the effective TB control programme. As the Mycobacterium tuberculosis cell envelope is closely associated with its virulence and resistance, it is very important to understand the cell envelope for better treatment of causative pathogens. Cell membrane plays a crucial role in imparting various cell functions. Proteins being the functional moiety, it is impossible to characterize the functional properties based on genetic analysis alone. Proteomic based research has indicated mycobacterial envelope as a good source of antigens/proteins. Envelope/membrane and associated proteins have an anticipated role in biological processes, which could be of vital importance to the microbe, and hence could qualify as drug targets. This review provides an overview of the prominent and biologically important cell envelope and highlights the different functions offered by the proteins associated with it. Selective targeting of the mycobacterial envelope offers an untapped opportunity to address the problems associated with the current drug regimen and also will lead to the development of more potent and safer drugs against all forms of tuberculous infections.

Background: The arginine deiminase enzyme of Mycoplasma arginini (MaADI) is a potential anti-cancer agent for treating arginine-auxotrophic cancers. Investigating the protein stability in the presence of osmolytes can help to increase protein stability under various stressed conditions. Methods: In this study, the stability and dynamics of MaADI were investigated in pure water and solutions of 1 M sorbitol, 10% (v/v) methanol, and 50% (v/v) methanol using molecular dynamics simulation. Results: Sorbitol was found to stabilize the protein, whereas high-concentrated methanol destabilized it. Sorbitol molecules interacted with the protein through hydrogen bonding and reduced the protein fluctuations. At 50% methanol, the flexibility of regions 4-8, 195-201, 314-324, and 332- 337 in the MaADI was increased, whereas residues 195-201 showed the highest variations. Conclusion: Thus, these regions of MaADI, especially 195-201, are the most sensitive regions in the presence of denaturing agents and can be subjected to protein engineering to improve the stability of MaADI.

Background: Drug-resistant tuberculosis remains a health security threat and resistance to second-line drugs limits the options for treatment. Consequently, there is an utmost need for identifying and characterizing new biomarkers/drug targets of prime importance. Membrane proteins have an anticipated role in biological processes and could qualify as biomarkers/drug targets. Streptomycin (SM) is recommended as a second-line treatment regimen only when amikacin resistance has been confirmed. As extensively drug-resistant (XDR) isolates are frequently cross-resistant to second-line injectable drugs, an untapped potential for the continued use of SM has been suggested. Objective: The study aimed to analyze the membrane proteins overexpressed in SM resistant isolates of Mycobacterium tuberculosis using proteomics approaches. Methods: Membrane proteins were extracted employing sonication and ultracentrifugation. Twodimensional gel electrophoresis (2DGE) of membrane proteins was performed and identification of proteins was done by liquid chromatography-mass spectrometry (LCMS) and bioinformatics tools. Results: On analyzing the two-dimensional (2D) gels, five protein spots were found overexpressed in the membrane of SM resistant isolates. Docking analysis revealed that SM might bind to the conserved domain of overexpressed proteins and Group-based prediction system-prokaryotic ubiquitinlike protein (GPS-PUP) predicted potential pupylation sites within them. Conclusion: These proteins might be of diagnostic importance for detecting the cases early and for exploring effective control strategies against drug-resistant tuberculosis, particularly SM.

Background: The machine learning computation paradigm touched new horizons with the development of deep learning architectures. It is widely used in complex problems and achieved significant results in many traditional applications like protein structure prediction, speech recognition, traffic management, health diagnostic systems and many more. Especially, Convolution neural network (CNN) has revolutionized visual data processing tasks. Objective: Protein structure is an important research area in various domains, from medical science and health sectors to drug designing. Fourier Transform Infrared Spectroscopy (FTIR) is the leading tool for protein structure determination. This review aims to study the existing deep learning approaches proposed in the literature to predict proteins' secondary structure and to develop a conceptual relation between FTIR spectra images and deep learning models to predict the structure of proteins. Methods: Various pre-trained CNN models are identified and interpreted to correlate the FTIR images of proteins containing Amide-I and Amide-II absorbance values and their secondary structure. Results: The concept of transfer learning is efficiently incorporated using the models like Visual Geometry Group (VGG), Inception, Resnet, and Efficientnet. The dataset of protein spectra images is applied as input, and these models significantly predict the secondary structure of proteins. Conclusion: As deep learning is recently being explored in this field of research, it worked remarkably in this application and needs continuous improvement with the development of new models.

Background: Heavy metals impact living organisms deleteriously when they exceed the required limits. Their remediation by bacteria is a much-pursued area of environmental research. In this study, we explored the quantitative changes of four heavy metals (cadmium, chromium, zinc, copper), on the global and membrane proteome of gram-negative S. nematodiphila MB307. This is a versatile bacterium, isolated from the rhizosphere of heavy metal tolerating plant and equipped with characteristics ranging from useful biopeptide production to remediation of metals. Methods: We explored changes in the static end products of coding DNA sequences, i.e., proteins after 24 incubation under metal stress, using LC-MS/MS. Data analysis was done using MaxQuant software coupled with the Perseus package. Results: Up and downregulated protein fractions consisted prominently of chaperones, membrane integrity proteins, mobility or transporter proteins. Comparative analysis with previously studied bacteria and the functional contribution of these proteins to metal stress offer evidence for the survival of S. nematodiphila under high concentrations of selected metals. Conclusion: The outcomes validate that this soil-derived bacterium is well attuned to removing these metals from the soil and water, and may be additionally useful for boosting the phytoremediation of metals. This study delivers interesting insights and overlays ground for further investigations on the mechanistic activity of this bacterium under pollutant stress.