In 1998 a nomenclature for the growing list of pesticidal proteins from Bacillus thuringiensis (Bt) was derived based solely on protein sequence comparisons. This nomenclature was widely adopted and provided a robust framework for the naming and classification of the proteins. The success of these proteins in integrated pest management schemes prompted an increased effort to find others with improved or more diverse activities. These discovery activities led to the characterization of proteins from a wider range of bacteria and with a variety of different protein folds. Since most of these new proteins were grouped together as Cry proteins it became apparent that the existing nomenclature had limitations in representing the diverse range of proteins that had been identified. This revised nomenclature retains the basic principles of the 1998 version but provides specific mnemonics to represent different structural groups. For the purposes of consistency, the vast majority of the proteins have either retained their name or have a new name that clearly references the previous one. Other pesticidal proteins not previously included in the nomenclature have been incorporated into this version.

Nanotechnology has arisen as a key player in the field of nanomedicine. Although the use of engineered nanoparticles is rapidly increasing, safety assessment is also important for the beneficial use of new nanomaterials. Considering that the experimental assessment of new nanomaterials is costly and laborious, in silico approaches hold promise. Several major challenges in nanotechnology indicate a need for nanoinformatics. New database initiatives such as ISA-TAB-Nano, caNanoLab, and Nanomaterial Registry will help in data sharing and developing data standards, and, as the amount of nanomaterials data grows, will provide a way to develop methods and tools specific to the nanolevel. In this review, we describe emerging databases and tools that should aid in the progress of nanotechnology research.

Proteins in the form of transcription factors (TFs) bind to specific DNA sites that regulate cell growth, differentiation, and cell development. The interactions between proteins and DNA are important toward maintaining and expressing genetic information. Without knowing TFs structures and DNA-binding properties, it is difficult to completely understand the mechanisms by which genetic information is transferred between DNA and proteins. The increasing availability of structural data on protein-DNA complexes and recognition mechanisms provides deeper insights into the nature of protein-DNA interactions and therefore, allows their manipulation. TFs utilize different mechanisms to recognize their cognate DNA (direct and indirect readouts). In this review, we focus on these recognition mechanisms as well as on the analysis of the DNA-binding domains of stem cell TFs, discussing the relative role of various amino acids toward facilitating such interactions. Unveiling such mechanisms will improve our understanding of the molecular pathways through which TFs are involved in repressing and activating gene expression.

TLR4 in complex with MD2 senses the presence of lipid A (LA) and initiates a signaling cascade that curb the infection. This complex is evolutionarily conserved and can initiate the immune system in response to a variety of LAs. In this study, molecular dynamics simulation (25[emsp14]ns) was performed to elucidate the differential behavior of TLR4/MD2 complex in response to Rhodobacter sphaeroides lipid A (RsLA). Penta-acyl chain-containing RsLA is at the verge of agonist (6 acyl-chains) and antagonist (4 acyl-chains) structure, and activates the TLR4 pathway in horses and hamsters, while inhibiting in humans and murine. In the time-evolved coordinates, the promising factors that dictated the differential response included the local and global mobility pattern of complexes, solvent-accessible surface area of ligand, and surface charge distributions of TLR4 and MD2. We showed that the GlcN1-GlcN2 backbone acquires agonist (3FXI)-like configurations in horses and hamsters, while acquiring antagonist (2E59)-like configurations in humans and murine systems. Moreover, analysis of F126 behavior in the MD2 F126 loop (amino acids 123-129) and loop EF (81-89) suggested that certain sequence variations also contribute to species-specific response. This study underlines the TLR4 signaling mechanism and provides new therapeutic opportunities.

Toll-like receptors (TLRs) recognize pathogen/damage-associated molecular patterns and initiate inflammatory signaling cascades. Occasionally, overexpression of TLRs leads to the onset of numerous inflammatory diseases, necessitating the development of selective inhibitors to allow a protective yet balanced immune response. Here, we demonstrate that a novel peptide (TIP1) derived from Toll/interleukin-1 receptor (TIR) domain-containing adapter protein inhibited multiple TLR signaling pathways (MyD88-dependent and MyD88-independent) in murine and human cell lines. TIP1 also inhibited NLRP3-mediated IL-1β secretion, as we validated at both the protein and mRNA levels. Biophysical experiments confirmed that TIP1 specifically binds to the BB loop of the TLR4-TIR domain. Animal studies revealed that TIP1 inhibited the secretion of lipopolysaccharide (LPS)-induced proinflammatory cytokines in collagen-induced arthritis (CIA) and kaolin/carrageenan-induced arthritis (K/C) rodent models. TIP1 also rescued animals from sepsis and from LPS-induced kidney/liver damage. Importantly, TIP1 ameliorated the symptoms of rheumatoid arthritis in CIA and K/C rodent models, suggesting that TIP1 has therapeutic potential for the treatment of TLR-mediated autoimmune/inflammatory diseases.

Cytarabine, daunorubicin, doxorubicin and vincristine are clinically used for combinatorial therapies of cancers in different combinations. However, the knowledge about the interaction of these drugs with the metabolizing enzyme cytochrome P450 is limited. Therefore, we utilized computational methods to predict and assess the drug-binding modes. In this study, we performed docking, MD simulations and free energy landscape analysis to understand the drug-enzyme interactions, protein domain motions and the most populated free energy minimum conformations of the docked protein-drug complexes, respectively. The outcome of docking and MD simulations predicted the productive, as well as the non-productive binding modes of the selected drugs. Based on these interaction studies, we observed that S119, R212 and R372 are the major drug-binding residues in CYP3A4. The molecular mechanics Poisson–Boltzmann surface area analysis revealed the dominance of hydrophobic forces in the CYP3A4-drug association. Further analyses predicted the residues that may contain favorable drug-specific interactions. The probable binding modes of the cancer drugs from this study may extend the knowledge of the protein-drug interaction and pave the way to design analogs with reduced toxicity. In addition, they also provide valuable insights into the metabolism of the cancer drugs.