Targeting Bacterial Cell Division: A Binding Site-Centered Approach to the Most Promising Inhibitors of the Essential Protein FtsZ.

Binary fission is the most common mode of bacterial cell division and is mediated by a multiprotein complex denominated the divisome. The constriction of the Z-ring splits the mother bacterial cell into two daughter cells of the same size. The Z-ring is formed by the polymerization of FtsZ, a bacterial protein homologue of eukaryotic tubulin, and it represents the first step of bacterial cytokinesis. The high grade of conservation of FtsZ in most prokaryotic organisms and its relevance in orchestrating the whole division system make this protein a fascinating target in antibiotic research. Indeed, FtsZ inhibition results in the complete blockage of the division system and, consequently, in a bacteriostatic or a bactericidal effect. Since many papers and reviews already discussed the physiology of FtsZ and its auxiliary proteins, as well as the molecular mechanisms in which they are involved, here, we focus on the discussion of the most compelling FtsZ inhibitors, classified by their main protein binding sites and following a medicinal chemistry approach.

Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins.

Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. This study attempts to use deep learning to predict GTP binding sites in Rab proteins, which is one of the most vital molecular functions in life science. A functional loss of GTP binding sites in Rab proteins has been implicated in a variety of human diseases (choroideremia, intellectual disability, cancer, Parkinson's disease). Therefore, creating a precise model to identify their functions is a crucial problem for understanding these diseases and designing the drug targets. Our deep learning model with two-dimensional convolutional neural network and position-specific scoring matrix profiles could identify GTP binding residues with achieved sensitivity of 92.3%, specificity of 99.8%, accuracy of 99.5%, and MCC of 0.92 for independent dataset. Compared with other published works, this approach achieved a significant improvement. Throughout the proposed study, we provide an effective model for predicting GTP binding sites in Rab proteins and a basis for further research that can apply deep learning in bioinformatics, especially in nucleotide binding site prediction.

Incorporating efficient radial basis function networks and significant amino acid pairs for predicting GTP binding sites in transport proteins.

BACKGROUND: Guanonine-protein (G-protein) is known as molecular switches inside cells, and is very important in signals transmission from outside to inside cell. Especially in transport protein, most of G-proteins play an important role in membrane trafficking; necessary for transferring proteins and other molecules to a variety of destinations outside and inside of the cell. The function of membrane trafficking is controlled by G-proteins via Guanosine triphosphate (GTP) binding sites. The GTP binding sites active G-proteins initiated to membrane vesicles by interacting with specific effector proteins. Without the interaction from GTP binding sites, G-proteins could not be active in membrane trafficking and consequently cause many diseases, i.e., cancer, Parkinson… Thus it is very important to identify GTP binding sites in membrane trafficking, in particular, and in transport protein, in general. RESULTS: We developed the proposed model with a cross-validation and examined with an independent dataset. We achieved an accuracy of 95.6% for evaluating with cross-validation and 98.7% for examining the performance with the independent data set. For newly discovered transport protein sequences, our approach performed remarkably better than similar methods such as GTPBinder, NsitePred and TargetSOS. Moreover, a friendly web server was developed for identifying GTP binding sites in transport proteins available for all users. CONCLUSIONS: We approached a computational technique using PSSM profiles and SAAPs for identifying GTP binding residues in transport proteins. When we included SAAPs into PSSM profiles, the predictive performance achieved a significant improvement in all measurement metrics. Furthermore, the proposed method could be a power tool for determining new proteins that belongs into GTP binding sites in transport proteins and can provide useful information for biologists.

Leucine-rich repeat kinase 2 inhibitor as Parkinson′s disease novel therapeutics:research advances / 国际药学研究杂志

Parkinson′s disease(PD)is a common disease caused by multiple factors and characterized by pathological degen?eration in the dopaminergic neural system. Based on its pathogenic factors,PD can be divided into several subtypes,so it is essential to develop therapeutic agents based on the main pathogenic factor of each subtype of PD. Recently it is confirmed that the mutation of leucine-rich repeat kinase 2(LRRK2)gene leads to increased activity of the LRRK2 notably,and then causes neurodegeneration. Thus developing LRRK2 inhibitors to modulate the kinase activity will be a novel therapy for the PD subtype which is caused by LRRK2 gene mutation. LRRK2,either a kinase or a GTPase,has two drug binding sites. Therefore,two types of LRRK2 inhibitors are being studied,one is the kinase inhibitor and the other is GTPase inhibitor. This paper summarizes the recent progress in the dis?covery and development of LRRK2 inhibitors.