TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Halogen-Based 17ß-HSD1 Inhibitors: Insights from DFT, Docking, and Molecular Dynamics Simulation Studies.

The high expression of 17ß-hydroxysteroid dehydrogenase type 1 (17ß-HSD1) mRNA has been found in breast cancer tissues and endometriosis. The current research focuses on preparing a range of organic molecules as 17ß-HSD1 inhibitors. Among them, the derivatives of hydroxyphenyl naphthol steroidomimetics are reported as one of the potential groups of inhibitors for treating estrogen-dependent disorders. Looking at the recent trends in drug design, many halogen-based drugs have been approved by the FDA in the last few years. Here, we propose sixteen potential hydroxyphenyl naphthol steroidomimetics-based inhibitors through halogen substitution. Our Frontier Molecular Orbitals (FMO) analysis reveals that the halogen atom significantly lowers the Lowest Unoccupied Molecular Orbital (LUMO) level, and iodine shows an excellent capability to reduce the LUMO in particular. Tri-halogen substitution shows more chemical reactivity via a reduced HOMO-LUMO gap. Furthermore, the computed DFT descriptors highlight the structure-property relationship towards their binding ability to the 17ß-HSD1 protein. We analyze the nature of different noncovalent interactions between these molecules and the 17ß-HSD1 using molecular docking analysis. The halogen-derived molecules showed binding energy ranging from -10.26 to -11.94 kcal/mol. Furthermore, the molecular dynamics (MD) simulations show that the newly proposed compounds provide good stability with 17ß-HSD1. The information obtained from this investigation will advance our knowledge of the 17ß-HSD1 inhibitors and offer clues to developing new 17ß-HSD1 inhibitors for future applications.

ProNAB: database for binding affinities of protein-nucleic acid complexes and their mutants.

Protein-nucleic acid interactions are involved in various biological processes such as gene expression, replication, transcription, translation and packaging. The binding affinities of protein-DNA and protein-RNA complexes are important for elucidating the mechanism of protein-nucleic acid recognition. Although experimental data on binding affinity are reported abundantly in the literature, no well-curated database is currently available for protein-nucleic acid binding affinity. We have developed a database, ProNAB, which contains more than 20 000 experimental data for the binding affinities of protein-DNA and protein-RNA complexes. Each entry provides comprehensive information on sequence and structural features of a protein, nucleic acid and its complex, experimental conditions, thermodynamic parameters such as dissociation constant (Kd), binding free energy (ΔG) and change in binding free energy upon mutation (ΔΔG), and literature information. ProNAB is cross-linked with GenBank, UniProt, PDB, ProThermDB, PROSITE, DisProt and Pubmed. It provides a user-friendly web interface with options for search, display, sorting, visualization, download and upload the data. ProNAB is freely available at https://web.iitm.ac.in/bioinfo2/pronab/ and it has potential applications such as understanding the factors influencing the affinity, development of prediction tools, binding affinity change upon mutation and design complexes with the desired affinity.