ProThermDB is an updated version of the thermodynamic database for proteins and mutants (ProTherm), which has ∼31 500 data on protein stability, an increase of 84% from the previous version. It contains several thermodynamic parameters such as melting temperature, free energy obtained with thermal and denaturant denaturation, enthalpy change and heat capacity change along with experimental methods and conditions, sequence, structure and literature information. Besides, the current version of the database includes about 120 000 thermodynamic data obtained for different organisms and cell lines, which are determined by recent high throughput proteomics techniques using whole-cell approaches. In addition, we provided a graphical interface for visualization of mutations at sequence and structure levels. ProThermDB is cross-linked with other relevant databases, PDB, UniProt, PubMed etc. It is freely available at https://web.iitm.ac.in/bioinfo2/prothermdb/index.html without any login requirements. It is implemented in Python, HTML and JavaScript, and supports the latest versions of major browsers, such as Firefox, Chrome and Safari.

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.

Membrane proteins are unique in that segments thereof concurrently reside in vastly different physicochemical environments: the extracellular space, the lipid bilayer, and the cytoplasm. Accordingly, the effects of missense variants disrupting their sequence depend greatly on the characteristics of the environment of the protein segment affected as well as the function it performs. Because membrane proteins have many crucial roles (transport, signal transduction, cell adhesion, etc.), compromising their functionality often leads to diseases including cancers, diabetes mellitus or cystic fibrosis. Here, we report a suite of sequence-based computational methods "Pred-MutHTP" for discriminating between disease-causing and neutral alterations in their sequence. With a data set of 11,846 disease-causing and 9,533 neutral mutations, we obtained an accuracy of 74% and 78% with 10-fold group-wise crossvalidation and test set, respectively. The features used in the models include evolutionary information, physiochemical properties, neighboring residue information, and specialized membrane protein attributes incorporating the number of transmembrane segments, substitution matrices specific to membrane proteins as well as residue distributions occurring in specific topological regions. Across 11 disease classes, the method achieved accuracies in the range of 75-85%. The model designed specifically for the transmembrane segments achieved an accuracy of 85% on the test set with a sensitivity and specificity of 86% and 83%, respectively. This renders our method the current state-of-the-art with regard to predicting the effects of variants in the transmembrane protein segments. Pred-MutHTP allows predicting the effect of any variant occurring in a membrane protein-available at

Human variant databases could be better exploited if the variant data available in multiple resources is integrated in a single comprehensive resource along with sequence and structural features. Such integration would improve the analyses of variants for disease prediction, prevention or treatment. The HuVarBase (HUmanVARiantdataBASE) assimilates publicly available human variant data at protein level and gene level into a comprehensive resource. Protein level data such as amino acid sequence, secondary structure of the mutant residue, domain, function, subcellular location and post-translational modification are integrated with gene level data such as gene name, chromosome number & genome position, DNA mutation, mutation type origin and rs ID number. Disease class has been added for the disease causing variants. The database is publicly available at https://www.iitm.ac.in/bioinfo/huvarbase. A total of 774,863 variant records, integrated in the HuVarBase, can be searched with options to display, visualize and download the results.

BACKGROUND: Excess cholesterol accumulation in lesional macrophages elicits complex responses in atherosclerosis. Epsins, a family of endocytic adaptors, fuel the progression of atherosclerosis; however, the underlying mechanism and therapeutic potential of targeting Epsins remains unknown. In this study, we determined the role of Epsins in macrophage-mediated metabolic regulation. We then developed an innovative method to therapeutically target macrophage Epsins with specially designed S2P-conjugated lipid nanoparticles, which encapsulate small-interfering RNAs to suppress Epsins. METHODS: We used single-cell RNA sequencing with our newly developed algorithm MEBOCOST to study cell-cell communications mediated by metabolites from sender cells and sensor proteins on receiver cells. Biomedical, cellular, and molecular approaches were utilized to investigate the role of macrophage Epsins in regulating lipid metabolism and transport. We performed this study using myeloid-specific Epsin double knockout (LysM-DKO) mice and mice with a genetic reduction of ABCG1 (ATP-binding cassette subfamily G member 1; LysM-DKO-ABCG1 fl/+ ). The nanoparticles targeting lesional macrophages were developed to encapsulate interfering RNAs to treat atherosclerosis. RESULTS: We revealed that Epsins regulate lipid metabolism and transport in atherosclerotic macrophages. Inhibiting Epsins by nanotherapy halts inflammation and accelerates atheroma resolution. Harnessing lesional macrophage-specific nanoparticle delivery of Epsin small-interfering RNAs, we showed that silencing of macrophage Epsins diminished atherosclerotic plaque size and promoted plaque regression. Mechanistically, we demonstrated that Epsins bound to CD36 to facilitate lipid uptake by enhancing CD36 endocytosis and recycling. Conversely, Epsins promoted ABCG1 degradation via lysosomes and hampered ABCG1-mediated cholesterol efflux and reverse cholesterol transport. In a LysM-DKO-ABCG1 fl/+ mouse model, enhanced cholesterol efflux and reverse transport due to Epsin deficiency was suppressed by the reduction of ABCG1. CONCLUSIONS: Our findings suggest that targeting Epsins in lesional macrophages may offer therapeutic benefits for advanced atherosclerosis by reducing CD36-mediated lipid uptake and increasing ABCG1-mediated cholesterol efflux.