CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict DeltaDeltaG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k-fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives >80% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link http://cupsat.uni-koeln.de.

ProTherm and ProNIT are two thermodynamic databases that contain experimentally determined thermodynamic parameters of protein stability and protein–nucleic acid interactions, respectively. The current versions of both the databases have considerably increased the total number of entries and enhanced search interface with added new fields, improved search, display and sorting options. As on September 2005, ProTherm release 5.0 contains 17 113 entries from 771 proteins, retrieved from 1497 scientific articles (∼20% increase in data from the previous version). ProNIT release 2.0 contains 4900 entries from 273 research articles, representing 158 proteins. Both databases can be queried using WWW interfaces. Both quick search and advanced search are provided on this web page to facilitate easy retrieval and display of the data from these databases. ProTherm is freely available online at http://gibk26.bse.kyutech.ac.jp/jouhou/Protherm/protherm.html and ProNIT at http://gibk26.bse.kyutech.ac.jp/jouhou/pronit/pronit.html.

A novel coronavirus (SARS-CoV-2) has caused a major outbreak in humans all over the world, and it is the latest pandemic in the series of other infectious diseases. The concept of drug repurposing has been used successfully for many years for known diseases. Considering the emergency and urgency, drug repurposing concept is being explored for coronavirus disease (COVID-19) as well. Recently, the combination of three known drugs, lopinavir, oseltamivir and ritonavir has been proposed to control the virulence to a great extent in COVID-19 affected patients within 48 hours. Hence, we tried to understand the effect of synergism of these drugs against the SARS-CoV-2 protease using sequential docking studies. As a result, combination of three drugs showed a better binding energy than that of individual drugs. Further, the complex was subjected to molecular dynamics simulations to get insights into the stability of the complex, considering the simultaneous interactions between three drugs and the protein. The protein complexed with three drugs remained stable during the simulations. Hence, these drugs can be explored further for drug repurposing against the successful inhibition of COVID-19.

Motivation: Though vitally important to cell function, the mechanism of protein–DNA binding has not yet been completely understood. We therefore analysed the relationship between DNA binding and protein sequence composition, solvent accessibility and secondary structure. Using non-redundant databases of transcription factors and protein–DNA complexes, neural network models were developed to utilize the information present in this relationship to predict DNA-binding proteins and their binding residues. Results: Sequence composition was found to provide sufficient information to predict the probability of its binding to DNA with nearly 69% sensitivity at 64% accuracy for the considered proteins; sequence neighbourhood and solvent accessibility information were sufficient to make binding site predictions with 40% sensitivity at 79% accuracy. Detailed analysis of binding residues shows that some three- and five-residue segments frequently bind to DNA and that solvent accessibility plays a major role in binding. Although, binding behaviour was not associated with any particular secondary structure, there were interesting exceptions at the residue level. Over-representation of some residues in the binding sites was largely lost at the total sequence level, but a different kind of compositional preference was observed in DNA-binding proteins. Availability: Online predictions of DNA-binding proteins and binding sites are available at http://www.netasa.org/dbs-pred/