Protein-protein interactions are important in carrying out many biological processes and functions. These interactions may be either permanent or of temporary nature. Several studies have employed tools like solvent accessibility and graph theory to identify these interactions, but still more studies need to be performed to quantify and validate them. Although we now have many databases available with predicted and experimental results on protein-protein interactions, we still do not have many databases which focus on providing structural details of the interacting complexes, their oligomerisation state and homologues. In this work, protein-protein interactions have been thoroughly investigated within the structural regime and quantified for their strength using calculated pseudoenergies. The PPCheck server, an in-house webserver, has been used for calculating the pseudoenergies like van der Waals, hydrogen bonds and electrostatic energy based on distances between atoms of amino acids from two interacting proteins. PPCheck can be visited at . Based on statistical data, as obtained by studying established protein-protein interacting complexes from earlier studies, we came to a conclusion that an average protein-protein interface consisted of about 51 to 150 amino acid residues and the generalized energy per residue ranged from -2 kJ mol(-1) to -6 kJ mol(-1). We found that some of the proteins have an exceptionally higher number of amino acids at the interface and it was purely because of their elaborate interface or extended topology i.e. some of their secondary structure regions or loops were either inter-mixing or running parallel to one another or they were taking part in domain swapping. Residue networks were prepared for all the amino acids of the interacting proteins involved in different types of interactions (like van der Waals, hydrogen-bonding, electrostatic or intramolecular interactions) and were analysed between the query domain-interacting partner pair and its remote homologue-interacting partner pair. We found that, in exceptional cases, homologous proteins belonging to the same superfamily, but with remote sequence similarity, can share similar interfaces.

BACKGROUNDModeling protein–protein interactions (PPIs) using docking algorithms is useful for understanding biomolecular interactions and mechanisms. Typically, a docking algorithm generates a large number of docking poses, and it is often challenging to select the best native-like pose. A further challenge is to recognize key residues, termed as hotspots, at protein–protein interfaces, which contribute more in stabilizing a protein–protein interface.RESULTSWe had earlier developed a computer algorithm, called PPCheck, which ascribes pseudoenergies to measure the strength of PPIs. Native-like poses could be successfully identified in 27 out of 30 test cases, when applied on a separate set of decoys that were generated using FRODOCK. PPCheck, along with conservation and accessibility scores, was able to differentiate ‘native-like and non-native-like poses from 1883 decoys of Critical Assessment of Prediction of Interactions (CAPRI) targets with an accuracy of 60%. PPCheck was trained on a 10-fold mixed dataset and tested on a 10-fold mixed test set for hotspot prediction. We obtain an accuracy of 72%, which is in par with other methods, and a sensitivity of 59%, which is better than most existing methods available for hotspot prediction that uses similar datasets. Other relevant tests suggest that PPCheck can also be reliably used to identify conserved residues in a protein and to perform computational alanine scanning.CONCLUSIONSPPCheck webserver can be successfully used to differentiate native-like and non-native-like docking poses, as generated by docking algorithms. The webserver can also be a convenient platform for calculating residue conservation, for performing computational alanine scanning, and for predicting protein–protein interface hotspots. While PPCheck can differentiate the generated decoys into native-like and non-native-like decoys with a fairly good accuracy, the results improve dramatically when features like conservation and accessibility are included. The method can be successfully used in ranking/scoring the decoys, as obtained from docking algorithms.

BackgroundKrishna Tulsi, a member of Lamiaceae family, is a herb well known for its spiritual, religious and medicinal importance in India. The common name of this plant is ‘Tulsi’ (or ‘Tulasi’ or ‘Thulasi’) and is considered sacred by Hindus. We present the draft genome of Ocimum tenuiflurum L (subtype Krishna Tulsi) in this report. The paired-end and mate-pair sequence libraries were generated for the whole genome sequenced with the Illumina Hiseq 1000, resulting in an assembled genome of 374 Mb, with a genome coverage of 61 % (612 Mb estimated genome size). We have also studied transcriptomes (RNA-Seq) of two subtypes of O. tenuiflorum, Krishna and Rama Tulsi and report the relative expression of genes in both the varieties.ResultsThe pathways leading to the production of medicinally-important specialized metabolites have been studied in detail, in relation to similar pathways in Arabidopsis thaliana and other plants. Expression levels of anthocyanin biosynthesis-related genes in leaf samples of Krishna Tulsi were observed to be relatively high, explaining the purple colouration of Krishna Tulsi leaves. The expression of six important genes identified from genome data were validated by performing q-RT-PCR in different tissues of five different species, which shows the high extent of urosolic acid-producing genes in young leaves of the Rama subtype. In addition, the presence of eugenol and ursolic acid, implied as potential drugs in the cure of many diseases including cancer was confirmed using mass spectrometry.ConclusionsThe availability of the whole genome of O.tenuiflorum and our sequence analysis suggests that small amino acid changes at the functional sites of genes involved in metabolite synthesis pathways confer special medicinal properties to this herb.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-015-0562-x) contains supplementary material, which is available to authorized users.