Ribosomally synthesized and post-translationally modified peptides (RiPPs) constitute a rapidly growing class of natural products with diverse structures and bioactivities. We have developed RiPPMiner, a novel bioinformatics resource for deciphering chemical structures of RiPPs by genome mining. RiPPMiner derives its predictive power from machine learning based classifiers, trained using a well curated database of more than 500 experimentally characterized RiPPs. RiPPMiner uses Support Vector Machine to distinguish RiPP precursors from other small proteins and classify the precursors into 12 sub-classes of RiPPs. For classes like lanthipeptide, cyanobactin, lasso peptide and thiopeptide, RiPPMiner can predict leader cleavage site and complex cross-links between post-translationally modified residues starting from genome sequences. RiPPMiner can identify correct cross-link pattern in a core peptide from among a very large number of combinatorial possibilities. Benchmarking of prediction accuracy of RiPPMiner on a large lanthipeptide dataset indicated high sensitivity, specificity, accuracy and precision. RiPPMiner also provides interfaces for visualization of the chemical structure, downloading of simplified molecular-input line-entry system and searching for RiPPs having similar sequences or chemical structures. The backend database of RiPPMiner provides information about modification system, precursor sequence, leader and core sequence, modified residues, cross-links and gene cluster for more than 500 experimentally characterized RiPPs. RiPPMiner is available at http://www.nii.ac.in/rippminer.html.

Genome guided discovery of novel natural products has been a promising approach for identification of new bioactive compounds. SBSPKS web-server has been a valuable resource for analysis of polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) gene clusters. We have developed an updated version - SBSPKSv2 which is based on comprehensive analysis of sequence, structure and secondary metabolite chemical structure data from 311 experimentally characterized PKS/NRPS gene clusters with known biosynthetic products. A completely new feature of SBSPKSv2 is the inclusion of features for search in chemical space. It allows the user to compare the chemical structure of a given secondary metabolite to the chemical structures of biosynthetic intermediates and final products. For identification of catalytic domains, SBSPKS now uses profile based searches, which are computationally faster and have high sensitivity. HMM profiles have also been added for a number of new domains and motif information has been used for distinguishing condensation (C), epimerization (E) and cyclization (Cy) domains of NRPS. In summary, the new and updated SBSPKSv2 is a versatile tool for genome mining and analysis of polyketide and non-ribosomal peptide biosynthetic pathways in chemical space. The server is available at: http://www.nii.ac.in/sbspks2.html.

Protein-protein interactions mediated by phosphotyrosine binding (PTB) domains play a crucial role in various cellular processes. In order to understand the structural basis of substrate recognition by PTB domains, multiple explicit solvent atomistic simulations of 100ns duration have been carried out on 6 PTB-peptide complexes with known binding affinities. MM/PBSA binding energy values calculated from these MD trajectories and residue based statistical pair potential score show good correlation with the experimental dissociation constants. Our analysis also shows that the modeled structures of PTB domains can be used to develop less compute intensive residue level statistical pair potential based approaches for predicting interaction partners of PTB domains.

BackgroundAntibody, the primary effector molecule of the immune system, evolves after initial encounter with the antigen from a precursor form to a mature one to effectively deal with the antigen. Antibodies of a lineage diverge through antigen-directed isolated pathways of maturation to exhibit distinct recognition potential. In the context of evolution in immune recognition, diversity of antigen cannot be ignored. While there are reports on antibody lineage, structural perspective with respect to diverse recognition potential in a lineage has never been studied. Hence, it is crucial to evaluate how maturation leads to topological tailoring within a lineage enabling them to interact with significantly distinct antigens.ResultsA data-driven approach was undertaken for the study. Global experimental mouse and human antibody-antigen complex structures from PDB were compiled into a coherent database of germline-linked antibodies bound with distinct antigens. Structural analysis of all lineages showed variations in CDRs of both H and L chains. Observations of conformational adaptation made from analysis of static structures were further evaluated by characterizing dynamics of interaction in two lineages, mouse VH1–84 and human VH5–51. Sequence and structure analysis of the lineages explained that somatic mutations altered the geometries of individual antibodies with common structural constraints in some CDRs. Additionally, conformational landscape obtained from molecular dynamics simulations revealed that incoming pathogen led to further conformational divergence in the paratope (as observed across datasets) even while maintaining similar overall backbone topology. MM-GB/SA analysis showed binding energies to be in physiological range. Results of the study are coherent with experimental observations.ConclusionsThe findings of this study highlight basic structural principles shaping the molecular evolution of a lineage for significantly diverse antigens. Antibodies of a lineage follow different developmental pathways while preserving the imprint of the germline. From the study, it can be generalized that structural diversification of the paratope is an outcome of natural selection of a conformation from an available ensemble, which is further optimized for antigen interaction. The study establishes that starting from a common lineage, antibodies can mature to recognize a wide range of antigens. This hypothesis can be further tested and validated experimentally.Electronic supplementary materialThe online version of this article (10.1186/s12900-018-0096-1) contains supplementary material, which is available to authorized users.

PDZ domains are important peptide recognition modules which usually recognize short C-terminal stretches of their interaction partners, but certain PDZ domains can also recognize internal peptides in the interacting proteins. Due to the scarcity of data on internal peptide recognition and lack of understanding of the mechanistic details of internal peptide recognition, identification of PDZ domains capable of recognizing internal peptides has been a difficult task.Since Par-6 PDZ domain can recognize both C-terminal and internal peptides, we have carried out multiple explicit solvent MD simulations of 1 μs duration on free and peptide bound Par-6 PDZ to decipher mechanistic details of internal peptide recognition. These simulations have been analyzed to identify residues which play a crucial role in internal peptide recognition by PDZ domains. Based on the conservation profile of the identified residues, we have predicted 47 human PDZ domains to be capable of recognizing internal peptides in human. We have also investigated how binding of CDC42 to the CRIB domain adjacent to the Par6 PDZ allosterically modulate the peptide recognition by Par6 PDZ. Our MD simulations on CRIB-Par6_PDZ didomain in isolation as well as in complex with CDC42, indicate that in absence of CDC42 the adjacent CRIB domain induces open loop conformation of PDZ facilitating internal peptide recognition. On the other hand, upon binding of CDC42 to the CRIB domain, Par6 PDZ adopts closed loop conformation required for recognition of C-terminus peptides. These results provide atomistic details of how binding of interaction partners onto adjacent domains can allosterically regulate substrate binding to PDZ domains. In summary, MD simulations provide novel insights into the modulation of substrate recognition preference of PDZ by specific peptides, adjacent domains and binding of interaction partners at allosteric sites.

BackgroundPDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces.ResultsWe have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions.ConclusionsIn summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html.ReviewersThis article was reviewed by Michael Gromiha and Zoltán Gáspári.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0151-4) contains supplementary material, which is available to authorized users.