ProsmORF-pred: a machine learning-based method for the identification of small ORFs in prokaryotic genomes.

Small open reading frames (smORFs) encoding proteins less than 100 amino acids (aa) are known to be important regulators of key cellular processes. However, their computational identification remains a challenge. Based on a comprehensive analysis of known prokaryotic small ORFs, we have developed the ProsmORF-pred resource which uses a machine learning (ML)-based method for prediction of smORFs in the prokaryotic genome sequences. ProsmORF-pred consists of two ML models, one for initiation site recognition in nucleic acid sequences upstream of putative start codons and the other uses translated amino acid sequences to decipher functional protein like sequences. The nucleotide sequence-based initiation site recognition model has been trained using longer ORFs (>100 aa) in the same genome while the ML model for identification of protein like sequences has been trained using annotated smORFs from Escherichia coli. Comprehensive benchmarking of ProsmORF-pred reveals that its performance is comparable to other state-of-the-art approaches on the annotated smORF set derived from 32 prokaryotic genomes. Its performance is distinctly superior to other tools like PRODIGAL and RANSEPS for prediction of newly identified smORFs which have a length range of 10-30 aa, where prediction of smORFs has been a major challenge. Apart from identification of smORFs in genomic sequences, ProsmORF-pred can also aid in functional annotation of the predicted smORFs based on sequence similarity and genomic neighbourhood similarity searches in ProsmORFDB, a well-curated database of known smORFs. ProsmORF-pred along with its backend database ProsmORFDB is available as a user-friendly web server (http://www.nii.ac.in/prosmorfpred.html).

Kupyaphores are zinc homeostatic metallophores required for colonization of Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) endures a combination of metal scarcity and toxicity throughout the human infection cycle, contributing to complex clinical manifestations. Pathogens counteract this paradoxical dysmetallostasis by producing specialized metal trafficking systems. Capture of extracellular metal by siderophores is a widely accepted mode of iron acquisition, and Mtb iron-chelating siderophores, mycobactin, have been known since 1965. Currently, it is not known whether Mtb produces zinc scavenging molecules. Here, we characterize low-molecular-weight zinc-binding compounds secreted and imported by Mtb for zinc acquisition. These molecules, termed kupyaphores, are produced by a 10.8 kbp biosynthetic cluster and consists of a dipeptide core of ornithine and phenylalaninol, where amino groups are acylated with isonitrile-containing fatty acyl chains. Kupyaphores are stringently regulated and support Mtb survival under both nutritional deprivation and intoxication conditions. A kupyaphore-deficient Mtb strain is unable to mobilize sufficient zinc and shows reduced fitness upon infection. We observed early induction of kupyaphores in Mtb-infected mice lungs after infection, and these metabolites disappeared after 2 wk. Furthermore, we identify an Mtb-encoded isonitrile hydratase, which can possibly mediate intracellular zinc release through covalent modification of the isonitrile group of kupyaphores. Mtb clinical strains also produce kupyaphores during early passages. Our study thus uncovers a previously unknown zinc acquisition strategy of Mtb that could modulate host-pathogen interactions and disease outcome.

Pf-Phospho: a machine learning-based phosphorylation sites prediction tool for Plasmodium proteins.

Even though several in silico tools are available for prediction of the phosphorylation sites for mammalian, yeast or plant proteins, currently no software is available for predicting phosphosites for Plasmodium proteins. However, the availability of significant amount of phospho-proteomics data during the last decade and advances in machine learning (ML) algorithms have opened up the opportunities for deciphering phosphorylation patterns of plasmodial system and developing ML-based phosphosite prediction tools for Plasmodium. We have developed Pf-Phospho, an ML-based method for prediction of phosphosites by training Random Forest classifiers using a large data set of 12 096 phosphosites of Plasmodium falciparum and Plasmodium bergei. Of the 12 096 known phosphosites, 75% of sites have been used for training/validation of the classifier, while remaining 25% have been used as completely unseen test data for blind testing. It is encouraging to note that Pf-Phospho can predict the kinase-independent phosphosites with 84% sensitivity, 75% specificity and 78% precision. In addition, it can also predict kinase-specific phosphosites for five plasmodial kinases-PfPKG, Plasmodium falciparum, PfPKA, PfPK7 and PbCDPK4 with high accuracy. Pf-Phospho (http://www.nii.ac.in/pfphospho.html) outperforms other widely used phosphosite prediction tools, which have been trained using mammalian phosphoproteome data. It also has been integrated with other widely used resources such as PlasmoDB, MPMP, Pfam and recently available ML-based predicted structures by AlphaFold2. Currently, Pf-phospho is the only bioinformatics resource available for ML-based prediction of phospho-signaling networks of Plasmodium and is a user-friendly platform for integrative analysis of phospho-signaling along with metabolic and protein-protein interaction networks.

SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2.

Small molecule modulators of protein-protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase.

A machine learning-based method for prediction of macrocyclization patterns of polyketides and non-ribosomal peptides.

MOTIVATION: Even though genome mining tools have successfully identified large numbers of non-ribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) biosynthetic gene clusters (BGCs) in bacterial genomes, currently no tool can predict the chemical structure of the secondary metabolites biosynthesized by these BGCs. Lack of algorithms for predicting complex macrocyclization patterns of linear PK/NRP biosynthetic intermediates has been the major bottleneck in deciphering the final bioactive chemical structures of PKs/NRPs by genome mining. RESULTS: Using a large dataset of known chemical structures of macrocyclized PKs/NRPs, we have developed a machine learning (ML) algorithm for distinguishing the correct macrocyclization pattern of PKs/NRPs from the library of all theoretically possible cyclization patterns. Benchmarking of this ML classifier on completely independent datasets has revealed ROC-AUC and PR-AUC values of 0.82 and 0.81, respectively. This cyclization prediction algorithm has been used to develop SBSPKSv3, a genome mining tool for completely automated prediction of macrocyclized structures of NRPs/PKs. SBSPKSv3 has been extensively benchmarked on a dataset of over 100 BGCs with known PKs/NRPs products. AVAILABILITY AND IMPLEMENTATION: The macrocyclization prediction pipeline and all the datasets used in this study are freely available at http://www.nii.ac.in/sbspks3.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RiPPMiner: a bioinformatics resource for deciphering chemical structures of RiPPs based on prediction of cleavage and cross-links.

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.

SBSPKSv2: structure-based sequence analysis of polyketide synthases and non-ribosomal peptide synthetases.

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.

Deciphering evolution of immune recognition in antibodies.

BACKGROUND: Antibody, 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. RESULTS: A 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. CONCLUSIONS: The 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.

Molecular Dynamics Simulations for Deciphering the Structural Basis of Recognition of Pre-let-7 miRNAs by LIN28.

LIN28 protein inhibits biogenesis of miRNAs belonging to the let-7 family by binding to precursor forms of miRNAs. Overexpression of LIN28 and low levels of let-7 miRNAs are associated with several forms of cancer cells. We have performed multiple explicit solvent molecular dynamics simulations ranging from 200 to 500 ns in length on different isoforms of preE-let-7 in complex with LIN28 and also in isolation to identify structural features and key specificity-determining residues (SDRs) that are important for the inhibitory role of LIN28. Our simulations suggest that a conserved structural feature of the loop regions of preE-let-7 miRNAs is more important for LIN28 recognition than sequence conservation among members of the let-7 family or the presence of the GGAG motif in the 3' region. The loop region consisting of a minimum of five nucleotides helps pre-miRNAs to acquire a conformation ideal for binding to LIN28, but pre-let-7c-2 prefers a conformation with a three-nucleotide loop. Thus, our simulations provide a theoretical rationale for the recent experimental observation of the escape of LIN28-mediated repression by pre-let-7c-2. The essential structural and sequence features highlighted in this study might aid in designing synthetic small molecule inhibitors for modulating LIN28-let-7 interaction in malignant cells. We have also identified crucial SDRs of the LIN28-preE-let-7 complex involving 13 residues of LIN28 and 10 residues of the pre-miRNA. On the basis of the conservation profile of these 13 SDRs, we have identified 10 novel proteins that are not annotated as LIN28 like but are similar in sequence, domain, or fold level to LIN28.

Deciphering the Molecular Basis of Functional Divergence in AMPylating Enzymes by Molecular Dynamics Simulations and Structure Guided Phylogeny.

The Fic domain was recently shown to catalyze AMPylation-the transfer of AMP from ATP to hydroxyl side chains of diverse eukaryotic proteins, ranging from RhoGTPases to chaperon BiP. We have carried out a series of explicit solvent molecular dynamics (MD) simulations up to 1 µs duration on the apo, holo, and substrate/product bound IbpA Fic domain (IbpAFic2). Simulations on holo-IbpAFic2 revealed that binding of Mg(2+) to α and ß phosphates is crucial for preserving catalytically important contacts involving ATP. Comparative analysis of the MD trajectories demonstrated how binding of ATP allosterically induces conformational changes in the distal switch II binding region of Fic domains thereby aiding in substrate recognition. Our simulations have also identified crucial aromatic-aromatic interactions which stabilize the orientation of the catalytic histidine for inline nucleophilic attack during AMPylation, thus providing a structural basis for the evolutionary conservation of these aromatic residue pairs in Fic domains. On the basis of analysis of interacting interface residue pairs that persist over the microsecond trajectory, we identified a tetrapeptide stretch involved in substrate recognition. The structure-based genome-wide search revealed a distinct conservation pattern for this segment in different Fic subfamilies, further supporting its proposed role in substrate recognition. In addition, combined use of simulations and phylogenetic analysis has helped in the discovery of a new subfamily of Fic proteins that harbor a conserved Lys/Arg in place of the inhibitory Glu of the regulatory helix. We propose the novel possibility of auto-enhancement of AMPylation activity in this new subfamily via the movement of regulatory helix, in contrast to auto-inhibition seen in most Fic proteins.

Deciphering kinase-substrate relationships by analysis of domain-specific phosphorylation network.

MOTIVATION: In silico prediction of site-specific kinase-substrate relationships (ssKSRs) is crucial for deciphering phosphorylation networks by linking kinomes to phosphoproteomes. However, currently available predictors for ssKSRs give rise to a large number of false-positive results because they use only a short sequence stretch around phosphosite as determinants of kinase specificity and do not consider the biological context of kinase-substrate recognition. RESULTS: Based on the analysis of domain-specific kinase-substrate relationships, we have constructed a domain-level phosphorylation network that implicitly incorporates various contextual factors. It reveals preferential phosphorylation of specific domains by certain kinases. These novel correlations have been implemented in PhosNetConstruct, an automated program for predicting target kinases for a substrate protein. PhosNetConstruct distinguishes cognate kinase-substrate pairs from a large number of non-cognate combinations. Benchmarking on independent datasets using various statistical measures demonstrates the superior performance of PhosNetConstruct over ssKSR-based predictors. AVAILABILITY AND IMPLEMENTATION: PhosNetConstruct is freely available at http://www.nii.ac.in/phosnetconstruct.html.

Mechanism of autophosphorylation of mycobacterial PknB explored by molecular dynamics simulations.

Mycobacterial Ser/Thr kinase, PknB, is essential for the growth of the pathogen. Unphosphorylated PknB is catalytically inactive, and its activation requires autophosphorylation of Thr residues on the activation loop. Autophosphorylation can in principle take place via two distinct mechanisms. Intermolecular trans autophosphorylation involves dimerization and phosphorylation of the activation loop of one chain in the catalytic pocket of the other chain. On the other hand, intramolecular cis autophosphorylation involves phosphorylation of the activation loop of the kinases in its own catalytic pocket within a monomer. On the basis of the crystal structure of PknB in the front-to-front dimeric form, it is currently believed that activation of PknB involves trans autophosphorylation. However, because of the lack of coordinates of the activation loop in the crystal structures, atomic details of the conformational changes associated with activation are yet to be deciphered. Therefore, to understand the conformational transitions associated with activation via autophosphorylation, a series of explicit solvent molecular dynamics simulations with a duration of 1 µs have been performed on each of the phosphorylated and nonphosphorylated forms of the PknB catalytic domain in monomeric and dimeric states. Simulations on phosphorylated PknB revealed a differential network of crucial electrostatic and hydrophobic residues that stabilize the phosphorylated form in the active conformation. Interestingly, in our simulations on nonphosphorylated monomers, the activation loop was observed to fold into its own active site, thereby opening the novel possibility of activation through intramolecular cis autophosphorylation. Thus, our simulations suggest that autophosphorylation of PknB might also involve cis initiation followed by trans amplification as reported for other eukaryotic kinases based on recent reaction kinetics studies.

RECQL4 is essential for the transport of p53 to mitochondria in normal human cells in the absence of exogenous stress.

Mutations in RECQL4 helicase are associated with Rothmund-Thomson syndrome (RTS). A subset of RTS patients is predisposed to cancer and is sensitive to DNA damaging agents. The enhanced sensitivity of cells from RTS patients correlates with the accumulation of transcriptionally active nuclear p53. We found that in untreated normal human cells these two nuclear proteins, p53 and RECQL4, instead colocalize in the mitochondrial nucleoids. RECQL4 accumulates in mitochondria in all phases of the cell cycle except S phase and physically interacts with p53 only in the absence of DNA damage. p53-RECQL4 binding leads to the masking of the nuclear localization signal of p53. The N-terminal 84 amino acids of RECQL4 contain a mitochondrial localization signal, which causes the localization of RECQL4-p53 complex to the mitochondria. RECQL4-p53 interaction is disrupted after stress, allowing p53 translocation to the nucleus. In untreated normal cells RECQL4 optimizes de novo replication of mtDNA, which is consequently decreased in fibroblasts from RTS patients. Wild-type RECQL4-complemented RTS cells show relocalization of both RECQL4 and p53 to the mitochondria, loss of p53 activation, restoration of de novo mtDNA replication and resistance to different types of DNA damage. In cells expressing Δ84 RECQL4, which cannot translocate to mitochondria, all the above functions are compromised. The recruitment of p53 to the sites of de novo mtDNA replication is also regulated by RECQL4. Thus these findings elucidate the mechanism by which p53 is regulated by RECQL4 in unstressed normal cells and also delineates the mitochondrial functions of the helicase.

Structure-based multiscale approach for identification of interaction partners of PDZ domains.

PDZ domains are peptide recognition modules which mediate specific protein-protein interactions and are known to have a complex specificity landscape. We have developed a novel structure-based multiscale approach which identifies crucial specificity determining residues (SDRs) of PDZ domains from explicit solvent molecular dynamics (MD) simulations on PDZ-peptide complexes and uses these SDRs in combination with knowledge-based scoring functions for proteomewide identification of their interaction partners. Multiple explicit solvent simulations ranging from 5 to 50 ns duration have been carried out on 28 PDZ-peptide complexes with known binding affinities. MM/PBSA binding energy values calculated from these simulations show a correlation coefficient of 0.755 with the experimental binding affinities. On the basis of the SDRs of PDZ domains identified by MD simulations, we have developed a simple scoring scheme for evaluating binding energies for PDZ-peptide complexes using residue based statistical pair potentials. This multiscale approach has been benchmarked on a mouse PDZ proteome array data set by calculating the binding energies for 217 different substrate peptides in binding pockets of 64 different mouse PDZ domains. Receiver operating characteristic (ROC) curve analysis indicates that, the area under curve (AUC) values for binder vs nonbinder classification by our structure based method is 0.780. Our structure based method does not require experimental PDZ-peptide binding data for training.

Retrobiosynthetic approach delineates the biosynthetic pathway and the structure of the acyl chain of mycobacterial glycopeptidolipids.

Glycopeptidolipids (GPLs) are dominant cell surface molecules present in several non-tuberculous and opportunistic mycobacterial species. GPLs from Mycobacterium smegmatis are composed of a lipopeptide core unit consisting of a modified C(26)-C(34) fatty acyl chain that is linked to a tetrapeptide (Phe-Thr-Ala-alaninol). The hydroxyl groups of threonine and terminal alaninol are further modified by glycosylations. Although chemical structures have been reported for 16 GPLs from diverse mycobacteria, there is still ambiguity in identifying the exact position of the hydroxyl group on the fatty acyl chain. Moreover, the enzymes involved in the biosynthesis of the fatty acyl component are unknown. In this study we show that a bimodular polyketide synthase in conjunction with a fatty acyl-AMP ligase dictates the synthesis of fatty acyl chain of GPL. Based on genetic, biochemical, and structural investigations, we determine that the hydroxyl group is present at the C-5 position of the fatty acyl component. Our retrobiosynthetic approach has provided a means to understand the biosynthesis of GPLs and also resolve the long-standing debate on the accurate structure of mycobacterial GPLs.

Novel insights into the regulation of malarial calcium-dependent protein kinase 1.

Calcium-dependent protein kinases (CDPKs) are major effectors of calcium signaling in apicomplexan parasites like Toxoplasma and Plasmodium and control important processes of the parasite life cycle. Despite recently reported crystal structures of Toxoplasma gondii (Tg)CDPKs, several important questions about their regulation remain unanswered. Plasmodium falciparum (Pf)CDPK1 has emerged as a key player in the life cycle of the malaria parasite, as it may be involved in the invasion of the host cells. Molecular modeling and site-directed mutagenesis studies on PfCDPK1 suggested that several residues in the regulatory domain play a dual role, as they seem to contribute to the stabilization of both the active and inactive kinase. Mass spectrometry revealed that PfCDPK1 was autophosphorylated at several sites; some of these were placed at strategic locations and therefore were found to be critical for kinase activation. The N-terminal extension of PfCDPK1 was found to be important for PfCDPK1 activation. Unexpectedly, an ATP binding site in the NTE of PfCDPK1 was identified. Our studies highlight several novel features of PfCDPK1 regulation, which may be shared by other members of the CDPK family. These findings may also aid design of inhibitors against these important targets, which are absent from the host.

Respiratory Quinone Switches from Menaquinone to Polyketide Quinone during the Development Cycle in Streptomyces sp. Strain MNU77.

Type III polyketide synthases (PKSs) found across Streptomyces species are primarily known for synthesis of a vast repertoire of clinically and industrially relevant secondary metabolites. However, our understanding of the functional relevance of these bioactive metabolites in Streptomyces physiology is still limited. Recently, a role of type III PKS harboring gene cluster in producing alternate electron carrier, polyketide quinone (PkQ) was established in a related member of the Actinobacteria, Mycobacteria, highlighting the critical role these secondary metabolites play in primary cellular metabolism of the producer organism. Here, we report the developmental stage-specific transcriptional regulation of homologous type III PKS containing gene cluster in freshwater Streptomyces sp. strain MNU77. Gene expression analysis revealed the type III PKS gene cluster to be stringently regulated, with significant upregulation observed during the dormant sporulation stage of Streptomyces sp. MNU77. In contrast, the expression levels of only known electron carrier, menaquinone biosynthetic genes were interestingly found to be downregulated. Our liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analysis of a metabolite extract from the Streptomyces sp. MNU77 spores also showed 10 times more metabolic abundance of PkQs than menaquinones. Furthermore, through heterologous complementation studies, we demonstrate that Streptomyces sp. MNU77 type III PKS rescues a respiratory defect of the Mycobacterium smegmatis type III PKS deletion mutant. Together, our studies reveal that freshwater Streptomyces sp. MNU77 robustly produces novel PkQs during the sporulation stage, suggesting utilization of PkQs as alternate electron carriers across Actinobacteria during dormant hypoxic conditions. IMPORTANCE The complex developmental life cycle of Streptomyces sp. mandates efficient cellular respiratory reconfiguration for a smooth transition from aerated nutrient-rich vegetative hyphal growth to the hypoxic-dormant sporulation stage. Polyketide quinones (PkQs) have recently been identified as a class of alternate electron carriers from a related member of the Actinobacteria, Mycobacteria, that facilitates maintenance of membrane potential in oxygen-deficient niches. Our studies with the newly identified freshwater Streptomyces sp. strain MNU77 show conditional transcriptional upregulation and metabolic abundance of PkQs in the spore state of the Streptomyces life cycle. In parallel, the levels of menaquinones, the only known Streptomyces electron carrier, were downregulated, suggesting deployment of PkQs as universal electron carriers in low-oxygen, unfavorable conditions across the Actinobacteria family.

Modeling holo-ACP:DH and holo-ACP:KR complexes of modular polyketide synthases: a docking and molecular dynamics study.

BACKGROUND: Modular polyketide synthases are multifunctional megasynthases which biosynthesize a variety of secondary metabolites using various combinations of dehydratase (DH), ketoreductase (KR) and enoyl-reductase (ER) domains. During the catalysis of various reductive steps these domains act on a substrate moiety which is covalently attached to the phosphopantetheine (P-pant) group of the holo-Acyl Carrier Protein (holo-ACP) domain, thus necessitating the formation of holo-ACP:DH and holo-ACP:KR complexes. Even though three dimensional structures are available for DH, KR and ACP domains, no structures are available for DH or KR domains in complex with ACP or substrate moieties. Since Ser of holo-ACP is covalently attached to a large phosphopantetheine group, obtaining complexes involving holo-ACP by standard protein-protein docking has been a difficult task. RESULTS: We have modeled the holo-ACP:DH and holo-ACP:KR complexes for identifying specific residues on DH and KR domains which are involved in interaction with ACP, phosphopantetheine and substrate moiety. A novel combination of protein-protein and protein-ligand docking has been used to first model complexes involving apo-ACP and then dock the phosphopantetheine and substrate moieties using covalent connectivity between ACP, phosphopantetheine and substrate moiety as constraints. The holo-ACP:DH and holo-ACP:KR complexes obtained from docking have been further refined by restraint free explicit solvent MD simulations to incorporate effects of ligand and receptor flexibilities. The results from 50 ns MD simulations reveal that substrate enters into a deep tunnel in DH domain while in case of KR domain the substrate binds a shallow surface exposed cavity. Interestingly, in case of DH domain the predicted binding site overlapped with the binding site in the inhibitor bound crystal structure of FabZ, the DH domain from E.Coli FAS. In case of KR domain, the substrate binding site identified by our simulations was in proximity of the known stereo-specificity determining residues. CONCLUSIONS: We have modeled the holo-ACP:DH and holo-ACP:KR complexes and identified the specific residues on DH and KR domains which are involved in interaction with ACP, phosphopantetheine and substrate moiety. Analysis of the conservation profile of binding pocket residues in homologous sequences of DH and KR domains indicated that, these results can also be extrapolated to reductive domains of other modular PKS clusters.

SBSPKS: structure based sequence analysis of polyketide synthases.

Polyketide synthases (PKSs) catalyze biosynthesis of a diverse family of pharmaceutically important secondary metabolites. Bioinformatics analysis of sequence and structural features of PKS proteins plays a crucial role in discovery of new natural products by genome mining, as well as in design of novel secondary metabolites by biosynthetic engineering. The availability of the crystal structures of various PKS catalytic and docking domains, and mammalian fatty acid synthase module prompted us to develop SBSPKS software which consists of three major components. Model_3D_PKS can be used for modeling, visualization and analysis of 3D structure of individual PKS catalytic domains, dimeric structures for complete PKS modules and prediction of substrate specificity. Dock_Dom_Anal identifies the key interacting residue pairs in inter-subunit interfaces based on alignment of inter-polypeptide linker sequences to the docking domain structure. In case of modular PKS with multiple open reading frames (ORFs), it can predict the cognate order of substrate channeling based on combinatorial evaluation of all possible interface contacts. NRPS-PKS provides user friendly tools for identifying various catalytic domains in the sequence of a Type I PKS protein and comparing them with experimentally characterized PKS/NRPS clusters cataloged in the backend databases of SBSPKS. SBSPKS is available at http://www.nii.ac.in/sbspks.html.

Allosteric regulation of the inactive to active state conformational transition in CDPK1 protein of Plasmodium falciparum.

The aim of the current study is to investigate the role of the CAD domain in the activation mechanism of calcium dependent protein kinase-1 of Plasmodium falciparum (PfCDPK1) and explore the possibility of allosteric inhibition of this kinase. PfCDPK1 belongs to CDPK family of apicomplexan kinases which have a C-terminal CAD domain. Microsecond scale MD simulations were performed on modeled structures of complete PfCDPK1 and its kinase domain alone. The simulations revealed that in absence of CAD the salt bridge between Glu116 in αC-helix and Lys85 in ß3-sheet of kinase breaks after 200 ns resulting in inactive conformation of the kinase, but the salt bridge stays intact in the complete protein stabilizing it in active conformation. These results highlight the novel CAD mediated allosteric stabilization of the crucial salt bridge which is a hallmark of active conformation of kinase domains. The mechanistic details of the allosteric activation revealed by our study, opens up the possibility for design of allosteric inhibitors of PfCDPK1 kinase by disrupting the kinase:CAD interactions. Using a combination of machine learning and structure-based in silico screening, we have identified novel PPI modulators for allosteric inactivation of PfCDPK1 kinase.