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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). [ABSTRACT FROM AUTHOR]

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 β3-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. • PfCDPK1 kinase - an important drug target, undergoes Ca2+ dependent conformational transitions through its CAD domain. • The study showed that CAD activates the kinase domain of PfCDPK1 allosterically. • The CAD stabilizes the conserved salt bridge in the active state of the kinase domain. • The Kinase:CAD domain interface provides the opportunity to design novel allosteric inhibitors for PfCDPK1. [ABSTRACT FROM AUTHOR]

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— Pf PKG, Plasmodium falciparum , Pf PKA, Pf PK7 and Pb CDPK4 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. [ABSTRACT FROM AUTHOR]

Author(s): Omita A. Trivedi; Pooja Arora; Vijayalakshmi Sridharan; Rashmi Tickoo; Debasisa Mohanty; Rajesh S. Gokhale (corresponding author) The metabolic repertoire in nature is augmented by generating hybrid metabolites from a [...]

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 com- pounds secreted and imported by Mtb for zinc acquisition. These molecules, termed kupyaphores, are produced by a 10.8 kbp bio- synthetic 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 dis- appeared 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. [ABSTRACT FROM AUTHOR]

Recent fragment‐based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS‐CoV‐2 Mpro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to Mpro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo‐ or small molecule fragment‐bound structures of Mpro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS‐CoV‐2 Mpro. It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC‐AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Background Enzymes belonging to acyl:CoA synthetase (ACS) superfamily activate wide variety of substrates and play major role in increasing the structural and functional diversity of various secondary metabolites in microbes and plants. However, due to the large sequence divergence within the superfamily, it is difficult to predict their substrate preference by annotation transfer from the closest homolog. Therefore, a large number of ACS sequences present in public databases lack any functional annotation at the level of substrate specificity. Recently, several examples have been reported where the enzymes showing high sequence similarity to luciferases or coumarate:CoA ligases have been surprisingly found to activate fatty acyl substrates in experimental studies. In this work, we have investigated the relationship between the substrate specificity of ACS and their sequence/structural features, and developed a novel computational protocol for in silico assignment of substrate preference. Results We have used a knowledge-based approach which involves compilation of substrate specificity information for various experimentally characterized ACS and derivation of profile HMMs for each subfamily. These HMM profiles can accurately differentiate probable cognate substrates from non-cognate possibilities with high specificity (Sp) and sensitivity (Sn) (Sn = 0.91-1.0, Sp = 0.96-1.0) values. Using homologous crystal structures, we identified a limited number of contact residues crucial for substrate recognition i.e. specificity determining residues (SDRs). Patterns of SDRs from different subfamilies have been used to derive predictive rules for correlating them to substrate preference. The power of the SDR approach has been demonstrated by correct prediction of substrates for enzymes which show apparently anomalous substrate preference. Furthermore, molecular modeling of the substrates in the active site has been carried out to understand the structural basis of substrate selection. A web based prediction tool http://www.nii.res.in/pred_acs_substr.html has been developed for automated functional classification of ACS enzymes. Conclusions We have developed a novel computational protocol for predicting substrate preference for ACS superfamily of enzymes using a limited number of SDRs. Using this approach substrate preference can be assigned to a large number of ACS enzymes present in various genomes. It can potentially help in rational design of novel proteins with altered substrate specificities.

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. [ABSTRACT FROM AUTHOR]

Abstract Background Secondary metabolites biosynthesized by polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) family of enzymes constitute several classes of therapeutically important natural products like erythromycin, rapamycin, cyclosporine etc. In view of their relevance for natural product based drug discovery, identification of novel secondary metabolite natural products by genome mining has been an area of active research. A number of different tailoring enzymes catalyze a variety of chemical modifications to the polyketide or nonribosomal peptide backbone of these secondary metabolites to enhance their structural diversity. Therefore, development of powerful bioinformatics methods for identification of these tailoring enzymes and assignment of their substrate specificity is crucial for deciphering novel secondary metabolites by genome mining. Results In this work, we have carried out a comprehensive bioinformatics analysis of methyltransferase (MT) domains present in multi functional type I PKS and NRPS proteins encoded by PKS/NRPS gene clusters having known secondary metabolite products. Based on the results of this analysis, we have developed a novel knowledge based computational approach for detecting MT domains present in PKS and NRPS megasynthases, delineating their correct boundaries and classifying them as N-MT, C-MT and O-MT using profile HMMs. Analysis of proteins in nr database of NCBI using these class specific profiles has revealed several interesting examples, namely, C-MT domains in NRPS modules, N-MT domains with significant homology to C-MT proteins, and presence of NRPS/PKS MTs in association with other catalytic domains. Our analysis of the chemical structures of the secondary metabolites and their site of methylation suggested that a possible evolutionary basis for the presence of a novel class of N-MT domains with significant homology to C-MT proteins could be the close resemblance of the chemical structures of the acceptor substrates, as in the case of pyochelin and yersiniabactin. These two classes of MTs recognize similar acceptor substrates, but transfer methyl groups to N and C positions on these substrates. Conclusion We have developed a novel knowledge based computational approach for identifying MT domains present in type I PKS and NRPS multifunctional enzymes and predicting their site of methylation. Analysis of nr database using this approach has revealed presence of several novel MT domains. Our analysis has also given interesting insight into the evolutionary basis of the novel substrate specificities of these MT proteins.

The remarkable substrate tolerance of the fatty acyl-CoA ligases (FACLs) to produce diverse acyl-CoAs and N-acetylcysteamine (NAC) analogues from their corresponding acids is reported. The acyl-activating enzymes (AAE) proteins provide an attractive route to expand the precursors available for polyketide biosynthesis.

Background: PDZ 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. Results: We 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. Conclusions: In 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. [ABSTRACT FROM AUTHOR]

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 Mg2+ to α and β 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. [ABSTRACT FROM AUTHOR]

With the recent discoveries of novel post-translational modifications (PTMs) which play important roles in signaling and biosynthetic pathways, identification of such PTM catalyzing enzymes by genome mining has been an area of major interest. Unlike well-known PTMs like phosphorylation, glycosylation, SUMOylation, no bioinformatics resources are available for enzymes associated with novel and unusual PTMs. Therefore, we have developed the novPTMenzy database which catalogs information on the sequence, structure, active site and genomic neighborhood of experimentally characterized enzymes involved in five novel PTMs, namely AMPylation, Eliminylation, Sulfation, Hydroxylation and Deamidation. Based on a comprehensive analysis of the sequence and structural features of these known PTM catalyzing enzymes, we have created Hidden Markov Model profiles for the identification of similar PTM catalyzing enzymatic domains in genomic sequences. We have also created predictive rules for grouping them into functional subfamilies and deciphering their mechanistic details by structure-based analysis of their active site pockets. These analytical modules have been made available as user friendly search interfaces of novPTMenzy database. It also has a specialized analysis interface for some PTMs like AMPylation and Eliminylation. The novPTMenzy database is a unique resource that can aid in discovery of unusual PTM catalyzing enzymes in newly sequenced genomes. Database URL: http://www.nii.ac.in/novptmenzy.html [ABSTRACT FROM AUTHOR]

Author(s): Omita A. Trivedi; Pooja Arora; Vijayalakshmi Sridharan; Rashmi Tickoo; Debasisa Mohanty; Rajesh S. Gokhale Nature 428, 441-445 (2004). The structures of lauroyl-AMP and lauroyl-CoA are incorrectly represented in Fig. [...]

Author(s): Omita A. Trivedi; Pooja Arora; Vijayalakshmi Sridharan; Rashmi Tickoo; Debasisa Mohanty; Rajesh S. Gokhale Nature 428, 441-445 (2004). In this Letter to Nature , lanes FadD17, -19, and -28 [...]

Bacterial phosphothreonine lyases catalyze a novel posttranslational modification involving formation of dehydrobutyrine/dehyroalanine by β elimination of the phosphate group of phosphothreonine or phosphoserine residues in their substrate proteins. Though there is experimental evidence for presence of dehydro amino acids in human proteins, no eukaryotic homologs of these lyases have been identified as of today. A comprehensive genome-wide search for identifying phosphothreonine lyase homologs in eukaryotes was carried out. Our fold-based search revealed structural and catalytic site similarity between bacterial phosphothreonine lyases and BLES03 (basophilic leukemia-expressed protein 03), a human protein with unknown function. Ligand induced conformational changes similar to bacterial phosphothreonine lyases, and movement of crucial arginines in the loop region to the catalytic pocket upon binding of phosphothreonine-containing peptides was seen during docking and molecular dynamics studies. Genome-wide search for BLES03 homologs using sensitive profile-based methods revealed their presence not only in eukaryotic classes such as chordata and fungi but also in bacterial and archaebacterial classes. The synteny of these archaebacterial BLES03-like proteins was remarkably similar to that of type IV lantibiotic synthetases which harbor LanL-like phosphothreonine lyase domains. Hence, context-based analysis reinforced our earlier sequence/structure-based prediction of phosphothreonine lyase catalytic function for BLES03. Our in silico analysis has revealed that BLES03-like proteins with previously unknown function are novel eukaryotic phosphothreonine lyases involved in biosynthesis of dehydro amino acids, whereas their bacterial and archaebacterial counterparts might be involved in biosynthesis of natural products similar to lantibiotics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Inhibitors of PDZ-peptide interactions have important implications in a variety of biological processes including treatment of cancer and Parkinson’s disease. Even though experimental studies have reported characterization of peptidomimetic inhibitors of PDZ-peptide interactions, the binding modes for most of them have not been characterized by structural studies. In this study we have attempted to understand the structural basis of the small molecule-PDZ interactions by in silico analysis of the binding modes and binding affinities of a set of 38 small molecules with known Ki or Kd values for PDZ2 and PDZ3 domains of PSD-95 protein. These two PDZ domains show differential selectivity for these compounds despite having a high degree of sequence similarity and almost identical peptide binding pockets. Optimum binding modes for these ligands for PDZ2 and PDZ3 domains were identified by using a novel combination of semi-flexible docking and explicit solvent molecular dynamics (MD) simulations. Analysis of the binding modes revealed most of the peptidomimectic ligands which had high Ki or Kd moved away from the peptide binding pocket, while ligands with high binding affinities remained in the peptide binding pocket. The differential specificities of the PDZ2 and PDZ3 domains primarily arise from differences in the conformation of the loop connecting βB and βC strands, because this loop interacts with the N-terminal chemical moieties of the ligands. We have also computed the MM/PBSA binding free energy values for these 38 compounds with both the PDZ domains from multiple 5 ns MD trajectories on each complex i.e. a total of 228 MD trajectories of 5 ns length each. Interestingly, computational binding free energies show good agreement with experimental binding free energies with a correlation coefficient of approximately 0.6. Thus our study demonstrates that combined use of docking and MD simulations can help in identification of potent inhibitors of PDZ-peptide complexes. [ABSTRACT FROM AUTHOR]

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. Plasmodiumfalciparum (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 regula- tory 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 Pfl:DPK1 activation. Unexpectedly, an ATP binding site in the NTE of PfCDPK1 was identified. Our studies highlight several novel features of Pfl2DPK1 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. [ABSTRACT FROM AUTHOR]

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 D84 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: The cell envelope of Mycobacterium tuberculosis (Mtb) possesses a repertoire of unusual lipids that are believed to play an important role in pathogenesis. In this review, we specifically focus on computational, biochemical and structural studies in lipid biosynthesis that have established functional role of polyketide synthases (PKSs) and fatty acyl-AMP ligases (FAALs). Mechanistic and structural studies with FAALs suggest that this group of proteins may have evolved from omnipresent fatty acyl-CoA ligases (FACLs). FAALs activate fatty acids as acyl-adenylates and transfer them on to the PKSs which then produce unusual acyl chains that are the components of mycobacterial lipids. FAALs are a newly discovered family of enzymes; whereas involvement of PKSs in lipid metabolism was not known prior to their discovery in Mtb. Since Mtb genome contains multiple homologs of FAALs and PKSs and owing to the conserved reaction mechanism and overlapping substrate specificity; there is tempting opportunity to develop ‘systemic drugs’ against these enzymes as anti-tuberculosis agents. [Copyright &y& Elsevier]

The life cycle of Dictyostelium discoideum is proposed to be regulated by expression of small metabolites. Genome sequencing studies have revealed a remarkable array of genes homologous to polyketide synthases (PKSs) that are known to synthesize secondary metabolites in bacteria and fungi. A crucial step in functional activation of PKSs involves their posttranslational modification catalyzed by phosphopantetheinyl transferases (PPTases). PPTases have been recently characterized from several bacteria; however, their relevance in complex life cycle of protozoa remains largely unexplored. Here we have identified and characterized two phosphopantetheinyl transferases from D. discoideum that exhibit distinct functional specificity. DiAcpS specifically modifies a stand-alone acyl carrier protein (ACP) that possesses a mitochondrial import signal. DiSfp in contrast is specific to Type I multifunctional PKS/fatty acid synthase proteins and cannot modify the stand-alone ACP. The mRNA of two PPTases can be detected during the vegetative as well as starvation-induced developmental pathway and the disruption of either of these genes results in non-viable amoebae. Our studies show that both PPTases play an important role in Dictyostelium biology and provide insight into the importance of PPTases in lower eukaryotes. [ABSTRACT FROM AUTHOR]

Background: Enzymes belonging to acyl:CoA synthetase (ACS) superfamily activate wide variety of substrates and play major role in increasing the structural and functional diversity of various secondary metabolites in microbes and plants. However, due to the large sequence divergence within the superfamily, it is difficult to predict their substrate preference by annotation transfer from the closest homolog. Therefore, a large number of ACS sequences present in public databases lack any functional annotation at the level of substrate specificity. Recently, several examples have been reported where the enzymes showing high sequence similarity to luciferases or coumarate: CoA ligases have been surprisingly found to activate fatty acyl substrates in experimental studies. In this work, we have investigated the relationship between the substrate specificity of ACS and their sequence/structural features, and developed a novel computational protocol for in silico assignment of substrate preference. Results: We have used a knowledge-based approach which involves compilation of substrate specificity information for various experimentally characterized ACS and derivation of profile HMMs for each subfamily. These HMM profiles can accurately differentiate probable cognate substrates from non-cognate possibilities with high specificity (Sp) and sensitivity (Sn) (Sn = 0.91-1.0, Sp = 0.96-1.0) values. Using homologous crystal structures, we identified a limited number of contact residues crucial for substrate recognition i.e. specificity determining residues (SDRs). Patterns of SDRs from different subfamilies have been used to derive predictive rules for correlating them to substrate preference. The power of the SDR approach has been demonstrated by correct prediction of substrates for enzymes which show apparently anomalous substrate preference. Furthermore, molecular modeling of the substrates in the active site has been carried out to understand the structural basis of substrate selection. A web based prediction tool http://www.nii.res.in/pred_acs_substr.html has been developed for automated functional classification of ACS enzymes. Conclusions: We have developed a novel computational protocol for predicting substrate preference for ACS superfamily of enzymes using a limited number of SDRs. Using this approach substrate preference can be assigned to a large number of ACS enzymes present in various genomes. It can potentially help in rational design of novel proteins with altered substrate specificities. [ABSTRACT FROM AUTHOR]

Sequence data arising from an increasing number of partial and complete genome projects is revealing the presence of the polyketide synthase (PKS) family of genes not only in microbes and fungi but also in plants and other eukaryotes. PKSs are huge multifunctional megasynthases that use a variety of biosynthetic paradigms to generate enormously diverse arrays of polyketide products that posses several pharmaceutically important properties. The remarkable conservation of these gene clusters across organisms offers abundant scope for obtaining novel insights into PKS biosynthetic code by computational analysis.We have carried out a comprehensive in silico analysis of modular and iterative gene clusters to test whether chemical structures of the secondary metabolites can be predicted from PKS protein sequences. Here, we report the success of our method and demonstrate the feasibility of deciphering the putative metabolic products of uncharacterized PKS clusters found in newly sequenced genomes. Profile Hidden Markov Model analysis has revealed distinct sequence features that can distinguish modular PKS proteins from their iterative counterparts. For iterative PKS proteins, structural models of iterative ketosynthase (KS) domains have revealed novel correlations between the size of the polyketide products and volume of the active site pocket. Furthermore, we have identified key residues in the substrate binding pocket that control the number of chain extensions in iterative PKSs. For modular PKS proteins, we describe for the first time an automated method based on crucial intermolecular contacts that can distinguish the correct biosynthetic order of substrate channeling from a large number of non-cognate combinatorial possibilities. Taken together, our in silico analysis provides valuable clues for formulating rules for predicting polyketide products of iterative as well as modular PKS clusters. These results have promising potential for discovery of novel natural products by genome mining and rational design of novel natural products. [ABSTRACT FROM AUTHOR]