Srikanth Duddela

Bio: Srikanth Duddela is an academic researcher from Saarland University. The author has contributed to research in topics: Protein ligand & Ligand (biochemistry). The author has an hindex of 4, co-authored 4 publications receiving 2187 citations. Previous affiliations of Srikanth Duddela include Indian Institute of Chemical Technology.

antiSMASH 3.0—a comprehensive resource for the genome mining of biosynthetic gene clusters

Abstract: Microbial secondary metabolism constitutes a rich source of antibiotics, chemotherapeutics, insecticides and other high-value chemicals. Genome mining of gene clusters that encode the biosynthetic pathways for these metabolites has become a key methodology for novel compound discovery. In 2011, we introduced antiSMASH, a web server and stand-alone tool for the automatic genomic identification and analysis of biosynthetic gene clusters, available at http://antismash.secondarymetabolites.org. Here, we present version 3.0 of antiSMASH, which has undergone major improvements. A full integration of the recently published ClusterFinder algorithm now allows using this probabilistic algorithm to detect putative gene clusters of unknown types. Also, a new dereplication variant of the ClusterBlast module now identifies similarities of identified clusters to any of 1172 clusters with known end products. At the enzyme level, active sites of key biosynthetic enzymes are now pinpointed through a curated pattern-matching procedure and Enzyme Commission numbers are assigned to functionally classify all enzyme-coding genes. Additionally, chemical structure prediction has been improved by incorporating polyketide reduction states. Finally, in order for users to be able to organize and analyze multiple antiSMASH outputs in a private setting, a new XML output module allows offline editing of antiSMASH annotations within the Geneious software.

Minimum Information about a Biosynthetic Gene cluster.

Abstract: A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.

Correlating chemical diversity with taxonomic distance for discovery of natural products in myxobacteria.

Abstract: Some bacterial clades are important sources of novel bioactive natural products. Estimating the magnitude of chemical diversity available from such a resource is complicated by issues including cultivability, isolation bias and limited analytical data sets. Here we perform a systematic metabolite survey of ~2300 bacterial strains of the order Myxococcales, a well-established source of natural products, using mass spectrometry. Our analysis encompasses both known and previously unidentified metabolites detected under laboratory cultivation conditions, thereby enabling large-scale comparison of production profiles in relation to myxobacterial taxonomy. We find a correlation between taxonomic distance and the production of distinct secondary metabolite families, further supporting the idea that the chances of discovering novel metabolites are greater by examining strains from new genera rather than additional representatives within the same genus. In addition, we report the discovery and structure elucidation of rowithocin, a myxobacterial secondary metabolite featuring an uncommon phosphorylated polyketide scaffold.

Probing the structure of human glucose transporter 2 and analysis of protein ligand interactions

Abstract: Diabetes is a metabolic disorder that has emerged recently as a major cause of global concern. Regulation of the blood glucose concentration is essential to maintain the homeostasis. GLUT2, a carrier protein, plays an important role in transporting sugar molecules across the membrane. To understand the function of this carrier molecule, knowledge of its three-dimensional structure is of paramount importance. Homology modeling approach was adopted to decipher the three-dimensional structure and features of human GLUT2. Ninety-eight percent residues of the modeled structure lie in the allowed region of the Ramachandran plot and a RMSD of 0.86 A with the template molecule confirms the reliability of the modeled structure. Comparative transmembrane helix prediction from primary sequence as well as analysis of model revealed presence of 12 helices, which is in agreement with the available literature. Molecular mechanical calculations and docking analysis were performed for the selected 33 compounds. Results showed Glipizide as the best interacting ligand based on the fitness values scored from the binding affinity and minimized energy of the docked complex. These results will aid in efficient designing of inhibitor molecules to curb diabetes.

antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline

Abstract: Secondary metabolites produced by bacteria and fungi are an important source of antimicrobials and other bioactive compounds. In recent years, genome mining has seen broad applications in identifying and characterizing new compounds as well as in metabolic engineering. Since 2011, the 'antibiotics and secondary metabolite analysis shell-antiSMASH' (https://antismash.secondarymetabolites.org) has assisted researchers in this, both as a web server and a standalone tool. It has established itself as the most widely used tool for identifying and analysing biosynthetic gene clusters (BGCs) in bacterial and fungal genome sequences. Here, we present an entirely redesigned and extended version 5 of antiSMASH. antiSMASH 5 adds detection rules for clusters encoding the biosynthesis of acyl-amino acids, β-lactones, fungal RiPPs, RaS-RiPPs, polybrominated diphenyl ethers, C-nucleosides, PPY-like ketones and lipolanthines. For type II polyketide synthase-encoding gene clusters, antiSMASH 5 now offers more detailed predictions. The HTML output visualization has been redesigned to improve the navigation and visual representation of annotations. We have again improved the runtime of analysis steps, making it possible to deliver comprehensive annotations for bacterial genomes within a few minutes. A new output file in the standard JavaScript object notation (JSON) format is aimed at downstream tools that process antiSMASH results programmatically.

Natural products in drug discovery: advances and opportunities.

Abstract: Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments — including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances — are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities. Natural products have historically made a major contribution to pharmacotherapy, but also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization. This Review discusses recent technological developments — including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances — that are enabling a revitalization of natural product-based drug discovery.

Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea

Abstract: We present two standards developed by the Genomic Standards Consortium (GSC) for reporting bacterial and archaeal genome sequences. Both are extensions of the Minimum Information about Any (x) Sequence (MIxS). The standards are the Minimum Information about a Single Amplified Genome (MISAG) and the Minimum Information about a Metagenome-Assembled Genome (MIMAG), including, but not limited to, assembly quality, and estimates of genome completeness and contamination. These standards can be used in combination with other GSC checklists, including the Minimum Information about a Genome Sequence (MIGS), Minimum Information about a Metagenomic Sequence (MIMS), and Minimum Information about a Marker Gene Sequence (MIMARKS). Community-wide adoption of MISAG and MIMAG will facilitate more robust comparative genomic analyses of bacterial and archaeal diversity.

antiSMASH 4.0-improvements in chemistry prediction and gene cluster boundary identification.

Abstract: Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the â € antibiotics and secondary metabolite analysis shell - antiSMASH' has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.

antiSMASH 6.0: improving cluster detection and comparison capabilities.

Abstract: Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the "antibiotics and secondary metabolite analysis shell-antiSMASH" (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters.