A mass spectrometry-guided genome mining approach for natural product peptidogenomics.

Peptide natural products show broad biological properties and are commonly produced by orthogonal ribosomal and nonribosomal pathways in prokaryotes and eukaryotes. To harvest this large and diverse resource of bioactive molecules, we introduce here natural product peptidogenomics (NPP), a new MS-guided genome-mining method that connects the chemotypes of peptide natural products to their biosynthetic gene clusters by iteratively matching de novo tandem MS (MS(n)) structures to genomics-based structures following biosynthetic logic. In this study, we show that NPP enabled the rapid characterization of over ten chemically diverse ribosomal and nonribosomal peptide natural products of previously unidentified composition from Streptomycete bacteria as a proof of concept to begin automating the genome-mining process. We show the identification of lantipeptides, lasso peptides, linardins, formylated peptides and lipopeptides, many of which are from well-characterized model Streptomycetes, highlighting the power of NPP in the discovery of new peptide natural products from even intensely studied organisms

Identification of macromolecular assemblies and determination of their structures

To understand the workings of a living cell, we need to identify its molecular components and determine how they associate with each other. To date, studies that identify new macromolecular assemblies have been mainly limited to low-throughput biochemistry assays. Structures of macromolecular assemblies also have been difficult to obtain, and are mostly available for a small subset of individual components or their assemblies amenable to conventional approaches, such as X-ray crystallography and nuclear magnetic resonance spectroscopy. In this dissertation, I describe my contributions to the development of four novel pipelines that utilize new technologies and datasets to facilitate the identification of macromolecular assemblies and the determination of their structures. First, we designed an algorithm to identify genes coding for biosynthetic macromolecular assemblies. Second, we developed a platform to identify previously unknown HIV-human protein assemblies based on affinity purification, mass spectrometry, and computational scoring. Third, to aid the structure determination of macromolecular assemblies that are challenging to isolate and purify, we proposed a new strategy based on in vivo measurements of genetic interaction and integrative modeling. Finally, to facilitate rational discovery of small molecule modulators of macromolecular assemblies and their components, we presented a new approach based on computational identification of putative ligand-binding sites that are not detectable in ligand-free structures, due to insufficient structure resolutions or flatness in the absence of a ligand

Unexpected evolutionary relationships within the rapamycin family.

<p><b>a</b>, Distinct scaffolds produced by pathways from related BGCs. The scatter plot shows the relationship between the sequence homology of a pair of BGCs (x-axis) and the structural homology of their small molecule products (y-axis), compared to rapamycin and its BGC. Each circle represents a gene cluster and its small molecule product. Meridamycin and FK520 are closely related to rapamycin, as are their BGCs. While the pladienolide BGC is closely related to the rapamycin BGC, the structure of pladienolide itself is not very similar to that of rapamycin. In particular, pladienolide has a much smaller macrocycle and lacks shikimate- or pipecolate-derived moieties, and, as a result, binds to a distinct protein target. Structural similarity is estimated by the Tanimoto coefficient using linear-path fingerprints (FP2) from Open Babel <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004016#pcbi.1004016-OBoyle1" target="_blank">[67]</a>, while sequence homology is represented as the Jaccard index defined on pairs of Pfam domains that share sequence identities within the top 10^th percentile of all-pair sequence identities. The number of domain pairs that share sequence identities within the top 10^th percentile and sequence identity of all domain pairs are shown as point sizes and colors, respectively. <b>b</b>, The role of concerted evolution in homogenizing domains within a BGC. Phylogenetic trees of KS and AT domains from the rapamycin, FK520, meridamycin, and pladienolide BGCs are shown (for detailed trees with accession numbers and bootstrap values, see <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004016#pcbi.1004016.s011" target="_blank">Figure S11</a></b>). The KS and AT sequences largely cluster into BGC-specific clades; for the AT domains, this is even the case for two different clusters encoding the same compound (meridamycin), showing the ability of concerted evolution to homogenize domains within a BGC. <b>c</b>, Chemical structures of rapamycin, meridamycin, FK520 and pladienolide. The sub-structure shared among rapamycin, meridamycin and FK520 is colored red, and the domains responsible for the biosynthesis of this sub-structure in each molecule are indicated with red circles in <b>b</b>.</p

The rapid and dynamic evolution of BGCs differs from the evolution of ribosomal gene clusters and primary metabolism.

<p><b>a</b>, Distributions of the best matching sequence homologs with respect to organism similarity (based on 16S rRNA) for predicted BGCs and histidine operons suggest significant differences in the ways they evolve. <b>b</b>, Number of detected rearrangements, indels and duplications plotted against the average percent identity in the aligned gene cluster pairs from which the events were deduced for predicted BGCs (top) and ribosomal gene clusters (bottom). Ribosomal gene clusters were selected for comparison based on their relatively large sizes (∼10–15 kb) compared to primary metabolic operons; to obtain a fair comparison with BGCs, only gene clusters of sizes 5–15 kb were taken into account. Counts are based on a systematic comparison of all gene clusters in our data set that share regions of >1000 bp with >70% identity, in which events were inferred from alignments of such 1000 bp blocks. Of the 10,096 BGC pairs meeting these criteria, 1,750 had a rearrangement, 1,140 had an indel, and 135 had a duplication, each of which were far more common than the corresponding evolutionary events in gene clusters encoding the translation apparatus. Interestingly, while indels and rearrangements could be detected in ∼16% and ∼19% of BGCs of all sizes, duplications are found far more commonly in gene clusters with sizes of >40 kb (7.6%) than in gene clusters with sizes of 10–20 kb (0.3%), suggesting a possible role for duplication and divergence in the evolution of large gene clusters. <b>c</b>, Size distribution of inserted/deleted fragments during recent gene cluster evolution, based on the indel analysis.</p

The Cyclin K/Cdk12 complex maintains genomic stability via regulation of expression of DNA damage response genes

Blazek et al. define the composition of human CycK complexes and describe CycK's association with Cdk12 and Cdk13 in two functionally distinct complexes. CycK/Cdk12 plays a key role in the cellular response to DNA damage by regulating the expression of DNA damage-responsive genes, including the critical regulators of genomic stability: BRCA1, ATR, FANC1, and FANCD2

decRiPPter datasets - Integration of machine learning and pan-genomics expands the biosynthetic landscape of RiPP natural products

Datasets for decRiPPter, a genome mining tool for novel types of ribosomally synthesized and post-translationally modified peptides (RiPPs). 1) All training data for the SVM and the scripts used to generate them, 2) The output from the analysis of 1,295 Streptomyces genomes, passed through the 'mild' and the 'strict' filter

A mass spectrometry-guided genome mining approach for natural product peptidogenomics.

Peptide natural products show broad biological properties and are commonly produced by orthogonal ribosomal and nonribosomal pathways in prokaryotes and eukaryotes. To harvest this large and diverse resource of bioactive molecules, we introduce here natural product peptidogenomics (NPP), a new MS-guided genome-mining method that connects the chemotypes of peptide natural products to their biosynthetic gene clusters by iteratively matching de novo tandem MS (MS(n)) structures to genomics-based structures following biosynthetic logic. In this study, we show that NPP enabled the rapid characterization of over ten chemically diverse ribosomal and nonribosomal peptide natural products of previously unidentified composition from Streptomycete bacteria as a proof of concept to begin automating the genome-mining process. We show the identification of lantipeptides, lasso peptides, linardins, formylated peptides and lipopeptides, many of which are from well-characterized model Streptomycetes, highlighting the power of NPP in the discovery of new peptide natural products from even intensely studied organisms

Molecular Architecture and Function of the SEA Complex, a Modulator of the TORC1 Pathway*

The TORC1 signaling pathway plays a major role in the control of cell growth and response to stress. Here we demonstrate that the SEA complex physically interacts with TORC1 and is an important regulator of its activity. During nitrogen starvation, deletions of SEA complex components lead to Tor1 kinase delocalization, defects in autophagy, and vacuolar fragmentation. TORC1 inactivation, via nitrogen deprivation or rapamycin treatment, changes cellular levels of SEA complex members. We used affinity purification and chemical cross-linking to generate the data for an integrative structure modeling approach, which produced a well-defined molecular architecture of the SEA complex and showed that the SEA complex comprises two regions that are structurally and functionally distinct. The SEA complex emerges as a platform that can coordinate both structural and enzymatic activities necessary for the effective functioning of the TORC1 pathway

IMG-ABC: An Atlas of Biosynthetic Gene Clusters to Fuel the Discovery of Novel Secondary Metabolites

In the discovery of secondary metabolites (SMs), large-scale analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack ofrelevant computational resources. We present IMG-ABC (https://img.jgi.doe.gov/abc/) -- An Atlas of Biosynthetic gene Clusters within the Integrated Microbial Genomes(IMG) system1. IMG-ABC is a rich repository of both validated and predicted biosynthetic clusters (BCs) in cultured isolates, single-cells and metagenomes linked withthe SM chemicals they produce and enhanced with focused analysis tools within IMG. The underlying scalable framework enables traversal of phylogenetic dark matter and chemical structure space -- serving as a doorway to a new era in the discovery of novel molecules