Superimposé: a 3D structural superposition server

The Superimposé webserver performs structural similarity searches with a preference towards 3D structure-based methods. Similarities can be detected between small molecules (e.g. drugs), parts of large structures (e.g. binding sites of proteins) and entire proteins. For this purpose, a number of algorithms were implemented and various databases are provided. Superimposé assists the user regarding the selection of a suitable combination of algorithm and database. After the computation on our server infrastructure, a visual assessment of the results is provided. The structure-based in silico screening for similar drug-like compounds enables the detection of scaffold-hoppers with putatively similar effects. The possibility to find similar binding sites can be of special interest in the functional analysis of proteins. The search for structurally similar proteins allows the detection of similar folds with different backbone topology. The Superimposé server is available at: http://bioinformatics.charite.de/superimpose

Screening, identification, and antibiotic activity of secondary metabolites of Penicillium sp. LPB2019K3-2 isolated from endemic amphipods of Lake Baikal

This study aimed to assess the influence of nutrient media content on the production of antibiotics and the ability of water fungi isolated from lake Baikal to synthesize novel natural products. Interest in this topic stems from the high demand for new drugs, and studies are carried out via the screening of new natural products with biological activity produced by unstudied or extremophilic microorganisms. For this study, a strain of Penicillium sp. was isolated from endemic Baikal phytophagous amphipod species. Here, we identified natural products using the following classical assays: biotechnological cultivation, MALDI identification of the strain, natural product extraction, antimicrobial activity determination, and modern methods such as HPLC-MS for the dereplication and description of natural products. It was found that many detected metabolites were not included in the most extensive database. Most of the identified metabolites were characterized by their biological activity and demonstrated antibiotic activity against model Gram-positive and Gram-negative bacteria. The isolated strain of water fungus produced penicolinate B, meleagrin A, austinoneol A, andrastin A, and other natural products. Additionally, we show that the synthesis of low-molecular-weight natural products depends on the composition of the microbiological nutrient media used for cultivation. Thus, although the golden age of antibiotics ended many years ago and microscopic fungi are well studied producers of known antibiotics, the water fungi of the Lake Baikal ecosystem possess great potential in the search for new natural products for the development of new drugs. These natural products can become new pharmaceuticals and can be used in therapy to treat new diseases such as SARS, MERS, H5N1, etc

Discovery and Heterologous Production of New Cyclic Depsibosamycins

Streptomyces are producers of valuable secondary metabolites with unique scaffolds that perform a plethora of biological functions. Nonribosomal peptides are of special interest due to their variety and complexity. They are synthesized by nonribosomal peptide synthetases, large biosynthetic machineries that are encoded in the genome of many Streptomyces species. The identification of new peptides and the corresponding biosynthetic gene clusters is of major interest since knowledge can be used to facilitate combinatorial biosynthesis and chemical semisynthesis of natural products. The recently discovered bosamycins are linear octapeptides with an interesting 5-OMe tyrosine moiety and various modifications at the N-terminus. In this study, the new cyclic depsibosamycins B, C, and D from Streptomyces aurantiacus LU19075 were discovered. In comparison to the linear bosamycins B, C, and D, which were also produced by the strain, the cyclic depsibosamycins showed a side-chain-to-tail lactonization of serine and glycine, leading to a ring of four amino acids. In silico identification and heterologous expression of the depsibosamycin (dbm) gene cluster indicated that the cyclic peptides, rather than the linear derivatives, are the main products of the cluster

Pathway Hunter Tool (PHT) � A Platform for Metabolic Network Analysis and Potential Drug Targeting

Metabolic network analysis will play a major role in �Systems Biology� in the future as they represent the backbone of molecular activity within the cell. Recent studies have taken a comparative approach toward interpreting these networks, contrasting networks of different species and molecular types, and under varying conditions. We have developed a robust algorithm to calculate shortest path in the metabolic network using metabolite chemical structure information. A divide and conquer technique using Maximal Common Subgraph (MCS) approach and binary fingerprint was used to map each substrate onto its corresponding product. Then for the calculation of the shortest paths (using modified Breadth First Search algorithm) the two biochemical criteria �local� and �global� structural similarity were used, where �local similarity� is defined as the similarity between two intermediate molecules and �global similarity� is defined as the amount of conserved structure found between the source metabolite and the destination metabolites after a series of reaction steps. The pathway alignment was introduced to find enzyme(s) preference in the pathway of various organisms (a local and global outlook to metabolic networks). This was also used to predict potentially missing enzymes in the pathway. A novel concept called �load points� and �choke points� identifies hot spots in the network. This was used to find important enzymes in the pathogens metabolic network for potential drug targets

New Alpiniamides From Streptomyces sp. IB2014/011-12 Assembled by an Unusual Hybrid Non-ribosomal Peptide Synthetase Trans-AT Polyketide Synthase Enzyme

The environment of Lake Baikal is a well-known source of microbial diversity. The strain Streptomyces sp. IB2014/011-12, isolated from samples collected at Lake Baikal, was found to exhibit potent activity against Gram-positive bacteria. Here, we report isolation and characterization of linear polyketide alpiniamide A (1) and its new derivatives B–D (2–5). The structures of alpiniamides A–D were established and their relative configuration was determined by combination of partial Murata’s method and ROESY experiment. The absolute configuration of alpiniamide A was established through Mosher’s method. The gene cluster, responsible for the biosynthesis of alpiniamides (alp) has been identified by genome mining and gene deletion experiments. The successful expression of the cloned alp gene cluster in a heterologous host supports these findings. Analysis of the architecture of the alp gene cluster and the feeding of labeled precursors elucidated the alpiniamide biosynthetic pathway. The biosynthesis of alpiniamides is an example of a rather simple polyketide assembly line generating unusual chemical diversity through the combination of domain/module skipping and double bond migration events

Molecular similarity searching based on deep learning for feature reduction

The concept of molecular similarity has been widely used in rational drug design, where structurally similar molecules are explored in molecular databases for retrieving functionally similar molecules. The most used conventional similarity methods are two-dimensional (2D) fingerprints to evaluate the similarity of molecules towards a target query. However, these descriptors include redundant and irrelevant features that might impact the effectiveness of similarity searching methods. Moreover, the majority of existing similarity searching methods often disregard the importance of some features over others and assume all features are equally important. Thus, this study proposed three approaches for identifying the important features of molecules in chemical datasets. The first approach was based on the representation of the molecular features using Autoencoder (AE), which removes irrelevant and redundant features. The second approach was the feature selection model based on Deep Belief Networks (DBN), which are used to select only the important features. In this approach, the DBN is used to find subset of features that represent the important ones. The third approach was conducted to include descriptors that complement to each other. Different important features from many descriptors were filtered through DBN and combined to form a new descriptor used for molecular similarity searching. The proposed approaches were experimented on the MDL Data Drug Report standard dataset (MDDR). Based on the test results, the three proposed approaches overcame some of the existing benchmark similarity methods, such as Bayesian Inference Networks (BIN), Tanimoto Similarity Method (TAN), Adapted Similarity Measure of Text Processing (ASMTP) and Quantum-Based Similarity Method (SQB). The results showed that the performance of the three proposed approaches proved to be better in term of average recall values, especially with the use of structurally heterogeneous datasets that could produce results than other methods used previously to improve molecular similarity searching

Metabolic and genomic profiling of actinobacteria strains : from new natural products to biosynthetic pathways

Marine actinomycetes are known to be a promising source for new natural products with putative application as therapeutic agents. Thus, the exploitation of novel discovered actinomycetes strains remains in the focus of NP research. The presented thesis deals on the one hand with the isolation and structural characterization of new NPs produced by streptomycetes species. On the other hand, the construction of a novel biosensor concept for the detection of secondary metabolite production is discussed. New spiroindimicins E and F were isolated from the Streptomyces sp. MP131-18 and structures were confirmed by NMR. Additionally, two new lagunapyrones D and E were identified by characteristic MS/MS fragmentation. The genome of MP131-18 has been sequenced and the gene cluster, responsible for the synthesis of bisindole compounds has been identified and connected to lynamicins/spiroindimicin production. Furthermore, four new alpiniamides B-D have been isolated from the Streptomyces sp. IB 2014/11-12. The sequenced genome enabled the identification of the gene cluster responsible for alpiniamide production. The predicted biosynthetic pathway was confirmed by feeding experiments and gene deletions in a heterologous host. Finally, a contribution to the construction of a repressor-based biosensor, which detects the products of awakened silent gene clusters in streptomycetes, has been made. This concept was successfully applied to the activated coelimycin gene cluster.  Marine Aktinomyceten stellen eine beliebte Quelle neuer Naturstoffe dar, welche unter Umständen Anwendung als therapeutische Arzneimittel finden. Daher steht die Untersuchung neuer Aktinomyceten Stämme im Fokus der Naturstoffforschung. Die dargelegte Arbeit handelt von der Isolierung und strukturellen Charakterisierung neuer Naturstoffe, welche von Streptomyceten produziert wurden. Desweiteren, wird die Konstruktion eines neuen Biosensors dargestellt, welcher die Produktion von Sekundärmetaboliten detektieren soll. Es wurden zwei neue spiroindimicine E und F aus der Streptomyces sp. MP131-18 isoliert und die chemische Struktur mittels NMR bestätigt. Das Genom von MP131-18 wurde sequenziert und der Gencluster, welcher verantwortlich ist für die Synthese der Bisindole-Substanzen identifiziert und den Lynamicinen/Spiroindmicinen zugeordnet. Weiterhin, wurden vier neue Alpiniamide B-D aus dem Stamm Streptomyces sp. IB 2014/11-12 isoliert. Das sequenzierte Genom ermöglichte die Identifizierung des Genclusters, welcher für die Alpiniamide Produktion verantwortlich ist. Die vorgeschlagene Biosyntheseroute wurde bestätigt durch Fütterungsexperimente und Gendeletierungen in einem Hostorganismus. Abschließend wurde ein Beitrag zur Konstruktion eines Repressor-basierender Biosensors geleistet, welcher die Aktivierung von zuvor inaktiven Gencluster in Streptomyceten detektieren soll. Dieses Konzept wurde erfolgreich an dem aktivierten Coelimycin Gencluster angewendet

Consensus rank orderings of molecular fingerprints illustrate the ‘most genuine’ similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

We compare several molecular fingerprint encodings for marketed, small molecule drugs, and assess how their rank order varies with the fingerprint in terms of the Tanimoto similarity to the most similar endogenous human metabolite as taken from Recon2. For the great majority of drugs, the rank order varies very greatly depending on the encoding used, and also somewhat when the Tanimoto similarity (TS) is replaced by the Tversky similarity. However, for a subset of such drugs, amounting to some 10 % of the set and a Tanimoto similarity of ~0.8 or greater, the similarity coefficient is relatively robust to the encoding used. This leads to a metric that, while arbitrary, suggests that a Tanimoto similarity of 0.75-0.8 or greater genuinely does imply a considerable structural similarity of two molecules in the drug-endogenite space. Although comparatively few ( 0.75). This is referred to as the Take Your Pick Improved Cheminformatic Analytical Likeness or TYPICAL encoding, and on this basis some 66 % of drugs are within a TS of 0.75 to an endogenite. We next explicitly recognise that natural evolution will have selected for the ability to transport dietary substances, including plant, animal and microbial ‘secondary’ metabolites, that are of benefit to the host. These should also be explored in terms of their closeness to marketed drugs. We thus compared the TS of marketed drugs with the contents of various databases of natural products. When this is done, we find that some 80 % of marketed drugs are within a TS of 0.7 to a natural product, even using just the MACCS encoding. For patterned and TYPICAL encodings, 80 % and 98 % of drugs are within a TS of 0.8 to (an endogenite or) an exogenous natural product. This implies strongly that it is these exogeneous (dietary and medicinal) natural products that are more to be seen as the ‘natural’ substrates of drug transporters (as is recognised, for instance, for the solute carrier SLC22A4 and ergothioneine). This novel analysis casts an entirely different light on the kinds of natural molecules that are to be seen as most like marketed drugs, and hence potential transporter substrates, and further suggests that a renewed exploitation of natural products as drug scaffolds would be amply rewarded

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.Siirretty Doriast