Molecular modeling of the reductase domain to elucidate the reaction mechanism of reduction of peptidyl thioester into its corresponding alcohol in non-ribosomal peptide synthetases

Structural and energetic analysis of stabilizing indel mutations

Amino acid insertions and deletions (indels) are among the most common protein mutations and necessitate changes to a protein's backbone geometry. Examining how indels affect protein folding stability (and especially how indels can increase stability) can help reveal the role of backbone energetics on stability and introduce new protein engineering strategies. Tsuboyama et al. measured folding stability for 57,698 single amino acid insertion or deletion mutants in 405 small domains, and this analysis identified 103 stabilizing mutants (ΔΔGunfolding > 1 kcal/mol). Here, we use computational modeling to analyze structural and energetic changes for these stabilizing indel mutants. We find that stabilizing indel mutations tend to have local structural effects and that stabilizing deletions (but less so insertions) are often found in regions of high backbone strain. We also find that stabilizing indels are typically correctly classified as stabilizing by the Rosetta energy function (which explicitly models backbone energetics), but not by an inverse folding (ESM-IF)-based analysis (Cagiada et al. 2024) which predicts absolute stability (ΔGunfolding).

A QSPR analysis of physical properties of antituberculosis drugs using neighbourhood degree-based topological indices and support vector regression

Topological indices are molecular descriptors used in QSPR modelling to predict the physicochemical properties of molecules. Topological indices are used in numerous applications in drug design. In this work, we compute the neighbourhood degree-based topological indices of 15 antituberculosis drugs, we studied the QSPR analysis of these drugs using support vector regression. The efficiency of support vector regression is determined by comparing it with the classical linear regression. Our QSPR model further shows the superiority of the SVR model as a better predictive model in QSPR analysis of the physical properties of antituberculosis drugs. The findings in this study are a further contribution to the field of chemical graph theory and drug design, providing a deeper understanding of neighbourhood degree-based topological indices and their predictive capabilities in QSPR model.

Structure of the Reductase Domain of a Fungal Carboxylic Acid Reductase and Its Substrate Scope in Thioester and Aldehyde Reduction

The synthesis of aldehydes from carboxylic acids has long been a challenge in chemistry. In contrast to the harsh chemically driven reduction, enzymes such as carboxylic acid reductases (CARs) are considered appealing biocatalysts for aldehyde production. Although structures of single- and didomains of microbial CARs have been reported, to date no full-length protein structure has been elucidated. In this study, we aimed to obtain structural and functional information regarding the reductase (R) domain of a CAR from the fungus Neurospora crassa (Nc). The NcCAR R-domain revealed activity for N-acetylcysteamine thioester (S-(2-acetamidoethyl) benzothioate), which mimics the phosphopantetheinylacyl-intermediate and can be anticipated as the minimal substrate for thioester reduction by CARs. The determined crystal structure of the NcCAR R-domain reveals a tunnel that putatively harbors the phosphopantetheinylacyl-intermediate, which is in good agreement with docking experiments performed with the minimal substrate. In vitro studies were performed with this highly purified R-domain and NADPH, demonstrating carbonyl reduction activity. The R-domain was able to accept not only a simple aromatic ketone but also benzaldehyde and octanal, which are typically considered to be the final product of carboxylic acid reduction by CAR. Also, the full-length NcCAR reduced aldehydes to primary alcohols. In conclusion, aldehyde overreduction can no longer be attributed exclusively to the host background.

Marine Cyclic Peptides: Antimicrobial Activity and Synthetic Strategies

Oceans are a rich source of structurally unique bioactive compounds from the perspective of potential therapeutic agents. Marine peptides are a particularly interesting group of secondary metabolites because of their chemistry and wide range of biological activities. Among them, cyclic peptides exhibit a broad spectrum of antimicrobial activities, including against bacteria, protozoa, fungi, and viruses. Moreover, there are several examples of marine cyclic peptides revealing interesting antimicrobial activities against numerous drug-resistant bacteria and fungi, making these compounds a very promising resource in the search for novel antimicrobial agents to revert multidrug-resistance. This review summarizes 174 marine cyclic peptides with antibacterial, antifungal, antiparasitic, or antiviral properties. These natural products were categorized according to their sources—sponges, mollusks, crustaceans, crabs, marine bacteria, and fungi—and chemical structure—cyclic peptides and depsipeptides. The antimicrobial activities, including against drug-resistant microorganisms, unusual structural characteristics, and hits more advanced in (pre)clinical studies, are highlighted. Nocathiacins I–III (91–93), unnarmicins A (114) and C (115), sclerotides A (160) and B (161), and plitidepsin (174) can be highlighted considering not only their high antimicrobial potency in vitro, but also for their promising in vivo results. Marine cyclic peptides are also interesting models for molecular modifications and/or total synthesis to obtain more potent compounds, with improved properties and in higher quantity. Solid-phase Fmoc- and Boc-protection chemistry is the major synthetic strategy to obtain marine cyclic peptides with antimicrobial properties, and key examples are presented guiding microbiologist and medicinal chemists to the discovery of new antimicrobial drug candidates from marine sources.

Acremopeptaibols A-F, 16-Residue Peptaibols from the Sponge-Derived Acremonium sp. IMB18-086 Cultivated with Heat-Killed Pseudomonas aeruginosa

Six new 16-residue peptaibols, acremopeptaibols A-F (1-6), along with five known compounds, were isolated from the cultures of the sponge-associated fungus Acremonium sp. IMB18-086 grown in the presence of the autoclaved bacterium Pseudomonas aeruginosa on solid rice medium. The peptaibol sequences were established based on comprehensive analysis of 1D and 2D NMR spectroscopic data in conjunction with HRESIMS/MS experiments. The configurations of the amino acid residues were determined by advanced Marfey's analysis. Compounds 1-6 feature the lack of the highly conserved Thr⁶ and Hyp¹⁰ residues in comparison with other members of the SF3 subfamily peptaibols. A plausible biosynthetic pathway of compounds 1-6 was proposed on the basis of genomic analysis. Compounds 1, 5, 7, and 10 exhibited significant antimicrobial activity against Staphylococcus aureus, methicillin-resistant S. aureus, Bacillus subtilis, and Candida albicans. Compounds 7-10 showed potent cytotoxicities against the A549 and/or HepG2 cancer cell lines.

Identification of Novel Rotihibin Analogues in Streptomyces scabies, Including Discovery of Its Biosynthetic Gene Cluster

Streptomyces scabies is a phytopathogen associated with common scab disease. This is mainly attributed to its ability to produce the phytotoxin thaxtomin A, the biosynthesis of which is triggered by cellobiose. During a survey of other metabolites released in the presence of cellobiose, we discovered additional compounds in the thaxtomin-containing extract from Streptomyces scabies. Structural analysis by mass spectrometry (MS) and nuclear magnetic resonance (NMR) revealed that these compounds are amino acid sequence variants of the TOR (target of rapamycin) kinase (TORK) pathway-inhibitory lipopeptide rotihibin A, and the main compounds were named rotihibins C and D. In contrast to thaxtomin, the production of rotihibins C and D was also elicited in the presence of glucose, indicating different regulation of their biosynthesis. Through a combination of shotgun and targeted proteomics, the putative rotihibin biosynthetic gene cluster rth was identified in the publicly available genome of S. scabies 87-22. This cluster spans 33 kbp and encodes 2 different nonribosomal peptide synthetases (NRPSs) and 12 additional enzymes. Homologous rth biosynthetic gene clusters were found in other publicly available and complete actinomycete genomes. Rotihibins C and D display herbicidal activity against Lemna minor and Arabidopsis thaliana at low concentrations, shown by monitoring the effects on growth and the maximal photochemistry efficiency of photosystem II. IMPORTANCE Rotihibins A and B are plant growth inhibitors acting on the TORK pathway. We report the isolation and characterization of new sequence analogues of rotihibin from Streptomyces scabies, a major cause of common scab in potato and other tuber and root vegetables. By combining proteomics data with genomic analysis, we found a cryptic biosynthetic gene cluster coding for enzyme machinery capable of rotihibin production. This work may lead to the biotechnological production of variants of this lipopeptide to investigate the exact mechanism by which it can target the plant TORK pathway in Arabidopsis thaliana. In addition, bioinformatics revealed the existence of other variants in plant-associated Streptomyces strains, both pathogenic and nonpathogenic species, raising new questions about the actual function of this lipopeptide. The discovery of a module in the nonribosomal peptide synthetase (NRPS) that incorporates the unusual citrulline residue may improve the prediction of peptides encoded by cryptic NRPS gene clusters.

Cyanochelins, an Overlooked Class of Widely Distributed Cyanobacterial Siderophores, Discovered by Silent Gene Cluster Awakening

Cyanobacteria require iron for growth and often inhabit iron-limited habitats, yet only a few siderophores are known to be produced by them. We report that cyanobacterial genomes frequently encode polyketide synthase (PKS)/nonribosomal peptide synthetase (NRPS) biosynthetic pathways for synthesis of lipopeptides featuring β-hydroxyaspartate (β-OH-Asp), a residue known to be involved in iron chelation. Iron starvation triggered the synthesis of β-OH-Asp lipopeptides in the cyanobacteria Rivularia sp. strain PCC 7116, Leptolyngbya sp. strain NIES-3755, and Rubidibacter lacunae strain KORDI 51-2. The induced compounds were confirmed to bind iron by mass spectrometry (MS) and were capable of Fe³⁺ to Fe²⁺ photoreduction, accompanied by their cleavage, when exposed to sunlight. The siderophore from Rivularia, named cyanochelin A, was structurally characterized by MS and nuclear magnetic resonance (NMR) and found to contain a hydrophobic tail bound to phenolate and oxazole moieties followed by five amino acids, including two modified aspartate residues for iron chelation. Phylogenomic analysis revealed 26 additional cyanochelin-like gene clusters across a broad range of cyanobacterial lineages. Our data suggest that cyanochelins and related compounds are widespread β-OH-Asp-featuring cyanobacterial siderophores produced by phylogenetically distant species upon iron starvation. Production of photolabile siderophores by phototrophic cyanobacteria raises questions about whether the compounds facilitate iron monopolization by the producer or, rather, provide Fe²⁺ for the whole microbial community via photoreduction. IMPORTANCE All living organisms depend on iron as an essential cofactor for indispensable enzymes. However, the sources of bioavailable iron are often limited. To face this problem, microorganisms synthesize low-molecular-weight metabolites capable of iron scavenging, i.e., the siderophores. Although cyanobacteria inhabit the majority of the Earth's ecosystems, their repertoire of known siderophores is remarkably poor. Their genomes are known to harbor a rich variety of gene clusters with unknown function. Here, we report the awakening of a widely distributed class of silent gene clusters by iron starvation to yield cyanochelins, β-hydroxy aspartate lipopeptides involved in iron acquisition. Our results expand the limited arsenal of known cyanobacterial siderophores and propose products with ecological function for a number of previously orphan gene clusters.

Pentadecaibins I-V: 15-Residue Peptaibols Produced by a Marine-Derived Trichoderma sp. of the Harzianum Clade

In the course of investigations on peptaibol chemodiversity from marine-derived Trichoderma spp., five new 15-residue peptaibols named pentadecaibins I-V (1-5) were isolated from the solid culture of the strain Trichoderma sp. MMS1255 belonging to the T. harzianum species complex. Phylogenetic analyses allowed precise positioning of the strain close to T. lentiforme lineage inside the Harzianum clade. Peptaibol sequences were elucidated on the basis of their MS/MS fragmentation and extensive 2D NMR experiments. Amino acid configurations were determined by Marfey's analyses. The pentadecaibins are based on the sequences Ac-Aib¹-Gly²-Ala³-Leu⁴-Aib/Iva⁵-Gln⁶-Aib/Iva⁷-Val/Leu⁸-Aib⁹-Ala¹⁰-Aib¹¹-Aib¹²-Aib¹³-Gln¹⁴-Pheol¹⁵. Characteristic of the pentadecaibin sequences is the lack of the Aib-Pro motif commonly present in peptaibols produced by Trichoderma spp. Genome sequencing of Trichoderma sp. MMS1255 allowed the detection of a 15-module NRPS-encoding gene closely associated with pentadecaibin biosynthesis. Pentadecaibins were assessed for their potential antiproliferative and antimicrobial activities.

Biosynthesis of depsipeptides, or Depsi: The peptides with varied generations

Depsipeptides are compounds that contain both ester bonds and amide bonds. Important natural product depsipeptides include the piscicide antimycin, the K⁺ ionophores cereulide and valinomycin, the anticancer agent cryptophycin, and the antimicrobial kutzneride. Furthermore, database searches return hundreds of uncharacterized systems likely to produce novel depsipeptides. These compounds are made by specialized nonribosomal peptide synthetases (NRPSs). NRPSs are biosynthetic megaenzymes that use a module architecture and multi-step catalytic cycle to assemble monomer substrates into peptides, or in the case of specialized depsipeptide synthetases, depsipeptides. Two NRPS domains, the condensation domain and the thioesterase domain, catalyze ester bond formation, and ester bonds are introduced into depsipeptides in several different ways. The two most common occur during cyclization, in a reaction between a hydroxy-containing side chain and the C-terminal amino acid residue in a peptide intermediate, and during incorporation into the growing peptide chain of an α-hydroxy acyl moiety, recruited either by direct selection of an α-hydroxy acid substrate or by selection of an α-keto acid substrate that is reduced in situ. In this article, we discuss how and when these esters are introduced during depsipeptide synthesis, survey notable depsipeptide synthetases, and review insight into bacterial depsipeptide synthetases recently gained from structural studies.

Engineering carboxylic acid reductase for selective synthesis of medium-chain fatty alcohols in yeast

Medium-chain fatty alcohols (MCFOHs, C6 to C12) are potential substitutes for fossil fuels, such as diesel and jet fuels, and have wide applications in various manufacturing processes. While today MCFOHs are mainly sourced from petrochemicals or plant oils, microbial biosynthesis represents a scalable, reliable, and sustainable alternative. Here, we aim to establish a Saccharomyces cerevisiae platform capable of selectively producing MCFOHs. This was enabled by tailoring the properties of a bacterial carboxylic acid reductase from Mycobacterium marinum (MmCAR). Extensive protein engineering, including directed evolution, structure-guided semirational design, and rational design, was implemented. MmCAR variants with enhanced activity were identified using a growth-coupled high-throughput screening assay relying on the detoxification of the enzyme's substrate, medium-chain fatty acids (MCFAs). Detailed characterization demonstrated that both the specificity and catalytic activity of MmCAR was successfully improved and a yeast strain harboring the best MmCAR variant generated 2.8-fold more MCFOHs than the strain expressing the unmodified enzyme. Through deletion of the native MCFA exporter gene TPO1, MCFOH production was further improved, resulting in a titer of 252 mg/L for the final strain, which represents a significant improvement in MCFOH production in minimal medium by S. cerevisiae.

Nonribosomal peptides in fungal cell factories: from genome mining to optimized heterologous production

Fungi are notoriously prolific producers of secondary metabolites including nonribosomal peptides (NRPs). The structural complexity of NRPs grants them interesting activities such as antibiotic, anti-cancer, and anti-inflammatory properties. The discovery of these compounds with attractive activities can be achieved by using two approaches: either by screening samples originating from various environments for their biological activities, or by identifying the related clusters in genomic sequences thanks to bioinformatics tools. This genome mining approach has grown tremendously due to recent advances in genome sequencing, which have provided an incredible amount of genomic data from hundreds of microbial species. Regarding fungal organisms, the genomic data have revealed the presence of an unexpected number of putative NRP-related gene clusters. This highlights fungi as a goldmine for the discovery of putative novel bioactive compounds. Recent development of NRP dedicated bioinformatics tools have increased the capacity to identify these gene clusters and to deduce NRPs structures, speeding-up the screening process for novel metabolites discovery. Unfortunately, the newly identified compound is frequently not or poorly produced by native producers due to a lack of expression of the related genes cluster. A frequently employed strategy to increase production rates consists in transferring the related biosynthetic pathway in heterologous hosts. This review aims to provide a comprehensive overview about the topic of NRPs discovery, from gene cluster identification by genome mining to the heterologous production in fungal hosts. The main computational tools and methods for genome mining are herein presented with an emphasis on the particularities of the fungal systems. The different steps of the reconstitution of NRP biosynthetic pathway in heterologous fungal cell factories will be discussed, as well as the key factors to consider for maximizing productivity. Several examples will be developed to illustrate the potential of heterologous production to both discover uncharacterized novel compounds predicted in silico by genome mining, and to enhance the productivity of interesting bio-active natural products.

Natural products from thioester reductase containing biosynthetic pathways

Covering: up to 2018 Thioester reductase domains catalyze two- and four-electron reductions to release natural products following assembly on nonribosomal peptide synthetases, polyketide synthases, and their hybrid biosynthetic complexes. This reductive off-loading of a natural product yields an aldehyde or alcohol, can initiate the formation of a macrocyclic imine, and contributes to important intermediates in a variety of biosyntheses, including those for polyketide alkaloids and pyrrolobenzodiazepines. Compounds that arise from reductase-terminated biosynthetic gene clusters are often reactive and exhibit biological activity. Biomedically important examples include the cancer therapeutic Yondelis (ecteinascidin 743), peptide aldehydes that inspired the first therapeutic proteasome inhibitor bortezomib, and numerous synthetic derivatives and antibody drug conjugates of the pyrrolobenzodiazepines. Recent advances in microbial genomics, metabolomics, bioinformatics, and reactivity-based labeling have facilitated the detection of these compounds for targeted isolation. Herein, we summarize known natural products arising from this important category, highlighting their occurrence in Nature, biosyntheses, biological activities, and the technologies used for their detection and identification. Additionally, we review publicly available genomic data to highlight the remaining potential for novel reductively tailored compounds and drug leads from microorganisms. This thorough retrospective highlights various molecular families with especially privileged bioactivity while illuminating challenges and prospects toward accelerating the discovery of new, high value natural products.

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Due to the advancement in sequence technologies that resulted an avalanche of protein sequence data. Therefore, it is necessary to develop an automated computational method to enable fast and accurate identification of novel PIPs within the vast number of candidate proteins and peptides. To address this, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest (RF) models, where each of the five different compositions, including amino acid, dipeptide, composition-transition-distribution, physicochemical properties, and amino acid index contains 10 RF models. PIP-EL achieves the Matthews' correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2-5% higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.

iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction

Identification of B-cell epitopes (BCEs) is a fundamental step for epitope-based vaccine development, antibody production, and disease prevention and diagnosis. Due to the avalanche of protein sequence data discovered in postgenomic age, it is essential to develop an automated computational method to enable fast and accurate identification of novel BCEs within vast number of candidate proteins and peptides. Although several computational methods have been developed, their accuracy is unreliable. Thus, developing a reliable model with significant prediction improvements is highly desirable. In this study, we first constructed a non-redundant data set of 5,550 experimentally validated BCEs and 6,893 non-BCEs from the Immune Epitope Database. We then developed a novel ensemble learning framework for improved linear BCE predictor called iBCE-EL, a fusion of two independent predictors, namely, extremely randomized tree (ERT) and gradient boosting (GB) classifiers, which, respectively, uses a combination of physicochemical properties (PCP) and amino acid composition and a combination of dipeptide and PCP as input features. Cross-validation analysis on a benchmarking data set showed that iBCE-EL performed better than individual classifiers (ERT and GB), with a Matthews correlation coefficient (MCC) of 0.454. Furthermore, we evaluated the performance of iBCE-EL on the independent data set. Results show that iBCE-EL significantly outperformed the state-of-the-art method with an MCC of 0.463. To the best of our knowledge, iBCE-EL is the first ensemble method for linear BCEs prediction. iBCE-EL was implemented in a web-based platform, which is available at http://thegleelab.org/iBCE-EL. iBCE-EL contains two prediction modes. The first one identifying peptide sequences as BCEs or non-BCEs, while later one is aimed at providing users with the option of mining potential BCEs from protein sequences.

AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest

The use of therapeutic peptides in various inflammatory diseases and autoimmune disorders has received considerable attention; however, the identification of anti-inflammatory peptides (AIPs) through wet-lab experimentation is expensive and often time consuming. Therefore, the development of novel computational methods is needed to identify potential AIP candidates prior to in vitro experimentation. In this study, we proposed a random forest (RF)-based method for predicting AIPs, called AIPpred (AIP predictor in primary amino acid sequences), which was trained with 354 optimal features. First, we systematically studied the contribution of individual composition [amino acid-, dipeptide composition (DPC), amino acid index, chain-transition-distribution, and physicochemical properties] in AIP prediction. Since the performance of the DPC-based model is significantly better than that of other composition-based models, we applied a feature selection protocol on this model and identified the optimal features. AIPpred achieved an area under the curve (AUC) value of 0.801 in a 5-fold cross-validation test, which was ∼2% higher than that of the control RF predictor trained with all DPC composition features, indicating the efficiency of the feature selection protocol. Furthermore, we evaluated the performance of AIPpred on an independent dataset, with results showing that our method outperformed an existing method, as well as 3 different machine learning methods developed in this study, with an AUC value of 0.814. These results indicated that AIPpred will be a useful tool for predicting AIPs and might efficiently assist the development of AIP therapeutics and biomedical research. AIPpred is freely accessible at www.thegleelab.org/AIPpred.

PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order to develop new antibacterial drugs. However, identification of such proteins using experimental techniques is expensive and often time consuming; hence, development of an efficient computational algorithm for the prediction of phage virion proteins (PVPs) prior to in vitro experimentation is needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called PVP-SVM, which was trained with 136 optimal features. A feature selection protocol was employed to identify the optimal features from a large set that included amino acid composition, dipeptide composition, atomic composition, physicochemical properties, and chain-transition-distribution. PVP-SVM achieved an accuracy of 0.870 during leave-one-out cross-validation, which was 6% higher than control SVM predictors trained with all features, indicating the efficiency of the feature selection method. Furthermore, PVP-SVM displayed superior performance compared to the currently available method, PVPred, and two other machine-learning methods developed in this study when objectively evaluated with an independent dataset. For the convenience of the scientific community, a user-friendly and publicly accessible web server has been established at www.thegleelab.org/PVP-SVM/PVP-SVM.html.

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html.

MLACP: machine-learning-based prediction of anticancer peptides

Cancer is the second leading cause of death globally, and use of therapeutic peptides to target and kill cancer cells has received considerable attention in recent years. Identification of anticancer peptides (ACPs) through wet-lab experimentation is expensive and often time consuming; therefore, development of an efficient computational method is essential to identify potential ACP candidates prior to in vitro experimentation. In this study, we developed support vector machine- and random forest-based machine-learning methods for the prediction of ACPs using the features calculated from the amino acid sequence, including amino acid composition, dipeptide composition, atomic composition, and physicochemical properties. We trained our methods using the Tyagi-B dataset and determined the machine parameters by 10-fold cross-validation. Furthermore, we evaluated the performance of our methods on two benchmarking datasets, with our results showing that the random forest-based method outperformed the existing methods with an average accuracy and Matthews correlation coefficient value of 88.7% and 0.78, respectively. To assist the scientific community, we also developed a publicly accessible web server at www.thegleelab.org/MLACP.html.

Analysis of the Sequences, Structures, and Functions of Product-Releasing Enzyme Domains in Fungal Polyketide Synthases

Product-releasing enzyme (PRE) domains in fungal non-reducing polyketide synthases (NR-PKSs) play a crucial role in catalysis and editing during polyketide biosynthesis, especially accelerating final biosynthetic reactions accompanied with product offloading. However, up to date, the systematic knowledge about PRE domains is deficient. In the present study, the relationships between sequences, structures, and functions of PRE domains were analyzed with 574 NR-PKSs of eight groups (I–VIII). It was found that the PRE domains in NR-PKSs could be mainly classified into three types, thioesterase (TE), reductase (R), and metallo-β-lactamase-type TE (MβL-TE). The widely distributed TE or TE-like domains were involved in NR-PKSs of groups I–IV, VI, and VIII. The R domains appeared in NR-PKSs of groups IV and VII, while the physically discrete MβL-TE domains were employed by most NR-PKSs of group V. The changes of catalytic sites and structural characteristics resulted in PRE functional differentiations. The phylogeny revealed that the evolution of TE domains was accompanied by complex functional divergence. The diverse sequence lengths of TE lid-loops affected substrate specificity with different chain lengths. The volume diversification of TE catalytic pockets contributed to catalytic mechanisms with functional differentiations. The above findings may help to understand the crucial catalysis of fungal aromatic polyketide biosyntheses and govern recombination of NR-PKSs to obtain unnatural target products.

Modification of S-Adenosyl-l-Homocysteine as Inhibitor of Nonstructural Protein 5 Methyltransferase Dengue Virus Through Molecular Docking and Molecular Dynamics Simulation

Dengue fever is still a major threat worldwide, approximately threatening two-fifths of the world's population in tropical and subtropical countries. Nonstructural protein 5 (NS5) methyltransferase enzyme plays a vital role in the process of messenger RNA capping of dengue by transferring methyl groups from S-adenosyl-l-methionine to N7 atom of the guanine bases of RNA and the RNA ribose group of 2'OH, resulting in S-adenosyl-l-homocysteine (SAH). The modification of SAH compound was screened using molecular docking and molecular dynamics simulation, along with computational ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) test. The 2 simulations were performed using Molecular Operating Environment (MOE) 2008.10 software, whereas the ADME-Tox test was performed using various software. The modification of SAH compound was done using several functional groups that possess different polarities and properties, resulting in 3460 ligands to be docked. After conducting docking simulation, we earned 3 best ligands (SAH-M331, SAH-M2696, and SAH-M1356) based on ΔG_binding and molecular interactions, which show better results than the standard ligands. Moreover, the results of molecular dynamics simulation show that the best ligands are still able to maintain the active site residue interaction with the binding site until the end of the simulation. After a series of molecular docking and molecular dynamics simulation were performed, we concluded that SAH-M1356 ligand is the most potential SAH-based compound to inhibit NS5 methyltransferase enzyme for treating dengue fever.

FANCM interacts with PCNA to promote replication traverse of DNA interstrand crosslinks

FANCM is a highly conserved DNA remodeling enzyme that promotes the activation of the Fanconi anemia DNA repair pathway and facilitates replication traverse of DNA interstrand crosslinks. However, how FANCM interacts with the replication machinery to promote traverse remains unclear. Here, we show that FANCM and its archaeal homolog Hef from Thermoplasma acidophilum interact with proliferating cell nuclear antigen (PCNA), an essential co-factor for DNA polymerases in both replication and repair. The interaction is mediated through a conserved PIP-box; and in human FANCM, it is strongly stimulated by replication stress. A FANCM variant carrying a mutation in the PIP-box is defective in promoting replication traverse of interstrand crosslinks and is also inefficient in promoting FANCD2 monoubiquitination, a key step of the Fanconi anemia pathway. Our data reveal a conserved interaction mode between FANCM and PCNA during replication stress, and suggest that this interaction is essential for FANCM to aid replication machines to traverse DNA interstrand crosslinks prior to post-replication repair.

Mining for Nonribosomal Peptide Synthetase and Polyketide Synthase Genes Revealed a High Level of Diversity in the Sphagnum Bog Metagenome

Sphagnum bog ecosystems are among the oldest vegetation forms harboring a specific microbial community and are known to produce an exceptionally wide variety of bioactive substances. Although the Sphagnum metagenome shows a rich secondary metabolism, the genes have not yet been explored. To analyze nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs), the diversity of NRPS and PKS genes in Sphagnum-associated metagenomes was investigated by in silico data mining and sequence-based screening (PCR amplification of 9,500 fosmid clones). The in silico Illumina-based metagenomic approach resulted in the identification of 279 NRPSs and 346 PKSs, as well as 40 PKS-NRPS hybrid gene sequences. The occurrence of NRPS sequences was strongly dominated by the members of the Protebacteria phylum, especially by species of the Burkholderia genus, while PKS sequences were mainly affiliated with Actinobacteria. Thirteen novel NRPS-related sequences were identified by PCR amplification screening, displaying amino acid identities of 48% to 91% to annotated sequences of members of the phyla Proteobacteria, Actinobacteria, and Cyanobacteria. Some of the identified metagenomic clones showed the closest similarity to peptide synthases from Burkholderia or Lysobacter, which are emerging bacterial sources of as-yet-undescribed bioactive metabolites. This report highlights the role of the extreme natural ecosystems as a promising source for detection of secondary compounds and enzymes, serving as a source for biotechnological applications.

Endogenous cross-talk of fungal metabolites

Non-ribosomal peptide (NRP) synthesis in fungi requires a ready supply of proteogenic and non-proteogenic amino acids which are subsequently incorporated into the nascent NRP via a thiotemplate mechanism catalyzed by NRP synthetases. Substrate amino acids can be modified prior to or during incorporation into the NRP, or following incorporation into an early stage amino acid-containing biosynthetic intermediate. These post-incorporation modifications involve a range of additional enzymatic activities including but not exclusively, monooxygenases, methyltransferases, epimerases, oxidoreductases, and glutathione S-transferases which are essential to effect biosynthesis of the final NRP. Likewise, polyketide biosynthesis is directly by polyketide synthase megaenzymes and cluster-encoded ancillary decorating enzymes. Additionally, a suite of additional primary metabolites, for example: coenzyme A (CoA), acetyl CoA, S-adenosylmethionine, glutathione (GSH), NADPH, malonyl CoA, and molecular oxygen, amongst others are required for NRP and polyketide synthesis (PKS). Clearly these processes must involve exquisite orchestration to facilitate the simultaneous biosynthesis of different types of NRPs, polyketides, and related metabolites requiring identical or similar biosynthetic precursors or co-factors. Moreover, the near identical structures of many natural products within a given family (e.g., ergot alkaloids), along with localization to similar regions within fungi (e.g., conidia) suggests that cross-talk may exist, in terms of biosynthesis and functionality. Finally, we speculate if certain biosynthetic steps involved in NRP and PKS play a role in cellular protection or environmental adaptation, and wonder if these enzymatic reactions are of equivalent importance to the actual biosynthesis of the final metabolite.

Heterologous expression of highly reducing polyketide synthase involved in betaenone biosynthesis

Heterologous expression of highly reducing polyketide synthase and trans-acting enoyl reductase provides insights into the skeletal construction of betaenones.

Random Coordinate Descent with Spinor-matrices and Geometric Filters for Efficient Loop Closure

Protein loop closure constitutes a critical step in loop and protein modeling whereby geometrically feasible loops must be found between two given anchor residues. Here, a new analytic/iterative algorithm denoted random coordinate descent (RCD) to perform protein loop closure is described. The algorithm solves loop closure through minimization as in cyclic coordinate descent but selects bonds for optimization randomly, updates loop conformations by spinor-matrices, performs loop closure in both chain directions, and uses a set of geometric filters to yield efficient conformational sampling. Geometric filters allow one to detect clashes and constrain dihedral angles on the fly. The RCD algorithm is at least comparable to state of the art loop closure algorithms due to an excellent balance between efficiency and intrinsic sampling capability. Furthermore, its efficiency allows one to improve conformational sampling by increasing the sampling number without much penalty. Overall, RCD turns out to be accurate, fast, robust, and applicable over a wide range of loop lengths. Because of the versatility of RCD, it is a solid alternative for integration with current loop modeling strategies.

Evolutionary, structural and functional interplay of the IκB family members

A primary level of control for nuclear factor kappa B (NF-κB) is effected through its interactions with the inhibitor protein, inhibitor of kappa B (IκB). Several lines of evidence confirm the existence of multiple forms of IκB that appear to regulate NF-κB by distinct mechanisms. Therefore, we performed a comprehensive bioinformatics analysis to understand the evolutionary history and intrinsic functional diversity of IκB family members. Phylogenetic relationships were constructed to trace the evolution of the IκB family genes. Our phylogenetic analysis revealed 10 IκB subfamily members that clustered into 5 major clades. Since the ankyrin (ANK) domain appears to be more ancient than the Rel homology domain (RHD), our phylogenetic analysis suggests that some undefined ancestral set of ANK repeats acquired an RHD before any duplication and was later duplicated and then diverged into the different IκB subfamilies. Functional analysis identified several functionally divergent sites in the ANK repeat domains (ARDs) and revealed that this region has undergone strong purifying selection, suggesting its functional importance in IκB genes. Structural analysis showed that the major variations in the number of ANK repeats and high conformational changes in the finger loop ARD region contribute to the differing binding partner specificities, thereby leading to distinct IκB functions. In summary, our study has provided useful information about the phylogeny and structural and functional divergence of the IκB family. Additionally, we identified a number of amino acid sites that contribute to the predicted functional divergence of these proteins.

Distribution of amino acids in functional sites of proteins with high melting temperature

The stability of proteins in its native state has an important implication on its function and evolution. The functional site analysis may lead to better understanding of how these amino acid distributions influence the melting temperature of proteins. It has been reported that increasing the fraction of hydrophobic contacts in a protein tends to raise melting temperature; increasing the fraction of repulsive charge contacts decrease the melting temperature and consistent with a destabilizing effect. The role of amino acid distribution as hydrophobic, charged and polar residues in proteins and mainly in its functional sites has been studied. Due to limited data availability, redundancy check and controlled environment parameters, the study was carried out with ten single chain-wild proteins having melting temperature above 80°C at pH 7. The analysis depicts that, the entire protein, hydrophobic residues are more frequent in single chain proteins and charged residues are more frequent in multi-chains proteins. In functional sites of these proteins, hydrophobic and charged residues are equally frequent in single chain proteins and charged residues are very high in multi-chains proteins. But, the polar residue distribution remains same for both single chain and multi-chain proteins and its functional sites.

Heterologous expression and structural characterisation of a pyrazinone natural product assembly line

Through a number of strategies nonribosomal peptide assembly lines give rise to a metabolic diversity not possible by ribosomal synthesis. One distinction within nonribosomal assembly is that products are elaborated on an enzyme-tethered substrate, and their release is enzyme catalysed. Reductive release by NAD(P)H-dependent catalysts is one observed nonribosomal termination and release strategy. Here we probed the selectivity of a terminal reductase domain by using a full-length heterologously expressed nonribosomal peptide synthetase for the dipeptide aureusimine and were able to generate 17 new analogues. Further, we generated an X-ray structure of aureusimine terminal reductase to gain insight into the structural details associated with this enzymatic domain.

Designing cyclopentapeptide inhibitor as potential antiviral drug for dengue virus ns5 methyltransferase

NS5 methyltransferase (Mtase) has a crucial role in the replication of dengue virus. There are two active sites on NS5 Mtase i.e., SAM and RNA-cap binding sites. Inhibition of the NS5 Mtase activity is expected to prevent the propagation of dengue virus. This study was conducted to design cyclic peptide ligands as enzyme inhibitors of dengue virus NS5 Mtase through computational approach. Cyclopentapeptides were designed as ligand of SAM binding site as much as 1635 and 736 cyclopentpeptides were designed as ligand of RNA-cap binding site. Interaction between ligand and NS5 Mtase has been conducted on the Docking simulation. The result shows that cyclopentapeptide CTWYC was the best peptide candidate on SAM binding site, with estimated free binding energy -30.72 kca/mol. Cyclopentapeptide CYEFC was the best peptide on RNA-cap binding site with estimated free binding energy -22.89 kcal/mol. Both peptides did not have tendency toward toxicity properties. So it is expected that both CTWYC and CYEFC ligands could be used as a potential antiviral drug candidates, which can inhibit the SAM and RNA-cap binding sites of dengue virus NS5 Mtase.

The reductase domain in a Type I fatty acid synthase from the apicomplexan Cryptosporidium parvum: restricted substrate preference towards very long chain fatty acyl thioesters

Background

The apicomplexan Cryptosporidium parvum genome possesses a 25-kb intronless open reading frame (ORF) that predicts a multifunctional Type I fatty acid synthase (CpFAS1) with at least 21 enzymatic domains. Although the architecture of CpFAS1 resembles those of bacterial polyketide synthases (PKSs), this megasynthase is predicted to function as a fatty acyl elongase as our earlier studies have indicated that the N-terminal loading unit (acyl-[ACP] ligase) prefers using intermediate to long chain fatty acids as substrates, and each of the three internal elongation modules contains a complete set of enzymes to produce a saturated fatty acyl chain. Although the activities of almost all domains were confirmed using recombinant proteins, that of the C-terminal reductase domain (CpFAS1-R) was yet undetermined. In fact, there were no published studies to report the kinetic features of any reductase domains in bacterial PKSs using purified recombinant or native proteins.

Results

In the present study, the identity of CpFAS1-R as a reductase is confirmed by in silico analysis on sequence similarity and characteristic motifs. Phylogenetic analysis based on the R-domains supports a previous notion on the bacterial origin of apicomplexan Type I FAS/PKS genes. We also developed a novel assay using fatty acyl-CoAs as substrates, and determined that CpFAS1-R could only utilize very long chain fatty acyl-CoAs as substrates (i.e., with activity on C26 > C24 > C22 > C20, but no activity on C18 and C16). It was capable of using both NADPH and NADH as electron donors, but prefers NADPH to NADH. The activity of CpFAS1-R displayed allosteric kinetics towards C26 hexacosanoyl CoA as a substrate (h = 2.0; V_max = 32.8 nmol min^-1 mg^-1 protein; and K₅₀ = 0.91 mM).

Conclusions

We have confirmed the activity of CpFAS1-R by directly assaying its substrate preference and kinetic parameters, which is for the first time for a Type I FAS, PKS or non-ribosomal peptide synthase (NRPS) reductase domain. The restricted substrate preference towards very long chain fatty acyl thioesters may be an important feature for this megasynthase to avoid the release of product(s) with undesired lengths.

Molecular modeling-based evaluation of hTLR10 and identification of potential ligands in Toll-like receptor signaling

Toll-like receptors (TLRs) are pattern recognition receptors that recognize pathogens based on distinct molecular signatures. The human (h)TLR1, 2, 6 and 10 belong to the hTLR1 subfamilies, which are localized in the extracellular regions and activated in response to diverse ligand molecules. Due to the unavailability of the hTLR10 crystal structure, the understanding of its homo and heterodimerization with hTLR2 and hTLR1 and the ligand responsible for its activation is limited. To improve our understanding of the TLR10 receptor-ligand interaction, we used homology modeling to construct a three dimensional (3D) structure of hTLR10 and refined the model through molecular dynamics (MD) simulations. We utilized the optimized structures for the molecular docking in order to identify the potential site of interactions between the homo and heterodimer (hTLR10/2 and hTLR10/1). The docked complexes were then used for interaction with ligands (Pam(3)CSK(4) and PamCysPamSK(4)) using MOE-Dock and ASEDock. Our docking studies have shown the binding orientations of hTLR10 heterodimer to be similar with other TLR2 family members. However, the binding orientation of hTLR10 homodimer is different from the heterodimer due to the presence of negative charged surfaces at the LRR11-14, thereby providing a specific cavity for ligand binding. Moreover, the multiple protein-ligand docking approach revealed that Pam(3)CSK(4) might be the ligand for the hTLR10/2 complex and PamCysPamSK(4,) a di-acylated peptide, might activate hTLR10/1 hetero and hTLR10 homodimer. Therefore, the current modeled complexes can be a useful tool for further experimental studies on TLR biology.