Structurally Uncommon Secondary Metabolites Derived from Endophytic Fungi

Among microorganisms, endophytic fungi are the least studied, but they have attracted attention due to their high biological diversity and ability to produce novel and bioactive secondary metabolites to protect their host plant against biotic and abiotic stress. These compounds belong to different structural classes, such as alkaloids, peptides, terpenoids, polyketides, and steroids, which could present significant biological activities that are useful for pharmacological or medical applications. Recent reviews on endophytic fungi have mainly focused on the production of novel bioactive compounds. Here, we focus on compounds produced by endophytic fungi, reported with uncommon bioactive structures, establishing the neighbor net and diversity of endophytic fungi. The review includes compounds published from January 2015 to December 2020 that were catalogued as unprecedented, rare, uncommon, or possessing novel structural skeletons from more than 39 different genera, with Aspergillus and Penicillium being the most mentioned. They were reported as displaying cytotoxic, antitumor, antimicrobial, antiviral, or anti-inflammatory activity. The solid culture, using rice as a carbon source, was the most common medium utilized in the fermentation process when this type of compound was isolated.

Identification of Compounds for Butyrylcholinesterase Inhibition

Butyrylcholinesterase (BChE) is a nonspecific cholinesterase enzyme that hydrolyzes choline-based esters. BChE plays a critical role in maintaining normal cholinergic function like acetylcholinesterase (AChE) through hydrolyzing acetylcholine (ACh). Selective BChE inhibition has been regarded as a viable therapeutic approach in Alzheimer’s disease. As of now, a limited number of selective BChE inhibitors are available. To identify BChE inhibitors rapidly and efficiently, we have screened 8998 compounds from several annotated libraries against an enzyme-based BChE inhibition assay in a quantitative high-throughput screening (qHTS) format. From the primary screening, we identified a group of 125 compounds that were further confirmed to inhibit BChE activity, including previously reported BChE inhibitors (e.g., bambuterol and rivastigmine) and potential novel BChE inhibitors (e.g., pancuronium bromide and NNC 756), representing diverse structural classes. These BChE inhibitors were also tested for their selectivity by comparing their IC50 values in BChE and AChE inhibition assays. The binding modes of these compounds were further studied using molecular docking analyses to identify the differences between the interactions of these BChE inhibitors within the active sites of AChE and BChE. Our qHTS approach allowed us to establish a robust and reliable process to screen large compound collections for potential BChE inhibitors.

Competing off-loading mechanisms of meropenem from an l,d-transpeptidase reduce antibiotic effectiveness

The carbapenem family of β-lactam antibiotics displays a remarkably broad spectrum of bactericidal activity, exemplified by meropenem’s phase II clinical trial success in patients with pulmonary tuberculosis, a devastating disease for which β-lactam drugs historically have been notoriously ineffective. The discovery and validation of l,d-transpeptidases (Ldts) as critical drug targets of bacterial cell-wall biosynthesis, which are only potently inhibited by the carbapenem and penem structural classes, gave an enzymological basis for the effectiveness of the first antitubercular β-lactams. Decades of study have delineated mechanisms of β-lactam inhibition of their canonical targets, the penicillin-binding proteins; however, open questions remain regarding the mechanisms of Ldt inhibition that underlie programs in drug design, particularly the optimization of kinetic behavior and potency. We have investigated critical features of mycobacterial Ldt inhibition and demonstrate here that the covalent inhibitor meropenem undergoes both reversible reaction and nonhydrolytic off-loading reactions from the cysteine transpeptidase LdtMt2 through a high-energy thioester adduct. Next-generation carbapenem optimization strategies should minimize adduct loss from unproductive mechanisms of Ldt adducts that reduce effective drug concentration.

IDOPS, a Profile HMM-Based Tool to Detect Pesticidal Sequences and Compare Their Genetic Context

Biopesticide-based crop protection is constantly challenged by insect resistance. Thus, expansion of available biopesticides is crucial for sustainable agriculture. Although Bacillus thuringiensis is the major agent for pesticide bioprotection, the number of bacteria species synthesizing proteins with biopesticidal potential is much higher. The Bacterial Pesticidal Protein Resource Center (BPPRC) offers a database of sequences for the control of insect pests, grouped in structural classes. Here we present IDOPS, a tool that detects novel biopesticidal sequences and analyzes them within their genetic environment. The backbone of the IDOPS detection unit is a curated collection of high-quality hidden Markov models that is in accordance with the BPPRC nomenclature. IDOPS was positively benchmarked with BtToxin_Digger and Cry_Processor. In addition, a scan of the UniProtKB database using the IDOPS models returned an abundance of new pesticidal protein candidates distributed across all of the structural groups. Gene expression depends on the genomic environment, therefore, IDOPS provides a comparative genomics module to investigate the genetic regions surrounding pesticidal genes. This feature enables the investigation of accessory elements and evolutionary traits relevant for optimal toxin expression and functional diversification. IDOPS contributes and expands our current arsenal of pesticidal proteins used for crop protection.

Protein structural class prediction using predicted secondary structure and hydropathy profile

This thesis explores machine learning models based on various feature sets to solve the protein structural class prediction problem which is a significant classification problem in bioinformatics. Knowledge of protein structural classes contributes to an understanding of protein folding patterns, and this has made structural class prediction research a major topic of interest. In this thesis, features are extracted from predicted secondary structure and hydropathy sequence using new strategies to classify proteins into one of the four major structural classes: all-α, all-β, α/β, and α+β. The prediction accuracy using these features compares favourably with some existing successful methods. We use Support Vector Machines (SVM), since this learning method has well-known efficiency in solving this classification problem. On a standard dataset (25PDB), the proposed system has an overall accuracy of 89% with as few as 22 features, whereas the previous best performing method had an accuracy of 88% using 2510 features.

Protein structural class prediction using predicted secondary structure and hydropathy profile

This thesis explores machine learning models based on various feature sets to solve the protein structural class prediction problem which is a significant classification problem in bioinformatics. Knowledge of protein structural classes contributes to an understanding of protein folding patterns, and this has made structural class prediction research a major topic of interest. In this thesis, features are extracted from predicted secondary structure and hydropathy sequence using new strategies to classify proteins into one of the four major structural classes: all-α, all-β, α/β, and α+β. The prediction accuracy using these features compares favourably with some existing successful methods. We use Support Vector Machines (SVM), since this learning method has well-known efficiency in solving this classification problem. On a standard dataset (25PDB), the proposed system has an overall accuracy of 89% with as few as 22 features, whereas the previous best performing method had an accuracy of 88% using 2510 features.

How Does the Phenol Structure Influence the Results of the Folin-Ciocalteu Assay?

Plants produce a diverse array of secondary metabolites that are generally nonessential but facilitate ecological interactions. Fruits, vegetables, seeds and nuts can accumulate bioactive secondary metabolites with health-promoting properties, including the potent antioxidant activities of phenolic compounds. Several in vitro assays have been developed to measure the polyphenol content and antioxidant activity of plant extracts, e.g., the simple and highly popular Folin-Ciocalteu (FC) assay. However, the literature contains a number of different descriptions of the assay and it is unclear whether the assay measures the polyphenol content or reducing capacity of the sample. To determine the influence of phenolic structures on the outcome of the FC assay, we tested phenols representing different subgroups (phenolic acids, flavonols, flavanols, dihydrochalcones and flavanones). We observed different results for each reference substance and subgroup. Accordingly, we concluded that the FC assay does not measure the polyphenol content of a sample but determines its reducing capacity instead. Assigning the substances to five structural classes showed that the FC results depend on the number of fulfilled Bors criteria. If a molecule fulfills none of the Bors criteria, the FC results depend on the number of OH groups. We did not find a correlation with other single electron transfer assays (e.g., ABTS and DPPH assays). Furthermore, the FC assay was compatible with all five subgroups and should be preferred over the DPPH assay, which is specific for extracts rich in dihydrochalcones or flavanones.

Structural coordinates: A novel approach to predict protein backbone conformation

Motivation Local protein structure is usually described via classifying each peptide to a unique class from a set of pre-defined structures. These classifications may differ in the number of structural classes, the length of peptides, or class attribution criteria. Most methods that predict the local structure of a protein from its sequence first rely on some classification and only then proceed to the 3D conformation assessment. However, most classification methods rely on homologous proteins’ existence, unavoidably lose information by attributing a peptide to a single class or suffer from a suboptimal choice of the representative classes. Results To alleviate the above challenges, we propose a method that constructs a peptide’s structural representation from the sequence, reflecting its similarity to several basic representative structures. For 5-mer peptides and 16 representative structures, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Our prediction method does not utilize information about protein homologues but relies only on the amino acids’ physicochemical properties and the resolved structures’ statistics. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions under various geometric constraints.

A protein structural study based on the centrality analysis of protein sequence feature networks

In this paper, we use network approaches to analyze the relations between protein sequence features for the top hierarchical classes of CATH and SCOP. We use fundamental connectivity measures such as correlation (CR), normalized mutual information rate (nMIR), and transfer entropy (TE) to analyze the pairwise-relationships between the protein sequence features, and use centrality measures to analyze weighted networks constructed from the relationship matrices. In the centrality analysis, we find both commonalities and differences between the different protein 3D structural classes. Results show that all top hierarchical classes of CATH and SCOP present strong non-deterministic interactions for the composition and arrangement features of Cystine (C), Methionine (M), Tryptophan (W), and also for the arrangement features of Histidine (H). The different protein 3D structural classes present different preferences in terms of their centrality distributions and significant features.

CheSPI: Chemical shift Secondary structure Population Inference

NMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI can assign up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.