PepFun: Open Source Protocols for Peptide-Related Computational Analysis

Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.     

What can a chemist learn from nature’s macrocycles? – A brief, conceptual view

Macrocyclic natural products often display remarkable biological activities, and many of these compounds (or their derivatives) are used as drugs. The chemical diversity of these compounds is immense and may provide inspiration for innovative drug design. Therefore, a database of naturally occurring macrocycles was analyzed for ring size, molecular weight distribution, and the frequency of some common substructural motifs. The underlying principles of the chemical diversity are reviewed in terms of biosynthetic origin and natures strategies for diversity and complexity generation in relation to the structural diversity and similarities found in the macrocycle database. Finally, it is suggested that synthetic chemists should use not only natures molecules, but also natures strategies as a source of inspiration. To illustrate this, the biosynthesis of macrocycles by non-ribosomal peptide synthetases and terpene and polyketide cyclases, as well as recent advances of these strategies in an integrated synthesis/biotechnology approach are briefly reviewed.     

Cover Picture: Turning Spiroketals Inside Out: A Rearrangement Triggered by an Enol Ether Epoxidation (ChemistryOpen 5/2015)

Invited for this month''s cover picture is the group of Professor Mark Peczuh at the University of Connecticut. The cover picture compares the rearrangement of a small molecule to the process of turning a stuffed animal inside out. The recycled, inside-out stuffed animals are both artistic and philosophically provocative. They capture the essence of the rearrangement reaction because the compounds themselves turn inside out over the course of the reaction, extending the diversity of products that can arise from simple starting materials. Small molecules often have functional groups with latent reactivity; under the appropriate conditions, those groups can react with other compounds (e.g., reagents) and also with other groups in the same molecule in an intramolecular reaction. The research team found that the epoxidation of some highly functionalized spiroketal compounds promoted rearrangements of their structures that turned them inside out. Some of the features of the products led them to use X-ray crystallography or a combination of computer-assisted structure elucidation, computation, and a new version of the 1,1-ADEQUATE NMR experiment to determine their structures. For more details, see the Communication on p. 577 ff.     

Computational Screening for the Anticancer Potential of Seed-Derived Antioxidant Peptides: A Cheminformatic Approach

Some seed-derived antioxidant peptides are known to regulate cellular modulators of ROS production, including those proposed to be promising targets of anticancer therapy. Nevertheless, research in this direction is relatively slow owing to the inevitable time-consuming nature of wet-lab experimentations. To help expedite such explorations, we performed structure-based virtual screening on seed-derived antioxidant peptides in the literature for anticancer potential. The ability of the peptides to interact with myeloperoxidase, xanthine oxidase, Keap1, and p47^phox was examined. We generated a virtual library of 677 peptides based on a database and literature search. Screening for anticancer potential, non-toxicity, non-allergenicity, non-hemolyticity narrowed down the collection to five candidates. Molecular docking found LYSPH as the most promising in targeting myeloperoxidase, xanthine oxidase, and Keap1, whereas PSYLNTPLL was the best candidate to bind stably to key residues in p47^phox. Stability of the four peptide-target complexes was supported by molecular dynamics simulation. LYSPH and PSYLNTPLL were predicted to have cell- and blood-brain barrier penetrating potential, although intolerant to gastrointestinal digestion. Computational alanine scanning found tyrosine residues in both peptides as crucial to stable binding to the targets. Overall, LYSPH and PSYLNTPLL are two potential anticancer peptides that deserve deeper exploration in future.     

The fundamentals behind solving for unknown molecular structures using computer‐assisted structure elucidation: a free software package at the undergraduate and graduate levels

The successful elucidation of an unknown compound's molecular structure often requires an analyst with profound knowledge and experience of advanced spectroscopic techniques, such as Nuclear Magnetic Resonance (NMR) spectroscopy and mass spectrometry. The implementation of Computer‐Assisted Structure Elucidation (CASE) software in solving for unknown structures, such as isolated natural products and/or reaction impurities, can serve both as elucidation and teaching tools. As such, the introduction of CASE software with 112 exercises to train students in conjunction with the traditional pen and paper approach will strengthen their overall understanding of solving unknowns and explore of various structural end points to determine the validity of the results quickly. Copyright © 2016 John Wiley & Sons, Ltd.     

An automated method for graph-based chemical space exploration and transition state finding

Algorithms that automatically explore the chemical space have been limited to chemical systems with a low number of atoms due to expensive involved quantum calculations and the large amount of possible reaction pathways. The method described here presents a novel solution to the problem of chemical exploration by generating reaction networks with heuristics based on chemical theory. First, a second version of the reaction network is determined through molecular graph transformations acting upon functional groups of the reacting. Only transformations that break two chemical bonds and form two new ones are considered, leading to a significant performance enhancement compared to previously presented algorithm. Second, energy barriers for this reaction network are estimated through quantum chemical calculations by a growing string method, which can also identify non-octet species missed during the previous step and further define the reaction network. The proposed algorithm has been successfully applied to five different chemical reactions, in all cases identifying the most important reaction pathways.     

Cheminformatic Profiling and Hit Prioritization of Natural Products with Activities against Methicillin-Resistant Staphylococcus aureus (MRSA)

Several natural products (NPs) have displayed varying in vitro activities against methicillin-resistant Staphylococcus aureus (MRSA). However, few of these compounds have not been developed into potential antimicrobial drug candidates. This may be due to the high cost and tedious and time-consuming process of conducting the necessary preclinical tests on these compounds. In this study, cheminformatic profiling was performed on 111 anti-MRSA NPs (AMNPs), using a few orally administered conventional drugs for MRSA (CDs) as reference, to identify compounds with prospects to become drug candidates. This was followed by prioritizing these hits and identifying the liabilities among the AMNPs for possible optimization. Cheminformatic profiling revealed that most of the AMNPs were within the required drug-like region of the investigated properties. For example, more than 76% of the AMNPs showed compliance with the Lipinski, Veber, and Egan predictive rules for oral absorption and permeability. About 34% of the AMNPs showed the prospect to penetrate the blood–brain barrier (BBB), an advantage over the CDs, which are generally non-permeant of BBB. The analysis of toxicity revealed that 59% of the AMNPs might have negligible or no toxicity risks. Structure–activity relationship (SAR) analysis revealed chemical groups that may be determinants of the reported bioactivity of the compounds. A hit prioritization strategy using a novel “desirability scoring function” was able to identify AMNPs with the desired drug-likeness. Hit optimization strategies implemented on AMNPs with poor desirability scores led to the design of two compounds with improved desirability scores.     

Identification and Analytical Characterization of a Novel Synthetic Cannabinoid-Type Substance in Herbal Material in Europe

The rapid diffusion of new psychoactive substances (NPS) presents unprecedented challenges to both customs authorities and analytical laboratories involved in their detection and characterization. In this study an analytical approach to the identification and structural elucidation of a novel synthetic cannabimimetic, quinolin-8-yl-3-[(4,4-difluoropiperidin-1-yl) sulfonyl]-4-methylbenzoate (2F-QMPSB), detected in seized herbal material, is detailed. An acid precursor 4-methyl-3-(4,4-difluoro-1-piperidinylsulfonyl) benzoic acid (2F-MPSBA), has also been identified in the same seized material. After extraction from the herbal material the synthetic cannabimimetic, also referred to as synthetic cannabinoid receptor agonists or “synthetic cannabinoids”, was characterized using gas chromatography-mass spectrometry (GC-MS), ¹H, ¹³C, ¹⁹F and ¹⁵N nuclear magnetic resonance (NMR) and high-resolution tandem mass spectrometry (HR-MS/MS) combined with chromatographic separation. A cheminformatics platform was used to manage and interpret the analytical data from these techniques.