CHUCKLES: a method for representing and searching peptide and peptoid sequences on both monomer and atomic levels

Development of human-machine language interfaces for the visual analysis of complex biologics and RNA modalities and associated experimental data

The advent of recombinant protein-based therapeutic agents in the 1980s and subsequent waves of innovation in molecular biology and engineering of biologics has permitted the production of an increasingly broad array of complex, high molecular weight constructs. While this has opened a powerful new toolbox of molecular scaffolds with which to probe and interdict biological processes, it also makes deciphering the architectural nuances between individual constructs intuitively difficult. Key to downstream data processes for the detection of data trends is the ability to unambiguously identify, compare, and communicate the nature of molecular compositions. Existing small molecule orientated software tools are not intended for structures such as peptides, proteins, antibodies, and RNA, and do not contain adequate atomistic or domain-level detail to appropriately convey their higher structural complexity. Similarly, there is a paucity of large molecule-focused data analysis and visualization tools. This article will describe four new approaches we developed for the graphical representation and analysis of complex large molecules and experimental data. These tools help fulfill key needs in scientific communication and structure-property analysis of complex biologics and modified oligonucleotide-based drug candidates.

BILN: A Human-Readable Line Notation for Complex Peptides

We present an easy, human-readable line notation to describe even complex peptides.

Proposal of the Annotation of Phosphorylated Amino Acids and Peptides Using Biological and Chemical Codes

Phosphorylation represents one of the most important modifications of amino acids, peptides, and proteins. By modifying the latter, it is useful in improving the functional properties of foods. Although all these substances are broadly annotated in internet databases, there is no unified code for their annotation. The present publication aims to describe a simple code for the annotation of phosphopeptide sequences. The proposed code describes the location of phosphate residues in amino acid side chains (including new rules of atom numbering in amino acids) and the diversity of phosphate residues (e.g., di- and triphosphate residues and phosphate amidation). This article also includes translating the proposed biological code into SMILES, being the most commonly used chemical code. Finally, it discusses possible errors associated with applying the proposed code and in the resulting SMILES representations of phosphopeptides. The proposed code can be extended to describe other modifications in the future.

Molecular representations in AI-driven drug discovery: a review and practical guide

The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.

BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities

The BIOPEP-UWM™ database of bioactive peptides (formerly BIOPEP) has recently become a popular tool in the research on bioactive peptides, especially on these derived from foods and being constituents of diets that prevent development of chronic diseases. The database is continuously updated and modified. The addition of new peptides and the introduction of new information about the existing ones (e.g., chemical codes and references to other databases) is in progress. New opportunities include the possibility of annotating peptides containing D-enantiomers of amino acids, batch processing option, converting amino acid sequences into SMILES code, new quantitative parameters characterizing the presence of bioactive fragments in protein sequences, and finding proteinases that release particular peptides.

Molecular docking predictions of fragrance binding to human leukocyte antigen molecules

BACKGROUND

Over 4000 small chemicals have been identified as allergens capable of inducing skin sensitization. Many sensitizers are hypothesized to act as haptens producing novel antigens, which can be presented to T cells by human leukocyte antigens (HLAs). Recent studies suggest that some chemical allergens use hapten-independent mechanisms.

OBJECTIVE

To determine whether molecular docking can identify HLA molecules that bind skin-sensitizing chemical allergens.

METHODS

Structural models of HLA molecules were used as the basis for molecular docking of 22 chemical allergens. Allergens predicted to bind HLA-B*57:01 were tested for their ability to stimulate T cells by the use of proliferation and interferon-gamma enzyme-linked immunospot assays.

RESULTS

Chemical allergens that did not satisfy the criteria for hapten activity in vitro were predicted to bind more strongly to common HLA isoforms than those with known hapten activity. HLA-B*57:01, which is an HLA allele required for drug hypersensitivity reactions, was predicted to bind several allergens, including benzyl benzoate, benzyl cinnamate, and benzyl salicylate. In in vitro T cell stimulation assays, benzyl salicylate and benzyl cinnamate were found to stimulate T cell responses from HLA-B*57:01 carriers.

CONCLUSIONS

These data suggest that small-molecule skin sensitizers have the potential to interact with HLA, and show that T cell-based in vitro assays may be used to evaluate the immunogenicity of skin-sensitizing chemicals.

rBAN: retro-biosynthetic analysis of nonribosomal peptides

Proteinogenic and non-proteinogenic amino acids, fatty acids or glycans are some of the main building blocks of nonribsosomal peptides (NRPs) and as such may give insight into the origin, biosynthesis and bioactivities of their constitutive peptides. Hence, the structural representation of NRPs using monomers provides a biologically interesting skeleton of these secondary metabolites. Databases dedicated to NRPs such as Norine, already integrate monomer-based annotations in order to facilitate the development of structural analysis tools. In this paper, we present rBAN (retro-biosynthetic analysis of nonribosomal peptides), a new computational tool designed to predict the monomeric graph of NRPs from their atomic structure in SMILES format. This prediction is achieved through the "in silico" fragmentation of a chemical structure and matching the resulting fragments against the monomers of Norine for identification. Structures containing monomers not yet recorded in Norine, are processed in a "discovery mode" that uses the RESTful service from PubChem to search the unidentified substructures and suggest new monomers. rBAN was integrated in a pipeline for the curation of Norine data in which it was used to check the correspondence between the monomeric graphs annotated in Norine and SMILES-predicted graphs. The process concluded with the validation of the 97.26% of the records in Norine, a two-fold extension of its SMILES data and the introduction of 11 new monomers suggested in the discovery mode. The accuracy, robustness and high-performance of rBAN were demonstrated in benchmarking it against other tools with the same functionality: Smiles2Monomers and GRAPE.

SPICES: a particle-based molecular structure line notation and support library for mesoscopic simulation

Simplified Particle Input ConnEction Specification (SPICES) is a particle-based molecular structure representation derived from straightforward simplifications of the atom-based SMILES line notation. It aims at supporting tedious and error-prone molecular structure definitions for particle-based mesoscopic simulation techniques like Dissipative Particle Dynamics by allowing for an interplay of different molecular encoding levels that range from topological line notations and corresponding particle-graph visualizations to 3D structures with support of their spatial mapping into a simulation box. An open Java library for SPICES structure handling and mesoscopic simulation support in combination with an open Java Graphical User Interface viewer application for visual topological inspection of SPICES definitions are provided.

Annotation of Peptide Structures Using SMILES and Other Chemical Codes-Practical Solutions

Contemporary peptide science exploits methods and tools of bioinformatics, and cheminformatics. These approaches use different languages to describe peptide structures—amino acid sequences and chemical codes (especially SMILES), respectively. The latter may be applied, e.g., in comparative studies involving structures and properties of peptides and peptidomimetics. Progress in peptide science “in silico” may be achieved via better communication between biologists and chemists, involving the translation of peptide representation from amino acid sequence into SMILES code. Recent recommendations concerning good practice in chemical information include careful verification of data and their annotation. This publication discusses the generation of SMILES representations of peptides using existing software. Construction of peptide structures containing unnatural and modified amino acids (with special attention paid on glycosylated peptides) is also included. Special attention is paid to the detection and correction of typical errors occurring in SMILES representations of peptides and their correction using molecular editors. Brief recommendations for training of staff working on peptide annotations, are discussed as well.

Chiral Brønsted Acid-Catalyzed Enantioselective α-Amidoalkylation Reactions: A Joint Experimental and Predictive Study

Enamides with a free NH group have been evaluated as nucleophiles in chiral Brønsted acid-catalyzed enantioselective α-amidoalkylation reactions of bicyclic hydroxylactams for the generation of quaternary stereocenters. A quantitative structure-reactivity relationship (QSRR) method has been developed to find a useful tool to rationalize the enantioselectivity in this and related processes and to orient the catalyst choice. This correlative perturbation theory (PT)-QSRR approach has been used to predict the effect of the structure of the substrate, nucleophile, and catalyst, as well as the experimental conditions, on the enantioselectivity. In this way, trends to improve the experimental results could be found without engaging in a long-term empirical investigation.

Jmol SMILES and Jmol SMARTS: specifications and applications

Background

SMILES and SMARTS are two well-defined structure matching languages that have gained wide use in cheminformatics. Jmol is a widely used open-source molecular visualization and analysis tool written in Java and implemented in both Java and JavaScript. Over the past 10 years, from 2007 to 2016, work on Jmol has included the development of dialects of SMILES and SMARTS that incorporate novel aspects that allow new and powerful applications.

Results

The specifications of “Jmol SMILES” and “Jmol SMARTS” are described. The dialects most closely resemble OpenSMILES and OpenSMARTS. Jmol SMILES is a superset of OpenSMILES, allowing a freer format, including whitespace and comments, the addition of “processing directives” that modify the meaning of certain aspects of SMILES processing such as aromaticity and stereochemistry, a more extensive treatment of stereochemistry, and several minor additions. Jmol SMARTS similarly adds these same modifications to OpenSMARTS, but also adds a number of additional “primitives” and elements of syntax tuned to matching 3D molecular structures and selecting their atoms. The result is an expansion of the capabilities of SMILES and SMARTS primarily for use in 3D molecular analysis, allowing a broader range of matching involving any combination of 3D molecular structures, SMILES strings, and SMARTS patterns. While developed specifically for Jmol, these dialects of SMILES and SMARTS are independent of the Jmol application itself.

Conclusions

Jmol SMILES and Jmol SMARTS add value to standard SMILES and SMARTS. Together they have proven exceptionally capable in extracting valuable information from 3D structural models, as demonstrated in Jmol. Capabilities in Jmol enabled by Jmol SMILES and Jmol SMARTS include efficient MMFF94 atom typing, conformational identification, SMILES comparisons without canonicalization, identification of stereochemical relationships, quantitative comparison of 3D structures from different sources (including differences in Kekulization), conformational flexible fitting, and atom mapping used to synchronize interactive displays of 2D structures, 3D structures, and spectral correlations, where data are being drawn from multiple sources.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-016-0160-4) contains supplementary material, which is available to authorized users.

Perturbation theory model of reactivity and enantioselectivity of palladium-catalyzed Heck–Heck cascade reactions

A new multi-output PT-QSRR model to correlate and predict the enantioselectivity and yield of Heck–Heck cascade reactions has been developed.

Smiles2Monomers: a link between chemical and biological structures for polymers

Background

The monomeric composition of polymers is powerful for structure comparison and synthetic biology, among others. Many databases give access to the atomic structure of compounds but the monomeric structure of polymers is often lacking. We have designed a smart algorithm, implemented in the tool Smiles2Monomers (s2m), to infer efficiently and accurately the monomeric structure of a polymer from its chemical structure.

Results

Our strategy is divided into two steps: first, monomers are mapped on the atomic structure by an efficient subgraph-isomorphism algorithm ; second, the best tiling is computed so that non-overlapping monomers cover all the structure of the target polymer. The mapping is based on a Markovian index built by a dynamic programming algorithm. The index enables s2m to search quickly all the given monomers on a target polymer. After, a greedy algorithm combines the mapped monomers into a consistent monomeric structure. Finally, a local branch and cut algorithm refines the structure. We tested this method on two manually annotated databases of polymers and reconstructed the structures de novo with a sensitivity over 90 %. The average computation time per polymer is 2 s.

Conclusion

s2m automatically creates de novo monomeric annotations for polymers, efficiently in terms of time computation and sensitivity. s2m allowed us to detect annotation errors in the tested databases and to easily find the accurate structures. So, s2m could be integrated into the curation process of databases of small compounds to verify the current entries and accelerate the annotation of new polymers. The full method can be downloaded or accessed via a website for peptide-like polymers at http://bioinfo.lifl.fr/norine/smiles2monomers.jsp.Graphical abstract:

Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental-Computational Study of NaGDC-DDAB Micelles

Studies of the self-aggregation of binary systems are of both theoretical and practical importance. They provide an opportunity to investigate the influence of the molecular structure of the hydrophobe on the nonideality of mixing. On the other hand, linear free energy relationship (LFER) models, such as Hansch's equations, may be used to predict the properties of chemical compounds such as drugs or surfactants. However, the task becomes more difficult once we want to predict simultaneaously the effect over multiple output properties of binary systems of perturbations under multiple input experimental boundary conditions (b(j)). As a consequence, we need computational chemistry or chemoinformatics models that may help us to predict different properties of the autoaggregation process of mixed surfactants under multiple conditions. In this work, we have developed the first model that combines perturbation theory (PT) and LFER ideas. The model uses as input covariance PT operators (CPTOs). CPTOs are calculated as the difference between covariance ΔCov((i)μ(k)) functions before and after multiple perturbations in the binary system. In turn, covariances calculated as the product of two Box-Jenkins operators (BJO) operators. BJOs are used to measure the deviation of the structure of different chemical compounds from a set of molecules measured under a given subset of experimental conditions. The best CPT-LFER model found predicted the effects of 25,000 perturbations over 9 different properties of binary systems. We also reported experimental studies of different experimental properties of the binary system formed by sodium glycodeoxycholate and didodecyldimethylammonium bromide (NaGDC-DDAB). Last, we used our CPT-LFER model to carry out a 1000 data point simulation of the properties of the NaGDC-DDAB system under different conditions not studied experimentally.

Experimental and computational studies of fatty acid distribution networks

A new PT-LFER model is useful for predicting a distribution network in terms of specific fatty acid distribution.

HELM: a hierarchical notation language for complex biomolecule structure representation

When biological macromolecules are used as therapeutic agents, it is often necessary to introduce non-natural chemical modifications to improve their pharmaceutical properties. The final products are complex structures where entities such as proteins, peptides, oligonucleotides, and small molecule drugs may be covalently linked to each other, or may include chemically modified biological moieties. An accurate in silico representation of these complex structures is essential, as it forms the basis for their electronic registration, storage, analysis, and visualization. The size of these molecules (henceforth referred to as "biomolecules") often makes them too unwieldy and impractical to represent at the atomic level, while the presence of non-natural chemical modifications makes it impossible to represent them by sequence alone. Here we describe the Hierarchical Editing Language for Macromolecules ("HELM") and demonstrate its utility in the representation of structures such as antisense oligonucleotides, short interference RNAs, peptides, proteins, and antibody drug conjugates.

CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures

CurlySMILES is a chemical line notation which extends SMILES with annotations for storage, retrieval and modeling of interlinked, coordinated, assembled and adsorbed molecules in supramolecular structures and nanodevices. Annotations are enclosed in curly braces and anchored to an atomic node or at the end of the molecular graph depending on the annotation type. CurlySMILES includes predefined annotations for stereogenicity, electron delocalization charges, extra-molecular interactions and connectivity, surface attachment, solutions, and crystal structures and allows extensions for domain-specific annotations. CurlySMILES provides a shorthand format to encode molecules with repetitive substructural parts or motifs such as monomer units in macromolecules and amino acids in peptide chains. CurlySMILES further accommodates special formats for non-molecular materials that are commonly denoted by composition of atoms or substructures rather than complete atom connectivity.

JenPep: a novel computational information resource for immunobiology and vaccinology

JenPep is a relational database containing a compendium of thermodynamic binding data for the interaction of peptides with a range of important immunological molecules: the major histocompatibility complex, TAP transporter, and T cell receptor. The database also includes annotated lists of B cell and T cell epitopes. Version 2.0 of the database is implemented in a bespoke postgreSQL database system and is fully searchable online via a perl/HTML interface (URL: http://www.jenner.ac.uk/JenPep).

Development and screening of a polyketide virtual library for drug leads against a motilide pharmacophore

A virtual library of macrocyclic polyketide molecules was generated and screened to identify novel, conformationally constrained potential motilin receptor agonists ("motilides"). A motilide pharmacophore model was generated from the potent 6,9-enol ether erythromycin and known derivatives from the literature. The pharmacophore for each molecular conformation was a point in a distance-volume space based on presentation of the putative binding moieties. Two methods, one fragment based method and the other reaction based, were explored for constructing the polyketide virtual library. First, a virtual library was assembled from monomeric fragments using the CHORTLES language. Second, the virtual library was assembled by the in silico application of all possible polyketide synthase enzyme reactions to generate the product library. Each library was converted to low-energy 3D conformations by distance geometry and standard minimization methods. The distance-volume metric was calculated for low-energy conformations of the members of the virtual polyketide library and screened against the enol ether pharmacophore. The goal was to identify novel macrocycles that satisfy the pharmacophore. We identified three conformationally constrained, novel polyketide series that have low-energy conformations satisfying the distance-volume constraints of the motilide pharmacophore.

RNA base-amino acid interaction strengths derived from structures and sequences

We investigate RNA base-amino acid interactions by counting their contacts in structures and their implicit contacts in various functional sequences where the structures can be assumed to be preserved. These frequencies are cast into equations to extract relative interaction energetics. Previously we used this approach in considering the major groove interactions of DNA, and here we apply it to the more diverse interactions observed in RNA. Structures considered are the three different tRNA synthetase complexes, the U1A spliceosomal protein with an RNA hairpin and the BIV TAR-Tat complex. We use binding data for the base frequencies for the seryl, aspartyl and glutaminyl tRNA-synthetase and U1 RNA-protein complexes. We compare with the previously reported DNA major groove peptide contacts the results for atoms of RNA bases, usually in the major groove. There are strong similarities between the rank orders of interacting bases in the DNA and the RNA cases. The apparent strongest RNA interaction observed is between arginine and guanine which was also one of the strongest DNA interactions. The similar data for base atomic interactions, whether base paired or not, support the importance of strong atomic interactions over local structure considerations, such as groove width and alpha-helicity.

Stigmata: an algorithm to determine structural commonalities in diverse datasets

An algorithm, Stigmata, is described, which extracts structural commonalities from chemical datasets. It is discussed using several illustrative examples and a pharmaceutically interesting set of dopamine D2 agonists. The commonalities are determined using two-dimensional topological chemical descriptions and are incorporated into the key feature of the algorithm, the modal fingerprint. Flexibility is built into the algorithm by means of a user-defined threshold value, which affects the information content of the modal fingerprint. The use of the modal fingerprint as a diversity assessment tool, as a database similarity query, and as a basis for color mapping the determined commonalities back onto the chemical structures is demonstrated.

BOOMSLANG: a program for combinatorial structure generation

An approach to exploiting pharmacophore models is described. Structures are assembled combinatorially from user-defined fragments and flexibly overlaid into the reference frame of the pharmacophore using distance geometry and molecular mechanics. The match with the pharmacophore is quantified by conformational energy and volume of overlap.

PRO_LIGAND: an approach to de novo molecular design. 6. Flexible fitting in the design of peptides

This paper describes the further development of the functionality of our in-house de novo design program, PRO_LIGAND. In particular, attention is focused on the implementation and validation of the 'direct tweak' method for the construction of conformationally flexible molecules, such as peptides, from molecular fragments. This flexible fitting method is compared to the original method based on libraries of prestored conformations for each fragment. It is shown that the directed tweak method produces results of comparable quality, with significant time savings. By removing the need to generate a set of representative conformers for any new library fragment, the flexible fitting method increases the speed and simplicity with which new fragments can be included in a fragment library and also reduces the disk space required for library storage. A further improvement to the molecular construction process within PRO_LIGAND is the inclusion of a constrained minimisation procedure which relaxes fragments onto the design model and can be used to reject highly strained structures during the structure generation phase. This relaxation is shown to be very useful in simple test cases, but restricts diversity for more realistic examples. The advantages and disadvantages of these additions to the PRO_LIGAND methodology are illustrated by three examples: similar design to an alpha helix region of dihydrofolate reductase, complementary design to the active site of HIV-1 protease and similar design to an epitope region of lysozyme.