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Kosugi T, Ohue M. Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold. Int J Mol Sci 2023; 24:13257. [PMID: 37686057 PMCID: PMC10487914 DOI: 10.3390/ijms241713257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/18/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.
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Affiliation(s)
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, G3-56-4259 Nagatsutacho, Midori-ku, Yokohama City 226-8501, Kanagawa, Japan;
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2
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Zakharova E, Orsi M, Capecchi A, Reymond JL. Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anti-Cancer Peptides. ChemMedChem 2022; 17:e202200291. [PMID: 35880810 PMCID: PMC9541320 DOI: 10.1002/cmdc.202200291] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/29/2022] [Indexed: 12/05/2022]
Abstract
Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic α‐helices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non‐hemolytic from hemolytic AMPs and ACPs to discover new non‐hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing. Experimental evaluation of thirty‐three peptides resulted in eleven active ACPs, four of which were non‐hemolytic, with properties resembling those of the natural ACP lasioglossin III. These experiments show the first example of direct machine learning guided discovery of non‐hemolytic ACPs.
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Affiliation(s)
- Elena Zakharova
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Markus Orsi
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Alice Capecchi
- University of Bern: Universitat Bern, Departement of Chemistry, Biochemistry and Pharmaceutical Sciences, SWITZERLAND
| | - Jean-Louis Reymond
- Universität Bern: Universitat Bern, Department of Chemistry and Biochemistry, Department of Chemistry and Biochemistry, Freiestrasse 3, 3012, Switzerland, 3012, Bern, SWITZERLAND
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3
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Solubility-Aware Protein Binding Peptide Design Using AlphaFold. Biomedicines 2022; 10:biomedicines10071626. [PMID: 35884931 PMCID: PMC9312799 DOI: 10.3390/biomedicines10071626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 01/02/2023] Open
Abstract
New protein–protein interactions (PPIs) are identified, but PPIs have different physicochemical properties compared with conventional targets, making it difficult to use small molecules. Peptides offer a new modality to target PPIs, but designing appropriate peptide sequences by computation is challenging. Recently, AlphaFold and RoseTTAFold have made it possible to predict protein structures from amino acid sequences with ultra-high accuracy, enabling de novo protein design. We designed peptides likely to have PPI as the target protein using the “binder hallucination” protocol of AfDesign, a de novo protein design method using AlphaFold. However, the solubility of the peptides tended to be low. Therefore, we designed a solubility loss function using solubility indices for amino acids and developed a solubility-aware AfDesign binder hallucination protocol. The peptide solubility in sequences designed using the new protocol increased with the weight of the solubility loss function; moreover, they captured the characteristics of the solubility indices. Moreover, the new protocol sequences tended to have higher affinity than random or single residue substitution sequences when evaluated by docking binding affinity. Our approach shows that it is possible to design peptide sequences that can bind to the interface of PPI while controlling solubility.
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4
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Saldívar-González FI, Medina-Franco JL. Approaches for enhancing the analysis of chemical space for drug discovery. Expert Opin Drug Discov 2022; 17:789-798. [PMID: 35640229 DOI: 10.1080/17460441.2022.2084608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Chemical space is a powerful, general, and practical conceptual framework in drug discovery and other areas in chemistry that addresses the diversity of molecules and it has various applications. Moreover, chemical space is a cornerstone of chemoinformatics as a scientific discipline. In response to the increase in the set of chemical compounds in databases, generators of chemical structures, and tools to calculate molecular descriptors, novel approaches to generate visual representations of chemical space in low dimensions are emerging and evolving. Such approaches include a wide range of commercial and free applications, software, and open-source methods. AREAS COVERED The current state of chemical space in drug design and discovery is reviewed. The topics discussed herein include advances for efficient navigation in chemical space, the use of this concept in assessing the diversity of different data sets, exploring structure-property/activity relationships for one or multiple endpoints, and compound library design. Recent advances in methodologies for generating visual representations of chemical space have been highlighted, thereby emphasizing open-source methods. EXPERT OPINION Quantitative and qualitative generation and analysis of chemical space require novel approaches for handling the increasing number of molecules and their information available in chemical databases (including emerging ultra-large libraries). In addition, it is of utmost importance to note that chemical space is a conceptual framework that goes beyond visual representation in low dimensions. However, the graphical representation of chemical space has several practical applications in drug discovery and beyond.
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Affiliation(s)
- Fernanda I Saldívar-González
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico
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5
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Hiener DC, Hutchison GR. Pareto Optimization of Oligomer Polarizability and Dipole Moment Using a Genetic Algorithm. J Phys Chem A 2022; 126:2750-2760. [PMID: 35471827 DOI: 10.1021/acs.jpca.2c01266] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
High-performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between the polarizability and dipole moment, indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polarizabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight-binding method for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. We also explored the underlying structure-function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes.
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Affiliation(s)
- Danielle C Hiener
- Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
| | - Geoffrey R Hutchison
- Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States.,Department of Chemical and Petroleum Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, Pennsylvania 15261, United States
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6
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Baylon JL, Ursu O, Muzdalo A, Wassermann AM, Adams GL, Spale M, Mejzlik P, Gromek A, Pisarenko V, Hancharyk D, Jenkins E, Bednar D, Chang C, Clarova K, Glick M, Bitton DA. PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization. J Chem Inf Model 2022; 62:1259-1267. [PMID: 35192366 DOI: 10.1021/acs.jcim.1c01360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure-activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.
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Affiliation(s)
- Javier L Baylon
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Oleg Ursu
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Anja Muzdalo
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Anne Mai Wassermann
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Gregory L Adams
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Martin Spale
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Petr Mejzlik
- AI & Big Data Analytics, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Anna Gromek
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Viktor Pisarenko
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Dzianis Hancharyk
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Esteban Jenkins
- Foundational Data and Analytics, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - David Bednar
- Foundational Data and Analytics, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Charlie Chang
- Discovery Research IT, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Kamila Clarova
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic.,Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague 166 28, Czech Republic
| | - Meir Glick
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Danny A Bitton
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
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7
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Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021; 14:245-270. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
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8
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Grübner M, Dunkel A, Steiner F, Hofmann T. Systematic Evaluation of Liquid Chromatography (LC) Column Combinations for Application in Two-Dimensional LC Metabolomic Studies. Anal Chem 2021; 93:12565-12573. [PMID: 34491041 DOI: 10.1021/acs.analchem.1c01857] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In comparison to proteomics, the application of two-dimensional liquid chromatography (2D LC) in the field of metabolomics is still premature. One reason might be the elevated chemical complexity and the associated challenge of selecting proper separation conditions in each dimension. As orthogonality of dimensions is a major issue, the present study aimed for the identification of successful stationary phase combinations. To determine the degree of orthogonality, first, six different metrics, namely, Pearson's correlation coefficient (1 - |R|), the nearest-neighbor distances (H̅NND), the "asterisk equations" (AO), and surface coverage by bins (SCG), convex hulls (SCCH), and α-convex hulls (SCαH), were critically assessed by 15 artificial 2D data sets, and a systematic parameter optimization of α-convex hulls was conducted. SGG, SCαH with α = 0.1, and H̅NND generated valid results with sensitivity toward space utilization and data distribution and, therefore, were applied to pairs of experimental retention time sets obtained for >350 metabolites, selected to represent the chemical space of human urine. Normalized retention data were obtained for 23 chromatographic setups, comprising reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), and mixed-mode separation systems with an ion exchange (IEX) contribution. As expected, no single LC setting provided separation of all considered analytes, but while conventional RP×HILIC combinations appeared rather complementary than orthogonal, the incorporation of IEX properties into the RP dimension substantially increased the 2D potential. Eventually, one of the most promising column combinations was implemented for an offline 2D LC time-of-flight mass spectrometry analysis of a lyophilized urine sample. Targeted screening resulted in a total of 164 detected metabolites and confirmed the outstanding coverage of the 2D retention space.
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Affiliation(s)
- Maria Grübner
- Chair of Food Chemistry and Molecular Sensory Science, Technical University of Munich, Lise-Meitner-Straße 34, Freising 85354, Germany.,Thermo Fisher Scientific, Dornierstraße 4, Germering 82110, Germany
| | - Andreas Dunkel
- Chair of Food Chemistry and Molecular Sensory Science, Technical University of Munich, Lise-Meitner-Straße 34, Freising 85354, Germany.,Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Straße 34, Freising 85354, Germany
| | - Frank Steiner
- Thermo Fisher Scientific, Dornierstraße 4, Germering 82110, Germany
| | - Thomas Hofmann
- Chair of Food Chemistry and Molecular Sensory Science, Technical University of Munich, Lise-Meitner-Straße 34, Freising 85354, Germany
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9
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Medina-Franco JL, Sánchez-Cruz N, López-López E, Díaz-Eufracio BI. Progress on open chemoinformatic tools for expanding and exploring the chemical space. J Comput Aided Mol Des 2021; 36:341-354. [PMID: 34143323 PMCID: PMC8211976 DOI: 10.1007/s10822-021-00399-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 06/14/2021] [Indexed: 01/10/2023]
Abstract
The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure-property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure-property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.
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Affiliation(s)
- José L Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico.
| | - Norberto Sánchez-Cruz
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico
| | - Edgar López-López
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico.,Departamento de Química y Programa de Posgrado en Farmacología, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Apartado 14-740, 07000, Mexico City, Mexico
| | - Bárbara I Díaz-Eufracio
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico
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10
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Capecchi A, Reymond JL. Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning. Biomolecules 2020; 10:E1385. [PMID: 32998475 PMCID: PMC7600738 DOI: 10.3390/biom10101385] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 09/22/2020] [Accepted: 09/25/2020] [Indexed: 12/20/2022] Open
Abstract
Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.
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Affiliation(s)
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland;
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11
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Díaz-Eufracio BI, Palomino-Hernández O, Arredondo-Sánchez A, Medina-Franco JL. D-Peptide Builder: A Web Service to Enumerate, Analyze, and Visualize the Chemical Space of Combinatorial Peptide Libraries. Mol Inform 2020; 39:e2000035. [PMID: 32558380 DOI: 10.1002/minf.202000035] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/18/2020] [Indexed: 11/07/2022]
Abstract
Peptide-based drug discovery is re-gaining attention in drug discovery. Similarly, combinatorial chemistry continues to be a useful technique for the rapid exploration of chemical space. A current challenge, however, is the enumeration of combinatorial peptide libraries using freely accessible tools. To facilitate the swift enumeration of combinatorial peptide libraries, we introduce herein D-Peptide Builder. In the current version, the user can build up to pentapeptides, linear or cyclic, using the natural pool of 20 amino acids. The user can use non- and/or N-methylated amino acids. The server also enables the rapid visualization of the chemical space of the newly enumerated peptides in comparison with other libraries relevant to drug discovery and preloaded in the server. D-Peptide Builder is freely accessible at http://dpeptidebuilder. quimica.unam.mx:4000/. It is also accessible through the open D-Tools platform (DIFACQUIM Tools for Chemoinformatics https://www.difacquim.com/d-tools/).
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Affiliation(s)
- Bárbara I Díaz-Eufracio
- DIFACQUIM research group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico
| | - Oscar Palomino-Hernández
- Computational Biomedicine, Institute of Advanced Simulation (IAS-5), and Institute of Neuroscience and Medicine (INM-9), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Aarón Arredondo-Sánchez
- DIFACQUIM research group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico
| | - José L Medina-Franco
- DIFACQUIM research group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, 04510, Mexico City, Mexico
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12
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One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. J Cheminform 2020; 12:43. [PMID: 33431010 PMCID: PMC7291580 DOI: 10.1186/s13321-020-00445-4] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 06/04/2020] [Indexed: 02/08/2023] Open
Abstract
Background Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules. Results Here we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints. Conclusion MAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/.![]()
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13
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Freire KA, Torres MDT, Lima DB, Monteiro ML, Bezerra de Menezes RRPP, Martins AMC, Oliveira VX. Wasp venom peptide as a new antichagasic agent. Toxicon 2020; 181:71-78. [PMID: 32360153 DOI: 10.1016/j.toxicon.2020.04.099] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/07/2020] [Accepted: 04/24/2020] [Indexed: 01/11/2023]
Abstract
Chagas disease is caused by Trypanosoma cruzi and affects approximately 10 million people a year worldwide. The only two treatment options, benznidazole and nifurtimox, have low efficacy and high toxicity towards human cells. Mastoporan peptide (MP) a small cationic AMP from the venom of the wasp Polybia paulista has been reported as a potent trypanocidal agent. Thus, we evaluated the antichagasic effect of another AMP from the venom of the same wasp Polybia paulista, polybia-CP (ILGTILGLLSKL-NH2), and investigated its mechanism of action against different stages of the trypanosomal cells life cycle. Polybia-CP was tested against the epimastigote, trypomastigote and amastigote forms of the T. cruzi Y strain (benznidazole-resistant strain) and inhibited the development of these forms. We also assessed the selectivity of the AMP against mammalian cells by exposing LLC-MK2 cells to polybia-CP, the peptide presented a high selectivity index (>106). The mechanism of action of polybia-CP on trypanosomal cells was investigated by flow cytometry, scanning electron microscopy (SEM) and enzymatic assays with T. cruzi GAPDH (tcGAPDH), enzyme that catalyzes the sixth step of glycolysis. Polybia-CP induced phosphatidylserine exposure, it also increased the formation of reactive species of oxigen (ROS) and reduced the transmembrane mitochondrial potential. Polybia-CP also led to cell shrinkage, evidencing apoptotic cell death. We did not observe the inhibition of tcGAPDH or autophagy induction. Altogether, polybia-CP has shown the features of a promising template for the development of new antichagasic agents.
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Affiliation(s)
| | - Marcelo Der Torossian Torres
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil; Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, Penn Institute for Computational Science, and Department of Bioengineering, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Dânya Bandeira Lima
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal do Ceará, Fortaleza, CE 60430372, Brazil
| | - Marilia Lopes Monteiro
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal do Ceará, Fortaleza, CE 60430372, Brazil
| | | | - Alice Maria Costa Martins
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal do Ceará, Fortaleza, CE 60430372, Brazil
| | - Vani Xavier Oliveira
- Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Santo André, SP, 09210580, Brazil; Departamento de Biofísica, Universidade Federal de São Paulo, São Paulo, SP, 04044020, Brazil.
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