Article
Biochemical Research Methods
Genki Kudo, Takumi Hirao, Ryunosuke Yoshino, Yasuteru Shigeta, Takatsugu Hirokawa
Summary: Understanding the binding site of the target protein is crucial for rational drug design. However, current pocket detection software often overestimates protein pockets compared to the actual volume of bound ligands. This study presents an alpha sphere-based refinement tool called P2C, which can accurately predict the shape and location of deep and druggable concavities in both ligand-free and ligand-bound modes. P2C is a valuable tool in Structure-Based Drug Design (SBDD) for identifying and designing desirable compounds.
Article
Pharmacology & Pharmacy
Riccardo Aguti, Erika Gardini, Martina Bertazzo, Sergio Decherchi, Andrea Cavalli
Summary: The choice of target pocket plays a crucial role in drug discovery, and in silico druggability prediction is used to support this process. Traditionally, druggability prediction is approached as a binary classification task, but the non-druggable class is conceptually ambiguous. Therefore, a one-class approach that focuses on druggable pockets is more appropriate. This study proposes using the import vector domain description (IVDD) algorithm, along with customized DrugPred descriptors computed via NanoShaper, to support this task. The results demonstrate the feasibility and effectiveness of the approach in removing or mitigating biases associated with labeling.
FRONTIERS IN PHARMACOLOGY
(2022)
Article
Chemistry, Medicinal
Giuseppe Lamanna, Pietro Delre, Gilles Marcou, Michele Saviano, Alexandre Varnek, Dragos Horvath, Giuseppe Felice Mangiatordi
Summary: This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, and its ability to de novo design promising candidates was assessed using docking programs PLANTS and GLIDE. The study demonstrates that GENERA can effectively perform multiobjective optimization and generate focused libraries with better scores compared to a starting set of known ACE-2 binders.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Medicinal
Niu Zhang, Zhicheng Zuo
Summary: The discovery of small-molecule Cas9 inhibitors provides a feasible approach to regulate CRISPR-Cas9 activity. Through computational analysis, a ligand binding site was identified within the carboxyl-terminal domain (CTD) of Cas9, which plays a role in recognizing the protospacer adjacent motif (PAM). Binding of the inhibitor BRD0539 induced structural rearrangements in the CTD, leading to the inhibition of Cas9 function. This study offers insights into the development of safer CRISPR-Cas9 technologies through improving existing ligands and discovering novel small-molecule brakes.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Biochemistry & Molecular Biology
Sarah Naomi Bolz, Sebastian Salentin, Gary Jennings, V. Joachim Haupt, Jared Sterneckert, Michael Schroeder
Summary: Mutations in LRRK2 are a frequent cause of Parkinson's disease, and reducing its kinase activity is considered a promising therapeutic strategy. Drug repositioning, comparing protein-ligand interactions in a large-scale screening, yielded potential LRRK2 inhibitors such as Sunitinib and Crizotinib. The study highlights the potential of structure-based methods for drug discovery and development amidst recent advancements in cryo-electron microscopy and structure prediction.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Article
Biochemistry & Molecular Biology
Ying Xia, Chunqiu Xia, Xiaoyong Pan, Hong-Bin Shen
Summary: Knowledge of protein-ligand interactions is crucial for biological process analysis and drug design. In this study, a web server called BindWeb is introduced for predicting ligand binding residues and pockets from protein structures. BindWeb benefits from the complementarity of two base methods, resulting in higher prediction accuracy.
Article
Biochemistry & Molecular Biology
Ying Zhou, Yintao Zhang, Donghai Zhao, Xinyuan Yu, Xinyi Shen, Yuan Zhou, Shanshan Wang, Yunqing Qiu, Yuzong Chen, Feng Zhu
Summary: Target discovery is crucial for drug development, and assessing target druggability characteristics is essential. The therapeutic target database (TTD) has collected established characteristic categories to facilitate the discovery and validation of innovative drug targets.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Chemistry, Medicinal
Illimar Hugo Rekand, Ruth Brenk
Summary: DrugPred_RNA is a structure-based druggability predictor developed to identify druggable RNA binding sites. The method performed well in discriminating druggable from less druggable protein binding sites and can contribute to direct drug discovery efforts for RNA targets. This predictor is robust against conformational and sequence changes in the binding sites, enhancing confidence in its performance.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2021)
Article
Biochemistry & Molecular Biology
David Jakubec, Petr Skoda, Radoslav Krivak, Marian Novotny, David Hoksza
Summary: This article presents significant enhancements to PrankWeb, including more accurate evolutionary conservation estimation and the ability to carry out LBS predictions using the AlphaFold model database.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Chemistry, Multidisciplinary
Yuri Kochnev, Jacob D. Durrant
Summary: Detecting macromolecular cavities where small molecules bind is a crucial step in computer-aided drug discovery. The popular algorithm fpocket requires users to download and install an executable file, and run it through the command-line interface. FPocketWeb improves on this by running fpocket3 executable directly in a web browser without installation, performing the calculations on the user's computer.
JOURNAL OF CHEMINFORMATICS
(2022)
Article
Chemistry, Medicinal
Timofey V. Losev, Igor S. Gerasimov, Maria V. Panova, Alexey A. Lisov, Yana R. Abdyusheva, Polina V. Rusina, Eugenia Zaletskaya, Oleg V. Stroganov, Michael G. Medvedev, Fedor N. Novikov
Summary: Bioisosteres are molecules with different substituents but similar shapes. They are widely used in drug design to modify metabolism, bioavailability, and activity. However, predicting the affinity of bioisosteres with computational methods has been challenging due to their similarity to standard force fields. In this study, a quantum mechanical (QM)-cluster approach based on the GFN2-xTB method was developed and successfully applied to predict the biological activity change of H -> F bioisosteric replacements. The method showed superior accuracy compared to the ChemPLP scoring function and comparable to in vitro experiments, with a standard deviation of 0.60 kcal/mol.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Multidisciplinary Sciences
Abhimanyu K. Singh, Sergio E. Martinez, Weijie Gu, Hoai Nguyen, Dominique Schols, Piet Herdewijn, Steven De Jonghe, Kalyan Das
Summary: The authors discovered a transient P-pocket created by HIV reverse transcriptase while sliding over a DNA substrate and developed a cryo-EM platform for lead optimization. By screening 300 drug-like fragments, they identified two leads that bind to the P-pocket, which is resilient to drug-resistance mutations and composed of structural elements from polymerase active site, primer grip, and template-primer. An engineered RT/DNA aptamer complex was utilized to trap the transient P-pocket and structures of the RT/DNA complex in the presence of an inhibitory fragment were determined.
NATURE COMMUNICATIONS
(2021)
Article
Chemistry, Medicinal
Joel Graef, Christiane Ehrt, Matthias Rarey
Summary: Binding site prediction on protein structures is crucial in early phase drug discovery. DoGSite is a widely used tool that uses a grid-based method for cavity detection on protein surface. A new version, DoGSite3, has been introduced to improve binding site detection in the presence of ligands and optimize parameters for more robust predictions. Comparative performance evaluation on published data sets has been conducted to assess the performance of both versions.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Multidisciplinary
Bo Cai, Casey J. Krusemark
Summary: A novel assay method combining DNA encoding with split-and-pool sample handling is developed to improve small-molecule binding assays to target proteins. The approach involves affinity labeling of DNA-linked ligands to a protein target, allowing for quantification of DNA barcodes to detect ligand binding. This method demonstrates potential utility in high-throughput small-molecule screening.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Chemistry, Medicinal
Hugo Guterres, Sang-Jun Park, Wei Jiang, Wonpil Im
Summary: This study presents a method to generate reliable holo protein structures from apo structures using molecular dynamics simulation, which significantly improves virtual screening performance. Results show successful refinement of apo binding-site structures towards holo conformations in 82% of test cases, and using the refined structures as receptors improves virtual screening performance with an average enrichment factor of 6.2.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2021)
Article
Chemistry, Medicinal
Simon Cross, Gabriele Cruciani
Summary: Understanding chemical modifications of known ligands is crucial in structure-based drug design. FragExplorer software helps users find fragments that best match molecular interaction fields in a protein binding site, allowing ligand expansion or replacement. FragExplorer offers fast computation speed and high fragment retrieval rate.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Chemistry, Multidisciplinary
Pim J. de Vink, Auke A. Koops, Giulia D'Arrigo, Gabriele Cruciani, Francesca Spyrakis, Luc Brunsveld
Summary: This study proposes a cooperativity mechanism for small molecule modulation of nuclear receptor-protein interactions. By using a thermodynamic model and three-body binding events, the reciprocal effects of nuclear receptor-coregulator binding and nuclear receptor-ligand binding are dissected and quantified. This provides a new conceptual way of thinking about structure-activity relationships for nuclear receptor ligands and has implications for the discovery and optimization of nuclear receptor modulators.
Article
Cell Biology
Maurizio Gianni, Laura Goracci, Anna Schlaefli, Alessandra Di Veroli, Mami Kurosaki, Luca Guarrera, Marco Bolis, Marika Foglia, Monica Lupi, Mario P. Tschan, Gabriele Cruciani, Mineko Terao, Enrico Garattini
Summary: This study investigates the role of lipids in the process of granulocytic differentiation activated by all-trans retinoic acid (ATRA) in Acute-Promyelocytic-Leukemia (APL) blasts. Using the NB4 cell-line as a model, the study found that exposure to ATRA resulted in a reduction in cardiolipin levels, leading to mitochondrial dysfunction and inhibition of granulocytic differentiation.
CELL DEATH & DISEASE
(2022)
Article
Multidisciplinary Sciences
Alice Cartoni Mancinelli, Alessandra Di Veroli, Simona Mattioli, Gabriele Cruciani, Alessandro Dal Bosco, Cesare Castellini
Summary: This study investigated the liver lipid metabolism of three chicken genotypes using LC/MS analysis. The results showed that Ross chickens have a metabolism primarily related to storage and structural roles, Leghorn chickens exhibited higher levels of n-3 NEFA, unsaturation level, and gene expression, and LxR chickens showed intermediate values between Ross and Leghorn chickens.
SCIENTIFIC REPORTS
(2022)
Article
Pharmacology & Pharmacy
Maria Smirnova, Laura Goracci, Gabriele Cruciani, Laetitia Federici, Xavier Decleves, Helene Chapy, Salvatore Cisternino
Summary: A computational approach was used to develop pharmacophore models for a drug/proton-antiporter, which successfully predicted known substrates and identified new candidates. The study provides valuable insights into the characterization and identification of substrates for this transporter, and has potential implications for designing drugs with improved ability to cross the blood-brain barrier.
Article
Hematology
Alberto J. Arribas, Sara Napoli, Luciano Cascione, Giulio Sartori, Laura Barnabei, Eugenio Gaudio, Chiara Tarantelli, Afua Adjeiwaa Mensah, Filippo Spriano, Antonella Zucchetto, Francesca M. Rossi, Andrea Rinaldi, Manuel Castro de Moura, Sandra Jovic, Roberta Bordone-Pittau, Alessandra Di Veroli, Anastasios Stathis, Gabriele Cruciani, Georg Stussi, Valter Gattei, Jennifer R. Brown, Manel Esteller, Emanuele Zucca, Davide Rossi, Francesco Bertoni
Summary: In this study, a model of secondary resistance to PI3K delta inhibitors was developed by exposing a splenic MZL cell line to idelalisib. The resistant cells showed upregulated genes, low-methylated promoters, and repression of certain miRNAs. Drug compounds targeting these pathways were found to be effective in the resistant cells. These findings suggest new therapeutic approaches to enhance the antitumor activity of PI3K delta inhibitors in B-cell lymphoid tumors.
Article
Chemistry, Medicinal
Dominique Sydow, Eva Assmann, Albert J. Kooistra, Friedrich Rippmann, Andrea Volkamer
Summary: Protein kinases are important drug targets, but developing selective inhibitors is challenging due to their structural conservation. This study presents a kinase fingerprint based on structural similarity that can predict off-targets. The fingerprint is a valuable tool in kinase research for guiding off-target and polypharmacology prediction.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Biochemistry & Molecular Biology
Tommaso Palomba, Massimo Baroni, Simon Cross, Gabriele Cruciani, Lydia Siragusa
Summary: Proteolysis-targeting chimeras (PROTACs) are novel therapeutics that utilize E3 ligases to degrade target proteins. Existing databases on E3 ligases have limited information on their structures and ligands. This study presents an accurate and comprehensive platform, ELIOT, to navigate and select new E3 ligases and ligands for the design of new PROTACs.
CHEMICAL BIOLOGY & DRUG DESIGN
(2023)
Article
Chemistry, Multidisciplinary
Siri C. van Keulen, Juliette Martin, Francesco Colizzi, Elisa Frezza, Daniel Trpevski, Nuria Cirauqui Diaz, Pietro Vidossich, Ursula Rothlisberger, Jeanette Hellgren Kotaleski, Rebecca C. Wade, Paolo Carloni
Summary: This study used simulation tools to uncover the molecular and subcellular mechanisms of AC function, with a focus on the AC5 isoform. The research revealed an inactive state of the enzyme in the presence of an inhibitory G alpha subunit, regardless of the presence of a stimulatory G alpha. The binding of G alpha subunits reshaped the free-energy landscape of the AC5 enzyme.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2023)
Article
Multidisciplinary Sciences
Debabrata Dey, Ariane Nunes-Alves, Rebecca C. Wade, Gideon Schreiber
Summary: Protein crowders have been found to significantly affect the diffusion rates of small molecules, primarily by influencing self-aggregation, interactions with proteins, and surface adsorption.
Review
Chemistry, Multidisciplinary
Abraham Muniz-Chicharro, Lane W. Votapka, Rommie E. Amaro, Rebecca C. Wade
Summary: Brownian dynamics (BD) is a computational method used to simulate molecular diffusion processes, and recent developments have improved its accuracy and expanded its applications. In biological research, BD is used to study the diffusive behavior of molecules under various conditions and to compute rate constants for molecular association and examine transport properties. The development of software packages for BD simulations has provided new features and expanded the range of questions that can be addressed.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2023)
Article
Biology
Luca Costantino, Stefania Ferrari, Matteo Santucci, Outi M. H. Salo-Ahen, Emanuele Carosati, Silvia Franchini, Angela Lauriola, Cecilia Pozzi, Matteo Trande, Gaia Gozzi, Puneet Saxena, Giuseppe Cannazza, Lorena Losi, Daniela Cardinale, Alberto Venturelli, Antonio Quotadamo, Pasquale Linciano, Lorenzo Tagliazucchi, Maria Gaetana Moschella, Remo Guerrini, Salvatore Pacifico, Rosaria Luciani, Filippo Genovese, Stefan Henrich, Silvia Alboni, Nuno Santarem, Anabela da Silva Cordeiro, Elisa Giovannetti, Godefridus J. Peters, Paolo Pinton, Alessandro Rimessi, Gabriele Cruciani, Robert M. Stroud, Rebecca C. Wade, Stefano Mangani, Gaetano Marverti, Domenico D'Arca, Glauco Ponterini, Maria Paola Costi
Summary: A new drug strategy that inhibits the activity of hTS protein and provides a new approach to fight drug-resistant cancers has been discovered.
Article
Chemistry, Physical
Ainara Claveras Cabezudo, Christina Athanasiou, Alexandros Tsengenes, Rebecca C. Wade
Summary: Reducing the nonbonded interactions between protein and water enables protein encapsulation in phospholipid micelles and bilayers.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Chemistry, Medicinal
Ina Poehner, Antonio Quotadamo, Joanna Panecka-Hofman, Rosaria Luciani, Matteo Santucci, Pasquale Linciano, Giacomo Landi, Flavio Di Pisa, Lucia Dello Iacono, Cecilia Pozzi, Stefano Mangani, Sheraz Gul, Gesa Witt, Bernhard Ellinger, Maria Kuzikov, Nuno Santarem, Anabela Cordeiro-da-Silva, Maria P. Costi, Alberto Venturelli, Rebecca C. Wade
Summary: In this study, a systematic and multidisciplinary approach was used to develop selective antiparasitic compounds. Through computational fragment-based design and crystallographic structure determination, compounds with activity against multiple targets were obtained. Additionally, the combination of polypharmacology design and parasite-specific optimization led to the discovery of compounds effective against T. brucei brucei.
JOURNAL OF MEDICINAL CHEMISTRY
(2022)
Article
Chemistry, Multidisciplinary
A. Toral-Lopez, D. B. Kokh, E. G. Marin, R. C. Wade, A. Godoy
Summary: Biological Field-Effect Transistors (BioFETs) have shown great potential for detecting small amounts of ions and molecules. The use of two-dimensional (2D) materials can enhance their performance and enable new applications, which is particularly relevant in the current pandemic where fast, reliable, and affordable detection methods are needed. However, there is a lack of comprehensive computational approaches to understand the mechanisms underlying sensor behavior. In this study, a multiscale platform is proposed that combines atomic models of molecules with mesoscopic device-level simulations, providing a detailed description of the sensor behavior.
NANOSCALE ADVANCES
(2022)