Article
Biochemistry & Molecular Biology
Che Yang, Fabian Sesterhenn, Jaume Bonet, Eva A. van Aalen, Leo Scheller, Luciano A. Abriata, Johannes T. Cramer, Xiaolin Wen, Stephane Rosset, Sandrine Georgeon, Theodore Jardetzky, Thomas Kreys, Martin Fussenegger, Maarten Merkx, Bruno E. Correia
Summary: This study successfully designed novel proteins using a bottom-up approach tailored to accommodate complex functional motifs. These proteins were functional components of biosensors and could modulate synthetic signaling receptors in engineered mammalian cells.
NATURE CHEMICAL BIOLOGY
(2021)
Article
Multidisciplinary Sciences
Gary Dixon, Heng Pan, Dapeng Yang, Bess P. Rosen, Therande Jashari, Nipun Verma, Julian Pulecio, Inbal Caspi, Kihyun Lee, Stephanie Stransky, Abigail Glezer, Chang Liu, Marco Rivas, Ritu Kumar, Yahui Lan, Ingrid Torregroza, Chuan He, Simone Sidoli, Todd Evans, Olivier Elemento, Danwei Huangfu
Summary: The previously unknown gene QSER1 plays a crucial role in regulating the methylation landscape, safeguarding bivalent promoters and poised enhancers of developmental genes, especially those in DNA methylation valleys. The genetic and biochemical interactions of QSER1 and TET1 support their cooperation in protecting transcriptional and developmental programs from de novo methylation mediated by DNMT3.
Article
Chemistry, Multidisciplinary
Lukas Friedrich, Gino Cingolani, Ying-Hui Ko, Mariaclara Iaselli, Morena Miciaccia, Maria Grazia Perrone, Konstantin Neukirch, Veronika Bobinger, Daniel Merk, Robert Klaus Hofstetter, Oliver Werz, Andreas Koeberle, Antonio Scilimati, Gisbert Schneider
Summary: The combination of natural products and synthetic molecules provides a new approach for drug discovery based on machine intelligence, creating new molecular designs inspired by bioactive natural products. Experimental results demonstrate that this method can successfully design molecules with specific target activities.
Article
Multidisciplinary Sciences
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vazquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank Dimaio, Minkyung Baek, David Baker
Summary: Considerable progress has been made in designing new proteins using deep-learning methods, but a general deep-learning framework for protein design that can solve a wide range of design challenges is still lacking. In this study, the researchers fine-tuned the RoseTTAFold structure prediction network to obtain a generative model of protein backbones that achieves outstanding performance in various protein design tasks.
Article
Multidisciplinary Sciences
Nathaniel R. Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, Frank DiMaio, Steven De Munck, Savvas N. Savvides, David Baker
Summary: Recently, researchers have found that incorporating deep learning methods can significantly improve the success rate of de novo design of high affinity protein binding proteins. By using AlphaFold2 or RoseTTAFold to assess the probability of a designed sequence adopting the desired structure and binding to the target protein, the success rates were increased nearly 10-fold. It was also found that using ProteinMPNN instead of Rosetta for sequence design greatly enhanced computational efficiency. Overall, this study demonstrates the importance of utilizing deep learning in protein binder design and its potential for improving success rates.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Zander Harteveld, Jaume Bonet, Stephene Rosset, Che Yang, Fabian Sesterhenn, Bruno E. Correia
Summary: De novo protein design is a powerful tool to explore new sequences and structures not found in nature. By using the TopoBuilder method, we were able to design sequences that adopt predicted folds and demonstrated their stability through experimental characterization.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Multidisciplinary Sciences
Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, Casper Goverde, Priscilla Turelli, Charlene Raclot, Alexandra Teslenko, Martin Pacesa, Stephane Rosset, Sandrine Georgeon, Jane Marsden, Aaron Petruzzella, Kefang Liu, Zepeng Xu, Yan Chai, Pu Han, George F. Gao, Elisa Oricchio, Beat Fierz, Didier Trono, Henning Stahlberg, Michael Bronstein, Bruno E. Correia
Summary: Physical interactions between proteins are crucial for biological processes. A geometric deep-learning framework is used to generate fingerprints on protein surfaces to describe the key aspects of molecular recognition. Using this approach, several de novo protein binders were designed and successfully bound to different protein targets.
Article
Multidisciplinary Sciences
Erika Davidoff Aguas, Abdul-Rahman Azizogli, Jatin Kashyap, Joseph Dodd-o, Zain Siddiqui, Jay Sy, Vivek Kumar
Summary: Chronic inflammation can lead to autoimmune diseases like rheumatoid arthritis and atherosclerosis. CCL2, also known as MCP-1, plays a crucial role in the progression of these diseases by attracting monocytes to the site of injury and promoting inflammation. In this study, computational modeling techniques were used to design high-affinity peptide binders for CCL2, with the aim of preventing its binding to CCR2 and reducing inflammation. Further analysis and experiments are needed to validate the accuracy of this computational approach in designing CCL2 cytokine binders.
ADVANCED THEORY AND SIMULATIONS
(2023)
Review
Chemistry, Multidisciplinary
Matthew J. Chalkley, Samuel I. Mann, William F. DeGrado
Summary: De novo protein design aims to define an amino acid sequence that encodes a specific structure and function of metalloproteins. By exploring ligand geometries, redox potentials, and catalytic reactions, significant progress has been made in designing diverse functional metalloproteins. The use of xenobiological metals and principles from inorganic chemistry also contributes to deriving new-to-nature functions in proteins.
NATURE REVIEWS CHEMISTRY
(2022)
Article
Multidisciplinary Sciences
Hiroto Murata, Hayao Imakawa, Nobuyasu Koga, George Chikenji
Summary: In this study, design rules for register shifts in beta alpha beta-motifs were proposed based on database analysis and physics-based simulations. The empirical rules were validated through simulations, showing that they are a result of physical interactions rather than evolutionary sampling bias. These design rules will serve as a guide for appropriate target structures in the de novo design of alpha beta-proteins.
Editorial Material
Chemistry, Multidisciplinary
Lijun Quan, Tingfang Wu, Qiang Lyu
Summary: This article discusses the importance of proteins as the foundation of life, specifically focusing on the significance of secondary structures for protein functioning. Buehler and his team introduce deep learning models to generate novel proteins with specific secondary structure constraints, likening it to constructing a functional house based on desired keystones.
Review
Biochemistry & Molecular Biology
Varnavas D. Mouchlis, Antreas Afantitis, Angela Serra, Michele Fratello, Anastasios G. Papadiamantis, Vassilis Aidinis, Iseult Lynch, Dario Greco, Georgia Melagraki
Summary: De novo drug design is a process of generating novel molecular structures using computational methods, with traditional approaches including structure-based and ligand-based design. Artificial intelligence and machine learning have a positive impact in this field.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Review
Biotechnology & Applied Microbiology
Wei Xiong, Bo Liu, Yujiao Shen, Keju Jing, Thomas R. Savage
Summary: Enzyme engineering with strategies like semi-rational design and artificial intelligence plays a crucial role in improving the catalytic performance of enzymes, aiming to enhance their functionality and specificity for industrial applications.
BIOCHEMICAL ENGINEERING JOURNAL
(2021)
Article
Materials Science, Multidisciplinary
Dong Zhang, Yijing Tang, Jintao Yang, Yijing Gao, Chunxin Ma, Lingbin Che, Jianguo Wang, Jiang Wu, Jie Zheng
Summary: Zwitterionic materials and allochroic materials offer distinct properties and functions for different applications. The development of zwitterionic, allochroic materials remains challenging. In this study, water-soluble and allochroic polymers of pVPES were designed and synthesized, which can self-polymerize into different architectures or co-polymerize with other functional polymers to become different smart devices. The pVPES-based hydrogel patches not only accelerated wound healing but also distinguished different types of wounds through color changes.
Article
Nanoscience & Nanotechnology
Keisuke Shimizu, Batsaikhan Mijiddorj, Masataka Usami, Ikuro Mizoguchi, Shuhei Yoshida, Shiori Akayama, Yoshio Hamada, Akifumi Ohyamas, Kenji Usui, Izuru Kawamura, Ryuji Kawano
Summary: A de novo designed peptide with a beta-hairpin structure is able to assemble into beta-barrelled nanopores for single-molecule detection of polynucleotides and polypeptide chains. By manipulating amino acid sequences, artificial proteins and peptides with desired functionality can be developed. Redesigning the peptide to create monodisperse pores allows for detection of single polypeptide chains, showing potential for creating artificial nanopores adjustable to target molecules.
NATURE NANOTECHNOLOGY
(2022)
Article
Biochemical Research Methods
Michael D. Lane, Burckhard Seelig
PROTEIN EXPRESSION AND PURIFICATION
(2016)
Editorial Material
Biochemistry & Molecular Biology
Burckhard Seelig
MOLECULAR MICROBIOLOGY
(2017)
Article
Biochemistry & Molecular Biology
Aleardo Morelli, Yari Cabezas, Lauren J. Mills, Burckhard Seelig
NUCLEIC ACIDS RESEARCH
(2017)
Article
Biochemistry & Molecular Biology
Misha V. Golynskiy, John C. Haugner, Burckhard Seelig
Review
Biochemistry & Molecular Biology
Michael D. Lane, Burckhard Seelig
CURRENT OPINION IN CHEMICAL BIOLOGY
(2014)
Article
Biochemistry & Molecular Biology
Fa-An Chao, Aleardo Morelli, John C. Haugner, Lewis Churchfield, Leonardo N. Hagmann, Lei Shi, Larry R. Masterson, Ritimukta Sarangi, Gianluigi Veglia, Burckhard Seelig
NATURE CHEMICAL BIOLOGY
(2013)
Article
Multidisciplinary Sciences
Aleardo Morelli, John Haugner, Burckhard Seelig
Article
Biochemistry & Molecular Biology
Matilda S. Newton, Dana J. Morrone, Kun-Hwa Lee, Burckhard Seelig
Letter
Biochemistry & Molecular Biology
Qiwei Shan, Nicholas J. Baltes, Paul Atkins, Elida R. Kirkland, Yong Zhang, Joshua A. Baller, Levi G. Lowder, Aimee A. Malzahn, John C. Haugner, Burckhard Seelig, Daniel F. Voytas, Yiping Qi
JOURNAL OF GENETICS AND GENOMICS
(2018)
Review
Biochemical Research Methods
Matilda S. Newton, Yari Cabezas-Perusse, Cher Ling Tong, Burckhard Seelig
ACS SYNTHETIC BIOLOGY
(2020)
Letter
Biochemistry & Molecular Biology
Celia Blanco, Samuel Verbanic, Burckhard Seelig, Irene A. Chen
JOURNAL OF MOLECULAR EVOLUTION
(2020)
Article
Multidisciplinary Sciences
Gloria Gamiz-Arco, Luis I. Gutierrez-Rus, Valeria A. Risso, Beatriz Ibarra-Molero, Yosuke Hoshino, Dusan Petrovic, Jose Justicia, Juan Manuel Cuerva, Adrian Romero-Rivera, Burckhard Seelig, Jose A. Gavira, Shina C. L. Kamerlin, Eric A. Gaucher, Jose M. Sanchez-Ruiz
Summary: The study revealed that the ancestral glycosidase has enhanced conformational flexibility but a comparatively rigid core, with stable activity and optimal temperature for thermophilic family-1 glycosidases. Unlike modern glycosidases, the ancestral glycosidase binds heme tightly and stoichiometrically at a well-defined buried site. The binding of heme rigidifies the TIM-barrel structure and allosterically enhances catalysis.
NATURE COMMUNICATIONS
(2021)
Article
Biochemistry & Molecular Biology
Cher Ling Tong, Nisha Kanwar, Dana J. Morrone, Burckhard Seelig
Summary: In this study, the catalytic activity of an artificial enzyme was improved by fusing different protein domains, demonstrating that domain fusion mechanism in nature can enhance the functionality of newly created proteins.
NUCLEIC ACIDS RESEARCH
(2022)
Article
Biochemistry & Molecular Biology
Encarnacion Medina-Carmona, Luis Gutierrez-Rus, Fadia Manssour-Triedo, Matilda S. Newton, Gloria Gamiz-Arco, Antonio J. Mota, Pablo Reine, Juan Manuel Cuerva, Mariano Ortega-Munoz, Eduardo Andres-Leon, Jose Luis Ortega-Roldan, Burckhard Seelig, Beatriz Ibarra-Molero, Jose M. Sanchez-Ruiz
Summary: Many metabolites in biochemical pathways are protected as protein-bound species to prevent side reactions and toxicity to cells. However, the leakage of sequestered metabolic intermediates can occur and contribute to organismal adaptation. In an experiment with Escherichia coli, the deletion of a crucial enzyme for proline biosynthesis led to cell growth inhibition, but single mutations in glutamine synthetase allowed the leakage of a reactive intermediate and restored cell growth. This study suggests that mutation-induced leakage of metabolic intermediates can enable new pathways and has potential applications in metabolic engineering.
MOLECULAR BIOLOGY AND EVOLUTION
(2023)
Article
Chemistry, Physical
Celia Blanco, Samuel Verbanic, Burckhard Seelig, Irene A. Chen
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2020)