Use the keywords feature with a free QxMD account.
Use Read by QxMD to access full text via your institution or open access sources.
Read also provides personalized recommendations to keep you up to date in your field.
Existing User
Sign InNew to Read
Sign Up#1
JOURNAL ARTICLE
Robbert J de Haas, Natalie Brunette, Alex Goodson, Justas Dauparas, Sue Y Yi, Erin C Yang, Quinton Dowling, Hannah Nguyen, Alex Kang, Asim K Bera, Banumathi Sankaran, Renko de Vries, David Baker, Neil P King
The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta...
March 26, 2024: Proceedings of the National Academy of Sciences of the United States of America
#2
Robbert J de Haas, Natalie Brunette, Alex Goodson, Justas Dauparas, Sue Y Yi, Erin C Yang, Quinton Dowling, Hannah Nguyen, Alex Kang, Asim K Bera, Banumathi Sankaran, Renko de Vries, David Baker, Neil P King
The design of novel protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. A new generation of deep learning methods promises to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta...
August 4, 2023: bioRxiv
#3
JOURNAL ARTICLE
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
Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the augmentation of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold...
May 6, 2023: Nature Communications
#4
JOURNAL ARTICLE
David E Kim, Davin R Jensen, David Feldman, Doug Tischer, Ayesha Saleem, Cameron M Chow, Xinting Li, Lauren Carter, Lukas Milles, Hannah Nguyen, Alex Kang, Asim K Bera, Francis C Peterson, Brian F Volkman, Sergey Ovchinnikov, David Baker
Small beta barrel proteins are attractive targets for computational design because of their considerable functional diversity despite their very small size (<70 amino acids). However, there are considerable challenges to designing such structures, and there has been little success thus far. Because of the small size, the hydrophobic core stabilizing the fold is necessarily very small, and the conformational strain of barrel closure can oppose folding; also intermolecular aggregation through free beta strand edges can compete with proper monomer folding...
March 14, 2023: Proceedings of the National Academy of Sciences of the United States of America
#5
JOURNAL ARTICLE
J Dauparas, I Anishchenko, N Bennett, H Bai, R J Ragotte, L F Milles, B I M Wicky, A Courbet, R J de Haas, N Bethel, P J Y Leung, T F Huddy, S Pellock, D Tischer, F Chan, B Koepnick, H Nguyen, A Kang, B Sankaran, A K Bera, N P King, D Baker
Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here, we describe a deep learning-based protein sequence design method, ProteinMPNN, that has outstanding performance in both in silico and experimental tests. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4% compared with 32.9% for Rosetta. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges...
October 7, 2022: Science
#6
Rebecca F Alford, Andrew Leaver-Fay, Jeliazko R Jeliazkov, Matthew J O'Meara, Frank P DiMaio, Hahnbeom Park, Maxim V Shapovalov, P Douglas Renfrew, Vikram K Mulligan, Kalli Kappel, Jason W Labonte, Michael S Pacella, Richard Bonneau, Philip Bradley, Roland L Dunbrack, Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, Jeffrey J Gray
No abstract text is available yet for this article.
July 12, 2022: Journal of Chemical Theory and Computation
#7
JOURNAL ARTICLE
Julia Koehler Leman, Sergey Lyskov, Steven M Lewis, Jared Adolf-Bryfogle, Rebecca F Alford, Kyle Barlow, Ziv Ben-Aharon, Daniel Farrell, Jason Fell, William A Hansen, Ameya Harmalkar, Jeliazko Jeliazkov, Georg Kuenze, Justyna D Krys, Ajasja Ljubetič, Amanda L Loshbaugh, Jack Maguire, Rocco Moretti, Vikram Khipple Mulligan, Morgan L Nance, Phuong T Nguyen, Shane Ó Conchúir, Shourya S Roy Burman, Rituparna Samanta, Shannon T Smith, Frank Teets, Johanna K S Tiemann, Andrew Watkins, Hope Woods, Brahm J Yachnin, Christopher D Bahl, Chris Bailey-Kellogg, David Baker, Rhiju Das, Frank DiMaio, Sagar D Khare, Tanja Kortemme, Jason W Labonte, Kresten Lindorff-Larsen, Jens Meiler, William Schief, Ora Schueler-Furman, Justin B Siegel, Amelie Stein, Vladimir Yarov-Yarovoy, Brian Kuhlman, Andrew Leaver-Fay, Dominik Gront, Jeffrey J Gray, Richard Bonneau
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling...
November 29, 2021: Nature Communications
#8
REVIEW
Zongyang Du, Hong Su, Wenkai Wang, Lisha Ye, Hong Wei, Zhenling Peng, Ivan Anishchenko, David Baker, Jianyi Yang
The trRosetta (transform-restrained Rosetta) server is a web-based platform for fast and accurate protein structure prediction, powered by deep learning and Rosetta. With the input of a protein's amino acid sequence, a deep neural network is first used to predict the inter-residue geometries, including distance and orientations. The predicted geometries are then transformed as restraints to guide the structure prediction on the basis of direct energy minimization, which is implemented under the framework of Rosetta...
December 2021: Nature Protocols
#9
JOURNAL ARTICLE
Ivan Anishchenko, Minkyung Baek, Hahnbeom Park, Naozumi Hiranuma, David E Kim, Justas Dauparas, Sanaa Mansoor, Ian R Humphreys, David Baker
The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to sequence information) both language model embeddings and template information weighted by sequence similarity to the target. We also developed a refinement pipeline that recombines models generated by template-free and template utilizing versions of trRosetta guided by the DeepAccNet accuracy predictor...
December 2021: Proteins
#10
JOURNAL ARTICLE
Ivan Vulovic, Qing Yao, Young-Jun Park, Alexis Courbet, Andrew Norris, Florian Busch, Aniruddha Sahasrabuddhe, Hannes Merten, Danny D Sahtoe, George Ueda, Jorge A Fallas, Sara J Weaver, Yang Hsia, Robert A Langan, Andreas Plückthun, Vicki H Wysocki, David Veesler, Grant J Jensen, David Baker
Protein nanomaterial design is an emerging discipline with applications in medicine and beyond. A long-standing design approach uses genetic fusion to join protein homo-oligomer subunits via α-helical linkers to form more complex symmetric assemblies, but this method is hampered by linker flexibility and a dearth of geometric solutions. Here, we describe a general computational method for rigidly fusing homo-oligomer and spacer building blocks to generate user-defined architectures that generates far more geometric solutions than previous approaches...
June 8, 2021: Proceedings of the National Academy of Sciences of the United States of America
#11
JOURNAL ARTICLE
Christoffer Norn, Basile I M Wicky, David Juergens, Sirui Liu, David Kim, Doug Tischer, Brian Koepnick, Ivan Anishchenko, David Baker, Sergey Ovchinnikov
The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case...
March 16, 2021: Proceedings of the National Academy of Sciences of the United States of America
#12
JOURNAL ARTICLE
Brian Coventry, David Baker
In aqueous solution, polar groups make hydrogen bonds with water, and hence burial of such groups in the interior of a protein is unfavorable unless the loss of hydrogen bonds with water is compensated by formation of new ones with other protein groups. For this reason, buried "unsatisfied" polar groups making no hydrogen bonds are very rare in proteins. Efficiently representing the energetic cost of unsatisfied hydrogen bonds with a pairwise-decomposable energy term during protein design is challenging since whether or not a group is satisfied depends on all of its neighbors...
March 2021: PLoS Computational Biology
#13
JOURNAL ARTICLE
Naozumi Hiranuma, Hahnbeom Park, Minkyung Baek, Ivan Anishchenko, Justas Dauparas, David Baker
We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error...
February 26, 2021: Nature Communications
#14
JOURNAL ARTICLE
Hahnbeom Park, Guangfeng Zhou, Minkyung Baek, David Baker, Frank DiMaio
Accurate and rapid calculation of protein-small molecule interaction free energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here, we describe a new approach in which small molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures...
March 9, 2021: Journal of Chemical Theory and Computation
#15
JOURNAL ARTICLE
Linna An, Gyu Rie Lee
While native proteins cover diverse structural spaces and achieve various biological events, not many of them can directly serve human needs. One reason is that the native proteins usually contain idiosyncrasies evolved for their native functions but disfavoring engineering requirements. To overcome this issue, one strategy is to create de novo proteins which are designed to possess improved stability, high environmental tolerance, and enhanced engineering potential. Compared to other protein engineering strategies, in silico design of de novo proteins has significantly expanded the protein structural and sequence spaces, reduced wet lab workload, and incorporated engineered features in a guided and efficient manner...
December 2020: Current Protocols in Protein Science
#16
JOURNAL ARTICLE
Jack B Maguire, Hugh K Haddox, Devin Strickland, Samer F Halabiya, Brian Coventry, Jermel R Griffin, Surya V S R K Pulavarti, Matthew Cummins, David F Thieker, Eric Klavins, Thomas Szyperski, Frank DiMaio, David Baker, Brian Kuhlman
The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we discover that this process disfavors larger amino acids in the protein core because the protein compresses in the early stages of refinement...
April 2021: Proteins
#17
JOURNAL ARTICLE
Daniel P Farrell, Ivan Anishchenko, Shabih Shakeel, Anna Lauko, Lori A Passmore, David Baker, Frank DiMaio
Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 Å), with visible secondary-structure elements but poorly resolved loops, making model building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta , and automated EM-map-guided complex assembly...
September 1, 2020: IUCrJ
#18
REVIEW
Julia Koehler Leman, Brian D Weitzner, Steven M Lewis, Jared Adolf-Bryfogle, Nawsad Alam, Rebecca F Alford, Melanie Aprahamian, David Baker, Kyle A Barlow, Patrick Barth, Benjamin Basanta, Brian J Bender, Kristin Blacklock, Jaume Bonet, Scott E Boyken, Phil Bradley, Chris Bystroff, Patrick Conway, Seth Cooper, Bruno E Correia, Brian Coventry, Rhiju Das, René M De Jong, Frank DiMaio, Lorna Dsilva, Roland Dunbrack, Alexander S Ford, Brandon Frenz, Darwin Y Fu, Caleb Geniesse, Lukasz Goldschmidt, Ragul Gowthaman, Jeffrey J Gray, Dominik Gront, Sharon Guffy, Scott Horowitz, Po-Ssu Huang, Thomas Huber, Tim M Jacobs, Jeliazko R Jeliazkov, David K Johnson, Kalli Kappel, John Karanicolas, Hamed Khakzad, Karen R Khar, Sagar D Khare, Firas Khatib, Alisa Khramushin, Indigo C King, Robert Kleffner, Brian Koepnick, Tanja Kortemme, Georg Kuenze, Brian Kuhlman, Daisuke Kuroda, Jason W Labonte, Jason K Lai, Gideon Lapidoth, Andrew Leaver-Fay, Steffen Lindert, Thomas Linsky, Nir London, Joseph H Lubin, Sergey Lyskov, Jack Maguire, Lars Malmström, Enrique Marcos, Orly Marcu, Nicholas A Marze, Jens Meiler, Rocco Moretti, Vikram Khipple Mulligan, Santrupti Nerli, Christoffer Norn, Shane Ó'Conchúir, Noah Ollikainen, Sergey Ovchinnikov, Michael S Pacella, Xingjie Pan, Hahnbeom Park, Ryan E Pavlovicz, Manasi Pethe, Brian G Pierce, Kala Bharath Pilla, Barak Raveh, P Douglas Renfrew, Shourya S Roy Burman, Aliza Rubenstein, Marion F Sauer, Andreas Scheck, William Schief, Ora Schueler-Furman, Yuval Sedan, Alexander M Sevy, Nikolaos G Sgourakis, Lei Shi, Justin B Siegel, Daniel-Adriano Silva, Shannon Smith, Yifan Song, Amelie Stein, Maria Szegedy, Frank D Teets, Summer B Thyme, Ray Yu-Ruei Wang, Andrew Watkins, Lior Zimmerman, Richard Bonneau
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability...
July 2020: Nature Methods
#19
REVIEW
Julia Koehler Leman, Brian D Weitzner, P Douglas Renfrew, Steven M Lewis, Rocco Moretti, Andrew M Watkins, Vikram Khipple Mulligan, Sergey Lyskov, Jared Adolf-Bryfogle, Jason W Labonte, Justyna Krys, Christopher Bystroff, William Schief, Dominik Gront, Ora Schueler-Furman, David Baker, Philip Bradley, Roland Dunbrack, Tanja Kortemme, Andrew Leaver-Fay, Charlie E M Strauss, Jens Meiler, Brian Kuhlman, Jeffrey J Gray, Richard Bonneau
Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area...
May 2020: PLoS Computational Biology
#20
JOURNAL ARTICLE
Brittney Hellner, Sarah Alamdari, Harley Pyles, Shuai Zhang, Arushi Prakash, Kayla G Sprenger, James J De Yoreo, David Baker, Jim Pfaendtner, François Baneyx
Solid-binding peptides (SBPs) recognizing inorganic and synthetic interfaces have enabled a broad range of materials science applications and hold promise as adhesive or morphogenetic control units that can be genetically encoded within desirable or designed protein frameworks. To date, the underlying relationships governing both SBP-surface and SBP-SBP interactions and how they give rise to different adsorption mechanisms remain unclear. Here, we combine protein engineering, surface plasmon resonance characterization, and molecular dynamics (MD) simulations initiated from Rosetta predictions to gain insights on the interplay of amino acid composition, structure, self-association, and adhesion modality in a panel of variants of the Car9 silica-binding peptide (DSARGFKKPGKR) fused to the C-terminus of superfolder green fluorescent protein (sfGFP)...
February 5, 2020: Journal of the American Chemical Society
Make the most of Read.
Download the mobile app.
Download the mobile app.
Fetching more papers... 
