UCSF ChimeraX: Tools for structure building and analysis

Protein Science, Journal Year: 2023, Volume and Issue: 32(11)

Published: Sept. 29, 2023

Advances in computational tools for atomic model building are leading to accurate models of large molecular assemblies seen electron microscopy, often at challenging resolutions 3-4 Å. We describe new methods the UCSF ChimeraX modeling package that take advantage machine-learning structure predictions, provide likelihood-based fitting maps, and compute per-residue scores identify errors. Additional model-building assist analysis mutations, post-translational modifications, interactions with ligands. present latest capabilities, including several community-developed extensions. is available free charge noncommercial use https://www.rbvi.ucsf.edu/chimerax.

Language: Английский

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Robust deep learning–based protein sequence design using ProteinMPNN

Science, Journal Year: 2022, Volume and Issue: 378(6615), P. 49 - 56

Published: Sept. 15, 2022

Although deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo designs have been generated using physically based approaches such as Rosetta. Here, we describe a learning-based sequence design method, ProteinMPNN, that outstanding performance in both silico and experimental tests. On native backbones, ProteinMPNN recovery of 52.4% compared with 32.9% for The amino acid at different positions can be coupled between single or multiple chains, enabling application to wide range current challenges. We demonstrate the broad utility high accuracy x-ray crystallography, cryo-electron microscopy, functional studies by rescuing previously failed designs, which were made Rosetta AlphaFold, monomers, cyclic homo-oligomers, tetrahedral nanoparticles, target-binding proteins.

Language: Английский

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BenchmarkingAlphaFoldfor protein complex modeling reveals accuracy determinants

Protein Science, Journal Year: 2022, Volume and Issue: 31(8)

Published: July 13, 2022

High-resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due the massive number and limitations there is a need for computational methods that can accurately model their structures. Here we explore use recently developed deep learning method, AlphaFold, predict structures protein complexes from sequence. With benchmark 152 diverse heterodimeric complexes, multiple implementations parameters AlphaFold were tested accuracy. Remarkably, many cases (43%) had near-native models (medium or high critical assessment predicted accuracy) generated as top-ranked predictions by greatly surpassing performance unbound docking (9% success rate models), however modeling antibody-antigen within our set was unsuccessful. We identified sequence features associated with lack success, also investigated impact alignment input. Benchmarking multimer-optimized version (AlphaFold-Multimer) released confirmed low (11% success), found T cell receptor-antigen are likewise not modeled algorithm, showing adaptive immune recognition poses challenge current algorithm model. Overall, study demonstrates end-to-end transient highlights areas improvement future developments reliably any interaction interest.

Language: Английский

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In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs

Cell, Journal Year: 2021, Volume and Issue: 184(7), P. 1865 - 1883.e20

Published: Feb. 9, 2021

Language: Английский

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Machine learning in protein structure prediction

Current Opinion in Chemical Biology, Journal Year: 2021, Volume and Issue: 65, P. 1 - 8

Published: May 18, 2021

Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical computational bases. While progress historically ebbed flowed, past two years saw dramatic advances driven by increasing "neuralization" prediction pipelines, whereby computations previously based on energy models sampling procedures are replaced neural networks. The extraction contacts evolutionary record; distillation sequence-structure patterns known structures; incorporation templates homologs in Protein Databank; refinement coarsely predicted structures into finely resolved ones have all reformulated using Cumulatively, this transformation resulted algorithms that can now predict single domains with a median accuracy 2.1 Å, setting stage foundational reconfiguration role biomolecular modeling within life sciences.

Language: Английский

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Cross-Linking Mass Spectrometry for Investigating Protein Conformations and Protein–Protein Interactions─A Method for All Seasons

Chemical Reviews, Journal Year: 2021, Volume and Issue: 122(8), P. 7500 - 7531

Published: Nov. 19, 2021

Mass spectrometry (MS) has become one of the key technologies structural biology. In this review, contributions chemical cross-linking combined with mass (XL-MS) for studying three-dimensional structures proteins and investigating protein–protein interactions are outlined. We summarize most important reagents, software tools, XL-MS workflows highlight prominent examples characterizing proteins, their assemblies, interaction networks in vitro vivo. Computational modeling plays a crucial role deriving 3D-structural information from data. Integrating other techniques biology, such as cryo-electron microscopy, been successful addressing biological questions that to date could not be answered. is therefore expected play an increasingly biology future.

Language: Английский

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Antibody structure prediction using interpretable deep learning

Patterns, Journal Year: 2021, Volume and Issue: 3(2), P. 100406 - 100406

Published: Dec. 9, 2021

Therapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design is hindered by reliance on experimental methods for determining antibody structures. Here, we present DeepAb, deep learning method predicting accurate FV structures from sequence. We evaluate DeepAb set structurally diverse, therapeutically relevant and find that our consistently outperforms leading alternatives. Previous have operated as "black boxes" offered few insights into their predictions. By introducing directly interpretable attention mechanism, show network attends to physically important residue pairs (e.g., proximal aromatics key hydrogen bonding interactions). Finally, novel mutant scoring metric derived confidence particular antibody, all eight top-ranked mutations improve binding affinity. This model will be useful broad range prediction tasks.

Language: Английский

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The impact of AlphaFold2 one year on

Nature Methods, Journal Year: 2022, Volume and Issue: 19(1), P. 15 - 20

Published: Jan. 1, 2022

Language: Английский

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Protein Design: From the Aspect of Water Solubility and Stability

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(18), P. 14085 - 14179

Published: Aug. 3, 2022

Water solubility and structural stability are key merits for proteins defined by the primary sequence 3D-conformation. Their manipulation represents important aspects of protein design field that relies on accurate placement amino acids molecular interactions, guided underlying physiochemical principles. Emulated designer with well-defined properties both fuel knowledge-base more precise computational models used in various biomedical nanotechnological applications. The continuous developments science, increasing computing power, new algorithms, characterization techniques provide sophisticated toolkits beyond guess work. In this review, we summarize recent advances respect to water stability. After introducing fundamental rules, discuss transmembrane solubilization de novo design. Traditional strategies enhance introduced. designs stable complexes high-order assemblies covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning specialty software dedicated evaluation aggregation, discussed. findings opportunities Cryo-EM presented. This review provides an overview significant progress prospects

Language: Английский

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Structure of Hsp90–Hsp70–Hop–GR reveals the Hsp90 client-loading mechanism

Nature, Journal Year: 2021, Volume and Issue: 601(7893), P. 460 - 464

Published: Dec. 22, 2021

Language: Английский

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