ProteinConformers: large-scale and energetically profiled descriptions of protein conformational landscapes
Curation statements for this article:-
Curated by eLife
eLife Assessment
This study presents a useful database resource containing protein conformations generated through molecular dynamics simulations, with extensive quality evaluation and benchmarking. While the database is well-constructed and professionally organized, the evidence supporting its claimed representation of protein conformational landscapes is incomplete, as the short simulation times and starting structure bias prevent true Boltzmann sampling of the conformational space.
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Abstract
Modeling protein conformational landscapes is essential for understanding dynamics, allostery, and drug discovery, yet existing resources lack diverse conformational coverage, energetic annotations, or benchmarking standards. ProteinConformers ( https://zhanggroup.org/ProteinConformers ) provides 2.7 million geometry-optimized conformations generated with a multi-seed molecular dynamics strategy, paired with 13.7 million energy evaluations and 5.5 million similarity annotations. It delivers continuous landscapes from non-native to near-native states, benchmarking framework for multi-conformation generators, and an interactive analysis platform.
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eLife Assessment
This study presents a useful database resource containing protein conformations generated through molecular dynamics simulations, with extensive quality evaluation and benchmarking. While the database is well-constructed and professionally organized, the evidence supporting its claimed representation of protein conformational landscapes is incomplete, as the short simulation times and starting structure bias prevent true Boltzmann sampling of the conformational space.
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Reviewer #1 (Public review):
Summary:
The authors describe a new database that rigorously explores protein conformations.
Strengths:
It is extremely well done, using state-of-the-art tools by a group at the top of the field of structural modeling. The evaluation of qualities and the benchmarking of the structures are outstanding, and it is expected that the new database will have a significant impact on the field.
Weaknesses:
The authors are using MD simulation to generate some of the structure, and therefore should have access to standard MD energies. I am surprised that no evaluation is provided based on these energies that can be extended to free energies.
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Reviewer #2 (Public review):
Summary:
The authors developed a dataset of protein conformations by running molecular dynamics simulations starting from both native and decoy conformations for a large number of proteins. These conformations were put together as a dataset for querying and downloading, along with their energies under different force fields. The authors suggest that such conformations represent the proteins' conformational landscape, so that they will be useful for evaluating methods generating multiple conformations of proteins.
Strengths:
The dataset is online and working. It has good documentation for others to use.
Weaknesses:
The biggest weakness is that the collected conformations very likely do not represent the true conformational landscape. To represent the conformational landscape, the structures need to be sampled …
Reviewer #2 (Public review):
Summary:
The authors developed a dataset of protein conformations by running molecular dynamics simulations starting from both native and decoy conformations for a large number of proteins. These conformations were put together as a dataset for querying and downloading, along with their energies under different force fields. The authors suggest that such conformations represent the proteins' conformational landscape, so that they will be useful for evaluating methods generating multiple conformations of proteins.
Strengths:
The dataset is online and working. It has good documentation for others to use.
Weaknesses:
The biggest weakness is that the collected conformations very likely do not represent the true conformational landscape. To represent the conformational landscape, the structures need to be sampled based on the Boltzmann distribution. However, in this study, conformations are generated by running very short (125ps to 375ps) MD simulations starting from near-native conformations and decoys. Such short simulations will produce small fluctuations around the starting conformations, so the distribution of conformations is largely dominated by the distribution of the initial conformations, which by one means are Boltzmann distributed. A conformation might be physically plausible, but it might have very small weight in the Boltzmann distribution. On the other hand, conformations with large weights might not be in the dataset.
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Reviewer #3 (Public review):
Summary:
This manuscript describes a web-based tool that allows researchers to compare large numbers of representative ("plausible") conformations of proteins. It also includes energetic analysis from multiple widely used structure-prediction methods.
Strengths:
This tool will likely be useful for students who want to learn more about the ensemble properties of proteins. The resource is well organized and it represents a large amount of computing resources.
Weaknesses:
It is not entirely clear how the database may be utilized by other groups to advance research. It could be helpful if the authors add a short section that provides example use cases that illustrate how this database can support new strategies for studying protein dynamics.
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