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Model Overview

EquiDock [1] is an SE(3)-equivariant model that can predict protein-protein complex formation from two invidual proteins. EquiDock treats both the proteins as rigid bodies and assumes no conformation change during the formation of the complex.

Intended Use

Almost all biological processes involve proteins interacting with each other. Prediction of protein complexes can be used understand disease progression and also in the design of novel proteins that can preferentially interact with other proteins of choice.

Model Architecture

EquiDock was implemented using the BioNeMo framework. EquiDock uses Independent E(3)-Equivariant Graph Matching Network (IEGMN) that performs graph feature embedding updates in addition to E(3)-equivariant coordinate updates. EquiDock uses a novel attention-based selection of keypoints to represent binding pocket of each protein via a differentiable variant of the Kabsch algortihm [2]. In addition, the EquiDock architecture also gaurantees commutativity ie., docking protein 1 with respect to protein 2 gives identical results as docking protein 2 with respect to protein 1.

Limitations

EquiDock is designed to treat proteins as rigid bodies and would not work well with proteins that undergo significant change in conformation upon binding.

Training

Dataset

The EquiDock model leverages two primary datasets: Docking Benchmark 5.5 (DB5.5) [3] and the Database of Interacting Protein Structures (DIPS) [4]. DB5.5, considered a gold standard dataset for data quality, consists of 253 structures. On the other hand, DIPS is a larger dataset of protein complex structures obtained from the Protein Data Bank (PDB), specifically tailored for rigid body docking. It's noted that DIPS contains only bound structures, making it suitable for rigid docking, while DB5.5 includes unbound protein structures, mostly displaying rigid structures.

Training involves initially training models on the training part of DIPS using the Adam optimizer with a learning rate of 2e-4. Early stopping is employed with a patience of 30 epochs. The best validation model is determined based on Ligand RMSD on the DIPS validation set.

For DB5.5, the DIPS pre-trained model is fine-tuned on the DB5.5 training set using a learning rate of 1e-4 and early stopping with 150 epochs patience. During training, roles of ligand and receptor are randomly assigned, and both ligand rotation and translation are randomly applied, even though the model is invariant to these operations.

Infrastructure and Configuration

The EquiDock model can be trained from scratch with the code and datasets provided. The equidock_dips.nemo and equidock_db5.nemo model checkpoints provided here were adapted from the public chekpoints available on GitHub.

Benchmarks

Two pretrained EquiDock checkpoints are available for download: equidock_dips.nemo and equidock_db5.nemo. These have been converted to the NeMo format from publicly available checkpoints. This section provides accuracy benchmarks for these models, as well as information on expected training speed performance. Currently, models trained from randomly initialized weights within the BioNeMo framework are not provided. The production of these models is ongoing work.

Accuracy Benchmarks

The accuracy of EquiDock was measured using the complex and interface root-mean-squared distance [1], which are measured over test datasets of DIPS and DB5.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

RMSD type	Checkpoint Name	Dataset	Dataset Split for Measurement	Metric	BioNeMo	Public
Complex RMSD	equidock_dips.nemo	DIPS	test	median	13.39	13.39
Complex RMSD	equidock_dips.nemo	DIPS	test	mean	14.52	14.52
Complex RMSD	equidock_dips.nemo	DIPS	test	std	7.13	7.13
Interface RMSD	equidock_dips.nemo	DIPS	test	median	10.24	10.24
Interface RMSD	equidock_dips.nemo	DIPS	test	mean	11.92	11.92
Interface RMSD	equidock_dips.nemo	DIPS	test	std	7.01	7.01
Complex RMSD	equidock_db5.nemo	DB5	test	median	14.13	14.13
Complex RMSD	equidock_db5.nemo	DB5	test	mean	14.73	14.72
Complex RMSD	equidock_db5.nemo	DB5	test	std	5.31	5.31
Interface RMSD	equidock_db5.nemo	DB5	test	median	11.97	11.97
Interface RMSD	equidock_db5.nemo	DB5	test	mean	13.23	13.23
Interface RMSD	equidock_db5.nemo	DB5	test	std	4.93	4.93

Training Performance Benchmarks

Training speed was tested on DGX-A100 systems with 8 IEGM layers over DIPS dataset for one epoch, on GPUs with 80GB of memory.

References

Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause. Independent se (3)-equivariant models for end-to-end rigid protein docking. arXiv preprint arXiv:2111.07786, 2021.
Wolfgang Kabsch. A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography, 32(5):922–923, 1976.
Thom Vreven, Iain H Moal, Anna Vangone, Brian G Pierce, Panagiotis L Kastritis, Mieczyslaw Torchala, Raphael Chaleil, Brian Jiménez-García, Paul A Bates, Juan Fernandez-Recio, and others. Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. Journal of molecular biology, 427(19):3031–3041, 2015.
Raphael Townshend, Rishi Bedi, Patricia Suriana, and Ron Dror. End-to-end learning on 3d protein structure for interface prediction. Advances in Neural Information Processing Systems, 2019.

License

EquiDock is provided under Apache 2.0 license

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EquiDock treats both the proteins as rigid bodies and assumes no conformation change during the formation of the complex.\n\n## Intended Use\n\nAlmost all biological processes involve proteins interacting with each other. Prediction of protein complexes can be used understand disease progression and also in the design of novel proteins that can preferentially interact with other proteins of choice.\n\n## Model Architecture\n\nEquiDock was implemented using the BioNeMo framework. EquiDock uses Independent E(3)-Equivariant Graph Matching Network (IEGMN) that performs graph feature embedding updates in addition to E(3)-equivariant coordinate updates. EquiDock uses a novel attention-based selection of keypoints to represent binding pocket of each protein via a differentiable variant of the Kabsch algortihm [2]. In addition, the EquiDock architecture also gaurantees commutativity ie., docking protein 1 with respect to protein 2 gives identical results as docking protein 2 with respect to protein 1.\n\n## Limitations\n\nEquiDock is designed to treat proteins as rigid bodies and would not work well with proteins that undergo significant change in conformation upon binding.\n\n## Training\n\n### Dataset\n\nThe EquiDock model leverages two primary datasets: Docking Benchmark 5.5 (DB5.5) [3] and the Database of Interacting Protein Structures (DIPS) [4]. DB5.5, considered a gold standard dataset for data quality, consists of 253 structures. On the other hand, DIPS is a larger dataset of protein complex structures obtained from the Protein Data Bank (PDB), specifically tailored for rigid body docking. It's noted that DIPS contains only bound structures, making it suitable for rigid docking, while DB5.5 includes unbound protein structures, mostly displaying rigid structures.\n\nTraining involves initially training models on the training part of DIPS using the Adam optimizer with a learning rate of 2e-4. Early stopping is employed with a patience of 30 epochs. The best validation model is determined based on Ligand RMSD on the DIPS validation set.\n\nFor DB5.5, the DIPS pre-trained model is fine-tuned on the DB5.5 training set using a learning rate of 1e-4 and early stopping with 150 epochs patience. During training, roles of ligand and receptor are randomly assigned, and both ligand rotation and translation are randomly applied, even though the model is invariant to these operations.\n\n### Infrastructure and Configuration\n\nThe EquiDock model can be trained from scratch with the code and datasets provided. The `equidock_dips.nemo` and `equidock_db5.nemo` model checkpoints provided here were adapted from the public chekpoints available on [GitHub](https://github.com/octavian-ganea/equidock_public/tree/main/checkpts).\n\n## Benchmarks\n\nTwo pretrained EquiDock checkpoints are available for download: `equidock_dips.nemo` and `equidock_db5.nemo`. These have been converted to the NeMo format from publicly available checkpoints. This section provides accuracy benchmarks for these models, as well as information on expected training speed performance. Currently, models trained from randomly initialized weights within the BioNeMo framework are not provided. The production of these models is ongoing work.\n\n### Accuracy Benchmarks\n\nThe accuracy of EquiDock was measured using the complex and interface root-mean-squared distance [1], which are measured over test datasets of DIPS and DB5.\n\n\n| RMSD type | Checkpoint Name | Dataset | Dataset Split for Measurement | Metric | BioNeMo | Public |\n|---------------------------------|----------------------|-------------------|-------------------------------|-----------|----------|----------|\n| Complex RMSD | equidock_dips.nemo | DIPS | test | median | 13.39 | 13.39 |\n| Complex RMSD | equidock_dips.nemo | DIPS | test | mean | 14.52 | 14.52 |\n| Complex RMSD | equidock_dips.nemo | DIPS | test | std | 7.13 | 7.13 |\n| Interface RMSD | equidock_dips.nemo | DIPS | test | median | 10.24 | 10.24 |\n| Interface RMSD | equidock_dips.nemo | DIPS | test | mean | 11.92 | 11.92 |\n| Interface RMSD | equidock_dips.nemo | DIPS | test | std | 7.01 | 7.01 |\n| Complex RMSD | equidock_db5.nemo | DB5 | test | median | 14.13 | 14.13 |\n| Complex RMSD | equidock_db5.nemo | DB5 | test | mean | 14.73 | 14.72 |\n| Complex RMSD | equidock_db5.nemo | DB5 | test | std | 5.31 | 5.31 |\n| Interface RMSD | equidock_db5.nemo | DB5 | test | median | 11.97 | 11.97 |\n| Interface RMSD | equidock_db5.nemo | DB5 | test | mean | 13.23 | 13.23 |\n| Interface RMSD | equidock_db5.nemo | DB5 | test | std | 4.93 | 4.93 |\n\n\n### Training Performance Benchmarks\n\nTraining speed was tested on DGX-A100 systems with 8 IEGM layers over DIPS dataset for one epoch, on GPUs with 80GB of memory.\n\n\n## References\n1. Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause. Independent se (3)-equivariant models for end-to-end rigid protein docking. arXiv preprint arXiv:2111.07786, 2021.\n2. Wolfgang Kabsch. A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A: Crystal Physics, Diffraction, Theoretical and General Crystallography, 32(5):922–923, 1976.\n3. Thom Vreven, Iain H Moal, Anna Vangone, Brian G Pierce, Panagiotis L Kastritis, Mieczyslaw Torchala, Raphael Chaleil, Brian Jiménez-García, Paul A Bates, Juan Fernandez-Recio, and others. Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. Journal of molecular biology, 427(19):3031–3041, 2015.\n4. Raphael Townshend, Rishi Bedi, Patricia Suriana, and Ron Dror. End-to-end learning on 3d protein structure for interface prediction. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526],[365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381],{"_382":488,"_207":488,"_168":169,"_355":489},{"_382":483,"_207":483,"_168":169,"_355":484},{"_382":479,"_207":479,"_168":169,"_355":480},{"_382":474,"_207":474,"_168":169,"_355":475},{"_382":469,"_207":469,"_168":169,"_355":470},{"_382":457,"_207":457,"_168":169,"_355":458},{"_382":445,"_207":445,"_168":169,"_355":446},{"_382":440,"_207":440,"_168":169,"_355":441},{"_382":429,"_207":429,"_168":169,"_355":430},{"_382":424,"_207":424,"_168":169,"_355":425},{"_382":419,"_207":419,"_168":169,"_355":420},{"_382":414,"_207":414,"_168":169,"_355":415},{"_382":409,"_207":409,"_168":169,"_355":410},{"_382":404,"_207":404,"_168":169,"_355":405},{"_382":399,"_207":399,"_168":169,"_355":400},{"_382":392,"_207":392,"_168":169,"_355":393},{"_382":383,"_207":383,"_168":169,"_355":384},"hostName","amazon-adsystem.com",[385,386],{"_79":391,"_388":383,"_389":31,"_168":390},{"_79":387,"_388":383,"_389":31,"_168":390},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[394,395],{"_79":398,"_388":392,"_389":31,"_168":397},{"_79":396,"_388":392,"_389":31,"_168":397},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[421],{"_79":422,"_388":419,"_389":31,"_168":423},"sp","Testing","google.com",[426],{"_79":427,"_388":424,"_389":31,"_168":428},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[431,432,433,434],{"_79":439,"_388":429,"_389":31,"_168":436},{"_79":438,"_388":429,"_389":31,"_168":436},{"_79":437,"_388":429,"_389":31,"_168":436},{"_79":435,"_388":429,"_389":31,"_168":436},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[442],{"_79":443,"_388":440,"_389":31,"_168":444},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[447,448,449,450],{"_79":456,"_388":445,"_389":31,"_168":452},{"_79":454,"_388":445,"_389":31,"_168":455},{"_79":453,"_388":445,"_389":31,"_168":452},{"_79":451,"_388":445,"_389":31,"_168":452},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[459,460,461,462],{"_79":468,"_388":457,"_389":31,"_168":466},{"_79":467,"_388":457,"_389":31,"_168":466},{"_79":465,"_388":457,"_389":31,"_168":466},{"_79":463,"_388":457,"_389":31,"_168":464},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[471],{"_79":472,"_388":469,"_389":31,"_168":473},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[476],{"_79":477,"_388":474,"_389":31,"_168":478},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[481],{"_79":482,"_388":479,"_389":31,"_168":423},"R","6sc.co",[485],{"_79":486,"_388":483,"_389":31,"_168":487},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634],[579,580,581,582,583],{"_382":474,"_207":474,"_168":169,"_355":605},{"_382":440,"_207":440,"_168":169,"_355":600},{"_382":593,"_207":593,"_168":169,"_355":594},{"_382":589,"_207":589,"_168":169,"_355":590},{"_382":584,"_207":584,"_168":169,"_355":585},"nvcrm.my.site.com",[586],{"_79":587,"_388":584,"_389":31,"_168":588},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[591],{"_79":592,"_388":589,"_389":31,"_168":423},"edge","github.com",[595,596],{"_79":599,"_388":593,"_389":31,"_168":598},{"_79":597,"_388":593,"_389":31,"_168":598},"logged_in","This domain is owned by Github. 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