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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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