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PatchMAN docking: Modeling peptide-protein interactions in the context of the receptor surface

03 Sep 2021-bioRxiv (Cold Spring Harbor Laboratory)-
TL;DR: PatchMAN as discussed by the authors uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures, which can be found not only within interfaces, but also within monomers.
Abstract: Peptide docking can be perceived as a subproblem of protein-protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation between the two partners, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about the peptide sequence to generate a representative conformer ensemble, which can then be rigid body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely that the protein surface defines the peptide bound conformation.We present PatchMAN (Patch-Motif AligNments), a novel peptide docking approach which uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a non-redundant set of protein-peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide-protein complexes in 62% / 81% within 2.5A / 5A RMSD, with corresponding sampling in 81% / 100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the conformation of the bound peptide is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide-protein association, and to the design of new peptide binders.

Summary (2 min read)

Introduction

  • Peptide-protein interactions - namely interactions mediated by short segments or motifs often located in disordered regions - are very common in the cell, constituting about 40% of the overall protein interactions (1).
  • Therefore structural characterization of such interactions is crucial for the understanding of many biological pathways and their potential in the development of therapeutic targets and other biotechnological applications (3).
  • To reduce the conformational space needed to sampling both the peptide conformation and its location on the receptor, many currently existing peptide-docking approaches tackle this problem by decoupling the folding and docking steps, generating a peptide conformational ensemble for subsequent docking (7–10).
  • This approach is also implemented in the InterPep docking protocol (15).
  • PatchMAN shows performance superior to current peptide-docking methods, including their recent implementation of AlphaFold2 (21) for peptide docking (22).

Results

  • General overview of the PatchMAN approach: docking by globally mapping the receptor surface with local motif templates.
  • For the sampling step, the authors first identify the surface residues based on surface accessible area (SASA).
  • The extracted peptide fragments are then superimposed back to the receptor protein using the rotational matrices from the patch-motif alignment.
  • Additional and more extensive details on each step, including specific parameters, are described in the Methods section.
  • Arrows indicate stretches that can be elongated.

PatchMAN performance

  • For the initial estimation of the method performance the authors ran PatchMAN on a non-redundant dataset containing 26 solved protein-peptide complexes previously used to assess performance of PIPER-FlexPepDock (PFPD) (11).
  • The authors analyzed the sequence similarity between the peptide templates and the docked peptide that led to generation of the near-native models .
  • These results indicate that peptide bound conformation can be sampled based on the receptor surface conformation only, without regard to the peptide sequence.
  • To test the ability of PatchMAN to deal with such conformational changes the authors attempted to dock a CD44-derived peptide, known to bind the F3b pocket of the FERM domain, starting from the structure with the closed pocket.
  • This case demonstrates that PatchMAN is able to identify cryptic binding pockets with its sampling approach that takes into account motifs with structural variability.

Discussion

  • Peptide-protein docking poses particular challenges due to the flexible nature of the peptide partner.
  • Many different approaches were developed to tackle this sampling challenge, usually by breaking it into several smaller, independent sampling steps.
  • In PatchMAN, instead of using the sequence of the peptide as the key to modeling its backbone conformation, the focus shifts towards the receptor context.
  • The receptor dictates the ensemble of possible peptide structures, making the sampling strategy invariant to peptide sequence.
  • To summarize, the authors presented here a robust, quick and high performing global peptide-docking protocol, and demonstrate that the PatchMAN approach is accurate and versatile.

Methods

  • Splitting the surface into structural patches Searching for local structural motif matches using MASTER Every patch is searched using the MASTER algorithm (23), against a database of non-redundant protein structures described in the original implementation of MASTER.
  • For the search, the authors used the RMSD cutoff of 1.5Å, and took the 50 lowest, as well as the 50 highest RMSD matches to ensure diversity.
  • Generating initial complexes for further refinement Using PyRosetta (35) the authors then identify the neighboring peptide stretches (Cα-Cα distance within 8Å), and finally, they elongate peptides longer than 2 amino acids to the desired length in both directions, if possible .

Refinement of the structures

  • Rosetta FlexPepDock was used to refine the structures to high resolution and to discriminate near-native models from the rest (as described previously (39)).
  • Here the authors use the refinement method without receptor backbone minimization for the main benchmark, and refinement with receptor backbone minimization for more challenging targets.

Criteria for measuring performance

  • The accuracy of performance was measured as in previous studies (11).
  • In short, the final top 1 percent of the decoys (based on the Rosetta reweighted score (6), using the Rosetta ref2015 scoring function (40)) are clustered and top 10 clusters representatives are analyzed.
  • All results were assessed using RMSD calculated over all interface peptide residue backbone atoms, after superposition of the receptor (i.e., rmsBB_if, as in previous studies, e.g. (11)).
  • Note that the PDB 1LVM was removed from the dataset (since the unbound structure is incorrect), but InterPep2 results are taken from (41) and include this structure.
  • All scripts and runline commands are provided in the Supplementary Methods section, and on github (https://github.cs.huji.ac.il/alisa/patchman/).

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PatchMAN docking: Modeling peptide-protein interactions in
the context of the receptor surface
Alisa Khramushin, Tomer Tsaban, Julia Varga, Orly Avraham, Ora Schueler-Furman*
Department of Microbiology and Molecular Genetics, Institute for Biomedical Research
Israel-Canada, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
*Corresponding author: Ora.furman-schueler@mail.huji.ac.il
Abstract
Peptide docking can be perceived as a subproblem of protein-protein docking. However, due to
the short length and flexible nature of peptides, many do not adopt one defined conformation
prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation
between the two partners, but also the bound conformation of the peptide needs to be modeled.
Traditional peptide-centered approaches use information about the peptide sequence to generate
a representative conformer ensemble, which can then be rigid body docked to the receptor.
Alternatively, one may look at this problem from the viewpoint of the receptor, namely that the
protein surface defines the peptide bound conformation.We present PatchMAN (Patch-Motif
AligNments), a novel peptide docking approach which uses structural motifs to map the receptor
surface with backbone scaffolds extracted from protein structures. On a non-redundant set of
protein-peptide complexes, starting from free receptor structures, PatchMAN successfully
models and identifies near-native peptide-protein complexes in 62% / 81% within 2.5Å /
RMSD, with corresponding sampling in 81% / 100% of the cases, outperforming other
approaches. PatchMAN leverages the observation that structural units of peptides with their
binding pocket can be found not only within interfaces, but also within monomers. We show that
the conformation of the bound peptide is sampled based on the structural context of the receptor
only, without taking into account any sequence information. Beyond peptide docking, this
approach opens exciting new avenues to study principles of peptide-protein association, and to
the design of new peptide binders.
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted September 3, 2021. ; https://doi.org/10.1101/2021.09.02.458699doi: bioRxiv preprint

Introduction
Peptide-protein interactions - namely interactions mediated by short segments or motifs often
located in disordered regions - are very common in the cell, constituting about 40% of the overall
protein interactions (1). Such interactions participate in many important cellular processes like
regulation and cell-signaling (2). Therefore structural characterization of such interactions is
crucial for the understanding of many biological pathways and their potential in the development
of therapeutic targets and other biotechnological applications (3). However, such interactions are
often weaker and more transient than globular protein interactions and therefore more
challenging to characterize experimentally, highlighting the need for developing computational
tools for modeling their structures.
The intuitive way to look at protein-peptide docking is as a sub-problem of protein-protein
docking. However, this approach presents several hurdles, since in addition to the problem of
finding the relative orientation between the two partners, the peptide conformation is often not
known or does not even assume a defined structure before binding the receptor (4). When the
binding site is known and a coarse model of a peptide-protein complex is available, it can be
further refined to high accuracy by local refinement protocols, such as Rosetta FlexPepDock
developed by us (5, 6). In the absence of such information however, global docking has to be
performed. To reduce the conformational space needed to sampling both the peptide
conformation and its location on the receptor, many currently existing peptide-docking
approaches tackle this problem by decoupling the folding and docking steps, generating a peptide
conformational ensemble for subsequent docking (7–10). For example in the
PIPER-FlexPepDock (PFPD) protocol previously developed by us (11), a conformer ensemble is
generated using the Rosetta Fragment Picker (12) (similar to the first step in traditional ab initio
folding (13)). This ensemble is then rigid body docked using PIPER (14) and further refined by
FlexPepDock. This approach is also implemented in the InterPep docking protocol (15).
MDockPeP uses sequence-similar fragments extracted from monomers (16), while in
HADDOCK and pepATTRACT, peptide conformations are represented by idealized secondary
structure fragments (8, 10), and the CABS-dock protocol uses random peptide conformations for
subsequent docking and refinement (17). All these approaches are united by the idea that the
peptide, as a separate protein, carries enough information for its separate folding, or at least the
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted September 3, 2021. ; https://doi.org/10.1101/2021.09.02.458699doi: bioRxiv preprint

determination of a conformer ensemble that represents its conformational preferences. But what
if the conformational ensemble of the peptide does not include the conformation that it adopts
upon binding? In such a case, the rigid-body step of the docking protocol will not be able to fit
the peptide into the binding pocket. An alternative solution for finding the bound conformation
of the peptide is template-based modeling. Many protein-protein interactions can be modeled
based on a solved structure of a homolog complex (18), and the same can be applied to
protein-peptide interactions (19). However, such an approach is restricted to a limited amount of
solved protein-peptide complexes.
We present here a novel approach for blind peptide docking, which we name PatchMAN
(Patch-Motif AligNments), that combines a global search with template-based modeling,
benefitting from both strategies. We look at peptide docking from the viewpoint of the receptor,
building on the assumption that the protein surface carries enough information to determine the
peptide bound conformation. This is based on the previously proposed theory that
peptide-protein interactions often mimic structural characteristics that are typical to monomeric
folds (20), hinting at a large reservoir of information that can further be used for peptide-protein
docking. PatchMAN uses surface patches, defined as bundles of disjoint backbone segments, to
search for similar “pockets” that contain a peptide stretch interacting with it in a dataset of
protein structures that includes monomers as well as protein-protein and protein-peptide
complexes. The backbone conformation of such peptide stretches is then superimposed back to
the receptor protein, and is used as a starting point for local peptide docking refinement.
PatchMAN shows performance superior to current peptide-docking methods, including our
recent implementation of AlphaFold2 (21) for peptide docking (22). As such, PatchMAN opens
new opportunities to model more complicated protein-peptide-like interactions, in addition to
facilitating design of new peptide binders.
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted September 3, 2021. ; https://doi.org/10.1101/2021.09.02.458699doi: bioRxiv preprint

Results
General overview of the PatchMAN approach: docking by globally mapping the receptor
surface with local motif templates
In general, protein-peptide as well as protein-protein interaction modeling can be split into two
categories: template-based modeling, in which new interactions are modeled based on solved
structures of similar interactions, and free modeling, in which a large number of new rigid body
orientations and internal degrees of freedom are sampled. In PatchMAN we suggest combining
the two, by generating peptide templates on the whole protein surface, thus sampling the binding
sites and “folding” the peptide at the same time. The protocol consists of 4 consecutive steps
(Figure 1): (1) Definition of surface patches on the receptor; (2) Identification of structural motif
matches in protein structures, and an interacting fragment that can be used as template for the
bound peptide; (3) Generation of the peptide-protein complex template structure, by
superimposing the interacting peptide back onto the receptor, and (4) Replacing side chains
according to the peptide sequence (threading), refinement and scoring of the model.
In the following we describe the protocol in more details (see also Methods section). For the
sampling step, we first identify the surface residues based on surface accessible area (SASA).
Next, the surface is split into patches consisting of one or more peptide segments. Those patches
are then used to search for similar motifs in a diverse non-redundant database of protein
structures (maximum 30% pairwise sequence identity), using MASTER (23). Peptide stretches
around every found motif are extracted (see Figure 1B). If an interacting fragment is shorter
than the required peptide length, it is elongated in both directions, so that even patches only
partially covering the binding site can lead to generation of a near-native template. The extracted
peptide fragments are then superimposed back to the receptor protein using the rotational
matrices from the patch-motif alignment. At this point the receptor protein surface is fully
mapped with templates for local peptide docking. Once the sampling is complete, the peptide
sequence is threaded onto the generated peptide templates. These starting structures are then
refined using Rosetta FlexPepDock (5). Finally, all models are scored and the best models are
selected. Additional and more extensive details on each step, including specific parameters, are
described in the Methods section.
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted September 3, 2021. ; https://doi.org/10.1101/2021.09.02.458699doi: bioRxiv preprint

Figure 1. The PatchMan protocol. (A) Flowchart. The input is a receptor PDB file and a peptide
sequence. (1) Definition of surface motifs on the receptor: The protein surface is defined based on solvent
accessibility, and then split into small structural surface patches. (2) Identification of structural matches in
protein structures: Matches are detected using MASTER search against a non-redundant dataset of
protein structures; (3) Generation of the peptide-protein complex structure: the peptide fragment is
determined (see (B)) and superimposed onto the receptor. Then the peptide sequence is threaded onto the
identified complementing fragment; (4) Refinement and scoring: the initial structures are refined using the
Rosetta FlexPepDock refinement protocol, and top-scoring models are selected as final predictions. (B)
Extracting peptide fragments. Neighboring residues (magenta) around the matching motif (green) are
.CC-BY-NC-ND 4.0 International licenseavailable under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprintthis version posted September 3, 2021. ; https://doi.org/10.1101/2021.09.02.458699doi: bioRxiv preprint

Citations
More filters
Posted ContentDOI
04 Oct 2021-bioRxiv
TL;DR: In this article, an AlphaFold model trained specifically for multimeric inputs of known stoichiometry was proposed, which significantly increases the accuracy of predicted multimimeric interfaces over input-adapted single-chain AlphaFolds.
Abstract: While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] [≥] 0.49) on 14 targets and high accuracy (DockQ [≥] 0.8) on 6 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,433 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ [≥] 0.23) in 67% of cases, and produce high accuracy predictions (DockQ [≥] 0.8) in 23% of cases, an improvement of +25 and +11 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric interfaces we successfully predict the interface in 69% of cases, and produce high accuracy predictions in 34% of cases, an improvement of +5 percentage points in both instances.

1,023 citations

Journal ArticleDOI
TL;DR: For example, AlphaFold2 as discussed by the authors generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor.
Abstract: Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

390 citations

Posted ContentDOI
07 Sep 2021-bioRxiv
TL;DR: In this paper, the authors combined AlphaFold2 with the physics-based docking program ClusPro, and the resulting 10 models were refined by AlphaFolds2 to improve the performance.
Abstract: It has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

36 citations

Journal ArticleDOI
TL;DR: The authors mine tertiary motifs from known structures to identify surface-complementing fragments or "seeds" and combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures.
Abstract: Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre‐defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface‐complementing fragments or “seeds.” We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM‐based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide‐protein complexes can be covered by seeds generated from single‐chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide‐covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher‐quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide‐binding site. These results demonstrate that known peptide‐binding structures can be constructed from TERMs in single‐chain structures and suggest that TERM information can be applied to efficiently design novel target‐complementing binders.

6 citations

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Frequently Asked Questions (2)
Q1. What have the authors contributed in "Patchman docking: modeling peptide-protein interactions in the context of the receptor surface" ?

The authors present PatchMAN ( Patch-Motif AligNments ), a novel peptide docking approach which uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. The authors show that the conformation of the bound peptide is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide-protein association, and to the design of new peptide binders. 4. 0 International license available under a ( which was not certified by peer review ) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 

Those findings suggest that using PatchMAN docking can be further improved by connecting templates on the protein surface, to model more complex interactions involving long intrinsically disordered proteins wrapping around a structured partner, a problem only addressed by few studies yet ( 34 ). The authors believe that enriching the hit pool with matches from receptor homologous structures will further improve PatchMAN performance, and specifically may be helpful for cases of conformational changes. 

Trending Questions (1)
What is the best way to perform molecular docking simulations of peptides?

The best way to perform molecular docking simulations of peptides is by using the PatchMAN approach, which models the bound conformation of the peptide based on the structural context of the receptor surface.