scholarly journals High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock

2017 ◽  
Author(s):  
Nawsad Alam ◽  
Oriel Goldstein ◽  
Bing Xia ◽  
Kathryn A. Porter ◽  
Dima Kozakov ◽  
...  
Keyword(s):  
High Resolution ◽  
Protein Interactions ◽  
Protein Docking ◽  
Academic Community ◽  
Conformational Space ◽  
Complex Structures ◽  
Docking Algorithm ◽  
Docking Protocol ◽  
Protein Interactome ◽  
Rigid Body Docking

AbstractPeptide-protein interactions contribute a significant fraction of the protein-protein interactome. Accurate modeling of these interactions is challenging due to the vast conformational space associated with interactions of highly flexible peptides with large receptor surfaces. To address this challenge we developed a fragment based high-resolution peptide-protein docking protocol. By streamlining the Rosetta fragment picker for accurate peptide fragment ensemble generation, the PIPER docking algorithm for exhaustive fragment-receptor rigid-body docking and Rosetta FlexPepDock for flexible full-atom refinement of PIPER docked models, we successfully addressed the challenge of accurate and efficient global peptide-protein docking at high-resolution with remarkable accuracy. Validation on a representative set of solved peptide-protein complex structures demonstrates the accuracy and robustness of our approach, and opens up the way to high-resolution modeling of many more peptide-protein interactions and to the detailed study of peptide-protein association in general. PIPER-FlexPepDock is freely available to the academic community as a server at http://piperfpd.furmanlab.cs.huji.ac.il.

scholarly journals Improved protein docking by predicted interface residues

2021 ◽  
Author(s):  
Gabriele Pozzati ◽  
Petras Kundrotas ◽  
Arne Elofsson
Keyword(s):  
State Of The Art ◽  
Protein Docking ◽  
Prediction Methods ◽  
Scoring Functions ◽  
Protein Interface ◽  
High Importance ◽  
Docking Algorithm ◽  
Docking Protocol ◽  
Template Free ◽  
Interface Prediction

ABSTRACTScoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.

scholarly journals Combining SAXS with Rosetta for Identification of Native-like Protein Complexes

2014 ◽  
Vol 70 (a1) ◽  
pp. C424-C424
Author(s):  
Pernille Sønderby ◽  
Günther Peters ◽  
Jens Bukrinski ◽  
Pernille Harris
Keyword(s):  
Molecular Level ◽  
Protein Complexes ◽  
Protein Docking ◽  
Complex Structures ◽  
Protein Drugs ◽  
X Ray Scattering ◽  
Protein Interfaces ◽  
Docking Protocol ◽  
Docking Tool ◽  
The Difference

It is well-known that recombinant human serum albumin (rHSA) has the ability to stabilize proteins in solution preventing protein adsorption, aggregation and oxidation. For this reason, rHSA is used as an excipient in the formulation of protein pharmaceuticals. To shed light on the molecular interactions, we have studied a variety of protein drugs that are known to bind to rHSA and thereby being stabilized. We observe that the interactions depend on protein concentrations and differ significantly between protein drugs. One approach to study these systems on a molecular level is the combination of small angle X-ray scattering (SAXS) and in-silico modelling. SAXS can be used to identify the overall shape of proteins and protein complexes and ab initio models can be derived from the scattering profile using programs such as Dammif [1]. These programs allow us to assess the overall conformation of the macromolecular structures, but cannot provide detailed information on the molecular level regarding protein-protein interfaces of the complexes. Here, the Rosetta modelling suite, a multipurpose software suite, can be utilized to perform protein-protein docking and to study the complexes. The challenge in using the Rosetta docking tool [2] is the difficulties in efficiently identifying the native-like structure. For better identification we apply SAXS constrains during the docking procedure. Although the method has been applied previously [3], no benchmarking has been published regarding the relative success of using SAXS constrains. We therefore have conducted an elaborate benchmarking, where we have used SAXS constrains for determination of complexes of non-identical components using the Rosetta docking protocol. A pool of complex structures has been chosen to evaluate the difference between conventional docking and docking performed using SAXS constrains. This allows us to optimize the parameters in the protocol and pave the way to study unknown complex structures.

scholarly journals Mapping the Energy Landscape of PROTAC-Mediated Protein-protein Interactions

2021 ◽  
Author(s):  
Jose A Villegas ◽  
Tasneem M Vaid ◽  
Michael E Johnson ◽  
Terry W Moore
Keyword(s):  
Protein Interactions ◽  
Degrees Of Freedom ◽  
Protein Complexes ◽  
Energy Landscape ◽  
Search Space ◽  
Protein Docking ◽  
Conformational Space ◽  
Conformational Sampling ◽  
Test Case ◽  
Coordinate Descent Algorithm

One of the principal difficulties in computational modeling of macromolecules is the vast conformational space that arises out of large numbers of atomic degrees of freedom. This problem is a familiar issue in the area of protein-protein docking, where models of protein complexes are generated from the monomeric subunits. Although restriction of molecular flexibility is a commonly used approximation that decreases the dimensionality of the problem, the seemingly endless number of possible ways two binding partners can interact generally necessitates the use of further approximations to explore the search space. Recently, growing interest in using computational tools to build predictive models of PROTAC-mediated complexes has led to the application of state-of-the-art protein-protein docking techniques to tackle this problem. Additionally, the atomic degrees of freedom introduced by flexibility of linkers used in the construction of PROTACs further expands the configurational search space, a problem that can be tackled with conformational sampling tools. However, repurposing existing tools to carry out protein-protein docking and linker conformer generation independently results in extensive sampling of structures incompatible with PROTAC-mediated complex formation. Here we show that it is possible to restrict the search to the space of protein-protein conformations that can be bridged by a PROTAC molecule with a given linker composition by using a cyclic coordinate descent algorithm to position PROTACs into complex-bound configurations. We use this methodology to construct a picture of the energy landscape of PROTAC-mediated interactions in a model test case, and show that the global minimum lies in the space of native-like conformations.

scholarly journals Harnessing protein folding neural networks for peptide-protein docking

2021 ◽  
Author(s):  
Tomer Tsaban ◽  
Julia K Varga ◽  
Orly Avraham ◽  
Ziv Ben Aharon ◽  
Alisa Khramushin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Docking ◽  
Peptide Sequence ◽  
Multiple Sequence ◽  
C Terminus ◽  
Docking Protocol ◽  
Wide Range ◽  
Simple Implementation

Highly accurate protein structure predictions by the recently published deep neural networks such as AlphaFold2 and RoseTTAFold are truly impressive achievements, and will have a tremendous impact far beyond structural biology. If peptide-protein binding can be seen as a final complementing step in the folding of a protein monomer, we reasoned that these approaches might be applicable to the modeling of such interactions. We present a simple implementation of AlphaFold2 to model the structure of peptide-protein interactions, enabled by linking the peptide sequence to the protein c-terminus via a poly glycine linker. We show on a large non-redundant set of 162 peptide-protein complexes that peptide-protein interactions can indeed be modeled accurately. Importantly, prediction is fast and works without multiple sequence alignment information for the peptide partner. We compare performance on a smaller, representative set to the state-of-the-art peptide docking protocol PIPER-FlexPepDock, and describe in detail specific examples that highlight advantages of the two approaches, pointing to possible further improvements and insights in the modeling of peptide-protein interactions. Peptide-mediated interactions play important regulatory roles in functional cells. Thus the present advance holds much promise for significant impact, by bringing into reach a wide range of peptide-protein complexes, and providing important starting points for detailed study and manipulation of many specific interactions.

Udock, the interactive docking entertainment system

2014 ◽  
Vol 169 ◽  
pp. 425-441 ◽  
Author(s):  
Guillaume Levieux ◽  
Guillaume Tiger ◽  
Stéphanie Mader ◽  
Jean-François Zagury ◽  
Stéphane Natkin ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Structures ◽  
Protein Docking ◽  
Conformational Space ◽  
Protein Protein Interactions ◽  
Binding Interface ◽  
Protein Interfaces ◽  
Almost All ◽  
Experimental Partner

Protein–protein interactions play a crucial role in biological processes. Protein docking calculations' goal is to predict, given two proteins of known structures, the associate conformation of the corresponding complex. Here, we present a new interactive protein docking system, Udock, that makes use of users' cognitive capabilities added up. In Udock, the users tackle simplified representations of protein structures and explore protein–protein interfaces’ conformational space using a gamified interactive docking system with on the fly scoring. We assumed that if given appropriate tools, a naïve user's cognitive capabilities could provide relevant data for (1) the prediction of correct interfaces in binary protein complexes and (2) the identification of the experimental partner in interaction among a set of decoys. To explore this approach experimentally, we conducted a preliminary two week long playtest where the registered users could perform a cross-docking on a dataset comprising 4 binary protein complexes. The users explored almost all the surface of the proteins that were available in the dataset but favored certain regions that seemed more attractive as potential docking spots. These favored regions were located inside or nearby the experimental binding interface for 5 out of the 8 proteins in the dataset. For most of them, the best scores were obtained with the experimental partner. The alpha version of Udock is freely accessible at http://udock.fr.

scholarly journals Harnessing protein folding neural networks for peptide-protein docking

2021 ◽  
Author(s):  
Tomer Tsaban ◽  
Julia Varga ◽  
Orly Avraham ◽  
Ziv Ben-Aharon ◽  
Alisa Khramushin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Docking ◽  
Peptide Sequence ◽  
Multiple Sequence ◽  
C Terminus ◽  
Docking Protocol ◽  
Wide Range ◽  
Simple Implementation

Abstract Highly accurate protein structure predictions by the recently published deep neural networks such as AlphaFold2 and RoseTTAFold are truly impressive achievements, and will have a tremendous impact far beyond structural biology. If peptide-protein binding can be seen as a final complementing step in the folding of a protein monomer, we reasoned that these approaches might be applicable to the modeling of such interactions. We present a simple implementation of AlphaFold2 to model the structure of peptide-protein interactions, enabled by linking the peptide sequence to the protein c-terminus via a poly glycine linker. We show on a large non-redundant set of 162 peptide-protein complexes that peptide-protein interactions can indeed be modeled accurately. Importantly, prediction is fast and works without multiple sequence alignment information for the peptide partner. We compare performance on a smaller, representative set to the state-of-the-art peptide docking protocol PIPER-FlexPepDock, and describe in detail specific examples that highlight advantages of the two approaches, pointing to possible further improvements and insights in the modeling of peptide-protein interactions. Peptide-mediated interactions play important regulatory roles in functional cells. Thus the present advance holds much promise for significant impact, by bringing into reach a wide range of peptide-protein complexes, and providing important starting points for detailed study and manipulation of many specific interactions.

scholarly journals Improved protein docking by predicted interface residues.

2021 ◽  
Author(s):  
Gabriele Pozzati ◽  
Petras Kundrotas ◽  
Arne Elofsson
Keyword(s):  
State Of The Art ◽  
Protein Docking ◽  
Prediction Methods ◽  
Scoring Functions ◽  
Protein Interface ◽  
High Importance ◽  
Docking Algorithm ◽  
Docking Protocol ◽  
Template Free ◽  
Interface Prediction

Scoring docking solutions is a difficult task, and many methods have been developed for this purpose. In docking, only a handful of the hundreds of thousands of models generated by docking algorithms are acceptable, causing difficulties when developing scoring functions. Today’s best scoring functions can significantly increase the number of top-ranked models but still fails for most targets. Here, we examine the possibility of utilising predicted residues on a protein-protein interface to score docking models generated during the scan stage of a docking algorithm. Many methods have been developed to infer the portions of a protein surface that interact with another protein, but most have not been benchmarked using docking algorithms. Different interface prediction methods are systematically tested for scoring >300.000 low-resolution rigid-body template free docking decoys. Overall we find that BIPSPI is the best method to identify interface amino acids and score docking solutions. Further, using BIPSPI provides better docking results than state of the art scoring functions, with >12% of first ranked docking models being acceptable. Additional experiments indicated precision as a high-importance metric when estimating interface prediction quality, focusing on docking constraints production. We also discussed several limitations for the adoption of interface predictions as constraints in a docking protocol.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

scholarly journals The Protein Interactome of Glycolysis in Escherichia coli

Proteomes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 16
Author(s):  
Shomeek Chowdhury ◽  
Stephen Hepper ◽  
Mudassir K. Lodi ◽  
Milton H. Saier ◽  
Peter Uetz
Keyword(s):  
Escherichia Coli ◽  
Protein Interactions ◽  
Allosteric Regulation ◽  
Glycolytic Enzymes ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Interacting Protein ◽  
E Coli ◽  
Protein Interactome ◽  
The Impact

Glycolysis is regulated by numerous mechanisms including allosteric regulation, post-translational modification or protein-protein interactions (PPI). While glycolytic enzymes have been found to interact with hundreds of proteins, the impact of only some of these PPIs on glycolysis is well understood. Here we investigate which of these interactions may affect glycolysis in E. coli and possibly across numerous other bacteria, based on the stoichiometry of interacting protein pairs (from proteomic studies) and their conservation across bacteria. We present a list of 339 protein-protein interactions involving glycolytic enzymes but predict that ~70% of glycolytic interactors are not present in adequate amounts to have a significant impact on glycolysis. Finally, we identify a conserved but uncharacterized subset of interactions that are likely to affect glycolysis and deserve further study.

scholarly journals High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling

2021 ◽  
Author(s):  
Théo Jaffrelot Inizan ◽  
Frédéric Célerse ◽  
Olivier Adjoua ◽  
Dina El Ahdab ◽  
Luc-Henri Jolly ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
High Resolution ◽  
Adaptive Sampling ◽  
Force Fields ◽  
Sampling Strategy ◽  
Md Simulations ◽  
Conformational Space ◽  
Many Body ◽  
Polarizable Force Fields ◽  
Main Protease

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

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