Less is more: Coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK

ABSTRACTPredicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing to model larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modelling platform. Docking and refinement are performed at the coarse-grained level and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ~7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.

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

10.1101/2021.08.31.458424 ◽

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.

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Exploring the landscape of model representations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2000098117 ◽

2020 ◽

Vol 117 (39) ◽

pp. 24061-24068 ◽

Cited By ~ 2

Author(s):

Thomas T. Foley ◽

Katherine M. Kidder ◽

M. Scott Shell ◽

W. G. Noid

Keyword(s):

Statistical Physics ◽

Degrees Of Freedom ◽

Large Scale ◽

Coarse Graining ◽

Order Parameters ◽

Microscopic Model ◽

Coarse Grained ◽

Proper Order ◽

Complex Phenomena ◽

Protein Fluctuations

The success of any physical model critically depends upon adopting an appropriate representation for the phenomenon of interest. Unfortunately, it remains generally challenging to identify the essential degrees of freedom or, equivalently, the proper order parameters for describing complex phenomena. Here we develop a statistical physics framework for exploring and quantitatively characterizing the space of order parameters for representing physical systems. Specifically, we examine the space of low-resolution representations that correspond to particle-based coarse-grained (CG) models for a simple microscopic model of protein fluctuations. We employ Monte Carlo (MC) methods to sample this space and determine the density of states for CG representations as a function of their ability to preserve the configurational information, I, and large-scale fluctuations, Q, of the microscopic model. These two metrics are uncorrelated in high-resolution representations but become anticorrelated at lower resolutions. Moreover, our MC simulations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative distinction between good and bad representations of proteins. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.

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Bottom-Up Coarse-Grained Modeling of DNA

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.645527 ◽

2021 ◽

Vol 8 ◽

Author(s):

Tiedong Sun ◽

Vishal Minhas ◽

Nikolay Korolev ◽

Alexander Mirzoev ◽

Alexander P. Lyubartsev ◽

...

Keyword(s):

Degrees Of Freedom ◽

Deoxyribonucleic Acid ◽

Persistence Length ◽

Coarse Graining ◽

Potential Of Mean Force ◽

Coarse Grained ◽

Bottom Up ◽

Slow Dynamic ◽

Molecular Systems ◽

Fine Grained

Recent advances in methodology enable effective coarse-grained modeling of deoxyribonucleic acid (DNA) based on underlying atomistic force field simulations. The so-called bottom-up coarse-graining practice separates fast and slow dynamic processes in molecular systems by averaging out fast degrees of freedom represented by the underlying fine-grained model. The resulting effective potential of interaction includes the contribution from fast degrees of freedom effectively in the form of potential of mean force. The pair-wise additive potential is usually adopted to construct the coarse-grained Hamiltonian for its efficiency in a computer simulation. In this review, we present a few well-developed bottom-up coarse-graining methods, discussing their application in modeling DNA properties such as DNA flexibility (persistence length), conformation, “melting,” and DNA condensation.

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Coarse-Grained Molecular Dynamics Simulation of Lysozyme Protein Crystals

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1544 ◽

2011 ◽

Vol 6 (1) ◽

Author(s):

Nafiseh Farhadian ◽

Mojtaba Shariaty-Niassar ◽

Kourosh Malek ◽

Ali Maghari

Keyword(s):

Molecular Dynamics ◽

Degrees Of Freedom ◽

Coarse Graining ◽

Dynamics Simulation ◽

Coarse Grained ◽

Protein Crystal ◽

Protein Crystals ◽

Biological Phenomena ◽

The Stability ◽

Coarse Grained Molecular Dynamics

Many biological phenomena of interest occur on a time scale that is too great to be studied by atomistic simulations. The use of coarse-graining methods to represent a system can alleviate this restriction by reducing the number of degrees of freedom thus extending the time and length scale in molecular modeling. Coarse-grained molecular dynamics (CGMD) technique was employed to simulate diffusion of water in the nanopores of lysozyme protein crystals. Good agreement was obtained between the atomistic and CG simulations in view of the stability of the protein crystal structure and water transport properties. Our simulations demonstrate that the CG method is a suitable technique for simulation the solvent diffusion process in the lysozyme protein crystal and also can be a good technique to predict the behavior of solvent and solutes in the biological systems at longer length and time scales.

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Minimalist models for proteins: a comparative analysis

Quarterly Reviews of Biophysics ◽

10.1017/s0033583510000132 ◽

2010 ◽

Vol 43 (3) ◽

pp. 333-371 ◽

Cited By ~ 79

Author(s):

Valentina Tozzini

Keyword(s):

Phase Diagram ◽

Comparative Analysis ◽

Amino Acid ◽

Degrees Of Freedom ◽

Predictive Power ◽

Coarse Graining ◽

Coarse Grained ◽

Molecular Systems ◽

Modern Molecular Biology ◽

Functional Forms

AbstractThe last decade has witnessed a renewed interest in the coarse-grained (CG) models for biopolymers, also stimulated by the needs of modern molecular biology, dealing with nano- to micro-sized bio-molecular systems and larger than microsecond timescale. This combination of size and timescale is, in fact, hard to access by atomic-based simulations. Coarse graining the system is a route to be followed to overcome these limits, but the ways of practically implementing it are many and different, making the landscape of CG models very vast and complex.In this paper, the CG models are reviewed and their features, applications and performances compared. This analysis, restricted to proteins, focuses on the minimalist models, namely those reducing at minimum the number of degrees of freedom without losing the possibility of explicitly describing the secondary structures. This class includes models using a single or a few interacting centers (beads) for each amino acid.From this analysis several issues emerge. The difficulty in building these models resides in the need for combining transferability/predictive power with the capability of accurately reproducing the structures. It is shown that these aspects could be optimized by accurately choosing the force field (FF) terms and functional forms, and combining different parameterization procedures. In addition, in spite of the variety of the minimalist models, regularities can be found in the parameters values and in FF terms. These are outlined and schematically presented with the aid of a generic phase diagram of the polypeptide in the parameter space and, hopefully, could serve as guidelines for the development of minimalist models incorporating the maximum possible level of predictive power and structural accuracy.

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Comparison of coarse-grained and all-atom representations by explicit energy landscape explorations

10.26434/chemrxiv-2021-blvfj ◽

2021 ◽

Author(s):

Yuxin Ma ◽

Antoine Monsavior ◽

Samuela Pasquali ◽

Konstantin Roeder

Keyword(s):

Resource Constraints ◽

Energy Landscape ◽

Computational Cost ◽

Computational Modelling ◽

Coarse Graining ◽

Careful Consideration ◽

Coarse Grained ◽

Computational Time ◽

Kinetic Properties ◽

Aquifex Aeolicus

Computational studies of large molecular systems are often hindered by resource constraints, such as the available computational time. A common approach to reduce the computational cost is to use a coarse-grained description instead of an all-atom representation. However, such a simpli�cation requires careful consideration of the coarse-graining scheme to identify potential artefacts introduced and the limitations of the model. In this contribution, we use the computational energy landscape framework to explicitly explore the energy landscapes for a coarse-grained (HiRE-RNA) and an all-atom potential (AMBER) for an example system, the Aquifex aeolicus tmRNA pseudoknot PK1. The method provides insight into structural, thermodynamic and kinetic properties within a common framework, and allows for a comparison of a variety of commonly computed observables, demonstrating the usefulness of this approach. For the speci�c case study, we observe that both potentials exhibit a number of common features, highlighting that the coarse-grained model captures essential physical features of the system. Nonetheless, we observe shortcomings, and we demonstrate how our approach allows us to improve the model based on the insight obtained from the computational modelling.

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Diffusion Wavelet Decomposition for Coarse-Graining of Polymer Chains

MRS Proceedings ◽

10.1557/opl.2015.106 ◽

2015 ◽

Vol 1753 ◽

Author(s):

B. Christopher Rinderspacher ◽

Jaydeep P. Bardhan ◽

Ahmed E. Ismail

Keyword(s):

Degrees Of Freedom ◽

Large Scale ◽

Coarse Graining ◽

Polymer Chains ◽

Coarse Grained ◽

Small Scale ◽

Bonding Structure ◽

Ring Structures ◽

Spatial Coordinates ◽

Molecular Bonding

ABSTRACTHere we present an alternative approach to coarse graining, based on the multiresolution diffusion-wavelet approach to operator compression, which does not require explicit atomistic-to-coarse-grained mappings. Our diffusion-wavelet method takes as input the topology and sparsity of the molecular bonding structure of a system, and returns as output a hierarchical set of degrees of freedom (DoFs) of system-specific coarse-grained variables. Importantly, the hierarchical compression provides a clear framework for modeling at many model scales (levels), beyond the common two-level CG representation. Our results show that the resulting hierarchy separates localized modes, such as a single C-C vibrational mode, from larger-scale motions, e.g., long-range concerted backbone vibrational modes. Our approach correctly captures small-scale chemical features, such as cellulose ring structures, and alkane side chains or CH2 units, as well as large-scale features of the backbone. In particular, the new method’s finest-scale modes describe DoFs similar to united atom models and other chemically-defined CG models. Modes at coarser levels describe increasingly large connected portions of the target polymers. For polyethylene and polystyrene, spatial coordinates and their associated forces were compressed by up to two orders of magnitude. The compression in forces is of particular interest as this allows larger timesteps as well as reducing the number of DoFs.

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Exploring Energy Profiles of Protein-Protein Interactions (PPIs) Using DFT Method

Letters in Drug Design & Discovery ◽

10.2174/1570180815666180815151141 ◽

2019 ◽

Vol 16 (6) ◽

pp. 670-677

Author(s):

Sanket Bapat ◽

Renu Vyas ◽

Muthukumarasamy Karthikeyan

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Hot Spot ◽

Energy Landscape ◽

Interaction Mechanism ◽

Dft Method ◽

Protein Docking ◽

Protein Protein Interactions ◽

Cumulative Energy ◽

Energy Profiles

Background: Large-scale energy landscape characterization of protein-protein interactions (PPIs) is important to understand the interaction mechanism and protein-protein docking methods. The experimental methods for detecting energy landscapes are tedious and the existing computational methods require longer simulation time. Objective: The objective of the present work is to ascertain the energy profiles at the interface regions in a rapid manner to analyze the energy landscape of protein-protein interactions. Methods: The atomic coordinates obtained from the X-ray and NMR spectroscopy data are considered as inputs to compute cumulative energy profiles for experimentally validated protein-protein complexes. The energies computed by the program were comparable to the standard molecular dynamics simulations. Results: The PPI Profiler not only enables rapid generation of energy profiles but also facilitates the detection of hot spot residue atoms involved therein. Conclusion: The hotspot residues and their computed energies matched with the experimentally determined hot spot residues and their energies which correlated well by employing the MM/GBSA method. The proposed method can be employed to scan entire proteomes across species at an atomic level to study the key PPI interactions.

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EROS-DOCK: protein–protein docking using exhaustive branch-and-bound rotational search

Bioinformatics ◽

10.1093/bioinformatics/btz434 ◽

2019 ◽

Vol 35 (23) ◽

pp. 5003-5010 ◽

Cited By ~ 1

Author(s):

Maria Elisa Ruiz Echartea ◽

Isaure Chauvot de Beauchêne ◽

David W Ritchie

Keyword(s):

Force Field ◽

Branch And Bound ◽

3D Structure ◽

Quality Criteria ◽

Search Space ◽

Protein Docking ◽

Coarse Grained ◽

Supplementary Information ◽

Binary Complex ◽

The Individual

Abstract Motivation Protein–protein docking algorithms aim to predict the 3D structure of a binary complex using the structures of the individual proteins. This typically involves searching and scoring in a 6D space. Many docking algorithms use FFT techniques to exhaustively cover the search space and to accelerate the scoring calculation. However, FFT docking results often depend on the initial protein orientations with respect to the Fourier sampling grid. Furthermore, Fourier-transforming a physics-base force field can involve a serious loss of precision. Results Here, we present EROS-DOCK, an algorithm to rigidly dock two proteins using a series of exhaustive 3D rotational searches in which non-clashing orientations are scored using the ATTRACT coarse-grained force field model. The rotational space is represented as a quaternion ‘π-ball’, which is systematically sub-divided in a ‘branch-and-bound’ manner, allowing efficient pruning of rotations that will give steric clashes. The algorithm was tested on 173 Docking Benchmark complexes, and results were compared with those of ATTRACT and ZDOCK. According to the CAPRI quality criteria, EROS-DOCK typically gives more acceptable or medium quality solutions than ATTRACT and ZDOCK. Availability and implementation The EROS-DOCK program is available for download at http://erosdock.loria.fr. Supplementary information Supplementary data are available at Bioinformatics online.

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Locating landmarks on high-dimensional free energy surfaces

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1418241112 ◽

2015 ◽

Vol 112 (11) ◽

pp. 3235-3240 ◽

Cited By ~ 38

Author(s):

Ming Chen ◽

Tang-Qing Yu ◽

Mark E. Tuckerman

Keyword(s):

Free Energy ◽

Degrees Of Freedom ◽

Energy Surface ◽

Saddle Points ◽

Coarse Graining ◽

Graph Representation ◽

Coarse Grained ◽

High Dimensional ◽

Free Energy Surface ◽

Energy Surfaces

Coarse graining of complex systems possessing many degrees of freedom can often be a useful approach for analyzing and understanding key features of these systems in terms of just a few variables. The relevant energy landscape in a coarse-grained description is the free energy surface as a function of the coarse-grained variables, which, despite the dimensional reduction, can still be an object of high dimension. Consequently, navigating and exploring this high-dimensional free energy surface is a nontrivial task. In this paper, we use techniques from multiscale modeling, stochastic optimization, and machine learning to devise a strategy for locating minima and saddle points (termed “landmarks”) on a high-dimensional free energy surface “on the fly” and without requiring prior knowledge of or an explicit form for the surface. In addition, we propose a compact graph representation of the landmarks and connections between them, and we show that the graph nodes can be subsequently analyzed and clustered based on key attributes that elucidate important properties of the system. Finally, we show that knowledge of landmark locations allows for the efficient determination of their relative free energies via enhanced sampling techniques.

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