scholarly journals Protein–Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

2021 ◽  
Vol 22 (14) ◽  
pp. 7341
Author(s):  
Mateusz Kurcinski ◽  
Sebastian Kmiecik ◽  
Mateusz Zalewski ◽  
Andrzej Kolinski
Keyword(s):  
Monte Carlo ◽  
Large Scale ◽  
Protein Complexes ◽  
Computational Cost ◽  
Protein Docking ◽  
Coarse Grained ◽  
Rigid Objects ◽  
Protein Model ◽  
Simulation Step ◽  
Monte Carlo Dynamics

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

scholarly journals Protein-protein docking with large-scale backbone flexibility

2021 ◽  
Author(s):  
Mateusz Kurcinski ◽  
Sebastian Kmiecik ◽  
Mateusz Zalewski ◽  
Andrzej Kolinski
Keyword(s):  
Binding Site ◽  
Structure Prediction ◽  
Large Scale ◽  
Protein Complexes ◽  
Complex Structure ◽  
Protein Docking ◽  
Coarse Grained ◽  
Backbone Flexibility ◽  
Docking Simulations ◽  
Docking Protocol

AbstractStructure prediction of protein-protein complexes is one of the most critical challenges in computational structural biology. It is often difficult to predict the complex structure, even for relatively rigid proteins. Modeling significant structural flexibility in protein docking remains an unsolved problem. This work demonstrates a protein-protein docking protocol with enhanced sampling that accounts for large-scale backbone flexibility. The docking protocol starts from unbound x-ray structures and is not using any binding site information. In docking, one protein partner undergoes multiple fold rearrangements, rotations, and translations during docking simulations, while the other protein exhibits small backbone fluctuations. Including significant backbone flexibility during the search for the binding site has been made possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics. In our simulations, we obtained acceptable quality models for the set of 12 protein-protein complexes, while for selected cases, models were close to high accuracy.

UNRES-Dock—protein–protein and peptide–protein docking by coarse-grained replica-exchange MD simulations

2020 ◽  
Author(s):  
Paweł Krupa ◽  
Agnieszka S Karczyńska ◽  
Magdalena A Mozolewska ◽  
Adam Liwo ◽  
Cezary Czaplewski
Keyword(s):  
Protein Complexes ◽  
Md Simulations ◽  
Protein Docking ◽  
Conformational Space ◽  
Coarse Grained ◽  
Supplementary Information ◽  
Replica Exchange ◽  
Variable Degree ◽  
Single Chain ◽  
Simulation Speed

Abstract Motivation The majority of the proteins in living organisms occur as homo- or hetero-multimeric structures. Although there are many tools to predict the structures of single-chain proteins or protein complexes with small ligands, peptide–protein and protein–protein docking is more challenging. In this work, we utilized multiplexed replica-exchange molecular dynamics (MREMD) simulations with the physics-based heavily coarse-grained UNRES model, which provides more than a 1000-fold simulation speed-up compared with all-atom approaches to predict structures of protein complexes. Results We present a new protein–protein and peptide–protein docking functionality of the UNRES package, which includes a variable degree of conformational flexibility. UNRES-Dock protocol was tested on a set of 55 complexes with size from 43 to 587 amino-acid residues, showing that structures of the complexes can be predicted with good quality, if the sampling of the conformational space is sufficient, especially for flexible peptide–protein systems. The developed automatized protocol has been implemented in the standalone UNRES package and in the UNRES server. Availability and implementation UNRES server: http://unres-server.chem.ug.edu.pl; UNRES package and data used in testing of UNRES-Dock: http://unres.pl. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Classification of protein–protein association rates based on biophysical informatics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kalyani Dhusia ◽  
Yinghao Wu
Keyword(s):  
Large Scale ◽  
Cross Validation ◽  
Protein Complexes ◽  
Dynamic Properties ◽  
Conformational Dynamics ◽  
Classification Model ◽  
Coarse Grained ◽  
Modeling Framework ◽  
Validation Data ◽  
Wide Range

Abstract Background Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles. Results To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model. Conclusions In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein–protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.

scholarly journals Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein–Protein Association

Biomolecules ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1056 ◽  
Author(s):  
Kalyani Dhusia ◽  
Zhaoqian Su ◽  
Yinghao Wu
Keyword(s):  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Kinetic Property ◽  
Simulation Method ◽  
Coarse Grained ◽  
Structural Level ◽  
Simulation Approach ◽  
Protein Association ◽  
Order Of Magnitude

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

scholarly journals The complementary graphene growth and etching revealed by large-scale kinetic Monte Carlo simulation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xiao Kong ◽  
Jianing Zhuang ◽  
Liyan Zhu ◽  
Feng Ding
Keyword(s):  
Monte Carlo ◽  
Large Scale ◽  
2D Materials ◽  
Kinetic Monte Carlo ◽  
Computational Cost ◽  
Atomic Scale ◽  
Graphene Growth ◽  
Graphene Island ◽  
Atomic Simulations ◽  
Hole Growth

AbstractTo fully understand the kinetics of graphene growth, large-scale atomic simulations of graphene islands evolution up to macro sizes (i.e., graphene islands of a few micrometers or with billions of carbon atoms) during growth and etching is essential, but remains a great challenge. In this paper, we developed a low computational cost large-scale kinetic Monte Carlo (KMC) algorithm, which includes all possible events of carbon attachments and detachments on various edge sites of graphene islands. Such a method allows us to simulate the evolution of graphene islands with sizes up to tens of micrometers during either growth or etching with a single CPU core. With this approach and the carefully fitted parameters, we have reproduced the experimentally observed evolution of graphene islands during both growth or etching on Pt(111) surface, and revealed more atomic details of graphene growth and etching. Based on the atomic simulations, we discovered a complementary relationship of graphene growth and etching—the route of graphene island shape evolution during growth is exactly the same as that of the etching of a hole in graphene and that of graphene island etching is exactly same as that of hole growth. The complementary relation brings us a basic principle to understand the growth and etching of graphene, and other 2D materials from atomic scale to macro size and the KMC algorithm is expected to be further developed into a standard simulation package for investigating the growth mechanism of 2D materials on various substrates.

scholarly journals From offshore to onshore probabilistic tsunami hazard assessment via efficient Monte-Carlo sampling

2021 ◽  
Author(s):  
Gareth Davies ◽  
Rikki Weber ◽  
Kaya Wilson ◽  
Phil Cummins
Keyword(s):  
Monte Carlo ◽  
High Resolution ◽  
Large Scale ◽  
Computational Cost ◽  
Deep Ocean ◽  
Analytical Techniques ◽  
Monte Carlo Sampling ◽  
Tsunami Hazard ◽  
Practical Applications ◽  
Inundation Simulation

Offshore Probabilistic Tsunami Hazard Assessments (offshore PTHAs) provide large-scale analyses of earthquake-tsunami frequencies and uncertainties in the deep ocean, but do not provide high-resolution onshore tsunami hazard information as required for many risk-management applications. To understand the implications of an offshore PTHA for the onshore hazard at any site, in principle the tsunami inundation should be simulated locally for every scenario in the offshore PTHA. In practice this is rarely feasible due to the computational expense of inundation models, and the large number of scenarios in offshore PTHAs. Monte-Carlo methods offer a practical and rigorous alternative for approximating the onshore hazard, using a random subset of scenarios. The resulting Monte-Carlo errors can be quantified and controlled, enabling high-resolution onshore PTHAs to be implemented at a fraction of the computational cost. This study develops novel Monte-Carlo sampling approaches for offshore-to-onshore PTHA. Modelled offshore PTHA wave heights are used to preferentially sample scenarios that have large offshore waves near an onshore site of interest. By appropriately weighting the scenarios, the Monte-Carlo errors are reduced without introducing any bias. The techniques are applied to a high-resolution onshore PTHA for the island of Tongatapu in Tonga. In this region, the new approaches lead to efficiency improvements equivalent to using 4-18 times more random scenarios, as compared with stratified-sampling by magnitude, which is commonly used for onshore PTHA. The greatest efficiency improvements are for rare, large tsunamis, and for calculations that represent epistemic uncertainties in the tsunami hazard. To facilitate the control of Monte-Carlo errors in practical applications, this study also provides analytical techniques for estimating the errors both before and after inundation simulations are conducted. Before inundation simulation, this enables a proposed Monte-Carlo sampling scheme to be checked, and potentially improved, at minimal computational cost. After inundation simulation, it enables the remaining Monte-Carlo errors to be quantified at onshore sites, without additional inundation simulations. In combination these techniques enable offshore PTHAs to be rigorously transformed into onshore PTHAs, with full characterisation of epistemic uncertainties, while controlling Monte-Carlo errors.

scholarly journals Large-scale simulation of biomembranes: bringing realistic kinetics to coarse-grained models

2019 ◽  
Author(s):  
Mohsen Sadeghi ◽  
Frank Noé
Keyword(s):  
Large Scale ◽  
Stochastic Dynamics ◽  
Computational Cost ◽  
Coarse Grained ◽  
Fluctuation Spectrum ◽  
Biologically Relevant ◽  
Membrane Models ◽  
Kinetic Effects ◽  
The Cost ◽  
Low Computational Cost

Biomembranes are two-dimensional assemblies of phospholipids that are only a few nanometres thick, but form micrometer-sized structures vital to cellular function. Explicit modelling of biologically relevant membrane systems is computationally expensive, especially when the large number of solvent particles and slow membrane kinetics are taken into account. While highly coarse-grained solvent-free models are available to study equilibrium behaviour of membranes, their efficiency comes at the cost of sacrificing realistic kinetics, and thereby the ability to predict pathways and mechanisms of membrane processes. Here, we present a framework for integrating coarse-grained membrane models with anisotropic stochastic dynamics and continuum-based hydrodynamics, allowing us to simulate large biomembrane systems with realistic kinetics at low computational cost. This paves the way for whole-cell simulations that still include nanometer/nanosecond spatiotemporal resolutions. As a demonstration, we obtain and verify fluctuation spectrum of a full-sized human red blood cell in a 150-milliseconds-long single trajectory. We show how the kinetic effects of different cytoplasmic viscosities can be studied with such a simulation, with predictions that agree with single-cell experimental observations.

scholarly journals Duplication accelerates the evolution of structural complexity in protein quaternary structure

2020 ◽  
Author(s):  
Alexander S. Leonard ◽  
Sebastian E. Ahnert
Keyword(s):  
Gene Duplication ◽  
Lattice Model ◽  
Self Assembly ◽  
Large Scale ◽  
Quaternary Structure ◽  
Protein Complexes ◽  
Structural Complexity ◽  
Data Bank ◽  
Coarse Grained ◽  
Duplication Events

AbstractGene duplication, from single genes to whole genomes, has been observed in organisms across all taxa. Despite its prevalence, the evolutionary benefits of this mechanism are the subject of ongoing debate. Gene duplication can significantly alter the self-assembly of protein quaternary structures, impacting the dosage or interaction proclivity. Here we use a lattice model of self-assembly as a coarse-grained representation of protein complex assembly, and show that it can be used to examine potential evolutionary advantages of duplication. Duplication provides a unique mechanism for increasing the evolvability of protein complexes by enabling the transformation of symmetric homomeric interactions into heteromeric ones. This transformation is extensively observed in in silico evolutionary simulations of the lattice model, with duplication events significantly accelerating the rate at which structural complexity increases. These coarse-grained simulation results are corroborated with a large-scale analysis of complexes from the Protein Data Bank.

scholarly journals A memetic algorithm enables global all-atom protein-protein docking with sidechain flexibility

2021 ◽  
Author(s):  
Daniel Varela ◽  
Ingemar André
Keyword(s):  
Energy Function ◽  
Memetic Algorithm ◽  
Computational Cost ◽  
Optimization Method ◽  
Protein Docking ◽  
Global Search ◽  
Local Optimization ◽  
Atomic Scale ◽  
Coarse Grained ◽  
Local Optimization Method

ABSTRACTProtein-protein docking plays a central role in the characterization and discovery of protein interactions in the cell. Complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires the use of simplified energy models, coarse-grained protein descriptions and rigid-body approximations. In this study we present EvoDOCK, which is an evolutionary-based docking algorithm that enables the identification of optimal docking orientations using an atomistic energy function and sidechain flexibility, employing a global search without prior information of the binding site. EvoDOCK is a memetic algorithm that combines the strength of a differential evolution algorithm for efficient exploration of the global search space with the benefits of a local optimization method, built on the Monte Carlo-based RosettaDOCK program, to optimize detailed atomic interactions. This approach resulted in substantial improvements in both sampling efficiency and computation speed compared to calculations using the local optimization method RosettaDOCK alone, with up to 35 times of reduction in computational cost. For all the ten systems investigated in this study, a highly accurate docking prediction could be identified as the lowest energy model with high efficiency. While protein-protein docking with EvoDOCK is still computationally expensive compared to many methods based on Fast Fourier Transforms (FFT), the results demonstrate the tractability of global docking proteins using an atomistic energy function while exploring sidechain flexibility. Comparison with FFT global docking demonstrated the benefits of using an all-atom energy function to identify native-like predictions. The sampling strategy in EvoDOCK can readily be tailored to include backbone flexibility in the search, which is often necessary to tackle more challenging docking challenges.

scholarly journals Capturing Protein-Ligand Recognition Pathways in Coarse-grained Simulation

2019 ◽  
Author(s):  
Bhupendra R. Dandekar ◽  
Jagannath Mondal
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Md Simulations ◽  
Coarse Grained ◽  
Attractive Alternative ◽  
Complex Process ◽  
Potential Binding ◽  
Allosteric Sites ◽  
Potential Binding Site ◽  
Complete Process

AbstractProtein-substrate recognition is highly dynamic and complex process in nature. A key approach in deciphering the mechanism underlying the recognition process is to capture the kinetic process of substrate in its act of binding to its designated protein cavity. Towards this end, microsecond long atomistic molecular dynamics (MD) simulation has recently emerged as a popular method of choice, due its ability to record these events at high spatial and temporal resolution. However, success in this approach comes at an exorbitant computational cost. Here we demonstrate that coarse grained models of protein, when systematically optimised to maintain its tertiary fold, can capture the complete process of spontaneous protein-ligand binding from bulk media to cavity, within orders of magnitude shorter wall clock time compared to that of all-atom MD simulations. The simulated and crystallographic binding pose are in excellent agreement. We find that the exhaustive sampling of ligand exploration in protein and solvent, harnessed by coarse-grained simulation at a frugal computational cost, in combination with Markov state modelling, leads to clearer mechanistic insights and discovery of novel recognition pathways. The result is successfully validated against three popular protein-ligand systems. Overall, the approach provides an affordable and attractive alternative of all-atom simulation and promises a way-forward for replacing traditional docking based small molecule discovery by high-throughput coarse-grained simulation for searching potential binding site and allosteric sites. This also provides practical avenues for first-hand exploration of bio-molecular recognition processes in large-scale biological systems, otherwise inaccessible in all-atom simulations.

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