scholarly journals Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Brian Olson ◽  
Irina Hashmi ◽  
Kevin Molloy ◽  
Amarda Shehu
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Optimization Problems ◽  
Protein Docking ◽  
Coarse Grained ◽  
Biological Macromolecules ◽  
Wide Range ◽  
Multiple Variables ◽  
Basin Hopping

Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.

scholarly journals Protein Structure Prediction: Assembly of Secondary Structure Elements by Basin-Hopping

ChemPhysChem ◽  
2014 ◽  
Vol 15 (15) ◽  
pp. 3378-3390 ◽  
Author(s):  
Falk Hoffmann ◽  
Ioan Vancea ◽  
Sanjay G. Kamat ◽  
Birgit Strodel
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Basin Hopping

scholarly journals Protein structure prediction using basin-hopping

2008 ◽  
Vol 128 (22) ◽  
pp. 225106 ◽  
Author(s):  
Michael C. Prentiss ◽  
David J. Wales ◽  
Peter G. Wolynes
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Basin Hopping

Error-estimation-guided rebuilding ofde novomodels increases the success rate ofab initiophasing

2012 ◽  
Vol 68 (11) ◽  
pp. 1522-1534 ◽  
Author(s):  
Rojan Shrestha ◽  
David Simoncini ◽  
Kam Y. J. Zhang
Keyword(s):  
Protein Structure ◽  
Ab Initio ◽  
Diffraction Data ◽  
Structure Prediction ◽  
De Novo ◽  
Coarse Grained ◽  
Data Sets ◽  
Molecular Replacement ◽  
High Quality ◽  
Protein Targets

Recent advancements in computational methods for protein-structure prediction have made it possible to generate the high-qualityde novomodels required forab initiophasing of crystallographic diffraction data using molecular replacement. Despite those encouraging achievements inab initiophasing usingde novomodels, its success is limited only to those targets for which high-qualityde novomodels can be generated. In order to increase the scope of targets to whichab initiophasing withde novomodels can be successfully applied, it is necessary to reduce the errors in thede novomodels that are used as templates for molecular replacement. Here, an approach is introduced that can identify and rebuild the residues with larger errors, which subsequently reduces the overall Cαroot-mean-square deviation (CA-RMSD) from the native protein structure. The error in a predicted model is estimated from the average pairwise geometric distance per residue computed among selected lowest energy coarse-grained models. This score is subsequently employed to guide a rebuilding process that focuses on more error-prone residues in the coarse-grained models. This rebuilding methodology has been tested on ten protein targets that were unsuccessful using previous methods. The average CA-RMSD of the coarse-grained models was improved from 4.93 to 4.06 Å. For those models with CA-RMSD less than 3.0 Å, the average CA-RMSD was improved from 3.38 to 2.60 Å. These rebuilt coarse-grained models were then converted into all-atom models and refined to produce improvedde novomodels for molecular replacement. Seven diffraction data sets were successfully phased using rebuiltde novomodels, indicating the improved quality of these rebuiltde novomodels and the effectiveness of the rebuilding process. Software implementing this method, calledMORPHEUS, can be downloaded from http://www.riken.jp/zhangiru/software.html.

scholarly journals LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Charles Christoffer ◽  
Vijay Bharadwaj ◽  
Ryan Luu ◽  
Daisuke Kihara
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Complexes ◽  
Protein Sequences ◽  
Data Bank ◽  
Protein Docking ◽  
Functional Mechanisms ◽  
Established Technique

Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.

A Geometric Complementarity-Based Tool for Protein–Protein Docking

2021 ◽  
Author(s):  
Sharon Sunny ◽  
P. B. Jayaraj
Keyword(s):  
Structure Prediction ◽  
Complex Structure ◽  
Protein Docking ◽  
Geometric Algorithms ◽  
Coarse Grained ◽  
Biological Impact ◽  
Searching Strategy ◽  
Determining Factor ◽  
Docking Benchmark ◽  
Conformation Space

The computationally hard protein–protein complex structure prediction problem is continuously fascinating to the scientific community due to its biological impact. The field has witnessed the application of geometric algorithms, randomized algorithms, and evolutionary algorithms to name a few. These techniques improve either the searching or scoring phase. An effective searching strategy does not generate a large conformation space that perhaps demands computational power. Another determining factor is the parameter chosen for score calculation. The proposed method is an attempt to curtail the conformations by limiting the search procedure to probable regions. In this method, partial derivatives are calculated on the coarse-grained representation of the surface residues to identify the optimal points on the protein surface. Contrary to the existing geometric-based algorithms that align the convex and concave regions of both proteins, this method aligns the concave regions of the receptor with convex regions of the ligand only and thus reduces the size of conformation space. The method’s performance is evaluated using the 55 newly added targets in Protein–Protein Docking Benchmark v 5 and is found to be successful for around 47% of the targets.

scholarly journals Rosetta and the Journey to Predict Proteins’ Structures, 20 Years on

2020 ◽  
Vol 15 (6) ◽  
pp. 611-628
Author(s):  
Jad Abbass ◽  
Jean-Christophe Nebel
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Docking ◽  
Ligand Docking ◽  
Ancient Egyptian ◽  
Docking Protein ◽  
Prediction Approach

For two decades, Rosetta has consistently been at the forefront of protein structure prediction. While it has become a very large package comprising programs, scripts, and tools, for different types of macromolecular modelling such as ligand docking, protein-protein docking, protein design, and loop modelling, it started as the implementation of an algorithm for ab initio protein structure prediction. The term ’Rosetta’ appeared for the first time twenty years ago in the literature to describe that algorithm and its contribution to the third edition of the community wide Critical Assessment of techniques for protein Structure Prediction (CASP3). Similar to the Rosetta stone that allowed deciphering the ancient Egyptian civilisation, David Baker and his co-workers have been contributing to deciphering ’the second half of the genetic code’. Although the focus of Baker’s team has expended to de novo protein design in the past few years, Rosetta’s ‘fame’ is associated with its fragment-assembly protein structure prediction approach. Following a presentation of the main concepts underpinning its foundation, especially sequence-structure correlation and usage of fragments, we review the main stages of its developments and highlight the milestones it has achieved in terms of protein structure prediction, particularly in CASP.

ALIGNING MULTIPLE PROTEIN STRUCTURES BY DETERMINISTIC ANNEALING

2005 ◽  
Vol 03 (04) ◽  
pp. 837-860 ◽  
Author(s):  
TIANSHOU ZHOU ◽  
LUONAN CHEN ◽  
YUN TANG ◽  
XIANGSUN ZHANG
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Protein Structures ◽  
Structure Alignment ◽  
Deterministic Annealing ◽  
Protein Structure Alignment ◽  
Protein Chain ◽  
Fold Family ◽  
Multiple Protein ◽  
Wide Range

Protein structure alignment plays a key role in protein structure prediction and fold family classification. An efficient method for multiple protein structure alignment in a mathematical manner is presented, based on deterministic annealing technique. The alignment problem is mapped onto a nonlinear continuous optimization problem (NCOP) with common consensus chain, matching assignment matrices and atomic coordinates as variables. At each step in the annealing procedure, the NCOP is decomposed into as many subproblems as the number of protein chains, each of which is actually an independent pairwise structure alignment between a protein chain and the consensus chain and hence can be efficiently solved by the parallel computation technique. The proposed method is robust with respect to choice of iteration parameters for a wide range of proteins, and performs well in both multiple and pairwise structure alignment cases, compared with existing alignment methods.

Characterization of Chaotic Motion of DNA in Linear Shear Flows

2003 ◽  
Author(s):  
D. G. Walker ◽  
M. A. Stremler
Keyword(s):  
Self Assembly ◽  
Dna Hybridization ◽  
Shear Flows ◽  
Coarse Grained ◽  
Stable Limit ◽  
Flow Conditions ◽  
Wide Range ◽  
Linear Shear ◽  
Stable Limit Cycles

Motion of macromolecules in flows is important to several disciplines such as DNA hybridization studies, self assembly of nanostructures, and transport of suspensions. The present study simulates the motion of macromolecular structures in linear shear flows. A molecular chain is modeled as a coarse-grained series of beads and springs. For a wide range flow conditions, the flow appears chaotic, where quasi-stable limit cycles are observed for several smaller ranges of flow conditions.

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