SABBAC: online Structural Alphabet-based protein BackBone reconstruction from Alpha-Carbon trace

SABBAC is an on-line service devoted to protein backbone reconstruction from alpha-carbon trace. It is based on the assembly of fragments taken from a library of reduced size, selected from the encoding of the protein trace in a hidden Markov model-derived structural alphabet. The assembly of the fragments is achieved by a greedy algorithm, using an energy-based scoring. Alpha-carbon coordinates remain unaffected. SABBAC simply positions the missing backbone atoms, no further refinement is performed. From our tests, SABBAC performs equal or better than other similar on-line approach and is robust to deviations on the alpha-carbon coordinates. It can be accessed at

CRANKITE: a fast polypeptide backbone conformation sampler

Background: CRANKITE is a suite of programs for simulating backbone conformations of polypeptides and proteins. The core of the suite is an efficient Metropolis Monte Carlo sampler of backbone conformations in continuous three-dimensional space in atomic details. Methods: In contrast to other programs relying on local Metropolis moves in the space of dihedral angles, our sampler utilizes local crankshaft rotations of rigid peptide bonds in Cartesian space. Results: The sampler allows fast simulation and analysis of secondary structure formation and conformational changes for proteins of average length

Combining Coarse-Grained Protein Models with Replica-Exchange All-Atom Molecular Dynamics

We describe a combination of all-atom simulations with CABS, a well-established coarse-grained protein modeling tool, into a single multiscale protocol. The simulation method has been tested on the C-terminal beta hairpin of protein G, a model system of protein folding. After reconstructing atomistic details, conformations derived from the CABS simulation were subjected to replica-exchange molecular dynamics simulations with OPLS-AA and AMBER99sb force fields in explicit solvent. Such a combination accelerates system convergence several times in comparison with all-atom simulations starting from the extended chain conformation, demonstrated by the analysis of melting curves, the number of native-like conformations as a function of time and secondary structure propagation. The results strongly suggest that the proposed multiscale method could be an efficient and accurate tool for high-resolution studies of protein folding dynamics in larger systems.Comment: 12 pages, 4 figure

Structural alphabets derived from attractors in conformational space

Background: The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as 'states'. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis.Results: A Structural Alphabet has been derived by clustering all four-residue fragments of a high-resolution subset of the protein data bank and extracting the high-density states as representative conformational states. Each fragment is uniquely defined by a set of three independent angles corresponding to its degrees of freedom, capturing in simple and intuitive terms the properties of the conformational space. The fragments of the Structural Alphabet are equivalent to the conformational attractors and therefore yield a most informative encoding of proteins. Proteins can be reconstructed within the experimental uncertainty in structure determination and ensembles of structures can be encoded with accuracy and robustness.Conclusions: The density-based Structural Alphabet provides a novel tool to describe local conformations and it is specifically suitable for application in studies of protein dynamics. © 2010 Pandini et al; licensee BioMed Central Ltd

Improving consensus structure by eliminating averaging artifacts

<p>Abstract</p> <p>Background</p> <p>Common structural biology methods (i.e., NMR and molecular dynamics) often produce ensembles of molecular structures. Consequently, averaging of 3D coordinates of molecular structures (proteins and RNA) is a frequent approach to obtain a consensus structure that is representative of the ensemble. However, when the structures are averaged, artifacts can result in unrealistic local geometries, including unphysical bond lengths and angles.</p> <p>Results</p> <p>Herein, we describe a method to derive representative structures while limiting the number of artifacts. Our approach is based on a Monte Carlo simulation technique that drives a starting structure (an extended or a 'close-by' structure) towards the 'averaged structure' using a harmonic pseudo energy function. To assess the performance of the algorithm, we applied our approach to Cα models of 1364 proteins generated by the TASSER structure prediction algorithm. The average RMSD of the refined model from the native structure for the set becomes worse by a mere 0.08 Å compared to the average RMSD of the averaged structures from the native structure (3.28 Å for refined structures and 3.36 A for the averaged structures). However, the percentage of atoms involved in clashes is greatly reduced (from 63% to 1%); in fact, the majority of the refined proteins had zero clashes. Moreover, a small number (38) of refined structures resulted in lower RMSD to the native protein versus the averaged structure. Finally, compared to PULCHRA <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>, our approach produces representative structure of similar RMSD quality, but with much fewer clashes.</p> <p>Conclusion</p> <p>The benchmarking results demonstrate that our approach for removing averaging artifacts can be very beneficial for the structural biology community. Furthermore, the same approach can be applied to almost any problem where averaging of 3D coordinates is performed. Namely, structure averaging is also commonly performed in RNA secondary prediction <abbrgrp><abbr bid="B2">2</abbr></abbrgrp>, which could also benefit from our approach.</p

Assessing Protein Conformational Sampling Methods Based on Bivariate Lag-Distributions of Backbone Angles

Despite considerable progress in the past decades, protein structure prediction remains one of the major unsolved problems in computational biology. Angular-sampling-based methods have been extensively studied recently due to their ability to capture the continuous conformational space of protein structures. The literature has focused on using a variety of parametric models of the sequential dependencies between angle pairs along the protein chains. In this article, we present a thorough review of angular-sampling-based methods by assessing three main questions: What is the best distribution type to model the protein angles? What is a reasonable number of components in a mixture model that should be considered to accurately parameterize the joint distribution of the angles? and What is the order of the local sequence–structure dependency that should be considered by a prediction method? We assess the model fits for different methods using bivariate lag-distributions of the dihedral/planar angles. Moreover, the main information across the lags can be extracted using a technique called Lag singular value decomposition (LagSVD), which considers the joint distribution of the dihedral/planar angles over different lags using a nonparametric approach and monitors the behavior of the lag-distribution of the angles using singular value decomposition. As a result, we developed graphical tools and numerical measurements to compare and evaluate the performance of different model fits. Furthermore, we developed a web-tool (http://www.stat.tamu.edu/∼madoliat/LagSVD) that can be used to produce informative animations

Serverification of Molecular Modeling Applications: the Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code's difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step 'serverification' protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org

Molecular Distance Geometry Approach to solve Alpha Carbon Trace Problem

To structural researchers, predicting protein structures currently remains a challenging task. During the past decades, different methodologies have been developed to address this issue. One such protein structure prediction problem is the Alpha Carbon Trace Problem. The Alpha Carbon trace problem is to determine the 3-D coordinates of the main chain atoms(C, N, and O) from just the CA carbon coordinates. This master\u27s thesis presents a novel approach for solving the CA trace problem by using a molecular distance geometry approach. The current approach uses the algorithms which are used to solve the Molecular Distance Geometry Problem to nd the coordinates of the atoms in the peptide plane of a given protein. Once, the coordinates of the atoms(CA, C, N, and O) in the single peptide plane are computed, the two CA atoms are aligned with the first two CA atoms in the CA trace by finding the appropriate rotation and translation. The same rotation and translation are applied to all the other atoms in the peptide plane(C, N, and O). The process is then repeated for the entire trace, and the coordinates of all the atoms in the main chain of the protein are retrieved. In order to predict the side-chain atoms from the main Chain, SCWRL4.0 is used. The output generated by SCWRL4.0 is then subjected to LBFGS energy minimizer using a tool called MESHI. The key advantage of using our approach is that it eliminates the building and searching for a huge protein fragment library. Experiments show that our approach is highly comparable to other approaches such as BBQ, PD2Main, and PULCHRA for solving the CA trace problem

Multiscale Molecular Dynamics Simulations of Histidine Kinase Activity

Zweikomponentensysteme (TCS), bestehend aus einer Sensorhistidinkinase (HK) und einem Antwortregulationsprotein, sind Schlüsselbausteine in bakteriellen Signaluebertragungsmechanismen. Die Fähigkeit von Bakterien auf eine breite Vielfalt von chemischen und physikalischen Stimuli angemessen zu reagieren ist ausschlaggebend für ihr Überleben. Es ist daher nicht überraschend, dass TCS zu den meistuntersuchten bakteriellen Proteinsystemen gehört. Sensorhistidinkinasen sind typischerweise in die Zellmembran integrierte, homodimere Proteine bestehend aus mehreren Domänen. Reizwahrnehmung an der Sensordomäne von HK löst eine Reihe von großskaligen Konformationsübergangen entlang der Domänen aus. Während sich die strukturellen Eigenschaften von verschiedenen HKs unterscheiden können, erhalten sie alle einen katalytischen ATP-bindenden Kern (CA) und dimerisierende Histidinphosphotransferdomänen (DHp). Während der Signalkaskade nimmt der Kern eine asymmetrische Konformation an, sodass die Kinase an einem der Protomere aktiv ist und die der anderen inaktiv. Das ermöglicht es dem ATP in einer der CA-Domänen seine $\gamma$-Phosphatgruppe an das Histidin der DHp abzugeben. Diese Phosphorylgrouppe wird anschließend an das Antwortregulationsprotein weitergegeben, die eine angemessene Reaktion der Zelle veranlasst. In der vorliegenden Arbeit untersuche ich die Konformationsdynamik des Kinasekerns mithilfe von Molekulardynamiksimulationen (MD). Der Fokus der Arbeit liegt auf zwei verschiedenen HKs: WalK und CpxA. Wegen der Größe der Systeme und den erforderlichen biologischen Zeitskalen, ist es nicht möglich die relevanten Konformationsübergänge in klassischen MD-Simulationen zu berechnen. Um dieses Problem zu umgehen, verwende ich ein strukturbasiertes Modell mit paarweisen harmonischen Potentialen. Diese Näherung erlaubt es, den Übergang zwischen dem inaktiven und den aktiven Zustand mit wesentlich geringerem rechnerischen Aufwand zu untersuchen. Nachdem ich das System mithilfe dieses vereinfachten Modells erkundet habe, benutze ich angereicherte Stichprobenverfahren mit atomistischen Modellen um detailliertere Einsichten in die Dynamik zu gewinnen. Die Ergebnisse in dieser Arbeit legen nahe, dass das Verhalten der einzelnen Unterdomänen des Kinasekerns eng miteinander gekoppelt ist