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14 pages, 46513 KiB

Open AccessArticle

Tracing Actin Filament Bundles in Three-Dimensional Electron Tomography Density Maps of Hair Cell Stereocilia

by Salim Sazzed, Junha Song, Julio A. Kovacs, Willy Wriggers, Manfred Auer and Jing He

Molecules 2018, 23(4), 882; https://doi.org/10.3390/molecules23040882 - 11 Apr 2018

Cited by 13 | Viewed by 5928

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, [...] Read more.

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin bundles, traditional approaches to filament detection and tracing have failed in these cases. In this study, we introduce BundleTrac, an effective method to trace hundreds of filaments in a bundle. A comparison between BundleTrac and manually tracing the actin filaments in a stereocilium showed that BundleTrac accurately built 326 of 330 filaments (98.8%), with an overall cross-distance of 1.3 voxels for the 330 filaments. BundleTrac is an effective semi-automatic modeling approach in which a seed point is provided for each filament and the rest of the filament is computationally identified. We also demonstrate the potential of a denoising method that uses a polynomial regression to address the resolution and high-noise anisotropic environment of the density map. Full article

(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))

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17 pages, 2590 KiB

Open AccessArticle

Feature-Based and String-Based Models for Predicting RNA-Protein Interaction

by Donald Adjeroh, Maen Allaga, Jun Tan, Jie Lin, Yue Jiang, Ahmed Abbasi and Xiaobo Zhou

Molecules 2018, 23(3), 697; https://doi.org/10.3390/molecules23030697 - 19 Mar 2018

Cited by 16 | Viewed by 5016

Abstract

In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI). In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included [...] Read more.

In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI). In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences), and structure information (protein and RNA secondary structures). This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods. Full article

(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))

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14 pages, 3744 KiB

Open AccessArticle

An Investigation of Atomic Structures Derived from X-ray Crystallography and Cryo-Electron Microscopy Using Distal Blocks of Side-Chains

by Lin Chen, Jing He, Salim Sazzed and Rayshawn Walker

Molecules 2018, 23(3), 610; https://doi.org/10.3390/molecules23030610 - 8 Mar 2018

Cited by 4 | Viewed by 3899

Abstract

Cryo-electron microscopy (cryo-EM) is a structure determination method for large molecular complexes. As more and more atomic structures are determined using this technique, it is becoming possible to perform statistical characterization of side-chain conformations. Two data sets were involved to characterize block lengths [...] Read more.

Cryo-electron microscopy (cryo-EM) is a structure determination method for large molecular complexes. As more and more atomic structures are determined using this technique, it is becoming possible to perform statistical characterization of side-chain conformations. Two data sets were involved to characterize block lengths for each of the 18 types of amino acids. One set contains 9131 structures resolved using X-ray crystallography from density maps with better than or equal to 1.5 Å resolutions, and the other contains 237 protein structures derived from cryo-EM density maps with 2–4 Å resolutions. The results show that the normalized probability density function of block lengths is similar between the X-ray data set and the cryo-EM data set for most of the residue types, but differences were observed for ARG, GLU, ILE, LYS, PHE, TRP, and TYR for which conformations with certain shorter block lengths are more likely to be observed in the cryo-EM set with 2–4 Å resolutions. Full article

(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))

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17 pages, 1784 KiB

Open AccessArticle

Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods

by Laurent Denarie, Ibrahim Al-Bluwi, Marc Vaisset, Thierry Siméon and Juan Cortés

Molecules 2018, 23(2), 373; https://doi.org/10.3390/molecules23020373 - 9 Feb 2018

Cited by 9 | Viewed by 3456

Abstract

This paper presents an approach to enhance conformational sampling of proteins employing stochastic algorithms such as Monte Carlo (MC) methods. The approach is based on a mechanistic representation of proteins and on the application of methods originating from robotics. We outline the general [...] Read more.

This paper presents an approach to enhance conformational sampling of proteins employing stochastic algorithms such as Monte Carlo (MC) methods. The approach is based on a mechanistic representation of proteins and on the application of methods originating from robotics. We outline the general ideas of our approach and detail how it can be applied to construct several MC move classes, all operating on a shared representation of the molecule and using a single mathematical solver. We showcase these sampling techniques on several types of proteins. Results show that combining several move classes, which can be easily implemented thanks to the proposed approach, significantly improves sampling efficiency. Full article

(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))

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13 pages, 5218 KiB

Open AccessArticle

Exploring Protein Cavities through Rigidity Analysis

by Stephanie Mason, Brian Y. Chen and Filip Jagodzinski

Molecules 2018, 23(2), 351; https://doi.org/10.3390/molecules23020351 - 7 Feb 2018

Cited by 6 | Viewed by 4412

Abstract

The geometry of cavities in the surfaces of proteins facilitates a variety of biochemical functions. To better understand the biochemical nature of protein cavities, the shape, size, chemical properties, and evolutionary nature of functional and nonfunctional surface cavities have been exhaustively surveyed in [...] Read more.

The geometry of cavities in the surfaces of proteins facilitates a variety of biochemical functions. To better understand the biochemical nature of protein cavities, the shape, size, chemical properties, and evolutionary nature of functional and nonfunctional surface cavities have been exhaustively surveyed in protein structures. The rigidity of surface cavities, however, is not immediately available as a characteristic of structure data, and is thus more difficult to examine. Using rigidity analysis for assessing and analyzing molecular rigidity, this paper performs the first survey of the relationships between cavity properties, such as size and residue content, and how they correspond to cavity rigidity. Our survey measured a variety of rigidity metrics on 120,323 cavities from 12,785 sequentially non-redundant protein chains. We used VASP-E, a volume-based algorithm for analyzing cavity geometry. Our results suggest that rigidity properties of protein cavities are dependent on cavity surface area. Full article

(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))

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18 pages, 1689 KiB

Open AccessArticle

Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability

by Ramin Dehghanpoor, Evan Ricks, Katie Hursh, Sarah Gunderson, Roshanak Farhoodi, Nurit Haspel, Brian Hutchinson and Filip Jagodzinski

Molecules 2018, 23(2), 251; https://doi.org/10.3390/molecules23020251 - 27 Jan 2018

Cited by 29 | Viewed by 7906

Abstract

Predicting how a point mutation alters a protein’s stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab [...] Read more.

Predicting how a point mutation alters a protein’s stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time as well as financial resources needed to assess the effect of even a single amino acid substitution. Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates. In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. We use these as features for our Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) methods. We validate the predictions of our in silico mutations against experimental

Δ Δ G

stability data, and attain Pearson Correlation values upwards of 0.71 for single mutations, and 0.81 for double mutations. We perform ablation studies to assess which features contribute most to a model’s success, and also introduce a voting scheme to synthesize a single prediction from the individual predictions of the three models. Full article

(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))

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17 pages, 2844 KiB

Open AccessArticle

Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem

by Kamal Al Nasr, Feras Yousef, Ruba Jebril and Christopher Jones

Molecules 2018, 23(2), 28; https://doi.org/10.3390/molecules23020028 - 23 Jan 2018

Cited by 7 | Viewed by 4295

Abstract

To take advantage of recent advances in genomics and proteomics it is critical that the three-dimensional physical structure of biological macromolecules be determined. Cryo-Electron Microscopy (cryo-EM) is a promising and improving method for obtaining this data, however resolution is often not sufficient to [...] Read more.

To take advantage of recent advances in genomics and proteomics it is critical that the three-dimensional physical structure of biological macromolecules be determined. Cryo-Electron Microscopy (cryo-EM) is a promising and improving method for obtaining this data, however resolution is often not sufficient to directly determine the atomic scale structure. Despite this, information for secondary structure locations is detectable. De novo modeling is a computational approach to modeling these macromolecular structures based on cryo-EM derived data. During de novo modeling a mapping between detected secondary structures and the underlying amino acid sequence must be identified. DP-TOSS (Dynamic Programming for determining the Topology Of Secondary Structures) is one tool that attempts to automate the creation of this mapping. By treating the correspondence between the detected structures and the structures predicted from sequence data as a constraint graph problem DP-TOSS achieved good accuracy in its original iteration. In this paper, we propose modifications to the scoring methodology of DP-TOSS to improve its accuracy. Three scoring schemes were applied to DP-TOSS and tested: (i) a skeleton-based scoring function; (ii) a geometry-based analytical function; and (iii) a multi-well potential energy-based function. A test of 25 proteins shows that a combination of these schemes can improve the performance of DP-TOSS to solve the topology determination problem for macromolecule proteins. Full article

(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))

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20 pages, 3203 KiB

Open AccessArticle

From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction

by Nasrin Akhter and Amarda Shehu

Molecules 2018, 23(1), 216; https://doi.org/10.3390/molecules23010216 - 19 Jan 2018

Cited by 30 | Viewed by 5461

Abstract

Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and [...] Read more.

Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins. Full article

(This article belongs to the Special Issue Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017))

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