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Special Issue "Selected Papers from the 10th Computational Structural Bioinformatics Workshop (CSBW-2017)"

A special issue of Molecules (ISSN 1420-3049).

Deadline for manuscript submissions: closed (20 December 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Amarda Shehu

Department of Computer Science, George Mason University, Fairfax, Virginia, USA
Website | E-Mail
Interests: stochastic optimization; macromolecular structure and dynamics; protein modeling
Guest Editor
Prof. Dr. Nurit Haspel

Department of Computer Science, University of Massachusetts at Boston, Boston, Massachusetts, USA
Website | E-Mail
Interests: computational structural biology and structural bioinformatics; nano-design; protein dynamics

Special Issue Information

Dear Colleagues,

This Special Issue is related to the 10th Computational Structural Biology Workshop (CSBW), which will be held on 20 August, 2017, co-located with the Association for Computing Machinery (ACM) Bioinformatics and Computational Biology (BCB) Conference in Boston, Massachusetts.

The rapid accumulation of macromolecular structures presents a unique set of challenges and opportunities in the analysis, comparison, modeling, and prediction of macromolecular structures and interactions. CSBW annually brings together researchers with expertise in bioinformatics, computational biology, structural biology, data mining, optimization and high performance computing to discuss new results, techniques, and research problems in computational structural biology and structural bioinformatics. The workshop novel methodological contributions driven by important biological problems and furthering our knowledge and understanding of the role of macromolecular structure in biological processes.

Participants of CSBW 2017 are cordially invited to contribute original research papers to this Special Issue of Molecules.

Prof. Amarda Shehu
Prof. Nurit Haspel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational structural biology
  • structural genomics
  • macromolecular structure and function
  • structural dynamics
  • interactions and assembly

Published Papers (5 papers)

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Research

Open AccessArticle Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods
Molecules 2018, 23(2), 373; doi:10.3390/molecules23020373
Received: 13 December 2017 / Revised: 22 January 2018 / Accepted: 1 February 2018 / Published: 9 February 2018
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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
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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
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Open AccessArticle Exploring Protein Cavities through Rigidity Analysis
Molecules 2018, 23(2), 351; doi:10.3390/molecules23020351 (registering DOI)
Received: 23 December 2017 / Revised: 25 January 2018 / Accepted: 31 January 2018 / Published: 7 February 2018
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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
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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
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Open AccessArticle Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability
Molecules 2018, 23(2), 251; doi:10.3390/molecules23020251
Received: 24 December 2017 / Revised: 15 January 2018 / Accepted: 19 January 2018 / Published: 27 January 2018
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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
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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
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Open AccessArticle Analytical Approaches to Improve Accuracy in Solving the Protein Topology Problem
Molecules 2018, 23(2), 28; doi:10.3390/molecules23020028
Received: 5 December 2017 / Revised: 19 January 2018 / Accepted: 19 January 2018 / Published: 23 January 2018
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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
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Open AccessArticle From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction
Molecules 2018, 23(1), 216; doi:10.3390/molecules23010216
Received: 8 December 2017 / Revised: 6 January 2018 / Accepted: 11 January 2018 / Published: 19 January 2018
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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
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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
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