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Biomolecules
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An Energy Landscape Perspective of Protein Structure Prediction and Analysis
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An Energy Landscape Perspective of Protein Structure Prediction and Analysis

A special issue of Biomolecules (ISSN 2218-273X).

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 5363

Special Issue Editor

Prof. Dr. Amarda Shehu

E-Mail Website
Guest Editor
Department of Computer Science, George Mason University, Fairfax, VA, USA
Interests: stochastic optimization; macromolecular structure and dynamics; protein modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the biomolecular structure recognized as central to understanding mechanisms in the cell, computational chemists and biophysicists have spent a significant amount of time on modeling and analyzing the relationship between macromolecular structure, dynamics, and function. In particular, due to the key role that protein molecules play in virtually any process in living cells, great effort across wet and dry laboratories has been dedicated to obtaining models of protein structure and dynamics as a first step toward understanding structure-mediated interactions.

The size and dimensionality of the structure space of even single-domain proteins continue to present outstanding challenges to scientific progress and discovery. Perhaps the most notable setting where such challenges have been harnessed into progress is that of template-free protein structure prediction, where the goal is to determine biologically-active tertiary structures of a protein sequence. Such advances have been primarily due to the design of sophisticated energetic models, molecular representations, and the molecular fragment replacement technique that is now the foundation of popular software frameworks.

The availability of such foundational models and techniques has allowed computational scientists to operate at a higher level and devise computational frameworks of ever-increasing exploration capabilities by building on powerful artificial intelligence algorithms for stochastic optimization and heuristic searches. Such algorithms have provided broad views of the structure space of a protein sequence and the associated energy landscape. More interestingly, the recognition that the protein energy landscape has been central to understanding the relationship between protein structure, dynamics, and function has motivated several recent approaches to the two facets of  template-free protein structure prediction, decoy generation, and decoy selection.

The goal of this Special Issue is to highlight recent algorithmic advances that address  the various challenges encountered in template-free protein structure prediction by leveraging the energy landscape view of the protein structure space. Other contributions sought in this Special Issue extend to any aspects of protein structure modeling and analysis that benefit from such a view. Critical reviews that synthesize the current research literature on protein structure modeling, prediction, and analysis and provide guidance for newcomers on emerging directions are also highly welcome.

Prof. Dr. Amarda Shehu
Guest Editor

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 submissions that pass pre-check are 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. Biomolecules 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 2700 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
  • Bioinformatics
  • Biophysics
  • Protein Structure, Dynamics, and Function
  • Protein Structure Modeling and Prediction
  • Energy Landscape
  • Stochastic Optimization
  • Machine Learning

Published Papers (2 papers)

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21 pages, 894 KiB  
Article
Reliable Generation of Native-Like Decoys Limits Predictive Ability in Fragment-Based Protein Structure Prediction
by Shaun M. Kandathil, Mario Garza-Fabre, Julia Handl and Simon C. Lovell
Biomolecules 2019, 9(10), 612; https://0-doi-org.brum.beds.ac.uk/10.3390/biom9100612 - 15 Oct 2019
Viewed by 2207
Abstract
Our previous work with fragment-assembly methods has demonstrated specific deficiencies in conformational sampling behaviour that, when addressed through improved sampling algorithms, can lead to more reliable prediction of tertiary protein structure when good fragments are available, and when score values can be relied [...] Read more.
Our previous work with fragment-assembly methods has demonstrated specific deficiencies in conformational sampling behaviour that, when addressed through improved sampling algorithms, can lead to more reliable prediction of tertiary protein structure when good fragments are available, and when score values can be relied upon to guide the search to the native basin. In this paper, we present preliminary investigations into two important questions arising from more difficult prediction problems. First, we investigated the extent to which native-like conformational states are generated during multiple runs of our search protocols. We determined that, in cases of difficult prediction, native-like decoys are rarely or never generated. Second, we developed a scheme for decoy retention that balances the objectives of retaining low-scoring structures and retaining conformationally diverse structures sampled during the course of the search. Our method succeeds at retaining more diverse sets of structures, and, for a few targets, more native-like solutions are retained as compared to our original, energy-based retention scheme. However, in general, we found that the rate at which native-like structural states are generated has a much stronger effect on eventual distributions of predictive accuracy in the decoy sets, as compared to the specific decoy retention strategy used. We found that our protocols show differences in their ability to access native-like states for some targets, and this may explain some of the differences in predictive performance seen between these methods. There appears to be an interaction between fragment sets and move operators, which influences the accessibility of native-like structures for given targets. Our results point to clear directions for further improvements in fragment-based methods, which are likely to enable higher accuracy predictions. Full article
(This article belongs to the Special Issue An Energy Landscape Perspective of Protein Structure Prediction and Analysis)
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21 pages, 1202 KiB  
Article
Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection
by Nasrin Akhter, Gopinath Chennupati, Kazi Lutful Kabir, Hristo Djidjev and Amarda Shehu
Biomolecules 2019, 9(10), 607; https://0-doi-org.brum.beds.ac.uk/10.3390/biom9100607 - 14 Oct 2019
Cited by 6 | Viewed by 2664
Abstract
The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging energy [...] Read more.
The energy landscape that organizes microstates of a molecular system and governs the underlying molecular dynamics exposes the relationship between molecular form/structure, changes to form, and biological activity or function in the cell. However, several challenges stand in the way of leveraging energy landscapes for relating structure and structural dynamics to function. Energy landscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins in them do not always correspond to stable structural states but are instead the result of inherent inaccuracies in semi-empirical molecular energy functions. Due to these challenges, energetics is typically ignored in computational approaches addressing long-standing central questions in computational biology, such as protein decoy selection. In the latter, the goal is to determine over a possibly large number of computationally-generated three-dimensional structures of a protein those structures that are biologically-active/native. In recent work, we have recast our attention on the protein energy landscape and its role in helping us to advance decoy selection. Here, we summarize some of our successes so far in this direction via unsupervised learning. More importantly, we further advance the argument that the energy landscape holds valuable information to aid and advance the state of protein decoy selection via novel machine learning methodologies that leverage supervised learning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitative evaluation of how leveraging protein energy landscapes advances an important problem in protein modeling. However, the ideas and concepts presented here are generally useful to make discoveries in studies aiming to relate molecular structure and structural dynamics to function. Full article
(This article belongs to the Special Issue An Energy Landscape Perspective of Protein Structure Prediction and Analysis)
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