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Novel Approaches in Protein Structure Prediction
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Novel Approaches in Protein Structure Prediction

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 6193

Special Issue Editors

Prof. Dr. Jozef Adam Liwo

E-Mail Website
Guest Editor
Faculty of Chemistry, University of Gdansk, 80-308 Gdansk, Poland
Interests: coarse-grained approaches to biomolecules; development of molecular simulation methods; physics-based and data-assisted protein structure prediction; modeling of protein dynamics
Prof. Dr. Cezary Czaplewski

E-Mail Website
Guest Editor
Department of Theoretical Chemistry, Faculty of Chemistry, University of Gdańsk, 80-308 Gdańsk, Poland
Interests: computer simulations; molecular modeling; molecular dynamics; global optimization; replica exchange; coarse-grained force fields; protein structure prediction; theoretical investigation of protein folding mechanisms; hydrophobic interactions; simulations of ionic liquids

Special Issue Information

Dear Colleagues,

The biological functions of proteins are determined by their structure and dynamics, and structural defects are often the cause of lethal diseases, such as cancer and Alzheimer’s disease. Therefore, knowledge of the structure and motion characteristics of these molecules is a necessary condition to learn the origin of diseases and design effective drugs and therapies. With thousands of new protein sequences discovered every year, experimental methods of structure determination are insufficient and theoretical methods for protein–structure predictions have become one of the pillars of structural biology.


This Special Issue will comprise papers about state-of-the-art methods for the prediction of protein structures, including the most successful, so far, such as comparative modeling, fragment assembly, threading, and physics-based methods, in which the native protein structure is searched as the structure with the lowest free energy, as well as combinations of these methods. Prediction of disordered regions of proteins and prediction assisted by sparse nuclear-magnetic resonance (NMR), small X-ray angle scattering (SAXS), and cryomicroscopy data, as well as prediction assisted by residue-residue contacts will also be addressed.

Prof. Adam Liwo
Prof. Cezary Czaplewski
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 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. Genes 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 2600 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

  • protein structure prediction
  • comparative modeling
  • threading
  • fragment assembly
  • physics-based modeling
  • data-assisted prediction
  • disordered regions of proteins

Published Papers (2 papers)

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Research

15 pages, 2113 KiB  
Article
Identification of Essential Proteins Based on Improved HITS Algorithm
by Xiujuan Lei, Siguo Wang and Fangxiang Wu
Genes 2019, 10(2), 177; https://doi.org/10.3390/genes10020177 - 25 Feb 2019
Cited by 13 | Viewed by 3000
Abstract
Essential proteins are critical to the development and survival of cells. Identifying and analyzing essential proteins is vital to understand the molecular mechanisms of living cells and design new drugs. With the development of high-throughput technologies, many protein–protein interaction (PPI) data are available, [...] Read more.
Essential proteins are critical to the development and survival of cells. Identifying and analyzing essential proteins is vital to understand the molecular mechanisms of living cells and design new drugs. With the development of high-throughput technologies, many protein–protein interaction (PPI) data are available, which facilitates the studies of essential proteins at the network level. Up to now, although various computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a novel method by applying Hyperlink-Induced Topic Search (HITS) on weighted PPI networks to detect essential proteins, named HSEP. First, an original undirected PPI network is transformed into a bidirectional PPI network. Then, both biological information and network topological characteristics are taken into account to weighted PPI networks. Pieces of biological information include gene expression data, Gene Ontology (GO) annotation and subcellular localization. The edge clustering coefficient is represented as network topological characteristics to measure the closeness of two connected nodes. We conducted experiments on two species, namely Saccharomyces cerevisiae and Drosophila melanogaster, and the experimental results show that HSEP outperformed some state-of-the-art essential proteins detection techniques. Full article
(This article belongs to the Special Issue Novel Approaches in Protein Structure Prediction)
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17 pages, 1129 KiB  
Article
Reoptimized UNRES Potential for Protein Model Quality Assessment
by Eshel Faraggi, Pawel Krupa, Magdalena A. Mozolewska, Adam Liwo and Andrzej Kloczkowski
Genes 2018, 9(12), 601; https://doi.org/10.3390/genes9120601 - 03 Dec 2018
Cited by 1 | Viewed by 2857
Abstract
Ranking protein structure models is an elusive problem in bioinformatics. These models are evaluated on both the degree of similarity to the native structure and the folding pathway. Here, we simulated the use of the coarse-grained UNited RESidue (UNRES) force field as a [...] Read more.
Ranking protein structure models is an elusive problem in bioinformatics. These models are evaluated on both the degree of similarity to the native structure and the folding pathway. Here, we simulated the use of the coarse-grained UNited RESidue (UNRES) force field as a tool to choose the best protein structure models for a given protein sequence among a pool of candidate models, using server data from the CASP11 experiment. Because the original UNRES was optimized for Molecular Dynamics simulations, we reoptimized UNRES using a deep feed-forward neural network, and we show that introducing additional descriptive features can produce better results. Overall, we found that the reoptimized UNRES performs better in selecting the best structures and tracking protein unwinding from its native state. We also found a relatively poor correlation between UNRES values and the model’s Template Modeling Score (TMS). This is remedied by reoptimization. We discuss some cases where our reoptimization procedure is useful. Full article
(This article belongs to the Special Issue Novel Approaches in Protein Structure Prediction)
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