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Special Issue "Selected Papers from the 12nd Computational Structural Bioinformatics Workshop (CSBW-2019)"

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

Deadline for manuscript submissions: closed (15 January 2020).

Special Issue Editors

Dr. Nurit Haspel
E-Mail Website
Guest Editor
Department of Computer Science, University of Massachusetts Boston, 100 Morrissey Blvd. Boston MA 02125, USA
Interests: protein structure; dynamics and function prediction; structural bioinformatics; algorithms; statistical learning
Special Issues and Collections in MDPI journals
Dr. Lin Chen
E-Mail Website
Guest Editor
Department of Computer Science, Valdosta State University, Valdosta, GA, USA
Interests: medical image processing; computational biology; human trafficking; high-performance computing; Monte Carlo simulation
Dr. Dong Si
E-Mail Website
Guest Editor
University of Washington, Bothell, 18115 Campus Way NE, Bothell, WA 98011, USA
Interests: machine learning and data mining; biomedical informatics; health informatics

Special Issue Information

Dear Colleagues,

This Special Issue is related to the 12th Computational Structural Biology Workshop (CSBW), which will be held on 8 September 2019, co-located with the Association for Computing Machinery (ACM) Bioinformatics and Computational Biology (BCB) Conference in Buffalo, NY.

The rapid accumulation of macromolecular structures presents a unique set of challenges and opportunities in the analysis, comparison, modeling, and prediction of biomolecules and their 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 bioinformatics. The novel methodological contributions presented at CSBW are driven by important biological problems and further our knowledge and understanding of the role of macromolecular structure in biological processes.

Participants of CSBW 2019 are cordially invited to contribute original research papers to this Special Issue of Molecules.

Dr. Nurit Haspel
Dr. Lin Chen
Dr. Dong Si
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 semimonthly 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 2000 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
  • Macromolecular Structure prediction
  • Structural Genomic
  • Machine Learning in Protein Structure Prediction
  • Biomolecular Simulations and Modeling

Published Papers (4 papers)

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Research

Article
Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering
Molecules 2020, 25(9), 2228; https://doi.org/10.3390/molecules25092228 - 09 May 2020
Cited by 2 | Viewed by 678
Abstract
Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood [...] Read more.
Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. In this paper, we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. Evaluations are related on both benchmark and CASP target proteins. Structure ensembles subjected to the proposed approach and the source code of the proposed approach are publicly-available at the links provided in Section 1. Full article
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Article
Outlier Profiles of Atomic Structures Derived from X-ray Crystallography and from Cryo-Electron Microscopy
by and
Molecules 2020, 25(7), 1540; https://doi.org/10.3390/molecules25071540 - 28 Mar 2020
Cited by 1 | Viewed by 759
Abstract
Background: As more protein atomic structures are determined from cryo-electron microscopy (cryo-EM) density maps, validation of such structures is an important task. Methods: We applied a histogram-based outlier score (HBOS) to six sets of cryo-EM atomic structures and five sets of X-ray atomic [...] Read more.
Background: As more protein atomic structures are determined from cryo-electron microscopy (cryo-EM) density maps, validation of such structures is an important task. Methods: We applied a histogram-based outlier score (HBOS) to six sets of cryo-EM atomic structures and five sets of X-ray atomic structures, including one derived from X-ray data with better than 1.5 Å resolution. Cryo-EM data sets contain structures released by December 2016 and those released between 2017 and 2019, derived from resolution ranges 0–4 Å and 4–6 Å respectively. Results: The distribution of HBOS values in five sets of X-ray structures show that HBOS is sensitive distinguishing sets of X-ray structures derived from different resolution ranges-higher than 1.5 Å, 1.5–2.0 Å, 2.0–2.5 Å, 2.5–3.0 Å, and 3.0–3.5 Å. The overall quality of cryo-EM structures is likely improved, as shown in a comparison of cryo-EM structures released before the end of 2016, those between 2017 and 2018, and those between 2018 and 2019. Our investigation shows that leucine (LEU) has a significantly higher rate of HBOS outliers than that of the reference data set (X-ray-1.5) and of other residue types in the cryo-EM data sets. HBOS was able to detect outliers for those residues that are currently marked as green in PDB validation reports. Conclusions: The HBOS profile of a dataset is a potential method to characterize the overall structural quality of the set. Residue LEU deserves special attention since it has a significantly higher HBOS outlier rate in sets of cryo-EM structures and those X-ray structures derived from X-ray data of lower than 2.5 Å resolutions. Most HBOS outlier residues from the EM-0-4-2019 set are located on loops for most types of residues. Full article
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Article
PETRA: Drug Engineering via Rigidity Analysis
Molecules 2020, 25(6), 1304; https://doi.org/10.3390/molecules25061304 - 12 Mar 2020
Viewed by 1429
Abstract
Rational drug design aims to develop pharmaceutical agents that impart maximal therapeutic benefits via their interaction with their intended biological targets. In the past several decades, advances in computational tools that inform wet-lab techniques have aided the development of a wide variety of [...] Read more.
Rational drug design aims to develop pharmaceutical agents that impart maximal therapeutic benefits via their interaction with their intended biological targets. In the past several decades, advances in computational tools that inform wet-lab techniques have aided the development of a wide variety of new medicines with high efficacies. Nonetheless, drug development remains a time and cost intensive process. In this work, we have developed a computational pipeline for assessing how individual atoms contribute to a ligand’s effect on the structural stability of a biological target. Our approach takes as input a protein-ligand resolved PDB structure file and systematically generates all possible ligand variants. We assess how the atomic-level edits to the ligand alter the drug’s effect via a graph theoretic rigidity analysis approach. We demonstrate, via four case studies of common drugs, the utility of our pipeline and corroborate our analyses with known biophysical properties of the medicines, as reported in the literature. Full article
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Article
Evaluating Autoencoder-Based Featurization and Supervised Learning for Protein Decoy Selection
Molecules 2020, 25(5), 1146; https://doi.org/10.3390/molecules25051146 - 04 Mar 2020
Cited by 2 | Viewed by 919
Abstract
Rapid growth in molecular structure data is renewing interest in featurizing structure. Featurizations that retain information on biological activity are particularly sought for protein molecules, where decades of research have shown that indeed structure encodes function. Research on featurization of protein structure is [...] Read more.
Rapid growth in molecular structure data is renewing interest in featurizing structure. Featurizations that retain information on biological activity are particularly sought for protein molecules, where decades of research have shown that indeed structure encodes function. Research on featurization of protein structure is active, but here we assess the promise of autoencoders. Motivated by rapid progress in neural network research, we investigate and evaluate autoencoders on yielding linear and nonlinear featurizations of protein tertiary structures. An additional reason we focus on autoencoders as the engine to obtain featurizations is the versatility of their architectures and the ease with which changes to architecture yield linear versus nonlinear features. While open-source neural network libraries, such as Keras, which we employ here, greatly facilitate constructing, training, and evaluating autoencoder architectures and conducting model search, autoencoders have not yet gained popularity in the structure biology community. Here we demonstrate their utility in a practical context. Employing autoencoder-based featurizations, we address the classic problem of decoy selection in protein structure prediction. Utilizing off-the-shelf supervised learning methods, we demonstrate that the featurizations are indeed meaningful and allow detecting active tertiary structures, thus opening the way for further avenues of research. Full article
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