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Take and Give: Protein Structure Analysis and Prediction with Statistical Scoring Functions

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 18666

Special Issue Editors

Prof. Dr. Peter Lackner

E-Mail Website
Guest Editor
Department of Molecular Biology, University of Salzburg, Salzburg, Austria
Prof. Dr. Markus Wiederstein

E-Mail Website
Guest Editor
Department of Molecular Biology, University of Salzburg, Salzburg, Austria

Special Issue Information

Dear Colleagues,

The PDB database provides more than 150,000 entries for biological macromolecular structures. The vast majority of the entries comprise proteins. Thus, we can resort to a large dataset which encodes information about sequence–structure–function relationships. Many bioinformatics approaches take advantage of this information and utilize it for a wealth of basic biological, biochemical, and biophysical problems. A well-established key approach is the statistical analysis of experimentally resolved structures for the subsequent derivation of statistical scoring functions (SSFs, also referred to as statistical energy functions, knowledge-based potentials or mean force potentials). Such SSFs are employed in numerous bioinformatics methods, e.g., for the assessment of experimentally determined structures or in the prediction of 3D protein structures, protein–protein interactions, protein–ligand interactions, protein stability, and many more. Methods may either utilize SSFs alone or combine them with physics-based force fields and employ different optimization or machine learning techniques.

The aim of this Special Issue is to focus on some of the most recent and interesting developments in SSF-based bioinformatics methods and their application in the analysis and prediction of biological macromolecular structures.

Prof. Dr. Peter Lackner
Prof. Dr. Markus Wiederstein
Guest Editors

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Published Papers (6 papers)

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Editorial

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2 pages, 146 KiB  
Editorial
Protein Structure Analysis and Prediction with Statistical Scoring Functions
by Peter Lackner and Markus Wiederstein
Int. J. Mol. Sci. 2021, 22(16), 8665; https://doi.org/10.3390/ijms22168665 - 12 Aug 2021
Viewed by 1402
Abstract
The PDB database provides more than 150,000 entries for biological macromolecular structures [...] Full article
(This article belongs to the Special Issue Take and Give: Protein Structure Analysis and Prediction with Statistical Scoring Functions)

Research

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13 pages, 9515 KiB  
Article
Prediction of Protein–Protein Binding Interactions in Dimeric Coiled Coils by Information Contained in Folding Energy Landscapes
by Panagiota S. Georgoulia and Sinisa Bjelic
Int. J. Mol. Sci. 2021, 22(3), 1368; https://doi.org/10.3390/ijms22031368 - 29 Jan 2021
Cited by 5 | Viewed by 2176
Abstract
Coiled coils represent the simplest form of a complex formed between two interacting protein partners. Their extensive study has led to the development of various methods aimed towards the investigation and design of complex forming interactions. Despite the progress that has been made [...] Read more.
Coiled coils represent the simplest form of a complex formed between two interacting protein partners. Their extensive study has led to the development of various methods aimed towards the investigation and design of complex forming interactions. Despite the progress that has been made to predict the binding affinities for protein complexes, and specifically those tailored towards coiled coils, many challenges still remain. In this work, we explore whether the information contained in dimeric coiled coil folding energy landscapes can be used to predict binding interactions. Using the published SYNZIP dataset, we start from the amino acid sequence, to simultaneously fold and dock approximately 1000 coiled coil dimers. Assessment of the folding energy landscapes showed that a model based on the calculated number of clusters for the lowest energy structures displayed a signal that correlates with the experimentally determined protein interactions. Although the revealed correlation is weak, we show that such correlation exists; however, more work remains to establish whether further improvements can be made to the presented model. Full article
(This article belongs to the Special Issue Take and Give: Protein Structure Analysis and Prediction with Statistical Scoring Functions)
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14 pages, 401 KiB  
Article
MHCII3D—Robust Structure Based Prediction of MHC II Binding Peptides
by Josef Laimer and Peter Lackner
Int. J. Mol. Sci. 2021, 22(1), 12; https://doi.org/10.3390/ijms22010012 - 22 Dec 2020
Cited by 10 | Viewed by 3116
Abstract
Knowledge of MHC II binding peptides is highly desired in immunological research, particularly in the context of cancer, autoimmune diseases, or allergies. The most successful prediction methods are based on machine learning methods trained on sequences of experimentally characterized binding peptides. Here, we [...] Read more.
Knowledge of MHC II binding peptides is highly desired in immunological research, particularly in the context of cancer, autoimmune diseases, or allergies. The most successful prediction methods are based on machine learning methods trained on sequences of experimentally characterized binding peptides. Here, we describe a complementary approach called MHCII3D, which is based on structural scaffolds of MHC II-peptide complexes and statistical scoring functions (SSFs). The MHC II alleles reported in the Immuno Polymorphism Database are processed in a dedicated 3D-modeling pipeline providing a set of scaffold complexes for each distinct allotype sequence. Antigen protein sequences are threaded through the scaffolds and evaluated by optimized SSFs. We compared the predictive power of MHCII3D with different sequence-based machine learning methods. The Pearson correlation to experimentally determine IC50 values for MHC II Automated Server Benchmarks data sets from IEDB (Immune Epitope Database) is 0.42, which is in the competitor methods range. We show that MHCII3D is quite robust in leaving one molecule out tests and is therefore not prone to overfitting. Finally, we provide evidence that MHCII3D can complement the current sequence-based methods and help to identify problematic entries in IEDB. Scaffolds and MHCII3D executables can be freely downloaded from our web pages. Full article
(This article belongs to the Special Issue Take and Give: Protein Structure Analysis and Prediction with Statistical Scoring Functions)
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16 pages, 3228 KiB  
Article
Protein–Protein Interactions Efficiently Modeled by Residue Cluster Classes
by Albros Hermes Poot Velez, Fernando Fontove and Gabriel Del Rio
Int. J. Mol. Sci. 2020, 21(13), 4787; https://doi.org/10.3390/ijms21134787 - 6 Jul 2020
Cited by 2 | Viewed by 3173
Abstract
Predicting protein–protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, [...] Read more.
Predicting protein–protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, among others. In the current work, we provide a representative protein structure that is amenable to PPI classification using machine learning approaches, referred to as residue cluster classes. Through sampling and optimization, we identified the best algorithm–parameter pair to classify PPI from more than 360 different training sets. We tested these classifiers against PPI datasets that were not included in the training set but shared sequence similarity with proteins in the training set to reproduce the situation of most proteins sharing sequence similarity with others. We identified a model with almost no PPI error (96–99% of correctly classified instances) and showed that residue cluster classes of protein pairs displayed a distinct pattern between positive and negative protein interactions. Our results indicated that residue cluster classes are structural features relevant to model PPI and provide a novel tool to mathematically model the protein structure/function relationship. Full article
(This article belongs to the Special Issue Take and Give: Protein Structure Analysis and Prediction with Statistical Scoring Functions)
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13 pages, 8926 KiB  
Article
Genomic Analysis of Intrinsically Disordered Proteins in the Genus Camelus
by Manal A. Alshehri, Manee M. Manee, Mohamed B. Al-Fageeh and Badr M. Al-Shomrani
Int. J. Mol. Sci. 2020, 21(11), 4010; https://doi.org/10.3390/ijms21114010 - 3 Jun 2020
Cited by 3 | Viewed by 2822
Abstract
Intrinsically disordered proteins/regions (IDPs/IDRs) fail to fold completely into 3D structures, but have major roles in determining protein function. While natively disordered proteins/regions have been found to fulfill a wide variety of primary cellular roles, the functions of many disordered proteins in numerous [...] Read more.
Intrinsically disordered proteins/regions (IDPs/IDRs) fail to fold completely into 3D structures, but have major roles in determining protein function. While natively disordered proteins/regions have been found to fulfill a wide variety of primary cellular roles, the functions of many disordered proteins in numerous species remain to be uncovered. Here, we perform the first large-scale study of IDPs/IDRs in the genus Camelus, one of the most important mammalians in Asia and North Africa, in order to explore the biological roles of these proteins. The study includes the prediction of disordered proteins/regions in Camelus species and in humans using multiple state-of-the-art prediction tools. Additionally, we provide a comparative analysis of Camelus and Homo sapiens IDPs/IDRs for the sake of highlighting the distinctive use of disorder in each genus. Our findings indicate that the human proteome is more disordered than the Camelus proteome. Gene Ontology analysis also revealed that Camelus IDPs are enriched in glutathione catabolism and lactose biosynthesis. Full article
(This article belongs to the Special Issue Take and Give: Protein Structure Analysis and Prediction with Statistical Scoring Functions)
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Review

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13 pages, 3809 KiB  
Review
Evolution as a Guide to Designing xeno Amino Acid Alphabets
by Christopher Mayer-Bacon, Neyiasuo Agboha, Mickey Muscalli and Stephen Freeland
Int. J. Mol. Sci. 2021, 22(6), 2787; https://doi.org/10.3390/ijms22062787 - 10 Mar 2021
Cited by 10 | Viewed by 5346
Abstract
Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life’s standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, “Protein Structure Analysis and Prediction with [...] Read more.
Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life’s standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, “Protein Structure Analysis and Prediction with Statistical Scoring Functions”, emerges from the ways in which current bioinformatics currently lacks empirical science when it comes to xenoproteins composed largely or entirely of amino acids from beyond the standard genetic code. Our intent is to present new perspectives on existing data from two different frontiers in order to suggest fresh ways in which their findings complement one another. These frontiers are origins/astrobiology research into the emergence of the standard amino acid alphabet, and empirical xenoprotein synthesis. Full article
(This article belongs to the Special Issue Take and Give: Protein Structure Analysis and Prediction with Statistical Scoring Functions)
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