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Protein, RNA, and Genome Structure Prediction

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 12118

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Special Issue Editors

Prof. Dr. Daisuke Kihara

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Guest Editor

Department of Biological Sciences, Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
Interests: protein docking; protein structure prediction; protein structure modeling for cryo-electron microscopy; protein function prediction; virtual drug screening; protein bioinformatics

Prof. Dr. Jianlin Cheng

E-Mail Website
Guest Editor

Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Interests: protein structure prediction; 3D genome modeling; protein function prediction; biological network modeling; machine learning

Special Issue Information

Dear Colleagues,

Significant progress has been observed in the protein structure prediction field in the recent years. It is partly due to effective usages of machine learning methods including deep learning, which made substantial improvement in many steps for the structure prediction, such as inter-residue contact and distance prediction. Keeping pace with protein structure prediction, there are many important developments achieved in related areas, such as protein-protein docking, RNA structure prediction, and 3D Genome prediction. This special issue is to capture the new exciting developments in these structure prediction fields.

Prof. Dr. Daisuke Kihara
Prof. Dr. Jianlin Cheng
Guest Editors

Manuscript Submission Information

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Keywords

Protein structure prediction
Protein structure model quality assessment
Protein-protein docking prediction
Protein structure refinement
Protein contact prediction
Protein distance prediction
Experimental data-assisted protein modeling
RNA structure prediction
3D genome structure modeling

Published Papers (6 papers)

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Research

20 pages, 3157 KiB

Open AccessArticle

Application of Homology Modeling by Enhanced Profile–Profile Alignment and Flexible-Fitting Simulation to Cryo-EM Based Structure Determination

by Yu Yamamori and Kentaro Tomii

Int. J. Mol. Sci. 2022, 23(4), 1977; https://doi.org/10.3390/ijms23041977 - 10 Feb 2022

Cited by 3 | Viewed by 1731

Abstract

Application of cryo-electron microscopy (cryo-EM) is crucially important for ascertaining the atomic structure of large biomolecules such as ribosomes and protein complexes in membranes. Advances in cryo-EM technology and software have made it possible to obtain data with near-atomic resolution, but the method is still often capable of producing only a density map with up to medium resolution, either partially or entirely. Therefore, bridging the gap separating the density map and the atomic model is necessary. Herein, we propose a methodology for constructing atomic structure models based on cryo-EM maps with low-to-medium resolution. The method is a combination of sensitive and accurate homology modeling using our profile–profile alignment method with a flexible-fitting method using molecular dynamics simulation. As described herein, this study used benchmark applications to evaluate the model constructions of human two-pore channel 2 (one target protein in CASP13 with its structure determined using cryo-EM data) and the overall structure of Enterococcus hirae V-ATPase complex. Full article

(This article belongs to the Special Issue Protein, RNA, and Genome Structure Prediction)

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11 pages, 1440 KiB

Open AccessArticle

by Xianjin Xu and Xiaoqin Zou

Int. J. Mol. Sci. 2021, 22(22), 12320; https://doi.org/10.3390/ijms222212320 - 15 Nov 2021

Cited by 7 | Viewed by 1633

Abstract

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies. Full article

(This article belongs to the Special Issue Protein, RNA, and Genome Structure Prediction)

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16 pages, 3381 KiB

Open AccessArticle

Four-Dimensional Chromosome Structure Prediction

by Max Highsmith and Jianlin Cheng

Int. J. Mol. Sci. 2021, 22(18), 9785; https://doi.org/10.3390/ijms22189785 - 10 Sep 2021

Cited by 4 | Viewed by 1894

Abstract

Chromatin conformation plays an important role in a variety of genomic processes, including genome replication, gene expression, and gene methylation. Hi-C data is frequently used to analyze structural features of chromatin, such as AB compartments, topologically associated domains, and 3D structural models. Recently, the genomics community has displayed growing interest in chromatin dynamics. Here, we present 4DMax, a novel method, which uses time-series Hi-C data to predict dynamic chromosome conformation. Using both synthetic data and real time-series Hi-C data from processes, such as induced pluripotent stem cell reprogramming and cardiomyocyte differentiation, we construct smooth four-dimensional models of individual chromosomes. These predicted 4D models effectively interpolate chromatin position across time, permitting prediction of unknown Hi-C contact maps at intermittent time points. Furthermore, 4DMax correctly recovers higher order features of chromatin, such as AB compartments and topologically associated domains, even at time points where Hi-C data is not made available to the algorithm. Contact map predictions made using 4DMax outperform naïve numerical interpolation in 87.7% of predictions on the induced pluripotent stem cell dataset. A/B compartment profiles derived from 4DMax interpolation showed higher similarity to ground truth than at least one profile generated from a neighboring time point in 100% of induced pluripotent stem cell experiments. Use of 4DMax may alleviate the cost of expensive Hi-C experiments by interpolating intermediary time points while also providing valuable visualization of dynamic chromatin changes. Full article

(This article belongs to the Special Issue Protein, RNA, and Genome Structure Prediction)

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21 pages, 91223 KiB

Open AccessArticle

Inferring Single-Cell 3D Chromosomal Structures Based on the Lennard-Jones Potential

by Mengsheng Zha, Nan Wang, Chaoyang Zhang and Zheng Wang

Int. J. Mol. Sci. 2021, 22(11), 5914; https://doi.org/10.3390/ijms22115914 - 31 May 2021

Cited by 3 | Viewed by 2303

Abstract

Reconstructing three-dimensional (3D) chromosomal structures based on single-cell Hi-C data is a challenging scientific problem due to the extreme sparseness of the single-cell Hi-C data. In this research, we used the Lennard-Jones potential to reconstruct both 500 kb and high-resolution 50 kb chromosomal structures based on single-cell Hi-C data. A chromosome was represented by a string of 500 kb or 50 kb DNA beads and put into a 3D cubic lattice for simulations. A 2D Gaussian function was used to impute the sparse single-cell Hi-C contact matrices. We designed a novel loss function based on the Lennard-Jones potential, in which the

ε

value, i.e., the well depth, was used to indicate how stable the binding of every pair of beads is. For the bead pairs that have single-cell Hi-C contacts and their neighboring bead pairs, the loss function assigns them stronger binding stability. The Metropolis–Hastings algorithm was used to try different locations for the DNA beads, and simulated annealing was used to optimize the loss function. We proved the correctness and validness of the reconstructed 3D structures by evaluating the models according to multiple criteria and comparing the models with 3D-FISH data. Full article

(This article belongs to the Special Issue Protein, RNA, and Genome Structure Prediction)

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17 pages, 3651 KiB

Open AccessArticle

Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

by Cheng-Peng Zhou, Di Wang, Xiaoyong Pan and Hong-Bin Shen

Int. J. Mol. Sci. 2021, 22(9), 4408; https://doi.org/10.3390/ijms22094408 - 23 Apr 2021

Cited by 1 | Viewed by 1735

Abstract

Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0. Full article

(This article belongs to the Special Issue Protein, RNA, and Genome Structure Prediction)

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33 pages, 4361 KiB

Open AccessArticle

CBCR: A Curriculum Based Strategy For Chromosome Reconstruction

by Van Hovenga and Oluwatosin Oluwadare

Int. J. Mol. Sci. 2021, 22(8), 4140; https://doi.org/10.3390/ijms22084140 - 16 Apr 2021

Viewed by 1803

Abstract

In this paper, we introduce a novel algorithm that aims to estimate chromosomes’ structure from their Hi-C contact data, called Curriculum Based Chromosome Reconstruction (CBCR). Specifically, our method performs this three dimensional reconstruction using cis-chromosomal interactions from Hi-C data. CBCR takes intra-chromosomal Hi-C [...] Read more.

x y z

coordinates that estimate the chromosome’s three dimensional structure in the form of a .pdb file. The algorithm relies on progressively training a distance-restraint-based algorithm with a strategy we refer to as curriculum learning. Curriculum learning divides the Hi-C data into classes based on contact frequency and progressively re-trains the distance-restraint algorithm based on the assumed importance of each curriculum in predicting the underlying chromosome structure. The distance-restraint algorithm relies on a modification of a Gaussian maximum likelihood function that scales probabilities based on the importance of features. We evaluate the performance of CBCR on both simulated and actual Hi-C data and perform validation on FISH, HiChIP, and ChIA-PET data as well. We also compare the performance of CBCR to several current methods. Our analysis shows that the use of curricula affects the rate of convergence of the optimization while decreasing the computational cost of our distance-restraint algorithm. Also, CBCR is more robust to increases in data resolution and therefore yields superior reconstruction accuracy of higher resolution data than all other methods in our comparison. Full article

(This article belongs to the Special Issue Protein, RNA, and Genome Structure Prediction)

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