scholarly journals De NovoPrediction of Human Chromosome Structures: Epigenetic Marking Patterns Encode Genome Architecture

2017 ◽  
Author(s):  
Michele Di Pierro ◽  
Ryan R. Cheng ◽  
Erez Lieberman Aiden ◽  
Peter G. Wolynes ◽  
Jos&eacute N. Onuchic
Keyword(s):  
Neural Network ◽  
Phase Separation ◽  
Structure Prediction ◽  
De Novo ◽  
Three Dimensional ◽  
Sufficient Information ◽  
Proximity Ligation ◽  
Genomic Annotation ◽  
Structural Ensembles ◽  
Chromatin Structural

Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predictedde novousing epigenetic data derived from ChIP-Seq. We exploit the idea that chromosomes encode a one-dimensional sequence of chromatin structural types. Interactions between these chromatin types determine the three-dimensional (3D) structural ensemble of chromosomes through a process similar to phase separation. First, a recurrent neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization (MiChroM) in order to generate an ensemble of 3D chromosome conformations. After training the model, dubbed MEGABASE (Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles), on odd numbered chromosomes, we predict the chromatin type sequences and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps as well as distances measured using 3D FISH experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and thatde novostructure prediction for whole genomes may be increasingly possible.

scholarly journals De novo prediction of human chromosome structures: Epigenetic marking patterns encode genome architecture

2017 ◽  
Vol 114 (46) ◽  
pp. 12126-12131 ◽  
Author(s):  
Michele Di Pierro ◽  
Ryan R. Cheng ◽  
Erez Lieberman Aiden ◽  
Peter G. Wolynes ◽  
José N. Onuchic
Keyword(s):  
Neural Network ◽  
Phase Separation ◽  
Structure Prediction ◽  
De Novo ◽  
Sufficient Information ◽  
Proximity Ligation ◽  
Genomic Annotation ◽  
Structural Ensembles ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Chromatin Structural

Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from chromatin immunoprecipitation-sequencing (ChIP-Seq). We exploit the idea that chromosomes encode a 1D sequence of chromatin structural types. Interactions between these chromatin types determine the 3D structural ensemble of chromosomes through a process similar to phase separation. First, a neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization [Minimal Chromatin Model (MiChroM)] to generate an ensemble of 3D chromosome conformations at a resolution of 50 kilobases (kb). After training the model, dubbed Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles (MEGABASE), on odd-numbered chromosomes, we predict the sequences of chromatin types and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps, as well as distances measured using 3D fluorescence in situ hybridization (FISH) experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible.

scholarly journals FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution

2020 ◽  
Author(s):  
Jiangyan Feng ◽  
Diwakar Shukla
Keyword(s):  
Ligand Binding ◽  
Structure Prediction ◽  
De Novo ◽  
Three Dimensional ◽  
Sequence Information ◽  
Structural Constraints ◽  
Complex Signals ◽  
Residue Contacts ◽  
Small Clusters ◽  
Functional Mechanisms

AbstractProteins are dynamic molecules which perform diverse molecular functions by adopting different three-dimensional structures. Recent progress in residue-residue contacts prediction opens up new avenues for the de novo protein structure prediction from sequence information. However, it is still difficult to predict more than one conformation from residue-residue contacts alone. This is due to the inability to deconvolve the complex signals of residue-residue contacts, i.e. spatial contacts relevant for protein folding, conformational diversity, and ligand binding. Here, we introduce a machine learning based method, called FingerprintContacts, for extending the capabilities of residue-residue contacts. This algorithm leverages the features of residue-residue contacts, that is, (1) a single conformation outperforms the others in the structural prediction using all the top ranking residue-residue contacts as structural constraints, and (2) conformation specific contacts rank lower and constitute a small fraction of residue-residue contacts. We demonstrate the capabilities of FingerprintContacts on eight ligand binding proteins with varying conformational motions. Furthermore, FingerprintContacts identifies small clusters of residue-residue contacts which are preferentially located in the dynamically fluctuating regions. With the rapid growth in protein sequence information, we expect FingerprintContacts to be a powerful first step in structural understanding of protein functional mechanisms.

scholarly journals Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction

2016 ◽  
Vol 24 (4) ◽  
pp. 577-607 ◽  
Author(s):  
Mario Garza-Fabre ◽  
Shaun M. Kandathil ◽  
Julia Handl ◽  
Joshua Knowles ◽  
Simon C. Lovell
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
De Novo ◽  
Memetic Algorithm ◽  
Scale Up ◽  
Three Dimensional ◽  
Limiting Factors ◽  
Genetic Operators

Computational approaches to de novo protein tertiary structure prediction, including those based on the preeminent “fragment-assembly” technique, have failed to scale up fully to larger proteins (on the order of 100 residues and above). A number of limiting factors are thought to contribute to the scaling problem over and above the simple combinatorial explosion, but the key ones relate to the lack of exploration of properly diverse protein folds, and to an acute form of “deception” in the energy function, whereby low-energy conformations do not reliably equate with native structures. In this article, solutions to both of these problems are investigated through a multistage memetic algorithm incorporating the successful Rosetta method as a local search routine. We found that specialised genetic operators significantly add to structural diversity and that this translates well to reaching low energies. The use of a generalised stochastic ranking procedure for selection enables the memetic algorithm to handle and traverse deep energy wells that can be considered deceptive, which further adds to the ability of the algorithm to obtain a much-improved diversity of folds. The results should translate to a tangible improvement in the performance of protein structure prediction algorithms in blind experiments such as CASP, and potentially to a further step towards the more challenging problem of predicting the three-dimensional shape of large proteins.

scholarly journals MSNovelist: De novo structure generation from mass spectra

2021 ◽  
Author(s):  
Michael A Stravs ◽  
Kai Dührkop ◽  
Sebastian Böcker ◽  
Nicola Zamboni
Keyword(s):  
Neural Network ◽  
Small Molecules ◽  
Natural Product ◽  
Structure Prediction ◽  
Mass Spectra ◽  
Unsolved Problem ◽  
De Novo ◽  
Reference Dataset ◽  
Structure Generation ◽  
Spectral Libraries

Structural elucidation of small molecules de novo from mass spectra is a longstanding, yet unsolved problem. Current methods rely on finding some similarity with spectra of known compounds deposited in spectral libraries, but do not solve the problem of predicting structures for novel or poorly represented compound classes. We present MSNovelist that combines fingerprint prediction with an encoder-decoder neural network to generate structures de novo from fragment spectra. In evaluation, MSNovelist correctly reproduced 61% of database annotations for a GNPS reference dataset. In a bryophyte MS2 dataset, our de novo structure prediction substantially outscored the best database candidate for seven features, and a potential novel natural product with a flavonoid core was identified. MSNovelist allows predicting structures solely from MS2 data, and is therefore ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.

scholarly journals Protofold II: Enhanced Model and Implementation for Kinetostatic Protein Folding1

2015 ◽  
Vol 6 (3) ◽  
Author(s):  
Pouya Tavousi ◽  
Morad Behandish ◽  
Horea T. Ilieş ◽  
Kazem Kazerounian
Keyword(s):  
Structure Prediction ◽  
Graphics Processing Units ◽  
De Novo ◽  
Sequence Data ◽  
Protein Structures ◽  
Three Dimensional ◽  
Small Time ◽  
Water Soluble ◽  
Folding Process ◽  
Aqueous Solvent

A reliable prediction of three-dimensional (3D) protein structures from sequence data remains a big challenge due to both theoretical and computational difficulties. We have previously shown that our kinetostatic compliance method (KCM) implemented into the Protofold package can overcome some of the key difficulties faced by other de novo structure prediction methods, such as the very small time steps required by the molecular dynamics (MD) approaches or the very large number of samples needed by the Monte Carlo (MC) sampling techniques. In this paper, we improve the free energy formulation used in Protofold by including the typically underrated entropic effects, imparted due to differences in hydrophobicity of the chemical groups, which dominate the folding of most water-soluble proteins. In addition to the model enhancement, we revisit the numerical implementation by redesigning the algorithms and introducing efficient data structures that reduce the expected complexity from quadratic to linear. Moreover, we develop and optimize parallel implementations of the algorithms on both central and graphics processing units (CPU/GPU) achieving speed-ups up to two orders of magnitude on the GPU. Our simulations are consistent with the general behavior observed in the folding process in aqueous solvent, confirming the effectiveness of model improvements. We report on the folding process at multiple levels, namely, the formation of secondary structural elements and tertiary interactions between secondary elements or across larger domains. We also observe significant enhancements in running times that make the folding simulation tractable for large molecules.

scholarly journals Large-scale determination of previously unsolved protein structures using evolutionary information

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Sergey Ovchinnikov ◽  
Lisa Kinch ◽  
Hahnbeom Park ◽  
Yuxing Liao ◽  
Jimin Pei ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Large Scale ◽  
De Novo ◽  
Structural Information ◽  
Sequence Similarity ◽  
Protein Structures ◽  
Three Dimensional ◽  
Public Access ◽  
Evolutionary Information ◽  
Blind Test

The prediction of the structures of proteins without detectable sequence similarity to any protein of known structure remains an outstanding scientific challenge. Here we report significant progress in this area. We first describe de novo blind structure predictions of unprecendented accuracy we made for two proteins in large families in the recent CASP11 blind test of protein structure prediction methods by incorporating residue–residue co-evolution information in the Rosetta structure prediction program. We then describe the use of this method to generate structure models for 58 of the 121 large protein families in prokaryotes for which three-dimensional structures are not available. These models, which are posted online for public access, provide structural information for the over 400,000 proteins belonging to the 58 families and suggest hypotheses about mechanism for the subset for which the function is known, and hypotheses about function for the remainder.

scholarly journals Transformer neural network for protein-specific de novo drug generation as a machine translation problem

2019 ◽  
Author(s):  
Daria Grechishnikova
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Drug Discovery ◽  
Network Architecture ◽  
De Novo ◽  
Three Dimensional ◽  
Physicochemical Characteristics ◽  
Dimensional Structure ◽  
Learning Approaches ◽  
De Novo Drug Design

AbstractDrug discovery for a protein target is a very laborious, long and costly process. Machine learning approaches and, in particular, deep generative networks can substantially reduce development time and costs. However, the majority of methods imply prior knowledge of protein binders, their physicochemical characteristics or the three-dimensional structure of the protein. The method proposed in this work generates novel molecules with predicted ability to bind a target protein by relying on its amino acid sequence only. We consider target-specific de novo drug design as a translational problem between the amino acid “language” and SMILES (Simplified Molecular Input Line Entry System) representation of the molecule. To tackle this problem, we apply Transformer neural network architecture, a state-of-the-art approach in sequence transduction tasks. Transformer is based on a self-attention technique, which allows the capture of long-range dependencies between items in sequence. The model generates realistic diverse compounds with structural novelty. The computed physicochemical properties and common metrics used in drug discovery fall within the plausible drug-like range of values.

scholarly journals Accurate contact predictions for thousands of protein families using PconsC3

2016 ◽  
Author(s):  
Marcin J. Skwark ◽  
Mirco Michel ◽  
David Menéndez Hurtado ◽  
Magnus Ekeberg ◽  
Arne Elofsson
Keyword(s):  
Structure Prediction ◽  
De Novo ◽  
Pfam Domain ◽  
Three Dimensional ◽  
Improved Method ◽  
Protein Families ◽  
Large Protein ◽  
Multiple Sequence ◽  
Residue Contact ◽  
Contact Predictions

Protein structure prediction was for decades one of the grand unsolved challenges in bioinformatics. A few years ago it was shown that by using a maximum entropy approach to describe couplings between columns in a multiple sequence alignment it was possible to significantly increase the accuracy of residue contact predictions. For very large protein families with more than 1000 effective sequences the accuracy is sufficient to produce accurate models of proteins as well as complexes. Today, for about half of all Pfam domain families no structure is known, but unfortunately most of these families have at most a few hundred members, i.e. are too small for existing contact prediction methods. To extend accurate contact predictions to the thousands of smaller protein families we present PconsC3, an improved method for protein contact predictions that can be used for families with as little as 100 effective sequence members. We estimate that PconsC3 provides accurate contact predictions for up to 4646 Pfam domain families. In addition, PconsC3 outperforms previous methods significantly independent on family size, secondary structure content, contact range, or the number of selected contacts. This improvement translates into improved de-novo prediction of three-dimensional structures. PconsC3 is available as a web server and downloadable version at http://c3.pcons.net. The downloadable version is free for all to use and licensed under the GNU General Public License, version 2.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

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