scholarly journals Intergenic ORFs as elementary structural modules of de novo gene birth and protein evolution

2021 ◽  
Author(s):  
Chris Papadopoulos ◽  
Isabelle Callebaut ◽  
Jean-Christophe Gelly ◽  
Isabelle Hatin ◽  
Olivier Namy ◽  
...  
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
De Novo ◽  
Protein Structures ◽  
Building Blocks ◽  
Amino Acid Sequences ◽  
Novel Genes ◽  
Noncoding Sequences ◽  
Ancestral Sequences ◽  
De Novo Gene

The noncoding genome plays an important role in de novo gene birth and in the emergence of genetic novelty. Nevertheless, how noncoding sequences’ properties could promote the birth of novel genes and shape the evolution and the structural diversity of proteins remains unclear. Therefore, by combining different bioinformatic approaches, we characterized the fold potential diversity of the amino acid sequences encoded by all intergenic open reading frames (ORFs) of S. cerevisiae with the aim of (1) exploring whether the structural states’ diversity of proteomes is already present in noncoding sequences, and (2) estimating the potential of the noncoding genome to produce novel protein bricks that could either give rise to novel genes or be integrated into pre-existing proteins, thus participating in protein structure diversity and evolution. We showed that amino acid sequences encoded by most yeast intergenic ORFs contain the elementary building blocks of protein structures. Moreover, they encompass the large structural state diversity of canonical proteins, with the majority predicted as foldable. Then, we investigated the early stages of de novo gene birth by reconstructing the ancestral sequences of 70 yeast de novo genes and characterized the sequence and structural properties of intergenic ORFs with a strong translation signal. This enabled us to highlight sequence and structural factors determining de novo gene emergence. Finally, we showed a strong correlation between the fold potential of de novo proteins and one of their ancestral amino acid sequences, reflecting the relationship between the noncoding genome and the protein structure universe.

scholarly journals Intergenic ORFs as elementary structural modules of de novo gene birth and protein evolution

2021 ◽  
Author(s):  
Chris Papadopoulos ◽  
Isabelle Callebaut ◽  
Jean-Christophe Gelly ◽  
Isabelle Hatin ◽  
Olivier Namy ◽  
...  
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
De Novo ◽  
Protein Structures ◽  
Structural Diversity ◽  
Building Blocks ◽  
Amino Acid Sequences ◽  
Novel Genes ◽  
Noncoding Sequences ◽  
De Novo Gene

The noncoding genome plays an important role in de novo gene birth and in the emergence of genetic novelty. Nevertheless, how noncoding sequences' properties could promote the birth of novel genes and shape the evolution and the structural diversity of proteins remains unclear. Therefore, by combining different bioinformatic approaches, we characterized the fold potential diversity of the amino acid sequences encoded by all intergenic ORFs (Open Reading Frames) of S. cerevisiae with the aim of (i) exploring whether the large structural diversity observed in proteomes is already present in noncoding sequences, and (ii) estimating the potential of the noncoding genome to produce novel protein bricks that can either give rise to novel genes or be integrated into pre-existing proteins, thus participating in protein structure diversity and evolution. We showed that amino acid sequences encoded by most yeast intergenic ORFs contain the elementary building blocks of protein structures. Moreover, they encompass the large structural diversity of canonical proteins with strikingly the majority predicted as foldable. Then, we investigated the early stages of de novo gene birth by identifying intergenic ORFs with a strong translation signal in ribosome profiling experiments and by reconstructing the ancestral sequences of 70 yeast de novo genes. This enabled us to highlight sequence and structural factors determining de novo gene emergence. Finally, we showed a strong correlation between the fold potential of de novo proteins and the one of their ancestral amino acid sequences, reflecting the relationship between the noncoding genome and the protein structure universe.

scholarly journals Unevolved De Novo Proteins Have Innate Tendencies to Bind Transition Metals

Life ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 8 ◽  
Author(s):  
Michael S. Wang ◽  
Kenric J. Hoegler ◽  
Michael H. Hecht
Keyword(s):  
Amino Acid ◽  
Transition Metals ◽  
Metal Binding ◽  
Combinatorial Library ◽  
De Novo ◽  
Protein Sequences ◽  
Amino Acid Sequences ◽  
Ancestral Sequences ◽  
Wide Range ◽  
Catalytic Functions

Life as we know it would not exist without the ability of protein sequences to bind metal ions. Transition metals, in particular, play essential roles in a wide range of structural and catalytic functions. The ubiquitous occurrence of metalloproteins in all organisms leads one to ask whether metal binding is an evolved trait that occurred only rarely in ancestral sequences, or alternatively, whether it is an innate property of amino acid sequences, occurring frequently in unevolved sequence space. To address this question, we studied 52 proteins from a combinatorial library of novel sequences designed to fold into 4-helix bundles. Although these sequences were neither designed nor evolved to bind metals, the majority of them have innate tendencies to bind the transition metals copper, cobalt, and zinc with high nanomolar to low-micromolar affinity.

scholarly journals De novo protein design by deep network hallucination

2020 ◽  
Author(s):  
Ivan Anishchenko ◽  
Tamuka M. Chidyausiku ◽  
Sergey Ovchinnikov ◽  
Samuel J. Pellock ◽  
David Baker
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Structures ◽  
Monte Carlo Sampling ◽  
Amino Acid Sequences ◽  
Wide Range ◽  
Physically Based ◽  
Folded Proteins

AbstractThere has been considerable recent progress in protein structure prediction using deep neural networks to infer distance constraints from amino acid residue co-evolution1–3. We investigated whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occuring proteins used in training the models. We generated random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting distance maps, which as expected are quite featureless. We then carried out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (KL-divergence) between the distance distributions predicted by the network and the background distribution. Optimization from different random starting points resulted in a wide range of proteins with diverse sequences and all alpha, all beta sheet, and mixed alpha-beta structures. We obtained synthetic genes encoding 129 of these network hallucinated sequences, expressed and purified the proteins in E coli, and found that 27 folded to monomeric stable structures with circular dichroism spectra consistent with the hallucinated structures. Thus deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins, and such networks and methods should contribute, alongside traditional physically based models, to the de novo design of proteins with new functions.

scholarly journals Amino acid torsion angles enable prediction of protein fold classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kun Tian ◽  
Xin Zhao ◽  
Xiaogeng Wan ◽  
Stephen S.-T. Yau
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Single Amino Acid ◽  
New Approach ◽  
Protein Functions

AbstractProtein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins are complex and require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction. Here we describe a new approach to predict of protein structures and structure classes from amino acid sequences. Our prediction model performs well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. The average accuracy is 92.5% for structure classification, which is higher than that of previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence. The results show that our method provides a new effective and reliable tool for protein structure prediction research.

Amino acid torsion angles enable prediction of protein fold classification

2020 ◽  
Author(s):  
Kun Tian ◽  
Xin Zhao ◽  
Xiaogeng Wan ◽  
Stephen Yau
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Prediction Model ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Protein Structures ◽  
Data Bank ◽  
Amino Acid Sequences ◽  
Single Amino Acid ◽  
New Approach

Abstract Background Protein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins, such as X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy, are complex and may require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction.Results Here we describe a new approach that uses integration and analysis of torsion angle information from the Protein Data Bank to enable prediction of protein structures from amino acid sequences. Our prediction model performed well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. This new prediction model performs well with an average of 92.5% accuracy for structure classification, which is higher than the previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence.Conclusions The results show that our method provides a new effective and reliable tool for protein structure prediction research.

scholarly journals In-silicoprediction and modeling of theEntamoeba histolyticaproteins: Serine-richEntamoeba histolyticaprotein and 29 kDa Cysteine-rich protease

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3160 ◽  
Author(s):  
Kumar Manochitra ◽  
Subhash Chandra Parija
Keyword(s):  
Amino Acid ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Treatment Modalities ◽  
Bioinformatic Tools ◽  
Complex Protein ◽  
A Cell ◽  
Quaternary Structures

BackgroundAmoebiasis is the third most common parasitic cause of morbidity and mortality, particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence there arises a necessity for a better diagnostic approach. Serine-richEntamoeba histolyticaprotein (SREHP), peroxiredoxin and Gal/GalNAc lectin are pivotal inE. histolyticavirulence and are extensively studied as diagnostic and vaccine targets. For elucidating the cellular function of these proteins, details regarding their respective quaternary structures are essential. However, studies in this aspect are scant. Hence, this study was carried out to predict the structure of these target proteins and characterize them structurally as well as functionally using appropriatein-silicomethods.MethodsThe amino acid sequences of the proteins were retrieved from National Centre for Biotechnology Information database and aligned using ClustalW. Bioinformatic tools were employed in the secondary structure and tertiary structure prediction. The predicted structure was validated, and final refinement was carried out.ResultsThe protein structures predicted by i-TASSER were found to be more accurate than Phyre2 based on the validation using SAVES server. The prediction suggests SREHP to be an extracellular protein, peroxiredoxin a peripheral membrane protein while Gal/GalNAc lectin was found to be a cell-wall protein. Signal peptides were found in the amino-acid sequences of SREHP and Gal/GalNAc lectin, whereas they were not present in the peroxiredoxin sequence. Gal/GalNAc lectin showed better antigenicity than the other two proteins studied. All the three proteins exhibited similarity in their structures and were mostly composed of loops.DiscussionThe structures of SREHP and peroxiredoxin were predicted successfully, while the structure of Gal/GalNAc lectin could not be predicted as it was a complex protein composed of sub-units. Also, this protein showed less similarity with the available structural homologs. The quaternary structures of SREHP and peroxiredoxin predicted from this study would provide better structural and functional insights into these proteins and may aid in development of newer diagnostic assays or enhancement of the available treatment modalities.

scholarly journals Protein structure prediction and design in a biologically-realistic implicit membrane

2019 ◽  
Author(s):  
Rebecca F. Alford ◽  
Patrick J. Fleming ◽  
Karen G. Fleming ◽  
Jeffrey J. Gray
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Protein Structure Prediction ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Computational Design ◽  
Amino Acid Distribution

ABSTRACTProtein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational bench-marks against experimental targets including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure dis-crimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.Significance StatementMembrane proteins participate in many life processes including transport, signaling, and catalysis. They constitute over 30% of all proteins and are targets for over 60% of pharmaceuticals. Computational design tools for membrane proteins will transform the interrogation of basic science questions such as membrane protein thermodynamics and the pipeline for engineering new therapeutics and nanotechnologies. Existing tools are either too expensive to compute or rely on manual design strategies. In this work, we developed a fast and accurate method for membrane protein design. The tool is available to the public and will accelerate the experimental design pipeline for membrane proteins.

scholarly journals Evolutionary Algorithms for Improving De Novo Peptide Sequencing

2021 ◽  
Author(s):  
◽  
Samaneh Azari
Keyword(s):  
Amino Acid ◽  
De Novo ◽  
Peptide Identification ◽  
Peptide Sequencing ◽  
De Novo Sequencing ◽  
Amino Acid Sequences ◽  
Full Length ◽  
Multi Objective ◽  
De Novo Peptide Sequencing ◽  
De Novo Peptide

<p>De novo peptide sequencing algorithms have been developed for peptide identification in proteomics from tandem mass spectra (MS/MS), which can be used to identify and discover novel peptides and proteins that do not have a database available. Despite improvements in MS instrumentation and de novo sequencing methods, a significant number of CID MS/MS spectra still remain unassigned with the current algorithms, often leading to low confidence of peptide assignments to the spectra. Moreover, current algorithms often fail to construct the completely matched sequences, and produce partial matches. Therefore, identification of full-length peptides remains challenging. Another major challenge is the existence of noise in MS/MS spectra which makes the data highly imbalanced. Also missing peaks, caused by incomplete MS fragmentation makes it more difficult to infer a full-length peptide sequence. In addition, the large search space of all possible amino acid sequences for each spectrum leads to a high false discovery rate. This thesis focuses on improving the performance of current methods by developing new algorithms corresponding to three steps of preprocessing, sequence optimisation and post-processing using machine learning for more comprehensive interrogation of MS/MS datasets. From the machine learning point of view, the three steps can be addressed by solving different tasks such as classification, optimisation, and symbolic regression. Since Evolutionary Algorithms (EAs), as effective global search techniques, have shown promising results in solving these problems, this thesis investigates the capability of EAs in improving the de novo peptide sequencing. In the preprocessing step, this thesis proposes an effective GP-based method for classification of signal and noise peaks in highly imbalanced MS/MS spectra with the purpose of having a positive influence on the reliability of the peptide identification. The results show that the proposed algorithm is the most stable classification method across various noise ratios, outperforming six other benchmark classification algorithms. The experimental results show a significant improvement in high confidence peptide assignments to MS/MS spectra when the data is preprocessed by the proposed GP method. Moreover, the first multi-objective GP approach for classification of peaks in MS/MS data, aiming at maximising the accuracy of the minority class (signal peaks) and the accuracy of the majority class (noise peaks) is also proposed in this thesis. The results show that the multi-objective GP method outperforms the single objective GP algorithm and a popular multi-objective approach in terms of retaining more signal peaks and removing more noise peaks. The multi-objective GP approach significantly improved the reliability of peptide identification. This thesis proposes a GA-based method to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full-length sequences. The proposed GA method benefits the GA capability of searching a large search space of potential amino acid sequences to find the most likely full-length sequence. The experimental results show that the proposed method outperforms the most commonly used de novo sequencing method at both amino acid level and peptide level. This thesis also proposes a novel method for re-scoring and re-ranking the peptide spectrum matches (PSMs) from the result of de novo peptide sequencing, aiming at minimising the false discovery rate as a post-processing approach. The proposed GP method evolves the computer programs to perform regression and classification simultaneously in order to generate an effective scoring function for finding the correct PSMs from many incorrect ones. The results show that the new GP-based PSM scoring function significantly improves the identification of full-length peptides when it is used to post-process the de novo sequencing results.</p>

Isolation and characterization of some novel genes of the apolipoprotein A-I family in Japanese eel, Anguilla japonica

2011 ◽  
Vol 6 (4) ◽  
pp. 545-557 ◽  
Author(s):  
Malay Choudhury ◽  
Takahiro Oku ◽  
Shoji Yamada ◽  
Masaharu Komatsu ◽  
Keita Kudoh ◽  
...  
Keyword(s):  
Amino Acids ◽  
Molecular Weight ◽  
Amino Acid ◽  
Consensus Sequence ◽  
Structural Features ◽  
Lipid Binding ◽  
Japanese Eel ◽  
Amino Acid Sequences ◽  
Novel Genes ◽  
Isolation And Characterization

AbstractApolipoproteins such as apolipoprotein (apo) A-I, apoA-IV, and apoE are lipid binding proteins synthesized mainly in the liver and the intestine and play an important role in the transfer of exogenous or endogenous lipids through the circulatory system. To investigate the mechanism of lipid transport in fish, we have isolated some novel genes of the apoA-I family, apoIA-I (apoA-I isoform) 1–11, from Japanese eel by PCR amplification. Some of the isolated genes of apoIA-I corresponded to 28kDa-1 cDNAs which had already been deposited into the database and encoded an apolipoprotein with molecular weight of 28 kDa in the LDL, whereas others seemed to be novel genes. The structural organization of all apoIA-Is consisted of four exons separated by three introns. ApoIA-I10 had a total length of 3232 bp, whereas other genes except for apoIA-I9 ranged from 1280 to 1441 bp. The sequences of apoIA-Is at the exon-intron junctions were mostly consistent with the consensus sequence (GT/AG) at exon-intron boundaries, whereas the sequences of 3′ splice acceptor in intron 1 of apoIA-I1-7 were (AC) but not (AG). The deduced amino acid sequences of all apoIA-Is contained a putative signal peptide and a propeptide of 17 and 5 amino acid residues, respectively. The mature proteins of apoIA-I1-3, 7, and 8 consisted of 237 amino acids, whereas those of apoIA-I4-6 consisted of 239 amino acids. The mature apoIA-I10 sequence showed 65% identity to amino acid sequence of apoIA-I11 which was associated with an apolipoprotein with molecular weight of 23 kDa in the VLDL. All these mature apoIA-I sequences satisfied the common structural features depicted for the exchangeable apolipoproteins such as apoA-I, apoA-IV, and apoE but apoIA-I11 lacked internal repeats 7, 8, and 9 when compared with other members of apoA-I family. Phylogenetic analysis showed that these novel apoIA-Is isolated from Japanese eel were much closer to apoA-I than apoA-IV and apoE, suggesting new members of the apoA-I family.

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