scholarly journals Protein sequence design by explicit energy landscape optimization

2020 ◽  
Author(s):  
Christoffer Norn ◽  
Basile I. M. Wicky ◽  
David Juergens ◽  
Sirui Liu ◽  
David Kim ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Large Fraction ◽  
Amino Acid Sequences ◽  
Alternative States ◽  
Point Energy ◽  
Sequence Design

AbstractThe protein design problem is to identify an amino acid sequence which folds to a desired structure. Given Anfinsen’s thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the lowest energy conformation is that structure. As this calculation involves not only all possible amino acid sequences but also all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest energy conformation for the designed sequence, and discarding the in many cases large fraction of designed sequences for which this is not the case. Here we show that by backpropagating gradients through the trRosetta structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures, and in one calculation explicitly design amino acid sequences predicted to fold into the desired structure and not any other. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by landscape optimization to the standard fixed backbone sequence design methodology in Rosetta, and show that the results of the former, but not the latter, are sensitive to the presence of competing low-lying states. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low resolution trRosetta model serves to disfavor alternative states, and the high resolution Rosetta model, to create a deep energy minimum at the design target structure.SignificanceComputational protein design has primarily focused on finding sequences which have very low energy in the target designed structure. However, what is most relevant during folding is not the absolute energy of the folded state, but the energy difference between the folded state and the lowest lying alternative states. We describe a deep learning approach which captures the entire folding landscape, and show that it can enhance current protein design methods.

scholarly journals Protein sequence design by conformational landscape optimization

2021 ◽  
Vol 118 (11) ◽  
pp. e2017228118
Author(s):  
Christoffer Norn ◽  
Basile I. M. Wicky ◽  
David Juergens ◽  
Sirui Liu ◽  
David Kim ◽  
...  
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Structure Prediction ◽  
Alternative Energy ◽  
De Novo ◽  
Amino Acid Sequences ◽  
Energy Landscapes ◽  
Point Energy ◽  
Sequence Design ◽  
Conformational Landscape

The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen’s thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure.

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 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 The amino acid sequence of cytochromes c-551 from three species of Pseudomonas

1973 ◽  
Vol 131 (3) ◽  
pp. 485-498 ◽  
Author(s):  
R. P. Ambler ◽  
Margaret Wynn
Keyword(s):  
Amino Acids ◽  
Pseudomonas Aeruginosa ◽  
Amino Acid ◽  
Cytochrome C ◽  
Amino Acid Sequence ◽  
Amino Acid Sequences ◽  
Mitochondrial Cytochrome ◽  
Cytochromes C ◽  
Lending Library ◽  
Single Peptide

The amino acid sequences of the cytochromes c-551 from three species of Pseudomonas have been determined. Each resembles the protein from Pseudomonas strain P6009 (now known to be Pseudomonas aeruginosa, not Pseudomonas fluorescens) in containing 82 amino acids in a single peptide chain, with a haem group covalently attached to cysteine residues 12 and 15. In all four sequences 43 residues are identical. Although by bacteriological criteria the organisms are closely related, the differences between pairs of sequences range from 22% to 39%. These values should be compared with the differences in the sequence of mitochondrial cytochrome c between mammals and amphibians (about 18%) or between mammals and insects (about 33%). Detailed evidence for the amino acid sequences of the proteins has been deposited as Supplementary Publication SUP 50015 at the National Lending Library for Science and Technology, Boston Spa, Yorks. LS23 7BQ, U.K., from whom copies can be obtained on the terms indicated in Biochem. J. (1973), 131, 5.

scholarly journals Factor D̄ of the alternative pathway of human complement. Purification, alignment and N-terminal amino acid sequences of the major cyanogen bromide fragments, and localization of the serine residue at the active site

1980 ◽  
Vol 187 (3) ◽  
pp. 863-874 ◽  
Author(s):  
D M Johnson ◽  
J Gagnon ◽  
K B Reid
Keyword(s):  
Amino Acid ◽  
Sequence Analysis ◽  
Amino Acid Sequence ◽  
Alternative Pathway ◽  
Amino Acid Sequences ◽  
Serine Residue ◽  
Amino Acid Sequence Analysis ◽  
Terminal Amino Acid ◽  
Terminal Amino ◽  
Terminal Amino Acid Sequence

The serine esterase factor D of the complement system was purified from outdated human plasma with a yield of 20% of the initial haemolytic activity found in serum. This represented an approx. 60 000-fold purification. The final product was homogeneous as judged by sodium dodecyl sulphate/polyacrylamide-gel electrophoresis (with an apparent mol.wt. of 24 000), its migration as a single component in a variety of fractionation procedures based on size and charge, and its N-terminal amino-acid-sequence analysis. The N-terminal amino acid sequence of the first 36 residues of the intact molecule was found to be homologous with the N-terminal amino acid sequences of the catalytic chains of other serine esterases. Factor D showed an especially strong homology (greater than 60% identity) with rat ‘group-specific protease’ [Woodbury, Katunuma, Kobayashi, Titani, & Neurath (1978) Biochemistry 17, 811-819] over the first 16 amino acid residues. This similarity is of interest since it is considered that both enzymes may be synthesized in their active, rather than zymogen, forms. The three major CNBr fragments of factor D, which had apparent mol.wts. of 15 800, 6600 and 1700, were purified and then aligned by N-terminal amino acid sequence analysis and amino acid analysis. By using factor D labelled with di-[1,3-14C]isopropylphosphofluoridate it was shown that the CNBr fragment of apparent mol.wt. 6600, which is located in the C-terminal region of factor D, contained the active serine residue. The amino acid sequence around this residue was determined.

Die primäre Proteinstruktur von Stämmen des Tabakmosaikvirus

1963 ◽  
Vol 18 (12) ◽  
pp. 1032-1049 ◽  
Author(s):  
B. Wittmann-Liebold ◽  
H. G. Wittmann
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Tobacco Mosaic Virus ◽  
Mosaic Virus ◽  
Amino Acid Sequences ◽  
Tryptic Peptides

The amino acid sequence of dahlemense, a naturally occuring strain of tobacco mosaic virus, has been determined and compared with that of the strain vulgare (Fig. 7). In this communication the experimental details are given for the elucidation of the amino acid sequences within two tryptic peptides with 65 amino acids.

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 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>

scholarly journals Diarrhea-Inducing Activity of Avian Rotavirus NSP4 Glycoproteins, Which Differ Greatly from Mammalian Rotavirus NSP4 Glycoproteins in Deduced Amino Acid Sequence, in Suckling Mice

2002 ◽  
Vol 76 (11) ◽  
pp. 5829-5834 ◽  
Author(s):  
Yoshio Mori ◽  
Mohammed Ali Borgan ◽  
Naoto Ito ◽  
Makoto Sugiyama ◽  
Nobuyuki Minamoto
Keyword(s):  
Escherichia Coli ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Amino Acid Sequences ◽  
Amino Acid Residues ◽  
Suckling Mice

ABSTRACT Avian rotavirus NSP4 glycoproteins expressed in Escherichia coli acted as enterotoxins in suckling mice, as did mammalian rotavirus NSP4 glycoproteins, despite great differences in the amino acid sequences. The enterotoxin domain of PO-13 NSP4 exists in amino acid residues 109 to 135, a region similar to that reported in SA11 NSP4.

scholarly journals The complete amino acid sequence of three alcohol dehydrogenase alleloenzymes (AdhN-11, AdhS and AdhUF) from the fruitfly Drosophila melanogaster

1980 ◽  
Vol 187 (3) ◽  
pp. 875-883 ◽  
Author(s):  
D R Thatcher
Keyword(s):  
Drosophila Melanogaster ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Alcohol Dehydrogenase ◽  
Aspartic Acid ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
British Library ◽  
Horse Liver ◽  
And Staphylococcus Aureus

The sequence of three alcohol dehydrogenase alleloenzymes from the fruitfly Drosophila melanogaster has been determined by the sequencing of peptides produced by trypsin, chymotrypsin, thermolysin, pepsin and Staphylococcus aureus-V8-proteinase digestion. The amino acid sequence shows no obvious homology with the published sequences of the horse liver and yeast enzymes, and secondary structure prediction suggests that the nucleotide-binding domain is located in the N-terminal half of the molecule. The amino acid substitutions between AdhN-11 (a point mutation of AdhF), AdhS and AdhUF alleloenzymes were identified. AdhN-11 alcohol dehydrogenase differed from the other two by a glycine-14-(AdhS and AdhUF)-to-aspartic acid substitution, the AdhS enzyme from AdhN-11 and AdhUF enzymes by a threonine-192-(AdhN-11 and AdhUF)-to-lysine (AdhS) substitution and the AdhUF enzyme was found to differ by an alanine-45-(AdhS and AdhN-11)-to-aspartic acid (AdhUF) charge substitution and a ‘silent’ asparagine-8-(AdhS and AdhN-11)-to-alanine (AdhUF) substitution. Detailed sequence evidence has been deposited as Supplementary Publication SUP 50107 (36 pages) at the British Library Lending Division, Boston Spa, Wetherby, West Yorkshire LS23 7BQ, U.K., from whom copies can be obtained on the terms indicated in Biochem. J. (1978) 169, 5.

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