scholarly journals Challenges in the computational design of proteins

2009 ◽  
Vol 6 (suppl_4) ◽  
Author(s):  
María Suárez ◽  
Alfonso Jaramillo
Keyword(s):  
Amino Acid ◽  
Structural Biology ◽  
Protein Design ◽  
Current Knowledge ◽  
Computational Design ◽  
Amino Acid Sequences ◽  
Computational Protein Design ◽  
Energy Functions ◽  
Physical Description ◽  
Atomic Interactions

Protein design has many applications not only in biotechnology but also in basic science. It uses our current knowledge in structural biology to predict, by computer simulations, an amino acid sequence that would produce a protein with targeted properties. As in other examples of synthetic biology, this approach allows the testing of many hypotheses in biology. The recent development of automated computational methods to design proteins has enabled proteins to be designed that are very different from any known ones. Moreover, some of those methods mostly rely on a physical description of atomic interactions, which allows the designed sequences not to be biased towards known proteins. In this paper, we will describe the use of energy functions in computational protein design, the use of atomic models to evaluate the free energy in the unfolded and folded states, the exploration and optimization of amino acid sequences, the problem of negative design and the design of biomolecular function. We will also consider its use together with the experimental techniques such as directed evolution. We will end by discussing the challenges ahead in computational protein design and some of their future applications.

A structural homology approach for computational protein design with flexible backbone

2018 ◽  
Vol 35 (14) ◽  
pp. 2418-2426 ◽  
Author(s):  
David Simoncini ◽  
Kam Y J Zhang ◽  
Thomas Schiex ◽  
Sophie Barbe
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Protein Sequence ◽  
Critical Role ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Computational Protein Design ◽  
Supplementary Information ◽  
Structural Homology ◽  
Homologous Proteins

Abstract Motivation Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Results We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%. Availability and implementation Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software. Supplementary information Supplementary data are available at Bioinformatics online.

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 Positive multistate protein design

2019 ◽  
Vol 36 (1) ◽  
pp. 122-130
Author(s):  
Jelena Vucinic ◽  
David Simoncini ◽  
Manon Ruffini ◽  
Sophie Barbe ◽  
Thomas Schiex
Keyword(s):  
Protein Design ◽  
Critical Role ◽  
Average Energy ◽  
Computational Design ◽  
Amino Acid Sequences ◽  
Supplementary Information ◽  
Backbone Flexibility ◽  
Identify Amino Acid ◽  
Design Software ◽  
Protein Redesign

Abstract Motivation Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. Results We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. Availability and implementation https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Insights from molecular dynamics simulations for computational protein design

2017 ◽  
Vol 2 (1) ◽  
pp. 9-33 ◽  
Author(s):  
Matthew Carter Childers ◽  
Valerie Daggett
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Structural Biology ◽  
Protein Design ◽  
Computational Protein Design ◽  
Grand Challenge ◽  
Dynamics Simulations

A grand challenge in the field of structural biology is to design and engineer proteins that exhibit targeted functions.

scholarly journals Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing

2022 ◽  
Author(s):  
Sandrine Legault ◽  
Derek Paco Fraser-Halberg ◽  
Ralph McAnelly ◽  
Matthew G Eason ◽  
Michael Thompson ◽  
...  
Keyword(s):  
Quantum Yield ◽  
Protein Design ◽  
Fluorescent Proteins ◽  
Computational Design ◽  
Computational Protein Design ◽  
Biological Research ◽  
Widespread Application ◽  
Red Fluorescent Proteins

Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield...

scholarly journals Fragger: a protein fragment picker for structural queries

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1722 ◽  
Author(s):  
Francois Berenger ◽  
David Simoncini ◽  
Arnout Voet ◽  
Rojan Shrestha ◽  
Kam Y.J. Zhang
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Data Bank ◽  
Amino Acid Sequences ◽  
Structural Fragment ◽  
Protein Fragment ◽  
Distance Threshold ◽  
Protein Fragments ◽  
Specific Subset ◽  
Design Activities

Protein modeling and design activities often require querying the Protein Data Bank (PDB) with a structural fragment, possibly containing gaps. For some applications, it is preferable to work on a specific subset of the PDB or with unpublished structures. These requirements, along with specific user needs, motivated the creation of a new software to manage and query 3D protein fragments. Fragger is a protein fragment picker that allows protein fragment databases to be created and queried. All fragment lengths are supported and any set of PDB files can be used to create a database. Fragger can efficiently search a fragment database with a query fragment and a distance threshold. Matching fragments are ranked by distance to the query. The query fragment can have structural gaps and the allowed amino acid sequences matching a query can be constrained via a regular expression of one-letter amino acid codes. Fragger also incorporates a tool to compute the backbone RMSD of one versus many fragments in high throughput. Fragger should be useful for protein design, loop grafting and related structural bioinformatics tasks.

scholarly journals Protein sequence design with a learned potential

2020 ◽  
Author(s):  
Namrata Anand-Achim ◽  
Raphael R. Eguchi ◽  
Alexander Derry ◽  
Russ B. Altman ◽  
Po-Ssu Huang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Amino Acid ◽  
Protein Design ◽  
Protein Sequence ◽  
State Of The Art ◽  
The State ◽  
Energy Functions ◽  
Sequence Design

AbstractThe primary challenge of fixed-backbone protein design is to find a distribution of sequences that fold to the backbone of interest. This task is central to nearly all protein engineering problems, as achieving a particular backbone conformation is often a prerequisite for hosting specific functions. In this study, we investigate the capability of a deep neural network to learn the requisite patterns needed to design sequences. The trained model serves as a potential function defined over the space of amino acid identities and rotamer states, conditioned on the local chemical environment at each residue. While most deep learning based methods for sequence design only produce amino acid sequences, our method generates full-atom structural models, which can be evaluated using established sequence quality metrics. Under these metrics we are able to produce realistic and variable designs with quality comparable to the state-of-the-art. Additionally, we experimentally test designs for a de novo TIM-barrel structure and find designs that fold, demonstrating the algorithm’s generalizability to novel structures. Overall, our results demonstrate that a deep learning model can match state-of-the-art energy functions for guiding protein design.SignificanceProtein design tasks typically depend on carefully modeled and parameterized heuristic energy functions. In this study, we propose a novel machine learning method for fixed-backbone protein sequence design, using a learned neural network potential to not only design the sequence of amino acids but also select their side-chain configurations, or rotamers. Factoring through a structural representation of the protein, the network generates designs on par with the state-of-the-art, despite having been entirely learned from data. These results indicate an exciting future for protein design driven by machine learning.

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 Leptodactylus latrans Amphibian Skin Secretions as a Novel Source for the Isolation of Antibacterial Peptides

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2943 ◽  
Author(s):  
Alvaro Siano ◽  
Maria Humpola ◽  
Eliandre de Oliveira ◽  
Fernando Albericio ◽  
Arturo Simonetta ◽  
...  
Keyword(s):  
Amino Acid ◽  
Antimicrobial Peptides ◽  
Current Knowledge ◽  
Antimicrobial Properties ◽  
Amino Acid Sequences ◽  
Aureus Strain ◽  
Potential Candidate ◽  
Amphibian Skin ◽  
Microbial Invasion ◽  
Skin Secretions

Amphibians´ skin produces a diverse array of antimicrobial peptides that play a crucial role as the first line of defense against microbial invasion. Despite the immense richness of wild amphibians in Argentina, current knowledge about the presence of peptides with antimicrobial properties is limited to a only few species. Here we used LC-MS-MS to identify antimicrobial peptides with masses ranging from 1000 to 4000 Da from samples of skin secretions of Leptodactylus latrans (Anura: Leptodactylidae). Three novel amino acid sequences were selected for chemical synthesis and further studies. The three synthetic peptides, named P1-Ll-1577, P2-Ll-1298, and P3-Ll-2085, inhibited the growth of two ATCC strains, namely Escherichia coli and Staphylococcus aureus. P3-Ll-2085 was the most active peptide. In the presence of trifluoroethanol (TFE) and anionic liposomes, it adopted an amphipathic α-helical structure. P2-Ll-1298 showed slightly lower activity than P3-Ll-2085. Comparison of the MIC values of these two peptides revealed that the addition of seven amino acid residues (GLLDFLK) on the N-terminal of P2-Ll-1298 significantly improved activity against both strains. P1-Ll-1577, which remarkably is an anionic peptide, showed interesting antimicrobial activity against E. coli and S. aureus strain, showing marked membrane selectivity and non-hemolysis. Due to this, P1-L1-1577 emerges as a potential candidate for the development of new antibacterial drugs.

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