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.

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 OSPREY 3.0: Open-Source Protein Redesign for You, with Powerful New Features

2018 ◽  
Author(s):  
Mark A. Hallen ◽  
Jeffrey W. Martin ◽  
Adegoke Ojewole ◽  
Jonathan D. Jou ◽  
Anna U. Lowegard ◽  
...  
Keyword(s):  
Protein Binding ◽  
Open Source ◽  
Protein Design ◽  
Open Source Software ◽  
Ease Of Use ◽  
Biophysical Modeling ◽  
Order Of Magnitude ◽  
Design Software ◽  
Protein Redesign ◽  
New Algorithms

We present OSPREY 3.0, a new and greatly improved release of the OSPREY protein design software. OSPREY 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of OSPREY when running the same algorithms on the same hardware. Moreover, OSPREY 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of OSPREY, OSPREY 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that OSPREY 3.0 accurately predicts the effect of mutations on protein-protein binding. OSPREY 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software.

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.

scholarly journals Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions

2019 ◽  
Author(s):  
Amanda L. Loshbaugh ◽  
Tanja Kortemme
Keyword(s):  
Protein Design ◽  
Computational Design ◽  
Design Methods ◽  
Backbone Flexibility ◽  
Functional Protein ◽  
Binding Interactions ◽  
Sequence Design ◽  
Sequence Profiles ◽  
Sampling Trajectory ◽  
Powerful Strategy

ABSTRACTComputational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.

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 Amino-acid site variability among natural and designed proteins

2013 ◽  
Author(s):  
Eleisha L. Jackson ◽  
Noah Ollikainen ◽  
Arthur W. Covert III ◽  
Tanja Kortemme ◽  
Claus O. Wilke
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Protein Sequences ◽  
Structural Constraints ◽  
Scoring Functions ◽  
Solvent Exposure ◽  
Backbone Flexibility ◽  
Hydrophobic Residues ◽  
Designed Proteins ◽  
Site Variability

Computational protein design attempts to create protein sequences that fold stably into pre-specified structures. Here we compare alignments of designed proteins to alignments of natural proteins and assess how closely designed sequences recapitulate patterns of sequence variation found in natural protein sequences. We design proteins using RosettaDesign, and we evaluate both fixed-backbone designs and variable-backbone designs with different amounts of backbone flexibility. We find that proteins designed with a fixed backbone tend to underestimate the amount of site variability observed in natural proteins while proteins designed with an intermediate amount of backbone flexibility result in more realistic site variability. Further, the correlation between solvent exposure and site variability in designed proteins is lower than that in natural proteins. This finding suggests that site variability is too uniform across different solvent exposure states (i.e., buried residues are too variable or exposed residues too conserved). When comparing the amino acid frequencies in the designed proteins with those in natural proteins we find that in the designed proteins hydrophobic residues are underrepresented in the core. From these results we conclude that intermediate backbone flexibility during design results in more accurate protein design and that either scoring functions or backbone sampling methods require further improvement to accurately replicate structural constraints on site variability.

Principles of Protein Stability and Their Application in Computational Design

2018 ◽  
Vol 87 (1) ◽  
pp. 105-129 ◽  
Author(s):  
Adi Goldenzweig ◽  
Sarel J. Fleishman
Keyword(s):  
Thermal Stability ◽  
Phylogenetic Analysis ◽  
Protein Design ◽  
Biomedical Research ◽  
Design Methodology ◽  
Computational Design ◽  
Marginal Stability ◽  
Computational Stability ◽  
Rational Engineering ◽  
The Past

Proteins are increasingly used in basic and applied biomedical research. Many proteins, however, are only marginally stable and can be expressed in limited amounts, thus hampering research and applications. Research has revealed the thermodynamic, cellular, and evolutionary principles and mechanisms that underlie marginal stability. With this growing understanding, computational stability design methods have advanced over the past two decades starting from methods that selectively addressed only some aspects of marginal stability. Current methods are more general and, by combining phylogenetic analysis with atomistic design, have shown drastic improvements in solubility, thermal stability, and aggregation resistance while maintaining the protein's primary molecular activity. Stability design is opening the way to rational engineering of improved enzymes, therapeutics, and vaccines and to the application of protein design methodology to large proteins and molecular activities that have proven challenging in the past.

Three hamster species with different scrapie incubation times and neuropathological features encode distinct prion proteins

1990 ◽  
Vol 10 (3) ◽  
pp. 1153-1163
Author(s):  
D H Lowenstein ◽  
D A Butler ◽  
D Westaway ◽  
M P McKinley ◽  
S J DeArmond ◽  
...  
Keyword(s):  
Incubation Time ◽  
Prion Proteins ◽  
Inbred Mice ◽  
Critical Role ◽  
Clinical Signs ◽  
Amino Acid Sequences ◽  
Reading Frame ◽  
Rna Analysis ◽  
Prp Gene

Given the critical role of the prion protein (PrP) in the transmission and pathogenesis of experimental scrapie, we investigated the PrP gene and its protein products in three hamster species, Chinese (CHa), Armenian (AHa), and Syrian (SHa), each of which were found to have distinctive scrapie incubation times. Passaging studies demonstrated that the host species, and not the source of scrapie prions, determined the incubation time for each species, and histochemical studies of hamsters with clinical signs of scrapie revealed characteristic patterns of neuropathology. Northern (RNA) analysis showed the size of PrP mRNA from CHa, AHa, and SHa hamsters to be 2.5, 2.4, and 2.1 kilobases, respectively. Immunoblotting demonstrated that the PrP isoforms were of similar size (33 to 35 kilodaltons); however, the monoclonal antibody 13A5 raised against SHa PrP did not react with the CHa or AHa PrP molecules. Comparison of the three predicted amino acid sequences revealed that each is distinct. Furthermore, differences within the PrP open reading frame that uniquely distinguish the three hamster species are within a hydrophilic segment of 11 amino acids that includes polymorphisms linked to scrapie incubation times in inbred mice and an inherited prion disease of humans. Single polymorphisms in this region correlate with the presence or absence of amyloid plaques for a given hamster species or mouse inbred strain. Our findings demonstrate distinctive molecular, pathological, and clinical characteristics of scrapie in three related species and are consistent with the hypothesis that molecular properties of the host PrP play a pivotal role in determining the incubation time and neuropathological features of scrapie.

iPattern

2017 ◽  
Vol 6 (2) ◽  
pp. 12-27
Author(s):  
Youmna Bassiouny ◽  
Rimon Elias ◽  
Philipp Paulsen
Keyword(s):  
Design Pattern ◽  
Design Patterns ◽  
Three Dimensional ◽  
Computational Design ◽  
Pattern Design ◽  
Design Problems ◽  
Innovative Design ◽  
Rule Based ◽  
Science And Art ◽  
Design Software

Computational design takes a computer science view of design, applying both the science and art of computational approaches and methodologies to design problems. This article proposes to convert design methodologies studied by designers into rule-based computational design software and help them by providing suggestions for designs to build upon given a set of primitive shapes and geometrical rules. iPattern is a pattern-making software dedicated to designers to generate innovative design patterns that can be used in a decorative manner. They may be applied on wallpapers, carpets, fabric textiles, three-dimensional lanterns, tableware, etc. The purpose is to create a modern pattern design collection that adds a new essence to the place. In order to generate creative design patterns, primitive shapes and geometrical rules are used. The generated design pattern is constructed based on the grid of the Flower of Life of the sacred geometry or similar grids constructed using primitive shapes (rectangles, squares and triangles) combined in the layout of the Flower of Life.

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