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 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 RosettaAntibodyDesign (RAbD): A General Framework for Computational Antibody Design

2017 ◽  
Author(s):  
Jared Adolf-Bryfogle ◽  
Oleks Kalyuzhniy ◽  
Michael Kubitz ◽  
Brian D. Weitzner ◽  
Xiaozhen Hu ◽  
...  
Keyword(s):  
Amino Acid ◽  
Risk Ratio ◽  
Protein Design ◽  
Three Dimensional ◽  
Design Procedure ◽  
Interface Energy ◽  
Design Strategies ◽  
Sequence Design ◽  
Antigen Antibody ◽  
Sequence Profiles

AbstractA structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al.,J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody–antigen complexes, using two design strategies – optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody–antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.Author SummaryAntibodies are proteins produced by the immune system to attack infections and cancer and are also used as drugs to treat cancer and autoimmune diseases. The mechanism that has evolved to produce them is able to make 10s of millions of different antibodies, each with a different surface used to bind the foreign or mutated molecule. We have developed a method to design antibodies computationally, based on the 1000s of experimentally determined three-dimensional structures of antibodies available. The method works by treating pieces of these structures as a collection of parts that can be combined in new ways to make better antibodies. Our method has been implemented in the protein modeling program Rosetta, and is called RosettaAntibodyDesign (RAbD). We tested RAbD both computationally and experimentally. The experimental test shows that we can improve existing antibodies by 10 to 50 fold, paving the way for design of entirely new antibodies in the future.

scholarly journals Breakthroughs in computational design methods open up new frontiers for de novo protein engineering

2021 ◽  
Vol 34 ◽  
Author(s):  
Ben A Meinen ◽  
Christopher D Bahl
Keyword(s):  
Protein Engineering ◽  
Chemical Reactions ◽  
Protein Design ◽  
De Novo ◽  
Computational Design ◽  
Design Methods ◽  
Computational Protein Design ◽  
The Past ◽  
New Methods ◽  
De Novo Protein Design

Abstract Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.

THE EVALUATION OF THE POTENTIAL OF THE COMPUTATIONAL DESIGN METHODS IN ARCHITECTURE WITH THE LIFE CYCLE MODEL

2020 ◽  
Vol 0 (21) ◽  
pp. 0-0
Author(s):  
Serkan PALABIYIK ◽  
Derya DEMİRCAN
Keyword(s):  
Life Cycle ◽  
Information And Communication Technologies ◽  
Architectural Design ◽  
Computational Design ◽  
Design Methods ◽  
Information Modeling ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Product Representation ◽  
Structure Information

Aim: Evolution process in information and communication technologies, architectural design and therefore in the field of architecture; It goes from product representation and communication use, which can be modeled in the computer environment, to the processing of data, information and information, to support creativity and decision making, an activity specific to human mental processes. In this process, many computational design methods have been developed that play an important role in contemporary design practices and guide the change of design culture in recent years. The top aim in this study presented; It is the evaluation of the methods developed in the field of computational design through the life cycle model. Method: At the point of investigating the spread and usage of the design methods included in the study within the sample area, bibliometric analysis, and content analysis methods, which are used to evaluate scientific studies, was used to draw repeatable and valid results regarding the content of the examined text. Results: The study shows that the most published design methods in the computational design are simulation-based design, structure information modeling, shape grammars, and genetic algorithms, respectively. In addition, it is determined that the most preferred international congress in this field is eCAADe. According to the evaluation of the total number of publications in this field, the contribution of Turkish researchers to the field was found to be 3%. Conclusion: This study evaluated when the upper scale, which are related to computational design methods in Turkey, we can conclude that there is insufficient scientific studies. In order to be able to use the rapidly developing computational design tools more effectively, to improve the computational design ability and to reflect the education process in the design process, it is important to have a voice in the architecture of the future.

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.

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