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 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 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 Hybrid approach to analysis of β-sheet structures based on signal processing and statistical consideration

2010 ◽  
Vol 467 (2128) ◽  
pp. 1052-1072
Author(s):  
V. Vojisavljevic ◽  
E. Pirogova ◽  
D. M. Davidovic ◽  
I. Cosic
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Protein Design ◽  
Hybrid Approach ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Spectra Analysis ◽  
Sheet Structure ◽  
The Relationship ◽  
Β Sheet

A number of biotechnology applications are based on protein design. For this design, the relationship between a protein’s primary structure and its conformation is of vital importance. A β-sheet is a common feature of a protein’s two-dimensional structure; therefore, elucidating the principles governing β-sheet structure and its stability is critical for understanding the protein-folding process. In the three-dimensional representation of protein molecules, C α carbon coordinates (carbon atom immediately adjacent to the carboxylate group) have often been employed instead of the complete set of coordinates for the corresponding residues. Using the C α carbon coordinates, we showed that particular amino acids are not randomly distributed within a β-sheet structure. On the basis of a new statistical approach for the analysis of a spatial distribution of amino acids in a protein, presented by their physico-chemical parameters, the electron–ion interaction potential (EIIP) and hydrophobicity, are described here. The relationship between amino acid positions inside the β-sheet and the EIIP and hydrophobicity parameters was established. The correlation between amino acid propensities related to the β-sheet was examined using multiple cross-spectra analysis. We also applied the continuous wavelet transform for the analysis of selected β-sheet structures using the EIIP and hydrophobicity parameters. The findings provide new insight into conformational propensities of amino acids for the adaption of β-sheet structures.

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 Molecular Characterization of Pax62Neu Through Pax610Neu: An Extension of the Pax6 Allelic Series and the Identification of Two Possible Hypomorph Alleles in the Mouse Mus musculus

Genetics ◽  
2001 ◽  
Vol 159 (4) ◽  
pp. 1689-1700
Author(s):  
Jack Favor ◽  
Heiko Peters ◽  
Thomas Hermann ◽  
Wolfgang Schmahl ◽  
Bimal Chatterjee ◽  
...  
Keyword(s):  
Amino Acid ◽  
Molecular Characterization ◽  
Amino Acid Substitution ◽  
Gene Dosage ◽  
Three Dimensional ◽  
Null Alleles ◽  
Target Sequence ◽  
Allelic Series ◽  
Phenotypic Analysis ◽  
Major Interest

Abstract Phenotype-based mutagenesis experiments will increase the mouse mutant resource, generating mutations at previously unmarked loci as well as extending the allelic series at known loci. Mapping, molecular characterization, and phenotypic analysis of nine independent Pax6 mutations of the mouse recovered in mutagenesis experiments is presented. Seven mutations result in premature termination of translation and all express phenotypes characteristic of null alleles, suggesting that Pax6 function requires all domains to be intact. Of major interest is the identification of two possible hypomorph mutations: Heterozygotes express less severe phenotypes and homozygotes develop rudimentary eyes and nasal processes and survive up to 36 hr after birth. Pax64Neu results in an amino acid substitution within the third helix of the homeodomain. Three-dimensional modeling indicates that the amino acid substitution interrupts the homeodomain recognition α-helix, which is critical for DNA binding. Whereas cooperative dimer binding of the mutant homeodomain to a paired-class DNA target sequence was eliminated, weak monomer binding was observed. Thus, a residual function of the mutated homeodomain may explain the hypomorphic nature of the Pax64Neu allele. Pax67Neu is a base pair substitution in the Kozak sequence and results in a reduced level of Pax6 translation product. The Pax64Neu and Pax67Neu alleles may be very useful for gene-dosage studies.

Nanoscale control of internal inhomogeneity enhances water transport in desalination membranes

Science ◽  
2020 ◽  
Vol 371 (6524) ◽  
pp. 72-75 ◽  
Author(s):  
Tyler E. Culp ◽  
Biswajit Khara ◽  
Kaitlyn P. Brickey ◽  
Michael Geitner ◽  
Tawanda J. Zimudzi ◽  
...  
Keyword(s):  
Water Transport ◽  
Water Permeability ◽  
Three Dimensional ◽  
Density Fluctuations ◽  
Active Layer Thickness ◽  
Chemical Compositions ◽  
Design Strategies ◽  
Nanoscale Structures ◽  
Density Maps ◽  
Similar Chemical

Biological membranes can achieve remarkably high permeabilities, while maintaining ideal selectivities, by relying on well-defined internal nanoscale structures in the form of membrane proteins. Here, we apply such design strategies to desalination membranes. A series of polyamide desalination membranes—which were synthesized in an industrial-scale manufacturing line and varied in processing conditions but retained similar chemical compositions—show increasing water permeability and active layer thickness with constant sodium chloride selectivity. Transmission electron microscopy measurements enabled us to determine nanoscale three-dimensional polyamide density maps and predict water permeability with zero adjustable parameters. Density fluctuations are detrimental to water transport, which makes systematic control over nanoscale polyamide inhomogeneity a key route to maximizing water permeability without sacrificing salt selectivity in desalination membranes.

scholarly journals Structural analysis of mutations in the Drosophila beta 2-tubulin isoform reveals regions in the beta-tubulin molecular required for general and for tissue-specific microtubule functions.

Genetics ◽  
1995 ◽  
Vol 139 (1) ◽  
pp. 267-286 ◽  
Author(s):  
J D Fackenthal ◽  
J A Hutchens ◽  
F R Turner ◽  
E C Raff
Keyword(s):  
Amino Acid ◽  
Three Dimensional ◽  
Variable Region ◽  
Internal Variable ◽  
Microtubule Assembly ◽  
Dimensional Structure ◽  
Wild Type ◽  
Beta Tubulin ◽  
Assembly Kinetics ◽  
Beta 2

Abstract We have determined the lesions in a number of mutant alleles of beta Tub85D, the gene that encodes the testis-specific beta 2-tubulin isoform in Drosophila melanogaster. Mutations responsible for different classes of functional phenotypes are distributed throughout the beta 2-tubulin molecule. There is a telling correlation between the degree of phylogenetic conservation of the altered residues and the number of different microtubule categories disrupted by the lesions. The majority of lesions occur at positions that are evolutionarily highly conserved in all beta-tubulins; these lesions disrupt general functions common to multiple classes of microtubules. However, a single allele B2t6 contains an amino acid substitution within an internal cluster of variable amino acids that has been identified as an isotype-defining domain in vertebrate beta-tubulins. Correspondingly, B2t6 disrupts only a subset of microtubule functions, resulting in misspecification of the morphology of the doublet microtubules of the sperm tail axoneme. We previously demonstrated that beta 3, a developmentally regulated Drosophila beta-tubulin isoform, confers the same restricted morphological phenotype in a dominant way when it is coexpressed in the testis with wild-type beta 2-tubulin. We show here by complementation analysis that beta 3 and the B2t6 product disrupt a common aspect of microtubule assembly. We therefore conclude that the amino acid sequence of the beta 2-tubulin internal variable region is required for generation of correct axoneme morphology but not for general microtubule functions. As we have previously reported, the beta 2-tubulin carboxy terminal isotype-defining domain is required for suprastructural organization of the axoneme. We demonstrate here that the beta 2 variant lacking the carboxy terminus and the B2t6 variant complement each other for mild-to-moderate meiotic defects but do not complement for proper axonemal morphology. Our results are consistent with the hypothesis drawn from comparisons of vertebrate beta-tubulins that the two isotype-defining domains interact in a three-dimensional structure in wild-type beta-tubulins. We propose that the integrity of this structure in the Drosophila testis beta 2-tubulin isoform is required for proper axoneme assembly but not necessarily for general microtubule functions. On the basis of our observations we present a model for regulation of axoneme microtubule morphology as a function of tubulin assembly kinetics.

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