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1.
Curr Opin Struct Biol ; 72: 226-236, 2021 Dec 25.
Artigo em Inglês | MEDLINE | ID: mdl-34963082

RESUMO

Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. Those generative models can learn protein representations that are often more informative of protein structure and function than hand-engineered features. Furthermore, they can be used to quickly propose millions of novel proteins that resemble the native counterparts in terms of expression level, stability, or other attributes. The protein design process can further be guided by discriminative oracles to select candidates with the highest probability of having the desired properties. In this review, we discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design.

2.
J Med Chem ; 64(20): 14955-14967, 2021 10 28.
Artigo em Inglês | MEDLINE | ID: mdl-34624194

RESUMO

Blocking the association between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) is an attractive therapeutic approach to prevent the virus from entering human cells. While antibodies and other modalities have been developed to this end, d-amino acid peptides offer unique advantages, including serum stability, low immunogenicity, and low cost of production. Here, we designed potent novel D-peptide inhibitors that mimic the ACE2 α1-binding helix by searching a mirror-image version of the PDB. The two best designs bound the RBD with affinities of 29 and 31 nM and blocked the infection of Vero cells by SARS-CoV-2 with IC50 values of 5.76 and 6.56 µM, respectively. Notably, both D-peptides neutralized with a similar potency the infection of two variants of concern: B.1.1.7 and B.1.351 in vitro. These potent D-peptide inhibitors are promising lead candidates for developing SARS-CoV-2 prophylactic or therapeutic treatments.


Assuntos
Peptídeos , SARS-CoV-2 , Animais , Chlorocebus aethiops , Simulação de Acoplamento Molecular , Células Vero
3.
STAR Protoc ; 2(2): 100505, 2021 Jun 18.
Artigo em Inglês | MEDLINE | ID: mdl-33997819

RESUMO

Computational generation of new proteins with a predetermined three-dimensional shape and computational optimization of existing proteins while maintaining their shape are challenging problems in structural biology. Here, we present a protocol that uses ProteinSolver, a pre-trained graph convolutional neural network, to quickly generate thousands of sequences matching a specific protein topology. We describe computational approaches that can be used to evaluate the generated sequences, and we show how select sequences can be validated experimentally. For complete details on the use and execution of this protocol, please refer to Strokach et al. (2020).

4.
J Mol Biol ; 433(11): 166810, 2021 05 28.
Artigo em Inglês | MEDLINE | ID: mdl-33450251

RESUMO

The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.


Assuntos
Biologia Computacional/métodos , Idioma , Mutação/genética , Redes Neurais de Computação , Software , Algoritmos , Bases de Dados de Proteínas , Internet , Dobramento de Proteína , Reprodutibilidade dos Testes , Interface Usuário-Computador
5.
Cell Syst ; 11(4): 402-411.e4, 2020 10 21.
Artigo em Inglês | MEDLINE | ID: mdl-32971019

RESUMO

Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction problem (CSP), akin to Sudoku puzzles. We trained ProteinSolver on over 70,000,000 real protein sequences corresponding to over 80,000 structures. We show that our method rapidly designs new protein sequences and benchmark them in silico using energy-based scores, molecular dynamics, and structure prediction methods. As a proof-of-principle validation, we use ProteinSolver to generate sequences that match the structure of serum albumin, then synthesize the top-scoring design and validate it in vitro using circular dichroism. ProteinSolver is freely available at http://design.proteinsolver.org and https://gitlab.com/ostrokach/proteinsolver. A record of this paper's transparent peer review process is included in the Supplemental Information.


Assuntos
Engenharia de Proteínas/métodos , Análise de Sequência de Proteína/métodos , Algoritmos , Sequência de Aminoácidos/genética , Simulação por Computador , Bases de Dados de Proteínas , Redes Neurais de Computação , Proteínas/metabolismo , Software
6.
Nucleic Acids Res ; 48(11): 6382-6402, 2020 06 19.
Artigo em Inglês | MEDLINE | ID: mdl-32383734

RESUMO

The Cys2His2 zinc finger is the most common DNA-binding domain expanding in metazoans since the fungi human split. A proposed catalyst for this expansion is an arms race to silence transposable elements yet it remains poorly understood how this domain is able to evolve the required specificities. Likewise, models of its DNA binding specificity remain error prone due to a lack of understanding of how adjacent fingers influence each other's binding specificity. Here, we use a synthetic approach to exhaustively investigate binding geometry, one of the dominant influences on adjacent finger function. By screening over 28 billion protein-DNA interactions in various geometric contexts we find the plasticity of the most common natural geometry enables more functional amino acid combinations across all targets. Further, residues that define this geometry are enriched in genomes where zinc fingers are prevalent and specificity transitions would be limited in alternative geometries. Finally, these results demonstrate an exhaustive synthetic screen can produce an accurate model of domain function while providing mechanistic insight that may have assisted in the domains expansion.


Assuntos
Modelos Moleculares , Domínios Proteicos/fisiologia , Dedos de Zinco/fisiologia , Sequência de Aminoácidos , Animais , Sequência de Bases , DNA/síntese química , DNA/genética , DNA/metabolismo , Aprendizado Profundo , Humanos , Ligação de Hidrogênio , Domínios Proteicos/genética , Reprodutibilidade dos Testes , Especificidade por Substrato/genética , Fatores de Transcrição/química , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo , Dedos de Zinco/genética
7.
J Org Chem ; 85(3): 1644-1651, 2020 02 07.
Artigo em Inglês | MEDLINE | ID: mdl-31893470

RESUMO

Hydrocarbon-stapled peptides are a class of bioactive α-helical ligands developed to target protein-protein interactions. Peptide stapling has benefited from the development of several chemical reactions to modulate their membrane permeability and binding affinity. However, in most current programs, choosing the best stapling positions is usually a trial-and-error process. Here, we develop a protocol to obtain optimal stapling positions computationally. Our method is based on molecular dynamics simulations and free energy calculations with nonequilibrium approaches; here, we predict the binding poses, hot-spot residues, and binding affinity differences of a set of perfluoroarene stapled α-helical peptides of the BIM BH3 peptide to the BCLXL receptor. The prediction of the hot-spot residues within the target peptide through computational alanine scanning anticipates not only the key residues for the receptor-peptide complex formation but also which positions should be avoided when applying the stapling groups. The staple moieties introduce local conformational changes not only in the replaced positions but also on their neighbor residues of the template peptide further affecting their binding behavior. Our approach is successful at rank-ordering the binding affinities of these stapled peptides with respect to the BIM BH3 peptide.


Assuntos
Simulação de Dinâmica Molecular , Peptídeos , Conformação Proteica em alfa-Hélice
8.
Mol Syst Biol ; 16(12): e9310, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-33438817

RESUMO

Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large-scale peptide-phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM-ligand complexes, indicating that a large majority of the phage-derived peptides are likely to target natural peptide-binding sites and could thus act as inhibitors of natural protein-protein interactions. The complete dataset has been assembled in an online database (http://www.prm-db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs.


Assuntos
Bases de Dados de Proteínas , Peptídeos/metabolismo , Inquéritos e Questionários , Sequência de Aminoácidos , Bacteriófagos/metabolismo , Humanos , Ligantes , Peptídeos/química
9.
Hum Mutat ; 40(9): 1414-1423, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31243847

RESUMO

Predicting the impact of mutations on proteins remains an important problem. As part of the CAGI5 frataxin challenge, we evaluate the accuracy with which Provean, FoldX, and ELASPIC can predict changes in the Gibbs free energy of a protein using a limited data set of eight mutations. We find that different methods have distinct strengths and limitations, with no method being strictly superior to other methods on all metrics. ELASPIC achieves the highest accuracy while also providing a web interface which simplifies the evaluation and analysis of mutations. FoldX is slightly less accurate than ELASPIC but is easier to run locally, as it does not depend on external tools or datasets. Provean achieves reasonable results while being computational less expensive than the other methods and not requiring a structure of the protein. In addition to methods submitted to the CAGI5 community experiment, and with the aim to inform about other methods with high accuracy, we also evaluate predictions made by Rosetta's ddg_monomer protocol, Rosetta's cartesian_ddg protocol, and thermodynamic integration calculations using Amber package. ELASPIC still achieves the highest accuracy, while Rosetta's catesian_ddg protocol appears to perform best in capturing the overall trend in the data.


Assuntos
Biologia Computacional/métodos , Proteínas de Ligação ao Ferro/química , Proteínas de Ligação ao Ferro/genética , Mutação , Humanos , Modelos Moleculares , Conformação Proteica , Dobramento de Proteína , Estabilidade Proteica , Termodinâmica
10.
Hum Mutat ; 40(9): 1392-1399, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31209948

RESUMO

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant ( Δ Δ G H 2 O ) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the Δ Δ G H 2 O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.


Assuntos
Substituição de Aminoácidos , Proteínas de Ligação ao Ferro/química , Proteínas de Ligação ao Ferro/genética , Algoritmos , Dicroísmo Circular , Humanos , Modelos Moleculares , Conformação Proteica , Dobramento de Proteína , Estabilidade Proteica
11.
ACS Synth Biol ; 8(5): 918-928, 2019 05 17.
Artigo em Inglês | MEDLINE | ID: mdl-30969105

RESUMO

The accurate determination of protein-protein interactions has been an important focus of molecular biology toward which much progress has been made due to the continuous development of existing and new technologies. However, current methods can have limitations, including scale and restriction to high affinity interactions, limiting our understanding of a large subset of these interactions. Here, we describe a modified bacterial-hybrid assay that employs combined selectable and scalable reporters that enable the sensitive screening of large peptide libraries followed by the sorting of positive interactions by the level of reporter output. We have applied this tool to characterize a set of human and E. coli PDZ domains. Our results are consistent with prior characterization of these proteins, and the improved sensitivity increases our ability to predict known and novel in vivo binding partners. This approach allows for the recovery of a wide range of affinities with a high throughput method that does not sacrifice the scale of the screen.


Assuntos
Escherichia coli/metabolismo , Ensaios de Triagem em Larga Escala/métodos , Peptídeos/metabolismo , Proteínas Adaptadoras de Transdução de Sinal/química , Proteínas Adaptadoras de Transdução de Sinal/metabolismo , Sequência de Aminoácidos , Genes Reporter , Humanos , Proteínas do Tecido Nervoso/química , Proteínas do Tecido Nervoso/metabolismo , Domínios PDZ , Biblioteca de Peptídeos , Peptídeos/química , Ligação Proteica
12.
J Mol Biol ; 431(2): 336-350, 2019 01 18.
Artigo em Inglês | MEDLINE | ID: mdl-30471255

RESUMO

Hydrophobic cores are often viewed as tightly packed and rigid, but they do show some plasticity and could thus be attractive targets for protein design. Here we explored the role of different functional pressures on the core packing and ligand recognition of the SH3 domain from human Fyn tyrosine kinase. We randomized the hydrophobic core and used phage display to select variants that bound to each of three distinct ligands. The three evolved groups showed remarkable differences in core composition, illustrating the effect of different selective pressures on the core. Changes in the core did not significantly alter protein stability, but were linked closely to changes in binding affinity and specificity. Structural analysis and molecular dynamics simulations revealed the structural basis for altered specificity. The evolved domains had significantly reduced core volumes, which in turn induced increased backbone flexibility. These motions were propagated from the core to the binding surface and induced significant conformational changes. These results show that alternative core packing and consequent allosteric modulation of binding interfaces could be used to engineer proteins with novel functions.


Assuntos
Regulação Alostérica/fisiologia , Ligação Proteica/fisiologia , Proteínas Proto-Oncogênicas c-fyn/metabolismo , Domínios de Homologia de src/fisiologia , Sequência de Aminoácidos , Humanos , Interações Hidrofóbicas e Hidrofílicas , Ligantes , Simulação de Dinâmica Molecular , Conformação Proteica
13.
Proteins ; 87(3): 236-244, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30520126

RESUMO

Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.


Assuntos
Endodesoxirribonucleases/química , Neoplasias Pancreáticas/tratamento farmacológico , Peptídeos/química , Termodinâmica , Aminoácidos/química , Sobrevivência Celular/efeitos dos fármacos , Endodesoxirribonucleases/antagonistas & inibidores , Endodesoxirribonucleases/genética , Humanos , Simulação de Dinâmica Molecular , Mutação/genética , Neoplasias Pancreáticas/genética , Peptídeos/genética , Peptídeos/farmacologia , Ligação Proteica
14.
Methods Mol Biol ; 1851: 1-17, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30298389

RESUMO

The function of a protein is largely determined by its three-dimensional structure and its interactions with other proteins. Changes to a protein's amino acid sequence can alter its function by perturbing the energy landscapes of protein folding and binding. Many tools have been developed to predict the energetic effect of amino acid changes, utilizing features describing the sequence of a protein, the structure of a protein, or both. Those tools can have many applications, such as distinguishing between deleterious and benign mutations and designing proteins and peptides with attractive properties. In this chapter, we describe how to use one of such tools, ELASPIC, to predict the effect of mutations on the stability of proteins and the affinity between proteins, in the context of a human protein-protein interaction network. ELASPIC uses a wide range of sequential and structural features to predict the change in the Gibbs free energy for protein folding and protein-protein interactions. It can be used both through a web server and as a stand-alone application. Since ELASPIC was trained using homology models and not crystal structures, it can be applied to a much broader range of proteins than traditional methods. It can leverage precalculated sequence alignments, homology models, and other features, in order to drastically lower the amount of time required to evaluate individual mutations and make tractable the analysis of millions of mutations affecting the majority of proteins in a genome.


Assuntos
Biologia Computacional/métodos , Mutação/genética , Proteínas/metabolismo , Ligação Proteica , Engenharia de Proteínas , Dobramento de Proteína , Estabilidade Proteica , Proteínas/genética , Termodinâmica
15.
Nat Struct Mol Biol ; 25(12): 1093-1102, 2018 12.
Artigo em Inglês | MEDLINE | ID: mdl-30455435

RESUMO

The cell division cycle consists of a series of temporally ordered events. Cell cycle kinases and phosphatases provide key regulatory input, but how the correct substrate phosphorylation and dephosphorylation timing is achieved is incompletely understood. Here we identify a PxL substrate recognition motif that instructs dephosphorylation by the budding yeast Cdc14 phosphatase during mitotic exit. The PxL motif was prevalent in Cdc14-binding peptides enriched in a phage display screen of native disordered protein regions. PxL motif removal from the Cdc14 substrate Cbk1 delays its dephosphorylation, whereas addition of the motif advances dephosphorylation of otherwise late Cdc14 substrates. Crystal structures of Cdc14 bound to three PxL motif substrate peptides provide a molecular explanation for PxL motif recognition on the phosphatase surface. Our results illustrate the sophistication of phosphatase-substrate interactions and identify them as an important determinant of ordered cell cycle progression.


Assuntos
Motivos de Aminoácidos/fisiologia , Divisão Celular , Saccharomyces cerevisiae/citologia , Proteínas de Ciclo Celular , Mitose , Modelos Moleculares , Fosforilação , Proteínas Tirosina Fosfatases , Saccharomyces cerevisiae/enzimologia , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae , Análise de Sequência de Proteína
16.
Curr Opin Struct Biol ; 50: 162-170, 2018 06.
Artigo em Inglês | MEDLINE | ID: mdl-29730529

RESUMO

Protein-protein interactions (PPIs) are essential to governing virtually all cellular processes. Of particular importance are the versatile motif-mediated interactions (MMIs), which are thus far underrepresented in available interaction data. This is largely due to technical difficulties inherent in the properties of MMIs, but due to the increasing recognition of the vital roles of MMIs in biology, several systematic approaches have recently been developed to detect novel MMIs. Consequently, rapidly growing numbers of motifs are being identified and pursued further for therapeutic applications. In this review, we discuss the current understanding on the diverse functions and disease-relevance of MMIs, the key methodologies for detection of MMIs, and the potential of MMIs for drug development.


Assuntos
Motivos de Aminoácidos , Domínios e Motivos de Interação entre Proteínas , Proteínas/química , Descoberta de Drogas , Ligação Proteica , Conformação Proteica , Mapeamento de Interação de Proteínas/métodos , Mapas de Interação de Proteínas/efeitos dos fármacos , Proteínas/metabolismo , Relação Quantitativa Estrutura-Atividade
17.
Proc Natl Acad Sci U S A ; 115(7): 1505-1510, 2018 02 13.
Artigo em Inglês | MEDLINE | ID: mdl-29378946

RESUMO

Biologics are a rapidly growing class of therapeutics with many advantages over traditional small molecule drugs. A major obstacle to their development is that proteins and peptides are easily destroyed by proteases and, thus, typically have prohibitively short half-lives in human gut, plasma, and cells. One of the most effective ways to prevent degradation is to engineer analogs from dextrorotary (D)-amino acids, with up to 105-fold improvements in potency reported. We here propose a general peptide-engineering platform that overcomes limitations of previous methods. By creating a mirror image of every structure in the Protein Data Bank (PDB), we generate a database of ∼2.8 million D-peptides. To obtain a D-analog of a given peptide, we search the (D)-PDB for similar configurations of its critical-"hotspot"-residues. As a proof of concept, we apply our method to two peptides that are Food and Drug Administration approved as therapeutics for diabetes and osteoporosis, respectively. We obtain D-analogs that activate the GLP1 and PTH1 receptors with the same efficacy as their natural counterparts and show greatly increased half-life.


Assuntos
Aminoácidos/química , Bases de Dados de Proteínas , Peptídeos/química , Engenharia de Proteínas/métodos , Algoritmos , Peptídeo 1 Semelhante ao Glucagon/agonistas , Peptídeo 1 Semelhante ao Glucagon/química , Peptídeo 1 Semelhante ao Glucagon/metabolismo , Receptor do Peptídeo Semelhante ao Glucagon 1/metabolismo , Células HEK293 , Meia-Vida , Humanos , Hormônio Paratireóideo/agonistas , Hormônio Paratireóideo/química , Hormônio Paratireóideo/metabolismo , Peptídeos/metabolismo , Peptídeos/farmacocinética , Conformação Proteica , Receptor Tipo 1 de Hormônio Paratireóideo/metabolismo , Reprodutibilidade dos Testes
18.
Nat Commun ; 9(1): 297, 2018 01 19.
Artigo em Inglês | MEDLINE | ID: mdl-29352116

RESUMO

Gene expression is a complex stochastic process composed of numerous enzymatic reactions with rates coupled to hidden cell-state variables. Despite advances in single-cell technologies, the lack of a theory accurately describing the gene expression process has restricted a robust, quantitative understanding of gene expression variability among cells. Here we present the Chemical Fluctuation Theorem (CFT), providing an accurate relationship between the environment-coupled chemical dynamics of gene expression and gene expression variability. Combined with a general, accurate model of environment-coupled transcription processes, the CFT provides a unified explanation of mRNA variability for various experimental systems. From this analysis, we construct a quantitative model of transcription dynamics enabling analytic predictions for the dependence of mRNA noise on the mRNA lifetime distribution, confirmed against stochastic simulation. This work suggests promising new directions for quantitative investigation into cellular control over biological functions by making complex dynamics of intracellular reactions accessible to rigorous mathematical deductions.


Assuntos
Expressão Gênica , Modelos Genéticos , RNA Mensageiro/metabolismo , Simulação por Computador , Meio Ambiente , Humanos , Processos Estocásticos , Transcrição Genética
19.
PLoS One ; 12(11): e0187524, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29108013

RESUMO

Redesigning protein surface topology to improve target binding holds great promise in the search for highly selective therapeutics. While significant binding improvements can be achieved using natural amino acids, the introduction of non-canonical residues vastly increases sequence space and thus the chance to significantly out-compete native partners. The potency of protein inhibitors can be further enhanced by synthesising mirror image, D-amino versions. This renders them non-immunogenic and makes them highly resistant to proteolytic degradation. Current experimental design methods often preclude the use of D-amino acids and non-canonical amino acids for a variety of reasons. To address this, we build an in silico pipeline for D-protein designs featuring non-canonical amino acids. For a test scaffold we use an existing D-protein inhibitor of VEGF: D-RFX001. We benchmark the approach by recapitulating previous experimental optimisation with canonical amino acids. Subsequent incorporation of non-canonical amino acids allows designs that are predicted to improve binding affinity by up to -7.18 kcal/mol.


Assuntos
Aminoácidos/química , Proteínas/química , Sequência de Aminoácidos , Proteínas/metabolismo , Fator A de Crescimento do Endotélio Vascular/antagonistas & inibidores
20.
Structure ; 25(10): 1598-1610.e3, 2017 10 03.
Artigo em Inglês | MEDLINE | ID: mdl-28890361

RESUMO

SH3 domains are protein modules that mediate protein-protein interactions in many eukaryotic signal transduction pathways. The majority of SH3 domains studied thus far act by binding to proline-rich sequences in partner proteins, but a growing number of studies have revealed alternative recognition mechanisms. We have comprehensively surveyed the specificity landscape of human SH3 domains in an unbiased manner using peptide-phage display and deep sequencing. Based on ∼70,000 unique binding peptides, we obtained 154 specificity profiles for 115 SH3 domains, which reveal that roughly half of the SH3 domains exhibit non-canonical specificities and collectively recognize a wide variety of peptide motifs, most of which were previously unknown. Crystal structures of SH3 domains with two distinct non-canonical specificities revealed novel peptide-binding modes through an extended surface outside of the canonical proline-binding site. Our results constitute a significant contribution toward a complete understanding of the mechanisms underlying SH3-mediated cellular responses.


Assuntos
Sequenciamento de Nucleotídeos em Larga Escala/métodos , Proteínas/química , Proteínas/metabolismo , Análise de Sequência de Proteína/métodos , Animais , Humanos , Modelos Moleculares , Biblioteca de Peptídeos , Peptídeos/metabolismo , Ligação Proteica , Conformação Proteica , Proteínas/genética , Domínios de Homologia de src
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