RESUMEN

Energetically favorable local interactions can overcome the entropic cost of chain ordering and cause otherwise flexible polymers to adopt regularly repeating backbone conformations. A prominent example is the α helix present in many protein structures, which is stabilized by i, i + 4 hydrogen bonds between backbone peptide units. With the increased chemical diversity offered by unnatural amino acids and backbones, it has been possible to identify regularly repeating structures not present in proteins, but to date, there has been no systematic approach for identifying new polymers likely to have such structures despite their considerable potential for molecular engineering. Here we describe a systematic approach to search through dipeptide combinations of 130 chemically diverse amino acids to identify those predicted to populate unique low-energy states. We characterize ten newly identified dipeptide repeating structures using circular dichroism spectroscopy and comparison with calculated spectra. NMR and X-ray crystallographic structures of two of these dipeptide-repeat polymers are similar to the computational models. Our approach is readily generalizable to identify low-energy repeating structures for a wide variety of polymers, and our ordered dipeptide repeats provide new building blocks for molecular engineering.

Asunto(s)

Péptidos , Péptidos/química , Estructura Secundaria de Proteína , Dipéptidos/química , Modelos Moleculares , Cristalografía por Rayos X

RESUMEN

RHOA mutations are found at diverse residues in various cancer types, implying mutation- and cell-specific mechanisms of tumorigenesis. Here, we focus on the underlying mechanisms of two gain-of-function RHOA mutations, A161P and A161V, identified in adult T-cell leukemia/lymphoma. We find that RHOAA161P and RHOAA161V are both fast-cycling mutants with increased guanine nucleotide dissociation/association rates compared with RHOAWT and show reduced GTP-hydrolysis activity. Crystal structures reveal an altered nucleotide association in RHOAA161P and an open nucleotide pocket in RHOAA161V. Both mutations perturb the dynamic properties of RHOA switch regions and shift the conformational landscape important for RHOA activity, as shown by 31P NMR and molecular dynamics simulations. Interestingly, RHOAA161P and RHOAA161V can interact with effectors in the GDP-bound state. 1H-15N HSQC NMR spectra support the existence of an active population in RHOAA161V-GDP. The distinct interaction mechanisms resulting from the mutations likely favor an RHOAWT-like "ON" conformation, endowing GDP-bound state effector binding activity.

Asunto(s)

Guanosina Difosfato , Simulación de Dinámica Molecular , Proteína de Unión al GTP rhoA , Proteína de Unión al GTP rhoA/metabolismo , Proteína de Unión al GTP rhoA/genética , Guanosina Difosfato/metabolismo , Humanos , Mutación , Cristalografía por Rayos X , Unión Proteica , Guanosina Trifosfato/metabolismo , Conformación Proteica , Mutación con Ganancia de Función

RESUMEN

A general approach to design proteins that bind tightly and specifically to intrinsically disordered regions (IDRs) of proteins and flexible peptides would have wide application in biological research, therapeutics, and diagnosis. However, the lack of defined structures and the high variability in sequence and conformational preferences has complicated such efforts. We sought to develop a method combining biophysical principles with deep learning to readily generate binders for any disordered sequence. Instead of assuming a fixed regular structure for the target, general recognition is achieved by threading the query sequence through diverse extended binding modes in hundreds of templates with varying pocket depths and spacings, followed by RFdiffusion refinement to optimize the binder-target fit. We tested the method by designing binders to 39 highly diverse unstructured targets. Experimental testing of ~36 designs per target yielded binders with affinities better than 100 nM in 34 cases, and in the pM range in four cases. The co-crystal structure of a designed binder in complex with dynorphin A is closely consistent with the design model. All by all binding experiments for 20 designs binding diverse targets show they are highly specific for the intended targets, with no crosstalk even for the closely related dynorphin A and dynorphin B. Our approach thus could provide a general solution to the intrinsically disordered protein and peptide recognition problem.

RESUMEN

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NEF and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB restraint violation report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.

Asunto(s)

Bases de Datos de Proteínas , Modelos Moleculares , Resonancia Magnética Nuclear Biomolecular , Conformación Proteica , Proteínas , Resonancia Magnética Nuclear Biomolecular/métodos , Proteínas/química , Programas Informáticos

RESUMEN

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.

RESUMEN

Multidimensional NMR spectra are the basis for studying proteins by NMR spectroscopy and crucial for the development and evaluation of methods for biomolecular NMR data analysis. Nevertheless, in contrast to derived data such as chemical shift assignments in the BMRB and protein structures in the PDB databases, this primary data is in general not publicly archived. To change this unsatisfactory situation, we present a standardized set of solution NMR data comprising 1329 2-4-dimensional NMR spectra and associated reference (chemical shift assignments, structures) and derived (peak lists, restraints for structure calculation, etc.) annotations. With the 100-protein NMR spectra dataset that was originally compiled for the development of the ARTINA deep learning-based spectra analysis method, 100 protein structures can be reproduced from their original experimental data. The 100-protein NMR spectra dataset is expected to help the development of computational methods for NMR spectroscopy, in particular machine learning approaches, and enable consistent and objective comparisons of these methods.

Asunto(s)

Imagen por Resonancia Magnética , Proteínas , Algoritmos , Espectroscopía de Resonancia Magnética , Resonancia Magnética Nuclear Biomolecular/métodos , Proteínas/química

RESUMEN

Biomolecules exhibit dynamic behavior that single-state models of their structures cannot fully capture. We review some recent advances for investigating multiple conformations of biomolecules, including experimental methods, molecular dynamics simulations, and machine learning. We also address the challenges associated with representing single- and multiple-state models in data archives, with a particular focus on NMR structures. Establishing standardized representations and annotations will facilitate effective communication and understanding of these complex models to the broader scientific community.

Asunto(s)

Simulación de Dinámica Molecular , Proteínas , Proteínas/química , Conformación Molecular , Espectroscopía de Resonancia Magnética , Conformación Proteica

RESUMEN

Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.

Asunto(s)

Lagartos , Péptidos Cíclicos , Animales , Péptidos Cíclicos/metabolismo , Lagartos/metabolismo , Péptidos/química , Conformación Molecular , Permeabilidad de la Membrana Celular

RESUMEN

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Asunto(s)

Furilfuramida , Proteínas , Conformación Proteica , Microscopía por Crioelectrón , Resonancia Magnética Nuclear Biomolecular/métodos , Proteínas/química

RESUMEN

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research. Highlights: AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.

RESUMEN

NMR is a valuable experimental tool in the structural biologist's toolkit to elucidate the structures, functions, and motions of biomolecules. The progress of machine learning, particularly in structural biology, reveals the critical importance of large, diverse, and reliable datasets in developing new methods and understanding in structural biology and science more broadly. Biomolecular NMR research groups produce large amounts of data, and there is renewed interest in organizing these data to train new, sophisticated machine learning architectures and to improve biomolecular NMR analysis pipelines. The foundational data type in NMR is the free-induction decay (FID). There are opportunities to build sophisticated machine learning methods to tackle long-standing problems in NMR data processing, resonance assignment, dynamics analysis, and structure determination using NMR FIDs. Our goal in this study is to provide a lightweight, broadly available tool for archiving FID data as it is generated at the spectrometer, and grow a new resource of FID data and associated metadata. This study presents a relational schema for storing and organizing the metadata items that describe an NMR sample and FID data, which we call Spectral Database (SpecDB). SpecDB is implemented in SQLite and includes a Python software library providing a command-line application to create, organize, query, backup, share, and maintain the database. This set of software tools and database schema allow users to store, organize, share, and learn from NMR time domain data. SpecDB is freely available under an open source license at https://github.rpi.edu/RPIBioinformatics/SpecDB.

Asunto(s)

Programas Informáticos , Espectroscopía de Resonancia Magnética/métodos , Resonancia Magnética Nuclear Biomolecular/métodos

RESUMEN

We use computational design coupled with experimental characterization to systematically investigate the design principles for macrocycle membrane permeability and oral bioavailability. We designed 184 6-12 residue macrocycles with a wide range of predicted structures containing noncanonical backbone modifications and experimentally determined structures of 35; 29 are very close to the computational models. With such control, we show that membrane permeability can be systematically achieved by ensuring all amide (NH) groups are engaged in internal hydrogen bonding interactions. 84 designs over the 6-12 residue size range cross membranes with an apparent permeability greater than 1 × 10-6 cm/s. Designs with exposed NH groups can be made membrane permeable through the design of an alternative isoenergetic fully hydrogen-bonded state favored in the lipid membrane. The ability to robustly design membrane-permeable and orally bioavailable peptides with high structural accuracy should contribute to the next generation of designed macrocycle therapeutics.

Asunto(s)

Amidas , Péptidos , Amidas/química , Hidrógeno , Enlace de Hidrógeno , Lípidos , Péptidos/química

RESUMEN

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.

RESUMEN

There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences1-3. Here we investigate whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occurring proteins used in training the models. We generate random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting residue-residue distance maps, which, as expected, are quite featureless. We then carry out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (Kullback-Leibler divergence) between the inter-residue distance distributions predicted by the network and background distributions averaged over all proteins. Optimization from different random starting points resulted in novel proteins spanning a wide range of sequences and predicted structures. We obtained synthetic genes encoding 129 of the network-'hallucinated' sequences, and expressed and purified the proteins in Escherichia coli; 27 of the proteins yielded monodisperse species with circular dichroism spectra consistent with the hallucinated structures. We determined the three-dimensional structures of three of the hallucinated proteins, two by X-ray crystallography and one by NMR, and these closely matched the hallucinated models. 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 physics-based models to the de novo design of proteins with new functions.

Asunto(s)

Redes Neurales de la Computación , Proteínas , Secuencia de Aminoácidos , Cristalografía por Rayos X , Alucinaciones , Humanos , Conformación Proteica , Proteínas/química , Proteínas/genética

RESUMEN

Effective control of COVID-19 requires antivirals directed against SARS-CoV-2. We assessed 10 hepatitis C virus (HCV) protease-inhibitor drugs as potential SARS-CoV-2 antivirals. There is a striking structural similarity of the substrate binding clefts of SARS-CoV-2 main protease (Mpro) and HCV NS3/4A protease. Virtual docking experiments show that these HCV drugs can potentially bind into the Mpro substrate-binding cleft. We show that seven HCV drugs inhibit both SARS-CoV-2 Mpro protease activity and SARS-CoV-2 virus replication in Vero and/or human cells. However, their Mpro inhibiting activities did not correlate with their antiviral activities. This conundrum is resolved by demonstrating that four HCV protease inhibitor drugs, simeprevir, vaniprevir, paritaprevir, and grazoprevir inhibit the SARS CoV-2 papain-like protease (PLpro). HCV drugs that inhibit PLpro synergize with the viral polymerase inhibitor remdesivir to inhibit virus replication, increasing remdesivir's antiviral activity as much as 10-fold, while those that only inhibit Mpro do not synergize with remdesivir.

Asunto(s)

Antivirales/farmacología , Tratamiento Farmacológico de COVID-19 , Proteasas Similares a la Papaína de Coronavirus/antagonistas & inhibidores , SARS-CoV-2/efectos de los fármacos , SARS-CoV-2/enzimología , Adenosina Monofosfato/análogos & derivados , Adenosina Monofosfato/farmacología , Alanina/análogos & derivados , Alanina/farmacología , COVID-19/virología , Técnicas de Cultivo de Célula , Línea Celular , Proteasas Similares a la Papaína de Coronavirus/metabolismo , Reposicionamiento de Medicamentos/métodos , Sinergismo Farmacológico , Hepacivirus/efectos de los fármacos , Hepatitis C/tratamiento farmacológico , Humanos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Inhibidores de Proteasas/farmacología , Replicación Viral/efectos de los fármacos

RESUMEN

Proanthocyanidins (condensed tannins) are important in food chemistry, agriculture, and health, driving demand for improvements in structure determination. We used ultrahigh resolution Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) methods to determine the exact composition of individual species in heterogeneous mixtures of proanthocyanidin polymers from Sorghum bicolor grain and Neptunia lutea leaves. Fragmentation patterns obtained with FT-ICR ESI MS-MS (electrospray ionization) confirmed structural details from thiolysis-high-performance liquid chromatography (HPLC)-diode array detection (DAD) and 1H-13C heteronuclear single quantum coherence (HSQC) NMR. We found that A-type linkages were characteristic of shorter polymers in predominantly B-linked proanthocyanidin. We suggest that supramolecular complex formation between proanthocyanidins and matrix components such as 2,5-dihydroxybenzoic acid was responsible for anomalous 152 dalton peaks, incorrectly assigned as 3-O-galloylation, when using FT-ICR matrix-assisted laser desorption ionization (MALDI-MS). Our data illustrate the power of the ultrahigh resolution FT-ICR methods but include the caveat that MALDI-MS must be paired with complementary analytical tools to avoid artifacts.

Asunto(s)

Fabaceae , Proantocianidinas , Cromatografía Líquida de Alta Presión , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción

RESUMEN

Rational design of protein-polymer bioconjugates is hindered by limited experimental data and mechanistic understanding on interactions between the two. In this communication, nuclear magnetic resonance (NMR) paramagnetic relaxation enhancement (PRE) reports on distances between paramagnetic spin labels and NMR active nuclei, informing on the conformation of conjugated polymers. 1H/15N-heteronuclear single quantum coherence (HSQC) NMR spectra were collected for ubiquitin (Ub) modified with block copolymers incorporating spin labels at different positions along their backbone. The resultant PRE data show that the conjugated polymers have conformations biased towards the nonpolar ß-sheet face of Ub, rather than behaving as if in solution. The bioconjugates are stabilized against denaturation by guanidine-hydrochloride, as measured by circular dichroism (CD), and this stabilization is attributed to the interaction between the protein and conjugated polymer.

RESUMEN

SP_0782 from Streptococcus pneumoniae is a dimeric protein that potentially binds with single-stranded DNA (ssDNA) in a manner similar to human PC4, the prototype of PC4-like proteins, which plays roles in transcription and maintenance of genome stability. In a previous NMR study, SP_0782 exhibited an ssDNA-binding property different from YdbC, a prokaryotic PC4-like protein from Lactococcus lactis, but the underlying mechanism remains unclear. Here, we show that although SP_0782 adopts an overall fold similar to those of PC4 and YdbC, the ssDNA length occupied by SP_0782 is shorter than those occupied by PC4 and YdbC. SP_0782 exhibits varied binding patterns for different lengths of ssDNA, and tends to form large complexes with ssDNA in a potential high-density binding manner. The structures of SP_0782 complexed with different ssDNAs reveal that the varied binding patterns are associated with distinct capture of nucleotides in two major DNA-binding regions of SP_0782. Moreover, a comparison of known structures of PC4-like proteins complexed with ssDNA reveals a divergence in the binding interface between prokaryotic and eukaryotic PC4-like proteins. This study provides insights into the ssDNA-binding mechanism of PC4-like proteins, and benefits further study regarding the biological function of SP_0782, probably in DNA protection and natural transformation.

Asunto(s)

Proteínas Bacterianas/química , ADN Bacteriano/química , ADN de Cadena Simple/química , Proteínas de Unión al ADN/química , Streptococcus pneumoniae/genética , Factores de Transcripción/química , Proteínas Bacterianas/genética , Proteínas Bacterianas/metabolismo , Sitios de Unión , Cristalografía por Rayos X , ADN Bacteriano/genética , ADN Bacteriano/metabolismo , ADN de Cadena Simple/genética , ADN de Cadena Simple/metabolismo , Proteínas de Unión al ADN/genética , Proteínas de Unión al ADN/metabolismo , Humanos , Cinética , Lactococcus lactis/genética , Lactococcus lactis/metabolismo , Modelos Moleculares , Conformación de Ácido Nucleico , Unión Proteica , Conformación Proteica en Hélice alfa , Conformación Proteica en Lámina beta , Pliegue de Proteína , Dominios y Motivos de Interacción de Proteínas , Streptococcus pneumoniae/metabolismo , Termodinámica , Factores de Transcripción/genética , Factores de Transcripción/metabolismo

RESUMEN

Protein CGL2373 from Corynebacterium glutamicum was previously proposed to be a member of the polyketide_cyc2 family, based on amino-acid sequence and secondary structure features derived from NMR chemical shift assignments. We report here the solution NMR structure of CGL2373, which contains three α-helices and one antiparallel ß-sheet and adopts a helix-grip fold. This structure shows moderate similarities to the representative polyketide cyclases, TcmN, WhiE, and ZhuI. Nevertheless, unlike the structures of these homologs, CGL2373 structure looks like a half-open shell with a much larger pocket, and key residues in the representative polyketide cyclases for binding substrate and catalyzing aromatic ring formation are replaced with different residues in CGL2373. Also, the gene cluster where the CGL2373-encoding gene is located in C. glutamicum contains additional genes encoding nucleoside diphosphate kinase, folylpolyglutamate synthase, and valine-tRNA ligase, different from the typical gene cluster encoding polyketide cyclase in Streptomyces. Thus, although CGL2373 is structurally a polyketide cyclase-like protein, the function of CGL2373 may differ from the known polyketide cyclases and needs to be further investigated. The solution structure of CGL2373 lays a foundation for in silico ligand screening and binding site identifying in future functional study.

Asunto(s)

Proteínas Bacterianas/genética , Corynebacterium glutamicum/ultraestructura , Complejos Multienzimáticos/ultraestructura , Conformación Proteica , Secuencia de Aminoácidos/genética , Proteínas Bacterianas/ultraestructura , Sitios de Unión/genética , Corynebacterium glutamicum/química , Cristalografía por Rayos X , Complejos Multienzimáticos/genética , Policétidos/química , Policétidos/metabolismo , Estructura Secundaria de Proteína , Streptomyces/genética

RESUMEN

Psilocybin, the prodrug of the psychoactive molecule psilocin, has demonstrated promising results in clinical trials for the treatment of addiction, depression, and post-traumatic stress disorder. The development of a psilocybin production platform in a highly engineerable microbe could lead to rapid advances towards the bioproduction of psilocybin for use in ongoing clinical trials. Here, we present the development of a modular biosynthetic production platform in the model microbe, Escherichia coli. Efforts to optimize and improve pathway performance using multiple genetic optimization techniques were evaluated, resulting in a 32-fold improvement in psilocybin titer. Further enhancements to this genetically superior strain were achieved through fermentation optimization, ultimately resulting in a fed-batch fermentation study, with a production titer of 1.16 g/L of psilocybin. This is the highest psilocybin titer achieved to date from a recombinant organism and a significant step towards demonstrating the feasibility of industrial production of biologically-derived psilocybin.

Asunto(s)

Técnicas de Cultivo Celular por Lotes , Escherichia coli , Ingeniería Metabólica , Psilocibina , Escherichia coli/genética , Escherichia coli/crecimiento & desarrollo , Psilocibina/biosíntesis , Psilocibina/genética