Biology subjects

DiMaio, F.

Publications and source records attributed to DiMaio, F..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Computational design of sequence-specific DNA-binding proteins

Sequence-specific DNA-binding proteins (DBPs) play critical roles in biology and biotechnology, and there has been considerable interest in the engineering of DBPs with new or altered specificities for genome editing and other applications. While there has been some success in reprogramming naturally occurring DBPs using selection methods, the computational design of new DBPs that recognize arbitrary target sites remains an outstanding challenge. We describe a computational method for the design of small DBPs that recognize specific target sequences through interactions with bases in the major groove, and employ this method in conjunction with experimental screening to generate binders for 5 distinct DNA targets. These binders exhibit specificity closely matching the computational models for the target DNA sequences at as many as 6 base positions and affinities as low as 30-100 nM. The crystal structure of a designed DBP-target site complex is in close agreement with the design model, highlighting the accuracy of the design method. The designed DBPs function in both Escherichia coli and mammalian cells to repress and activate transcription of neighboring genes. Our method is a substantial step towards a general route to small and hence readily deliverable sequence-specific DBPs for gene regulation and editing.

synthetic biology↗

bioRxiv2023

De novo design of highly selective miniprotein inhibitors of integrins αvβ6 and αvβ8

The RGD (Arg-Gly-Asp)-binding integrins v{beta}6 and v{beta}8 are clinically validated cancer and fibrosis targets of considerable therapeutic importance. Compounds that can discriminate between the two closely related integrin proteins and other RGD integrins, stabilize specific conformational states, and have sufficient stability enabling tissue restricted administration could have considerable therapeutic utility. Existing small molecules and antibody inhibitors do not have all of these properties, and hence there is a need for new approaches. Here we describe a method for computationally designing hyperstable RGD-containing miniproteins that are highly selective for a single RGD integrin heterodimer and conformational state, and use this strategy to design inhibitors of v{beta}6 and v{beta}8 with high selectivity. The v{beta}6 and v{beta}8 inhibitors have picomolar affinities for their targets, and >1000-fold selectivity over other RGD integrins. CryoEM structures are within 0.6-0.7[A] root-mean-square deviation (RMSD) to the computational design models; the designed v{beta}6 inhibitor and native ligand stabilize the open conformation in contrast to the therapeutic anti-v{beta}6 antibody BG00011 that stabilizes the bent-closed conformation and caused on-target toxicity in patients with lung fibrosis, and the v{beta}8 inhibitor maintains the constitutively fixed extended-closed v{beta}8 conformation. In a mouse model of bleomycin-induced lung fibrosis, the v{beta}6 inhibitor potently reduced fibrotic burden and improved overall lung mechanics when delivered via oropharyngeal administration mimicking inhalation, demonstrating the therapeutic potential of de novo designed integrin binding proteins with high selectivity.

biochemistry↗

bioRxiv2023

Efficient and accurate prediction of protein structure using RoseTTAFold2

AlphaFold2 and RoseTTAFold predict protein structures with very high accuracy despite substantial architecture differences. We sought to develop an improved method combining features of both. The resulting method, RoseTTAFold2, extends the original three-track architecture of RoseTTAFold over the full network, incorporating the concepts of Frame-aligned point error, recycling during training, and the use of a distillation set from AlphaFold2. We also took from AlphaFold2 the idea of structurally coherent attention in updating pair features, but using a more computationally efficient structure-biased attention as opposed to triangle attention. The resulting model has the accuracy of AlphaFold2 on monomers, and AlphaFold2-multimer on complexes, with better computational scaling for large proteins and complexes. This excellent performance is achieved without hallmark features of AlphaFold2, invariant point attention and triangle attention, indicating that these are not essential for high accuracy prediction. Almost all recent work on protein structure prediction has re-used the basic AlphaFold2 architecture; our results show that excellent performance can be achieved with a broader class of models, opening the door for further exploration.

biochemistry↗

bioRxiv2023

Cyclic peptide structure prediction and design using AlphaFold

Deep learning networks offer considerable opportunities for accurate structure prediction and design of biomolecules. While cyclic peptides have gained significant traction as a therapeutic modality, developing deep learning methods for designing such peptides has been slow, mostly due to the small number of available structures for molecules in this size range. Here, we report approaches to modify the AlphaFold network for accurate structure prediction and design of cyclic peptides. Our results show this approach can accurately predict the structures of native cyclic peptides from a single sequence, with 36 out of 49 cases predicted with high confidence (pLDDT > 0.85) matching the native structure with root mean squared deviation (RMSD) less than 1.5 [A]. Further extending our approach, we describe computational methods for designing sequences of peptide backbones generated by other backbone sampling methods and for de novo design of new macrocyclic peptides. We extensively sampled the structural diversity of cyclic peptides between 7-13 amino acids, and identified around 10,000 unique design candidates predicted to fold into the designed structures with high confidence. X-ray crystal structures for seven sequences with diverse sizes and structures designed by our approach match very closely with the design models (root mean squared deviation < 1.0 [A]), highlighting the atomic level accuracy in our approach. The computational methods and scaffolds developed here provide the basis for custom-designing peptides for targeted therapeutic applications.

biochemistry↗

bioRxiv2022

Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

There has been considerable recent progress in designing new proteins using deep learning methods1-9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.

biochemistry↗

bioRxiv2022

Robust residue-level error detection in cryo-electron microscopy models

Building accurate protein models into moderate resolution (3-5[A]) cryo-electron microscopy (cryo-EM) maps is challenging and error-prone. While the majority of solved cryo-EM structures are at these resolutions, there are few model validation metrics that can precisely evaluate the local quality of atomic models built into these maps. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model to identify residue-level errors in protein structures built into cryo-EM maps. Trained on a set of errors from obsoleted protein structures, our model draws off two major sources of information to predict errors: the local agreement of model and map compared to expected, and how "native-like" the neighborhood around a residue looks, as predicted by a deep learning model. MEDIC is validated on a set of 28 structures that were subsequently solved to higher-resolutions, where our model identifies the differences between low- and high-resolution structures with 68% precision and 60% recall. We additionally use this model to rebuild 12 deposited structures, fixing 2 sequence registration errors, 51 areas with improper secondary structure, 51 incorrect loops, and 16 incorrect carbonyls, showing the value of this approach to guide model building.

biochemistry↗

bioRxiv2022

Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA

Protein-nucleic acid complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein-nucleic acid complexes without homology to known complexes is a largely unsolved problem. Here we extend the RoseTTAFold end-to-end deep learning approach to modeling of nucleic acid and protein-nucleic acid complexes. We develop a single trained network, RoseTTAFoldNA, that rapidly produces 3D structure models with confidence estimates for protein-DNA and protein-RNA complexes, and for RNA tertiary structures. In all three cases, confident predictions have considerably higher accuracy than current state of the art methods. RoseTTAFoldNA should be broadly useful for modeling the structure of naturally occurring protein-nucleic acid complexes, and for designing sequence specific RNA and DNA binding proteins.

bioinformatics↗

bioRxiv2022

Automatic and accurate ligand structure determination guided by cryo-electron microscopy maps

Advances in cryo-electron microscopy (cryoEM) and deep-learning guided protein structure prediction have expedited structural studies of protein complexes. However, methods for accurately determining ligand conformations are lacking. In this manuscript, we develop a tool for automatically determining ligand structures guided by medium-resolution cryoEM density. We show this method is robust at predicting ligands in maps as low as 6[A] resolution, and is able to correct receptor sidechain errors. Combining this with a measure of placement confidence, and running on all protein/ligand structures in EMDB, we show that 58% of ligands replicate the deposited model, 16% confidently find alternate conformations, 22% have ambiguous density where multiple conformations might be present, and 4% are incorrectly placed. For five cases where our approach finds an alternate conformation with high confidence, high-resolution crystal structures validate our placement. This tool and the resulting analysis should prove critical in using cryoEM to investigate protein-ligand complexes.

biochemistry↗

bioRxiv2022

Improving de novo Protein Binder Design with Deep Learning

We explore the improvement of energy-based protein binder design using deep learning. We find that using AlphaFold2 or RoseTTAFold to assess the probability that a designed sequence adopts the designed monomer structure, and the probability that this structure binds the target as designed, increases design success rates nearly 10-fold. We find further that sequence design using ProteinMPNN rather than Rosetta considerably increases computational efficiency.

bioengineering↗

bioRxiv2022

Hallucinating protein assemblies

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 [A]), as are 3 cryoEM structures of giant rings with up to 1550 residues, C33 symmetry, and 10 nanometer in diameter; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be created using deep learning, and pave the way for the design of increasingly complex nanomachines and biomaterials. One-sentence summaryDeep network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.

biophysics↗

bioRxiv2022

Structural basis underlying specific biochemical activities of non-muscle tropomyosin isoforms

The actin cytoskeleton is critical for cell migration, morphogenesis, endocytosis, organelle dynamics, and cytokinesis. To support diverse cellular processes, actin filaments form a variety of structures with specific architectures and dynamic properties. Key proteins specifying actin filaments are tropomyosins. Non-muscle cells express several functionally non-redundant tropomyosin isoforms, which differentially control the interactions of other proteins, including myosins and ADF/cofilin, with actin filaments. However, the underlying molecular mechanisms have remained elusive. By determining the cryogenic electron microscopy structures of actin filaments decorated by two functionally distinct non-muscle tropomyosin isoforms, Tpm1.6 and Tpm3.2, we reveal that actin filament conformation remains unaffected upon binding. However, Tpm1.6 and Tpm3.2 follow different paths along the major groove of the actin filament, providing an explanation for their incapability to co-polymerize on actin filaments. The structures and biochemical work also elucidate the molecular basis underlying specific roles of Tpm1.6 and Tpm3.2 in myosin II activation and protecting actin filaments from ADF/cofilin-catalysed severing.

biochemistry↗

bioRxiv2022

AlphaFold accurately predicts distinct conformations based on oligomeric state of a de novo designed protein

Using the molecular modeling program Rosetta, we designed a de novo protein, called SEWN0.1, that binds the heterotrimeric G protein Gq. The design is helical, well-folded, and primarily monomeric in solution at a concentration of 10 uM. However, when we solved the crystal structure of SEWN0.1, we observed a dimer in a conformation incompatible with binding Gq. Unintentionally, we had designed a protein that adopts alternate conformations depending on its oligomeric state. Recently, there has been tremendous progress in the field of protein structure prediction as new methods in artificial intelligence have been used to predict structures with high accuracy. We were curious if the structure prediction method AlphaFold could predict the structure of SEWN0.1 and if the prediction depended on oligomeric state. When AlphaFold was used to predict the structure of monomeric SEWN0.1, it produced a model that resembles the Rosetta design model and is compatible with binding Gq, but when used to predict the structure of a dimer, it predicted a conformation that closely resembles the SEWN0.1 crystal structure. AlphaFolds ability to predict multiple conformations for a single protein sequence should be useful for engineering protein switches.

biophysics↗

bioRxiv2021

Cryo-EM Structures of CTP Synthase Filaments Reveal Mechanism of pH-Sensitive Assembly During Budding Yeast Starvation

Many metabolic enzymes self-assemble into micron-scale filaments to organize and regulate metabolism. The appearance of these assemblies often coincides with large metabolic changes as in development, cancer, and stress. Yeast undergo cytoplasmic acidification upon starvation, triggering the assembly of many metabolic enzymes into filaments. However, it is unclear how these filaments assemble at the molecular level and what their role is in the yeast starvation response. CTP Synthase (CTPS) assembles into metabolic filaments across many species. Here, we characterize in vitro polymerization and investigate in vivo consequences of CTPS assembly in yeast. Cryo-EM structures reveal a pH-sensitive assembly mechanism and highly ordered filament bundles that stabilize an inactive state of the enzyme, features unique to yeast CTPS. Disruption of filaments in cells with non-assembly or hyper-assembly mutations decreases growth rate, reflecting the importance of regulated CTPS filament assembly in homeotstasis.

biochemistry↗

bioRxiv2021

Genome sequencing of 196 Treponema pallidum strains from six continents reveals additional variability in vaccine candidate genes and dominance of Nichols clade strains in Madagascar

In spite of its immutable susceptibility to penicillin, Treponema pallidum (T. pallidum) subsp. pallidum continues to cause millions of cases of syphilis each year worldwide, resulting in significant morbidity and mortality and underscoring the urgency of developing an effective vaccine to curtail the spread of the infection. Several technical challenges, including absence of an in vitro culture system until very recently, have hampered efforts to catalog the diversity of strains collected worldwide. Here, we provide near-complete genomes from 196 T. pallidum strains - including 191 T. pallidum subsp. pallidum - sequenced directly from patient samples collected from 8 countries and 6 continents. Maximum likelihood phylogeny revealed that samples from most sites were predominantly SS14 clade. However, 99% (84/85) of the samples from Madagascar formed two of the five distinct Nichols subclades. Although recombination was uncommon in the evolution of modern circulating strains, we found multiple putative recombination events between T. pallidum subsp. pallidum and subsp. endemicum, shaping the genomes of several subclades. Temporal analysis dated the most recent common ancestor of Nichols and SS14 clades to 1717 (95% HPD: 1543-1869), in agreement with other recent studies. Rates of SNP accumulation varied significantly among subclades, particularly among different Nichols subclades, and was associated in the Nichols A subclade with a C394F substitution in TP0380, a ERCC3-like DNA repair helicase. Our data highlight the role played by variation in genes encoding putative surface-exposed outer membrane proteins in defining separate lineages, and provide a critical resource for the design of broadly protective syphilis vaccines targeting surface antigens. Author SummaryEach year, millions of new cases of venereal and congenital syphilis, caused by the bacterium Treponema pallidum (T. pallidum) subsp. pallidum, are diagnosed worldwide, resulting in significant morbidity and mortality. Alongside endemic circulation of syphilis in low-income countries, disease resurgence in high-income nations has underscored the need for a vaccine. Due to prior technological limitations in culturing and sequencing the organism, the extent of the genetic diversity within modern strains of T. pallidum subsp. pallidum remains poorly understood, hampering development of a broadly protective vaccine. In this study, we obtained 196 near-complete T. pallidum genomes directly from clinical swabs from eight countries across six continents. Of these, 191 were identified as T. pallidum subsp. pallidum, including 90 Nichols clade genomes. Bayesian analysis revealed a high degree of variance in mutation rate among subclades. Interestingly, a Nichols subclade with a particularly high mutation rate harbors a non-synonymous mutation in a putative DNA repair helicase. Coupling sequencing data with protein structure prediction, we identified multiple novel amino acid variants in several proteins previously identified as potential vaccine candidates. Our data help inform current efforts to develop a broadly protective syphilis vaccine.

genomics↗

bioRxiv2021

Seipin forms a flexible cage at lipid droplet formation sites

Lipid droplets (LDs) form in the endoplasmic reticulum by phase separation of neutral lipids. This process is facilitated by the seipin protein complex, which consists of a ring of seipin monomers, with yet unclear function. Here, we report a structure of yeast seipin based on cryo-electron microscopy and structural modeling data. Seipin forms a decameric, cage-like structure with the lumenal domains forming a stable ring at the cage floor and transmembrane segments forming the cage sides and top. The transmembrane segments interact with adjacent monomers in two distinct, alternating conformations. These conformations result from changes in switch regions, located between the lumenal domains and the transmembrane segments, that are required for seipin function. Our data suggest a model for LD formation in which a closed seipin cage enables TG phase separation and subsequently switches to an open conformation to allow LD growth and budding.

molecular biology↗

bioRxiv2021

Accurate prediction of protein structures and interactions using a 3-track network

DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. One-Sentence SummaryAccurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function.

bioinformatics↗

bioRxiv2021

Structure of the phosphoinositide 3-kinase p110γ-p101 complex reveals molecular mechanism of GPCR activation

The class IB phosphoinositide 3-kinase (PI3K), PI3K{gamma}, is a master regulator of immune cell function, and a promising drug target for both cancer and inflammatory diseases. Critical to PI3K{gamma} function is the association of the p110{gamma} catalytic subunit to either a p101 or p84 regulatory subunit, which mediates activation by G-protein coupled receptors (GPCRs). Here, we report the cryo-EM structure of a heterodimeric PI3K{gamma} complex, p110{gamma}-p101. This structure reveals a unique assembly of catalytic and regulatory subunits that is distinct from other class I PI3K complexes. p101 mediates activation through its G{beta}{gamma} binding domain, recruiting the heterodimer to the membrane and allowing for engagement of a secondary G{beta}{gamma} binding site in p110{gamma}. Multiple oncogenic mutations mapped to these novel interfaces and enhanced G{beta}{gamma} activation. A nanobody that specifically binds to the p101-G{beta}{gamma} interface blocks activation providing a novel tool to study and target p110{gamma}-p101-specific signaling events in vivo.

biochemistry↗

bioRxiv2021

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

bioinformatics↗