Biology subjects

Baker, D.

Publications and source records attributed to Baker, D..

At least 235 records · Page 13Linked to original sources

bioRxiv2020

Learning a force field from small-molecule crystal lattice predictions enables consistent sub-Angstrom protein-ligand docking

Accurate and rapid calculation of protein-small molecule interaction energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here we describe a new approach in which small-molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures. We optimize parameters by requiring that the experimentally determined molecular lattice arrangements have lower energy than all alternative lattice arrangements. Thousands of independent crystal lattice-prediction simulations were run on each of 1,386 small molecule crystal structures, and energy function parameters of an implicit solvent energy model were optimized so native crystal lattice arrangements had lowest energy. The resulting energy model was implemented in Rosetta, together with a rapid genetic algorithm docking method employing grid based scoring and receptor flexibility. The success rate of bound structure recapitulation in cross-docking on 1,112 complexes was improved by more than 10% over previously published methods, with solutions within <1 [A] in over half of the cases. Our results demonstrate that small molecule crystal structures are a rich source of information for systematically improving computational drug discovery.

biophysics↗

bioRxiv2020

De novo design of picomolar SARS-CoV-2 miniprotein inhibitors

We used two approaches to design proteins with shape and chemical complementarity to the receptor binding domain (RBD) of SARS-CoV-2 Spike protein near the binding site for the human ACE2 receptor. Scaffolds were built around an ACE2 helix that interacts with the RBD, or de novo designed scaffolds were docked against the RBD to identify new binding modes. In both cases, designed sequences were optimized first in silico and then experimentally for target binding, folding and stability. Nine designs bound the RBD with affinities ranging from 100pM to 10nM, and blocked bona fide SARS-CoV-2 infection of Vero E6 cells with IC50 values ranging from 35 pM to 35 nM; the most potent of these -- 56 and 64 residue hyperstable proteins made using the second approach -- are roughly six times more potent on a per mass basis (IC50 ~ 0.23 ng/ml) than the best monoclonal antibodies reported thus far. Cryo-electron microscopy structures of the SARS-CoV-2 spike ectodomain trimer in complex with the two most potent minibinders show that the structures of the designs and their binding interactions with the RBD are nearly identical to the computational models, and that all three RBDs in a single Spike protein can be engaged simultaneously. These hyperstable minibinders provide promising starting points for new SARS-CoV-2 therapeutics, and illustrate the power of computational protein design for rapidly generating potential therapeutic candidates against pandemic threats.

synthetic biology↗

bioRxiv2020

Hierarchical design of multi-scale protein complexes by combinatorial assembly of oligomeric helical bundle and repeat protein building blocks

A goal of de novo protein design is to develop a systematic and robust approach to generating complex nanomaterials from stable building blocks. Due to their structural regularity and simplicity, a wide range of monomeric repeat proteins and oligomeric helical bundle structures have been designed and characterized. Here we describe a stepwise hierarchical approach to building up multi-component symmetric protein assemblies using these structures. We first connect designed helical repeat proteins (DHRs) to designed helical bundle proteins (HBs) to generate a large library of heterodimeric and homooligomeric building blocks; the latter have cyclic symmetries ranging from C2 to C6. All of the building blocks have repeat proteins with accessible termini, which we take advantage of in a second round of architecture guided rigid helical fusion (WORMS) to generate larger symmetric assemblies including C3 and C5 cyclic and D2 dihedral rings, a tetrahedral cage, and a 120 subunit icosahedral cage. Characterization of the structures by small angle x-ray scattering, x-ray crystallography, and cryo-electron microscopy demonstrates that the hierarchical design approach can accurately and robustly generate a wide range of macromolecular assemblies; with a diameter of 43nm, the icosahedral nanocage is the largest structurally validated designed cage to date. The computational methods and building block sets described here provide a very general route to new de novo designed symmetric protein nanomaterials.

biochemistry↗

bioRxiv2020

Protein sequence design by explicit energy landscape optimization

The protein design problem is to identify an amino acid sequence which folds to a desired structure. Given Anfinsens 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.

biophysics↗

bioRxiv2020

De novo protein design by deep network hallucination

There has been considerable recent progress in protein structure prediction using deep neural networks to infer distance constraints from amino acid residue co-evolution1-3. We investigated whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occuring proteins used in training the models. We generated random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting distance maps, which as expected are quite featureless. We then carried out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (KL-divergence) between the distance distributions predicted by the network and the background distribution. Optimization from different random starting points resulted in a wide range of proteins with diverse sequences and all alpha, all beta sheet, and mixed alpha-beta structures. We obtained synthetic genes encoding 129 of these network hallucinated sequences, expressed and purified the proteins in E coli, and found that 27 folded to monomeric stable structures with circular dichroism spectra consistent with the hallucinated structures. 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 physically based models, to the de novo design of proteins with new functions.

bioengineering↗

bioRxiv2020

De novo design of modular and tunable allosteric biosensors

Naturally occurring allosteric protein switches have been repurposed for developing novel biosensors and reporters for cellular and clinical applications 1, but the number of such switches is limited, and engineering them is often challenging as each is different. Here, we show that a very general class of allosteric protein-based biosensors can be created by inverting the flow of information through de novo designed protein switches in which binding of a peptide key triggers biological outputs of interest 2. Using broadly applicable design principles, we allosterically couple binding of protein analytes of interest to the reconstitution of luciferase activity and a bioluminescent readout through the association of designed lock and key proteins. Because the sensor is based purely on thermodynamic coupling of analyte binding to switch activation, only one target binding domain is required, which simplifies sensor design and allows direct readout in solution. We demonstrate the modularity of this platform by creating biosensors that, with little optimization, sensitively detect the anti-apoptosis protein Bcl-2, the hIgG1 Fc domain, the Her2 receptor, and Botulinum neurotoxin B, as well as biosensors for cardiac Troponin I and an anti-Hepatitis B virus (HBV) antibody that achieve the sub-nanomolar sensitivity necessary to detect clinically relevant concentrations of these molecules. Given the current need for diagnostic tools for tracking COVID-19 3, we use the approach to design sensors of antibodies against SARS-CoV-2 protein epitopes and of the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein. The latter, which incorporates a de novo designed RBD binder, has a limit of detection of 15pM with an up to seventeen fold increase in luminescence upon addition of RBD. The modularity and sensitivity of the platform should enable the rapid construction of sensors for a wide range of analytes and highlights the power of de novo protein design to create multi-state protein systems with new and useful functions.

synthetic biology↗

bioRxiv2020

Improved protein structure refinement guided by deep learning based accuracy estimation

We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules.

bioinformatics↗

bioRxiv2020

Generation of ordered protein assemblies using rigid three-body fusion

Protein nanomaterial design is an emerging discipline with applications in medicine and beyond. A longstanding design approach uses genetic fusion to join protein homo-oligomer subunits via -helical linkers to form more complex symmetric assemblies, but this method is hampered by linker flexibility and a dearth of geometric solutions. Here, we describe a general computational method that performs rigid three-body fusion of homo-oligomer and spacer building blocks to generate user-defined architectures, while at the same time significantly increasing the number of geometric solutions over typical symmetric fusion. The fusion junctions are then optimized using Rosetta to minimize flexibility. We apply this method to design and test 92 dihedral symmetric protein assemblies from a set of designed homo-dimers and repeat protein building blocks. Experimental validation by native mass spectrometry, small angle X-ray scattering, and negative-stain single-particle electron microscopy confirms the assembly states for 11 designs. Most of these assemblies are constructed from DARPins (designed ankyrin repeat proteins), anchored on one end by -helical fusion and on the other by a designed homo-dimer interface, and we explored their use for cryo-EM structure determination by incorporating DARPin variants selected to bind targets of interest. Although the target resolution was limited by preferred orientation effects, small scaffold size, and the low-order symmetry of these dihedral scaffolds, we found that the dual anchoring strategy reduced the flexibility of the target-DARPIN complex with respect to the overall assembly, suggesting that multipoint anchoring of binding domains could contribute to cryo-EM structure determination of small proteins.

synthetic biology↗

bioRxiv2020

Protein sequence optimization with a pairwise decomposable penalty for buried unsatisfied hydrogen bonds

In aqueous solution, polar groups make hydrogen bonds with water, and hence burial of such groups in the interior of a protein is unfavorable unless the loss of hydrogen bonds with water is compensated by formation of new ones with other protein groups. Hence, buried "unsatisfied" polar groups making no hydrogen bonds are very rare in proteins. Efficiently representing the energetic cost of unsatisfied hydrogen bonds with a pairwise-decomposable energy term during protein design is challenging since whether or not a group is satisfied depends on all of its neighbors. Here we describe a method for assigning a pairwise-decomposable energy to sidechain rotamers such that following combinatorial sidechain packing, buried unsaturated polar atoms are penalized. The penalty can be any quadratic function of the number of unsatisfied polar groups, and can be computed very rapidly. We show that inclusion of this term in Rosetta sidechain packing calculations substantially reduces the number of buried unsatisfied polar groups.

molecular biology↗

bioRxiv2020

Elicitation of broadly protective immunity to influenza by multivalent hemagglutinin nanoparticle vaccines

Influenza vaccines that confer broad and durable protection against diverse virus strains would have a major impact on global health. However, next-generation vaccine design efforts have been complicated by challenges including the genetic plasticity of the virus and the immunodominance of certain epitopes in its glycoprotein antigens. Here we show that computationally designed, two-component nanoparticle immunogens induce potently neutralizing and broadly protective antibody responses against a wide variety of influenza viruses. The nanoparticle immunogens display 20 hemagglutinin (HA) trimers in a highly immunogenic array, and their assembly in vitro enables precisely controlled co-display of multiple distinct HAs in defined ratios. Nanoparticle immunogens displaying the four HAs of licensed quadrivalent influenza vaccines (QIV) elicited hemagglutination inhibition and neutralizing antibody responses to vaccine-matched strains that were equivalent or superior to commercial QIV in mice, ferrets, and nonhuman primates. The nanoparticle immunogens--but not QIV--simultaneously induced broadly protective antibody responses to heterologous viruses, including H5N1 and H7N9, by targeting the subdominant yet conserved HA stem. Unlike previously reported influenza vaccine candidates, our nanoparticle immunogens can alter the intrinsic immunodominance hierarchy of HA to induce both potent receptor-blocking and broadly cross-reactive stem-directed antibody responses and are attractive candidates for a next-generation influenza vaccine that could replace current seasonal vaccines. One Sentence SummaryNanoparticle immunogens displaying four seasonal influenza hemagglutinins elicit neutralizing antibodies directed at both the immunodominant head and the conserved stem and confer broad protective immunity.

immunology↗

bioRxiv2020

Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM

Cryo-electron microscopy of protein complexes often leads to moderate resolution maps (4-8 [A]), with visible secondary structure elements but poorly resolved loops, making model-building challenging. In the absence of high-resolution structures of homologues, only coarse-grained structural features are typically inferred from these maps, and it is often impossible to assign specific regions of density to individual protein subunits. This paper describes a new method for overcoming these difficulties that integrates predicted residue distance distributions from a deep-learned convolutional neural network, computational protein folding using Rosetta, and automated EM-map-guided complex assembly. We apply this method to a 4.6 [A] resolution cryoEM map of Fanconi Anemia core complex (FAcc), an E3 ubiquitin ligase required for DNA interstrand crosslink repair, which was previously challenging to interpret as it is comprised of 6557 residues, only 1897 of which are covered by homology models. In the published structure built from this map, only 387 residues could be assigned to specific subunits. By building and placing into density 42 deep-learning guided models containing 4795 residues not included in the previously published structure, we are able to determine an almost-complete atomic model of FAcc, in which 5182 of the 6557 residues were placed. The resulting model is consistent with previously published biochemical data, and facilitates interpretation of disease related mutational data. We anticipate that our approach will be broadly useful for cryoEM structure determination of large complexes containing many subunits for which there are no homologues of known structure.

biochemistry↗

bioRxiv2020

The genomes of invasive coral Tubastraea spp. (Dendrophylliidae) as tool for the development of biotechnological solutions

Corals have been attracting huge attention due to the impact of climate change and ocean acidification on reef formation and resilience. Nevertheless, some species like Tubastraea coccinea and T. tagusensis have been spreading very fast replacing the native ones which affect the local environment and decrease biodiversity of corals and other organisms associated with them. Despite some focal efforts to understand the biology of these organisms, they remain understudied at the molecular level. This knowledge gap hinders the development of cost-effective strategies for both conservation and management of invasive species. In this circumstance, it is expected that genome sequencing would provide powerful insights that could lead to better strategies for prevention, management, and control of this and other invasive species. Here, we present three genomes of Tubastraea spp. in one of the most comprehensive biological studies of corals, that includes flow cytometry, karyotyping, transcriptomics, genomics, and phylogeny. The genome of T. tagusensis is organized in 23 chromosomes pairs and has 1.1 Gb, the T. coccinea genome is organized in 22 chromosome pairs and has 806 Mb, and the Tubastraea sp. genome is organized in 21 chromosome pairs and has 795 Mb. The hybrid assembly of T. tagusensis using short and long-reads has a N50 of 227,978 bp, 7,996 contigs and high completeness estimated as 91.6% of BUSCO complete genes, of T. coccinea has a N50 of 66,396 bp, 17,214 contigs and 88.1% of completeness, and of Tubastraea sp. has a N50 of 82,672 bp, 12,922 contigs and also 88.1% of completeness. We inferred that almost half of the genome consists of repetitive elements, mostly interspersed repeats. We provide evidence for exclusive Scleractinia and Tubastraea gene content related to adhesion and immunity. The Tubastraea spp. genomes are a fundamental study which promises to provide insights not only about the genetic basis for the extreme invasiveness of this particular coral genus, but to understand the adaptation flaws of some reef corals in the face of anthropic-induced environmental disturbances. We expect the data generated in this study will foster the development of efficient technologies for the management of coral species, whether invasive or threatened.

evolutionary biology↗

bioRxiv2020

ProteinGCN: Protein model quality assessment using GraphConvolutional Networks

Blind estimation of local (per-residue) and global (for the whole structure) accuracies in protein structure models is an essential step in many protein modeling applications. With the recent developments in deep-learning, single-model quality assessment methods have been also advanced, primarily through the use of 2D and 3D convolutional deep neural networks. Here we explore an alternative approach and train a graph convolutional network with nodes representing protein atoms and edges connecting spatially adjacent atom pairs on the dataset Rosetta-300k which contains a set of 300k conformations from 2,897 proteins. We show that our proposed architecture, PO_SCPLOWROTEINC_SCPLOWGCN, is capable of predicting both local and global accuracies in protein models at state-of-the-art levels. Further, the number of free parameters in PO_SCPLOWROTEINC_SCPLOWGCN is almost 1-2 orders of magnitude smaller compared to the 3D convolutional networks proposed earlier. We provide the source code of our work to encourage reproducible research.1

bioinformatics↗

bioRxiv2020

A generative algorithm for de novo design of proteins with diverse pocket structures

To create new enzymes and biosensors from scratch, precise control over the structure of small molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed a generative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The generative algorithm was tested and refined through feedback from two rounds of large scale experimental testing, involving in total, the assembly of synthetic genes encoding 7896 generated designs and assessment of their stability on the yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

biochemistry↗

bioRxiv2020

Genomic diversity of Escherichia coli isolates from non-human primates in the Gambia

Increasing contact between humans and non-human primates provides an opportunity for the transfer of potential pathogens or antimicrobial resistance between host species. We have investigated genomic diversity, and antimicrobial resistance in Escherichia coli isolates from four species of non-human primate in the Gambia: Papio papio (n=22), Chlorocebus sabaeus (n=14), Piliocolobus badius (n=6) and Erythrocebus patas (n=1). We performed Illumina whole-genome sequencing on 101 isolates from 43 stools, followed by nanopore long-read sequencing on eleven isolates. We identified 43 sequence types (STs) by the Achtman scheme (ten of which are novel), spanning five of the eight known phylogroups of E. coli. The majority of simian isolates belong to phylogroup B2--characterised by strains that cause human extraintestinal infections--and encode factors associated with extraintestinal disease. A subset of the B2 strains (ST73, ST681 and ST127) carry the pks genomic island, which encodes colibactin, a genotoxin associated with colorectal cancer. We found little antimicrobial resistance and only one example of multi-drug resistance among the simian isolates. Hierarchical clustering showed that simian isolates from ST442 and ST349 are closely related to isolates recovered from human clinical cases (differences in 50 and seven alleles respectively), suggesting recent exchange between the two host species. Conversely, simian isolates from ST73, ST681 and ST127 were distinct from human isolates, while five simian isolates belong to unique core-genome ST complexes--indicating novel diversity specific to the primate niche. Our results are of public health importance, considering the increasing contact between humans and wild non-human primates. Impact statementLittle is known about the population structure, virulence potential and the burden of antimicrobial resistance among Escherichia coli from wild non-human primates, despite increased exposure to humans through the fragmentation of natural habitats. Previous studies, primarily involving captive animals, have highlighted the potential for bacterial exchange between non-human primates and humans living nearby, including strains associated with intestinal pathology. Using multiple-colony sampling and whole-genome sequencing, we investigated the strain distribution and population structure of E. coli from wild non-human primates from the Gambia. Our results indicate that these monkeys harbour strains that can cause extraintestinal infections in humans. We document the transmission of virulent E. coli strains between monkeys of the same species sharing a common habitat and evidence of recent interaction between strains from humans and wild non-human primates. Also, we present complete genome assemblies for five novel sequence types of E. coli. Author notesAll supporting data, code and protocols have been provided within the article or through supplementary data files. Nine supplementary figures and six supplementary files are available with the online version of this article. AbbreviationsExPEC, Extraintestinal pathogenic Escherichia coli; ST, Sequence type; AMR, Antimicrobial resistance; MLST, Multi-locus sequence typing; VFDB, Virulence factors database; SNP, single nucleotide polymorphism; SPRI, Solid phase reversible immobilisation. Data summaryThe raw sequences and polished assemblies from this study are available in the National Center for Biotechnology Information (NCBI) Short Read Archive, under the BioProject accession number PRJNA604701. The full list and characteristics of these strains and other reference strains used in the analyses are presented in Table 1 and Supplementary Files 1-4 (available with the online version of this article).

genomics↗

bioRxiv2020

Targeting HIV Env immunogens to B cell follicles in non-human primates through immune complex or protein nanoparticle formulations

Following immunization, high affinity antibody responses develop within germinal centers (GCs), specialized sites within follicles of the lymph node (LN) where B cells proliferate and undergo somatic hypermutation. Antigen availability within GCs is important, as B cells must acquire and present antigen to follicular helper T cells to drive this process. However, recombinant protein immunogens such as soluble HIV envelope (Env) trimers do not efficiently accumulate in follicles following traditional immunization. Here we demonstrate two strategies to concentrate HIV Env immunogens in follicles, via the formation of immune complexes (ICs) or by employing self-assembling protein nanoparticles for multivalent display of Env antigens. Using rhesus macaques, we show that within a few days following immunization, free trimers were present in a diffuse pattern in draining LNs, while trimer ICs and Env nanoparticles accumulated in B cell follicles. Whole LN imaging strikingly revealed that ICs and trimer nanoparticles concentrated in as many as 500 follicles in a single lymph node within 2 days after immunization. Imaging of LNs collected 7 days post-immunization showed that Env nanoparticles persisted on follicular dendritic cells in the light zone of nascent germinal centers. These findings suggest that the form of antigen administered in vaccination can dramatically impact localization in lymphoid tissues and provides a new rationale for the enhanced immune responses observed following immunization with immune complexes or nanoparticles.

immunology↗

bioRxiv2020

Promoter keyholes enable specific and persistent multi-gene expression programs in primary T cells without genome modification

Human genes are regulated quantitatively, yet the ability to specify the expression level of a native gene accurately and specifically using a defined reagent has remained elusive. Here we show that precise targeting of KRAB repressive domain within regulatory DNA unlocks an endogenous quantitative dial that can be engaged at nucleotide resolution to program expression levels across a wide physiologic range, with single-gene specificity and high reproducibly in primary cells.

synthetic biology↗

bioRxiv2020

Self-assembly based post-translational protein oscillators

Recent advances in synthetic post-translational protein circuits are significantly impacting the landscape of biomimicry engineering. However, designing sustained dynamic phenomena in these circuits remains an outstanding challenge. Inspired by the KaiABC system regulating the circadian clock in cyanobacteria, we develop two experimentally realizable post-translational oscillators. The oscillators rely on a small number of components interacting only through reversible binding and phosphorylation/dephosphorylation reactions.

systems biology↗