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 α-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 α-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

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 α-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 α-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

On Robustness Computation and Optimization in BIOCHAM-4

Long version with appendicesInternational audienceBIOCHAM-4 is a tool for modeling, analyzing and synthesizing biochemical reaction networks with respect to some formal, yet possibly imprecise, specification of their behavior. We focus here on one new capability of this tool to optimize the robustness of a parametric model with respect to a specification of its dynamics in quantitative temporal logic. More precisely, we present two complementary notions of robustness: the statistical notion of model robustness to parameter perturbations, defined as its mean functionality, and a metric notion of formula satisfaction robustness, defined as the penetration depth in the validity domain of the temporal logic constraints. We show how the formula robustness can be used in BIOCHAM-4 with no extra cost as an objective function in the parameter optimization procedure, to actually improve the model robustness. We illustrate these unique features with a classical example of the hybrid systems community and provide some performance figures on a model of MAPK signalling with 37 parameters

Modeling Inhomogeneous DNA Replication Kinetics

In eukaryotic organisms, DNA replication is initiated at a series of chromosomal locations called origins, where replication forks are assembled proceeding bidirectionally to replicate the genome. The distribution and firing rate of these origins, in conjunction with the velocity at which forks progress, dictate the program of the replication process. Previous attempts at modeling DNA replication in eukaryotes have focused on cases where the firing rate and the velocity of replication forks are homogeneous, or uniform, across the genome. However, it is now known that there are large variations in origin activity along the genome and variations in fork velocities can also take place. Here, we generalize previous approaches to modeling replication, to allow for arbitrary spatial variation of initiation rates and fork velocities. We derive rate equations for left- and right-moving forks and for replication probability over time that can be solved numerically to obtain the mean-field replication program. This method accurately reproduces the results of DNA replication simulation. We also successfully adapted our approach to the inverse problem of fitting measurements of DNA replication performed on single DNA molecules. Since such measurements are performed on specified portion of the genome, the examined DNA molecules may be replicated by forks that originate either within the studied molecule or outside of it. This problem was solved by using an effective flux of incoming replication forks at the model boundaries to represent the origin activity outside the studied region. Using this approach, we show that reliable inferences can be made about the replication of specific portions of the genome even if the amount of data that can be obtained from single-molecule experiments is generally limited

On Chemical Reaction Network Design by a Nested Evolution Algorithm

International audienceOne goal of synthetic biology is to implement useful functions with biochemical reactions, either by reprogramming living cells or programming artificial vesicles. In this perspective, we consider Chemical Reaction Networks (CRN) as a programming language, and investigate the CRN program synthesis problem. Recent work has shown that CRN interpreted by differential equations are Turing-complete and can be seen as analog computers where the molecular concentrations play the role of information carriers. Any real function that is computable by a Turing machine in arbitrary precision can thus be computed by a CRN over a finite set of molecular species. The proof of this result gives a numerical method to generate a finite CRN for implementing a real function presented as the solution of a Polynomial Initial Values Problem (PIVP). In this paper, we study an alternative method based on artificial evolution to build a CRN that approximates a real function given on finite sets of input values. We present a nested search algorithm that evolves the structure of the CRN and optimizes the kinetic parameters at each generation. We evaluate this algorithm on the Heaviside and Cosine functions both as functions of time and functions of input molecular species. We then compare the CRN obtained by artificial evolution both to the CRN generated by the numerical method from a PIVP definition of the function, and to the natural CRN found in the BioModels repository for switches and oscillators

Pre-replication complex proteins assemble at regions of low nucleosome occupancy within the Chinese hamster dihydrofolate reductase initiation zone

Genome-scale mapping of pre-replication complex proteins has not been reported in mammalian cells. Poor enrichment of these proteins at specific sites may be due to dispersed binding, poor epitope availability or cell cycle stage-specific binding. Here, we have mapped sites of biotin-tagged ORC and MCM protein binding in G1-synchronized populations of Chinese hamster cells harboring amplified copies of the dihydrofolate reductase (DHFR) locus, using avidin-affinity purification of biotinylated chromatin followed by high-density microarray analysis across the DHFR locus. We have identified several sites of significant enrichment for both complexes distributed throughout the previously identified initiation zone. Analysis of the frequency of initiations across stretched DNA fibers from the DHFR locus confirmed a broad zone of de-localized initiation activity surrounding the sites of ORC and MCM enrichment. Mapping positions of mononucleosomal DNA empirically and computing nucleosome-positioning information in silico revealed that ORC and MCM map to regions of low measured and predicted nucleosome occupancy. Our results demonstrate that specific sites of ORC and MCM enrichment can be detected within a mammalian intitiation zone, and suggest that initiation zones may be regions of generally low nucleosome occupancy where flexible nucleosome positioning permits flexible pre-RC assembly sites