Proteins are one of the main functional building blocks of the cell ., The ability to create novel proteins outside of the natural realm has opened the path towards innovative achievements , such as new pathways 1 , cellular functions 2 , and therapeutic leads 3–5 ., Computational protein design is the rational and structure-based approach to solve the inverse folding problem , i . e . the search for the best putative sequence capable of fitting and stabilizing a protein’s three-dimensional conformation 6 ., As such , a great deal of effort has been placed into understanding the rules of protein folding and stability 7 , 8 and its relation to the appropriate sequence space 9 ., Computational protein design approaches focus on exploring two interconnected landscapes related to sampling of the conformational and sequence spaces ., Fixed backbone approaches use static protein backbone conformations , which greatly constrain the sequence space explored by the computational algorithm 9 ., Following the same principles of naturally occurring homologs , which often exhibit confined structural diversity , flexible backbone approaches enhance the sequence diversity , adding the challenge of identifying energetically favorable sequence variants that are correctly coupled to structural perturbations 10 ., Another variation for computational design approaches is de novo design , in which protein backbones are assembled in silico , followed by sequence optimization to fold into a pre-defined three-dimensional conformation without being constrained by previous sequence information 11–13 ., This approach tests our understanding of the rules governing the structure of different protein folds ., The failures and successes of this approach confirm and correct the principles used for the protein design process 7 , 8 ., One of the main aims of computational protein design is the rational design of functional proteins capable of carrying existing or novel functions into new structural contexts 14 ., Broadly , there are three main approaches for the design of functional proteins: redesigning of pre-existing functions , grafting of functional sites onto heterologous proteins , and designing of novel functions not found in the protein repertoire ., The redesign of a pre-existing function to alter its catalytic activity 15 or improve its binding target recognition 16 can be considered the most conservative approach ., It is typically accomplished by point mutations around the functional area of interest and tends to have little impact on global structure of the designed protein ., On the other extreme , the design of fully novel functions has most noticeably been achieved by applying chemical principles that tested our fundamental knowledge of enzyme catalysis 17 , 18 ., Between these two approaches resides protein grafting ., This method aims to repurpose natural folds as carriers for exogenous known functions ., It relies on the strong structure-function relationship present in proteins , to endow an heterologous protein with an exogenous function by means of transferring a structural motif that performs such function 3–5 , 19–22 ., At the biochemical level , grafting approaches have been used to design high binding affinity protein-protein interactions , by stabilizing binding motifs removing the entropic cost of binding ( e . g . flexible peptides ) 21 , and also by extending the binding interfaces to allow for additional energetically favorable interactions ., The extended interfaces also provide opportunities to tune the specificity of the designed proteins 21 ., On the practical side , some of the most notable applications of protein grafting thus far , have been the design of novel viral inhibitors 21 , 23 and epitope-focused immunogens for vaccine design 3–5 ., Following this strategy one can easily imagine applications to functionalize protein-based biomaterials 24 or to design novel biosensors 25 ., The importance of robust grafting approaches to functionalize heterologous proteins is related to the fact that the proteins that naturally perform these functions , may lack the best biochemical properties in terms of size , affinity , solubility , immunogenicity and other application specific factors ., Thus far , the most successful grafting approaches are highly dependent on structural similarity between the functional motif and the insertion region in the protein scaffold ., When the functional motif and the insertion region are identical in backbone conformation , the motif transfer can be performed by side-chain grafting , i . e . mutating the target residues into those of the functional motif 3 , 5 ., In much more challenging scenarios , full backbone grafting may be used in conjunction with directed evolution 19 ., Nevertheless , motif transfer is limited between very similar structural regions , which greatly constrains the subset of putative scaffolds that can be used for this purpose ., The lack of compatibility between the putative scaffolds and the functional sites has been referred to as a “designability” problem 26 , 27 , which refers to the likelihood of a protein backbone to host and stabilize a structural motif ., The designability problem becomes more obvious as the structural complexity of the functional motif grows , drastically limiting the types of functional motifs that can be transferred ., Previously , we have demonstrated the possibility of expanding protein grafting to scaffolds with segments that have low structural similarity ., To accomplish that task , we developed the prototype protocol Rosetta Fold From Loops ( FFL ) 4 , 21 ., The distinctive feature of our protocol is the coupling of the folding and design stages to bias the sampling towards structural conformations and sequences that stabilize the grafted functional motif ., In the past , FFL was used to obtain designs that were functional ( synthetic immunogens 4 and protein-based inhibitors 21 ) and where the experimentally determined crystal structures closely resembled the computational models ., However , the structures of the functional sites were structurally very close to the insertion segments of the hosting scaffolds ., The architecture of FFL was intrinsically limited in the types of constraints available and the grafting of linear , single segment functional motifs ., Here , we present a complete re-implementation of FFL with enhanced functionalities , simplified user interface and complete integration with other Rosetta protocols ., We have called this new , more generalist protocol Rosetta Functional Folding and Design ( FunFolDes ) ., We benchmarked FunFolDes extensively , unveiling important technical details to better exploit and expand the capabilities of the protocol ., Furthermore , we challenged FunFolDes with two design tasks of transplanting viral epitopes to heterologous scaffolds , and by doing so probe the applicability of the protocol ., The design tasks were centered on using distant structural templates as hosting scaffolds , and functionalizing a de novo designed protein , —FunFolDes succeeded in both challenges ., These results are encouraging and provide a solid basis for the broad applicability of FunFolDes as a strategy for the robust computational design of functionalized proteins ., The original prototype of the Rosetta Fold From Loops ( FFL ) protocol was successfully used to transplant the structural motif of the Respiratory Syncytial Virus protein F ( RSVF ) site II neutralizing epitope into a protein scaffold in the context of a vaccine design application 4 ., FFL enabled the insertion and conformational stabilization of the structural motif into a defined protein topology by using Rosetta’s fragment insertion machinery to fold an extended polypeptide chain to adopt the desired topology 28 which was then sequence designed ., Information from the scaffold structure was used to guide the folding , ensuring an overall similar topology while allowing for the conformational changes needed to stabilize the inserted structural motif ., The final implementation of FunFolDes is schematically represented in Fig 1 , and fully described in Materials and Methods ., Our upgrades to FFL focused on three main aims: I ) improve the applicability of the system to handle more complex structural motifs ( i . e . multiple discontinuous backbone segments ) ; II ) enhance the design of functional proteins by including binding partners in the simulations; III ) increase the control over each stage of the simulation improving the usability for non-experts ., These three aims were achieved through the implementation of five core technical improvements described below ., Insertion of multi-segment functional sites ., Most functional sites in proteins typically entail , at the structural level , multiple discontinuous segments , as is the case for protein-protein interfaces , enzyme active-sites , and others 29 , 30 ., FunFolDes handles functional sites with any number of discontinuous segments , ensuring the native orientations of each of the segments ., These new features enhance the types of structural motifs that can be handled by FunFolDes , widening the applicability of the computational protocol ., Structural folding and sequence design in the presence of a binding target ., Many of the functional roles of proteins in cells require physical interaction with other proteins , nucleic acids , or metabolites 31 ., The inclusion of the binder has two main advantages: I ) explicit representation of functional constraints to bias the designed protein towards a functional sequence space , resolving putative clashes derived from the template scaffold; II ) facilitate the design of new additional contact residues ( outside of the motif ) that may afford enhanced affinity and/or specificity ., Region-specific structural constraints ., FunFolDes can collect from full-template to region-specific constraints , allowing greater levels of flexibility in areas of the scaffold that can be critical for function ( e . g . segments close to the interface of a target protein ) ., The type of distance constrains used in the protocol are soft constrains with score penalties if the defined standard deviation is exceeded in the upper and lower bounds ., Furthermore , FunFolDes is no longer limited to atom-pair distance constraints 32 and can incorporate other types of kinematic constraints , such as angle and dihedral constraints 33 , which have been used extensively to design beta-rich topologies 8 ., On-the-fly fragment picking ., Classically , fragment libraries are generated through sequence-based predictions of secondary structure and dihedral angles that rely on external computational methods 34 ., We leveraged internal functionalities in Rosetta so that FunFolDes can assemble fragment sets on-the-fly ., Using this feature , we can assemble fragment sets based on the structure of the input scaffold ., Sequence-based fragments remain an option , however this feature removes the need for secondary applications , boosting the usability of FunFolDes ., Lastly , the on-the-fly fragment picking enables the development of protocols with mutable fragment sets along the procedure ., Compatibility with other Rosetta modules ., Finally , FunFolDes is compatible with Rosetta’s modular xml-interface—Rosetta Scripts ( RS ) 35 ., Enabling customization of the FunFolDes protocol and , more importantly , cross-talk with other protocols and filters available through the RS interface ., We devised two benchmark scenarios to test the performance of FunFolDes ., One of these aimed to capture conformational changes in small protein domains caused by sequence insertions or deletions , and the second scenario assessed protocol performance to fold and design a binder in the presence of the binding target ., Typical protein design benchmarks are assembled by stripping native side chains from known protein structures and evaluating the sequence recovery of the design algorithm 9 ., The main design aim of FunFolDes is to insert structural motifs into protein folds while allowing flexibility across the overall structure ., This conformational freedom allows the full protein scaffold to adapt and stabilize the functional motif’s conformation ., This is a main distinctive point from other approaches to design functional proteins that rely on a mostly rigid scaffold 2 , 3 , 11 , 19 , 30 , 36 ., For many modeling problems , such as protein structure prediction , protein-protein and protein-ligand docking , and protein design , standardized benchmark datasets are available 37 or easily accessible ., Devising a benchmark for designed proteins with propagating conformational changes across the structure is challenging , as we are assessing both structural accuracy as well as sequence recovery of the protocol ., To address this problem , we analyzed structural domains found repeatedly in natural proteins and clustered them according to their definition in the CATH database 38 ., As a result , we selected a set of 14 benchmark targets labeled T01 through T14 ( Fig 2A ) ., A detailed description on the construction of the benchmark can be found in the Materials and Methods section ., Briefly , for the benchmark we selected proteins with less than 100 residues , where each test case was composed of two proteins of the same CATH domain cluster ., One of the proteins is the template , and serves as a structural representative of the CATH domain ., The second protein , dubbed target , contains structural insertions or deletions ( motif region ) , to which a structural change in a different segment of the protein could be attributed ( query region ) ., The motif and query regions for all the targets are shown in Fig 2A and quantified by the percentage of overall secondary structure in S1A Fig . To a great extent , these structural changes due to natural sequence insertions and deletions are analogous to those occurring in the design scenarios for which FunFolDes was conceived ., Using FunFolDes , we folded and designed the target proteins while maintaining the motif segment structurally fixed , mimicking a structural motif insertion ., Distance constraints between residues were extracted from the template in the regions of shared structural elements of the template and the target , and were used to guide the folding simulations ., To check whether FunFolDes enhances sequence and structural sampling , we compared the simulations to constrained ab initio ( cst-ab initio ) simulations 33 ., As Rosetta conformational sampling is highly dependent upon the fragment set 39 , in this benchmark we also tested the influence of structure- and sequence-based fragments ., The performance of the two protocols was analyzed regarding global and local recovery of both structure and sequence ., Structural recovery was assessed through two main metrics:, ( a ) global RMSD of the full decoys against the target and, ( b ) local RMSD of the query region ., When evaluating the distributions for global RMSD in the designed ensembles , FunFolDes outperformed cst-ab initio by consistently producing populations of decoys with lower mean RMSD ( mostly found below 5 Å ) , a result observed in all 14 targets ( Fig 2B , S1B Fig ) ., This result is especially reassuring considering that FunFolDes simulations contain more structural information of the target topology than the cst-ab initio simulations ., The local RMSDs of the query unconstrained regions presented less clear results across the benchmark ( S1B Fig ) ., In 13 targets , FunFolDes outperformed cst-ab initio , showing lower mean RMSDs but in some targets with minor differences ., When comparing fragment sets ( structure- vs sequence-based ) , both achieved similar mean RMSDs in the decoy populations; nonetheless , the structure-based fragments more often reached the lowest RMSDs for overall and query RMSDs ( Fig 2B , S1 Fig ) ., This is consistent with what would be expected from the structural information content within each fragment set ., When paired with the technical simplicity of use , time-saving and enhanced sampling of the desired topology , the structure-based fragments are an added value for FunFolDes ., We also quantified sequence recovery , both in terms of sequence identity and similarity according to the BLOSUM62 matrix 40 ( Fig 3A ) ., In all targets , FunFolDes showed superior recoveries than cst-ab initio , and at the levels of other design protocols using Rosetta 10 ( Fig 3A ) ., This type of metrics has been shown to be highly dependent on the exact backbone conformation used as input 9 , 10 ., Given that FunFolDes is exploring larger conformational spaces , as a proxy for the quality of the sequences generated , we used the target’s Hidden Markov Models ( HMM ) 41 and quantified the designed sequences that were identified as part of the target’s CATH superfamily according to its HMM definition ( Fig 3B ) ., FunFolDes decoy populations systematically outperformed those from cst-ab initio ( Fig 3B ) ., The performance of the two fragment sets shows no significant differences ., In summary , this benchmark highlights the ability of FunFolDes to generate close-to-native scaffold proteins to stabilize inserted structural motifs ., FunFolDes aims to refit protein scaffolds towards the structural requirements of a functional motif ., It is thus critical , to explore within certain topological boundaries , structural variations around the original templates ., This benchmark points to several variables in the protocol that resulted in enhanced structural and sequence sampling ., The computational design of proteins that can bind with high affinity and specificity to targets of interest remains a largely unsolved problem 42 ., Within the FunFolDes conceptual approach of coupling folding with sequence design , we sought to add the structure of the binding target ( Fig 1 ) to attempt to bias sampling towards functional structural and sequence spaces ., Previously , we used FFL to design a new binder ( BINDI ) to BHRF1 ( Fig 4A ) , an Epstein-Barr virus protein with anti-apoptotic properties directly linked to the tumorigenic activity of EBV 21 ., FFL designs bound to BHRF1 with a dissociation constant ( KD ) of 58–60 nM , and after affinity maturation reached a KD of 220±50 pM ., BINDI was designed in the absence of the target and then docked to BHRF1 through the known interaction motif ., A striking observation from the overall approach was that the FFL stage was highly inefficient , generating a large fraction of backbone conformations incompatible with the binding mode of the complex ., To test whether the presence of the target could improve structural and sequence sampling , we leveraged the structural and sequence information available for the BINDI-BHRF1 system and benchmarked FunFolDes for this design problem ., As described by Procko and colleagues , when comparing the topological template provided to FFL and the BINDI crystal structure , the last helix of the bundle ( helix 3 ) was shifted relative to the template ensuring structural compatibility between BINDI and BHRF1 ( Fig 4B ) ., We used this case study to assess the capabilities of FunFolDes to sample closer conformations to those observed in the BINDI-BHRF1 crystal structure ., In addition , we used the saturation mutagenesis data generated for BINDI 21 to evaluate the sequence space sampled by FunFolDes ., A detailed description of this benchmark can be found in the Materials and Methods section ., Briefly , we performed four different FunFolDes simulations: I ) binding target absent ( no_target ) ; II ) binding target present with no conformational freedom ( static ) ; III ) binding target present with side-chain repacking ( pack ) ; IV ) binding target present with side-chain repacking plus minimization and backbone minimization ( packmin ) ., no_target simulations generated a low number of conformations compatible with the target ( <10% of the total generated designs ) ( S2A Fig ) ., Upon global minimization more than 60% ( S2A Fig ) of the decoys were compatible with the binding target , at the cost of considerable structural drifts for both binder ( mean RMSD 3 . 3 Å ) and target ( mean RMSD 7 . 7 Å ) ( Fig 4C ) ., These structural drifts reflect the energy optimization requirements by the relaxation algorithms but are deemed biologically irrelevant due to the profound structural reconfigurations ., In contrast , simulations performed in the presence of the target clearly biased the sampling to more productive conformational spaces ., RMSD drifts upon minimization were less than 1 Å for both designs and binding target ( Fig 4C ) ., Global structural alignments of the designs fail to emphasize the differences of the helical arrangements ( S2B Fig ) ., Thus , we aligned all the designs on the conserved binding motif ( Fig 4A ) and measured the RMSD over the three helices that compose the fold ., FunFolDes simulations in the presence of the target sampled a mean RMSD of 3 Å ( lowest ≈ 2 Å ) compared to the BINDI structure ( Fig 4D ) , with the closest designs at approximately 2 Å , while the no_target simulation showed a mean RMSD of 4 . 5 Å ( lowest ≈ 2 . 5 Å ) ., While we acknowledge that these structural differences are modest , the data suggests that they can be important to sample conformations and sequences competent for binding ., We also analyzed Rosetta energy distributions of designs in the unbound state for the different simulations ., We observed noticeable differences for the designs generated in the absence ( no_target ) and the presence of the binding target , -320 and -280 Rosetta Energy Units ( REUs ) , respectively ( Fig 4E ) ., This difference is significant , particularly for a small protein ( 116 residues ) ., We also observed considerable differences for the binding energies ( ΔΔG ) of the no_target and the bound simulations with mean ΔΔGs of -10 and -50 REUs , respectively ( Fig 4E ) ., The energy metrics provide interesting insights regarding the design of functional proteins ., Although the sequence and structure optimization for the designs in the absence of the target reached lower energies , these designs are structurally incompatible with the binding target and , even after refinement , their functional potential ( as assessed by the ΔΔG ) is not nearly as favorable as those performed in the presence of the binding target ( Fig 4F ) ., These data suggest that , in many cases , to optimize function it may be necessary to sacrifice the overall computed energy of the protein , a common proxy to the experimental thermodynamic stability of the protein 43 ., The existence of stability-function tradeoffs has been the subject of many experimental studies 44 , 45 , however , it remains a much less explored strategy in computational design , where it may also be necessary to design proteins with lower stability to ensure that the functional requirements can be accommodated ., This observation provides a compelling argument to perform biased simulations in the presence of the binding target , which can be broadly defined as a “functional constraint” ., To evaluate sequence sampling quality , we compared the computationally designed sequences to a saturation mutagenesis dataset available for BINDI 21 ., The details of the dataset and scoring scheme can be found in the methods and S2 Fig . Briefly , point mutations beneficial to the binding affinity to BHRF1 have a positive score , deleterious mutants a negative score , and neutral score 0 ., Such a scoring scheme , will yield a score of 0 for the BINDI sequence ., Designs performed in the presence of the binding target obtained higher mean scores as compared to the no_target designs ( Fig 4G ) ., The pack simulation , showed the highest distribution mean , having one design scoring better than the BINDI sequence ., In some key positions at the protein-protein interface , the pack designs clearly outperformed those generated by the no_target simulation , when quantified by a per-position score ( Fig 4H ) ; meaning that amino-acids productive for binding interactions were sampled more often ., This benchmark provides an example of the benefits of using a “functional constraint” ( binding target ) to improve the quality of the sequences obtained by computational design ., Overall , the BINDI benchmark provided important insights regarding the best FunFolDes protocol to improve the design of functional proteins ., To further test FunFolDes’s design capabilities , we sought to transplant a contiguous viral epitope that is recognized by a monoclonal antibody with high affinity ( Fig 5A ) ., For this design , we used the RSVF site II epitope ( PDB ID: 3IXT 46 ) as the functional motif ., This epitope adopts a helix-loop-helix conformation recognized by the antibody motavizumab ( mota ) 46 ., In previous work we have designed proteins with this epitope , but started from a structurally similar template , where the RMSD between the epitope and the scaffold segment was approximately 1 Å over the helical residues ., Here , we sought to challenge FunFolDes by using a distant structural template where the local RMSDs of the epitope and the segment onto which it was transplanted were higher than 2 Å ., We used MASTER 47 to perform the structural search ( detailed description in Materials and Methods ) and selected as template scaffold the structure of the A6 protein of the Antennal Chemosensory system from the moth Mamestra brassicae ( PDB ID: 1KX8 48 ) ( Fig 5A ) ., The backbone RMSD between the conformation of the epitope and the insertion region in 1kx8 is 2 . 37 Å ( Fig 5B ) ., The A6 protein is involved in chemical communication and has been shown to bind to fatty-acid molecules with hydrophobic alkyl chains composed of 12–18 carbons ., Two prominent features are noticeable in the structure: two disulfide bonds ( Fig 5A ) and a considerable void volume in the protein core ( S3 Fig ) , thought to be the binding site for fatty acids ., These features emphasize that the initial design template is likely not a very stable protein ., In the design process we performed two stages of FunFolDes simulations to obtain a proper insertion of the motif in the topology ( Fig 5C ) ., A detailed description of the workflow and metrics used for selection ( S3 Fig ) can be found in the Materials and Methods ., A striking feature of our designs , when compared to the starting template , is that they had a much lower void volume , showing that FunFolDes generated structures and sequences that yielded well packed structures ( S3 Fig ) ., We started by testing experimentally seven designs ., Those that expressed in bacteria were further characterized using size exclusion chromatography coupled to a multi-angle light scatter ( SEC-MALS ) to determine the solution oligomerization state ., To assess their folding and thermal stability ( Tm ) we used Circular Dichroism ( CD ) spectroscopy , and finally to assess their functional properties we used surface plasmon resonance ( SPR ) to determine binding dissociation constants ( KDs ) to the mota antibody ., Out of the seven designs , six were purified and characterized further ., The majority of the designs were monomers in solution and showed CD spectra typical of helical proteins ., Regarding , thermal stabilities we obtained designs that were not very stable and did not unfold cooperatively ( 1kx8_02 ) , however we also obtained very stable designs that did not fully unfold under high temperatures ( 1kx8_07 ) ( S4 Fig ) ., The determined binding affinities to mota ranged from 34 to 208 nM , which was an encouraging result ., Nevertheless , compared to the peptide epitope ( KD = 20 nM ) and other designs previously published ( KD = 20 pM ) 4 , there was room for improvement ., Therefore , we generated a second round of designs to attempt to improve stability and binding affinities ., Driven by the observation that the native fold has two disulfide bonds , in the second round , we tested eight designed variants with different disulfide bonds and , if necessary , additional mutations to accommodate them ., The disulfide bonded positions were selected according to the spatial orientation of residues in the designed models , with most of the disulfide bonds being placed at distal locations from the epitope ( >20 Å ) ., All eight designs were soluble after purification and two were monomeric: 1kx8_d2 and 1kx8_3_d1 , showing CD spectra typical of helical proteins ( Fig 5D ) with melting temperatures ( Tms ) of 43 and 48°C ( Fig 5E ) , respectively ., Remarkably , 1kx8_d2 showed a KD of 1 . 14 nM ( Fig 5F ) , an improvement of approximately 30-fold compared to the best variants of the first round ., 1kx8_d2 binds to mota with approximately 20-fold higher affinity than the peptide-epitope ( KD ≈ 20 nM ) , and 50-fold lower compared to previously designed scaffolds ( KD = 20 pM ) 4 ., This difference in binding is likely reflective of how challenging it can be to accomplish the repurposing of protein structures with distant structural similarity ., Post-design analyses were performed to compare the sequence and structure of the best design model with the initial template ., The global RMSD between the two structures is 2 . 25 Å ., Much of the structural variability arises from the inserted motif , while the surrounding segments adopt a configuration similar to the original template scaffold ., The sequence identity of 1kx8_d2 as compared to the native protein is approximately 13% ., The sequence conservation per-position ( Fig 5G ) was evaluated through the BLOSUM62 matrix , where positive scores are attributed to the original residue or favorable substitutions and negative if unfavorable ., Overall , 38 . 5% of the residues in 1kx8_d2 scored positively , and 61 . 4% of the residues had a score equal or lower than 0 ., This is particularly interesting , from the perspective that several residues , unfavorable according to BLOSUM62 , yielded well folded and functional proteins ., To further substantiate our experimental results , we performed structure prediction simulations of the designed sequences , where we observed that 1kx8_d2 presents a higher folding propensity than the WT protein ( S5A Fig ) ., To evaluate if the predicted models presented the correct epitope conformation , we performed docking simulations and observed that they obtained lower binding energies than the native peptide-antibody complex , within similar RMSD fluctuations ( S5A Fig ) ., The successful design of this protein is a relevant demonstration of the broad usability of FunFolDes and the overall strategy of designing functional proteins by coupled folding and design to incorporate functional motifs in unrelated protein scaffolds ., Advances in computational design methodologies have achieved remarkable results in the design of de novo protein sequences and structures 7 , 8 , 11 ., However , the majority of the designed proteins are “functionless” and were designed to test the performance of computational algorithms for structural accuracy ., Here , we sought to use one of the hallmark proteins from de novo design efforts–TOP7 13 ( Fig 6A ) –and functionalize it using FunFolDes ., The functional site selected to insert into TOP7 was a different viral epitope from RSVF , site IV , which is recognized by the 101F antibody 49 ., When bound to the 101F antibody , site IV adopts a β-strand-like conformation ( Fig 6B ) , which in terms of secondary structure content is compatible with one of the edge strands of the TOP7 topology ( Fig 6C ) ., Despite the secondary structure similarity , the RMSD of the site IV backbone in comparison with TOP7 is 2 . 1 Å over 7 residues , and the antibody orientation in this particular alignment reveals steric clashes with TOP7 ., Therefore , this design challenge is yet another prototypical application for FunFolDes , and we followed two distinct design routes: I ) a conservative approach where we fixed the amino-acid identities of roughly half of the core of TOP7 and allowed mutations mostly on the contacting shell of the e
The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability ., The low success rates of computational design protocols and the extensive in vitro optimization often required , highlight the challenge of designing proteins that perform essential biochemical functions , such as binding or catalysis ., One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins ., The structural complexity of the functional motif largely determines how readily one can find host protein structures that are “designable” , meaning that are likely to present the functional motif in the desired conformation ., One promising route to enhance the “designability” of protein structures is to allow backbone flexibility ., Here , we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins—Rosetta Functional Folding and Design ( FunFolDes ) ., We performed extensive computational benchmarks , where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein ., An observation consistent with several experimental studies that have revealed function-stability tradeoffs ., To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo “functionless” fold , which represent two typical challenges where the designability problem arises ., The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies , making them valuable candidates for vaccine design endeavors ., Overall , we present an accessible strategy to repurpose old protein folds for new functions ., This may lead to important improvements on the computational design of proteins , with structurally complex functional sites , that can perform elaborate biochemical functions related to binding and catalysis .
The ability to use computational tools to manipulate the structure and function of proteins has the potential to impact many facets of fundamental and translational science ., Due to our limited understanding of the principles that govern protein function and structure , the computational design of functional proteins remains challenging ., We developed a computational protocol ( Rosetta FunFolDes ) to facilitate the insertion of functional motifs into heterologous proteins ., We performed extensive in silico benchmarks , and found that when the design of function is required the global energy minima may not be the optimal solution , in line with previously reported experimental studies ., Further , we used FunFolDes to design two novel functional proteins , displaying two viral epitopes that can be of interest for vaccine development ., The designed proteins were experimentally characterized , showing that functionalization was successfully achieved ., These results highlight the capability of FunFolDes to address common challenges on the design of functional proteins ., In particular , the reduced structural compatibility between functional sites and host scaffolds , effectively enabling the repurposing of old protein folds for new functions ., Overall , FunFolDes provides new means to accomplish the challenging task of functionalizing computationally designed proteins .
crystal structure, markov models, engineering and technology, synthetic biology, condensed matter physics, synthetic bioengineering, mathematics, protein structure, sequence motif analysis, macromolecular design, crystallography, research and analysis methods, bioengineering, sequence analysis, solid state physics, bioinformatics, proteins, hidden markov models, biological databases, molecular biology, probability theory, physics, protein structure comparison, biochemistry, biochemical simulations, sequence databases, database and informatics methods, biology and life sciences, physical sciences, computational biology, macromolecular engineering, macromolecular structure analysis