Background: The emergence of artemisinin resistance in South East Asia calls for urgent discovery of new drug compounds that have antiplasmodial activity. Unlike the classical compound screening drug discovery methods, the rational approach involving targeted drug discovery is less cumbersome and therefore key for innovation of new antiplasmodial compounds. Plasmodium falciparum (Pf) utilizes the process of host erythrocyte remodeling using Plasmodium-helical interspersed sub-telomeric domain (PHIST) containing proteins, which are amenable drug targets. The aim of this study is to identify inhibitors of PHIST from sulfated polysaccharides as new antimalarials.Methods: 251 samples from an ongoing study of epidemiology of malaria and drug resistance sensitivity patterns in Kenya were sequenced for PHISTb/RLP1 gene using Sanger sequencing. The sequenced reads were mapped to the reference Pf3D7 protein sequence of PHISTb/RLP1 using CLC Main Workbench. Homology modeling of both reference and mutant protein structures was achieved using the LOMETs tool. The models were refined using ModRefiner for energy minimization. Ramachandran plot was generated by ProCheck to assess the conformation of amino acids in the protein model. Protein binding sites predictions were assessed using FT SITE software. We searched for prospective antimalarials from PubChem. Docking experiments were achieved using AutoDock Vina and analysis results visualized in PyMOL.Results: Sanger sequencing generated 86 complete sequences. Upon mapping of the sequences to the reference, 12 non-synonymous single nucleotide polymorphisms were considered for mutant protein structure analysis. Eleven drug compounds with antiplasmodial activity were identified. Both modeled PHISTb/RLP1 reference and mutant structures had a Ramachandran score of >90% of the amino acids in the favored region. Ten of the drug compounds interacted with amino acid residues in PHISTb and RESA domains, showing potential activity against these proteins.Conclusion: This research identifies inhibitors of exported proteins that can be used inin vitro tests against thePlasmodium parasite. This protein has been involved in enabling attachment of infected erythrocytes to organs and blood vessels, as well as erythrocyte remodeling mechanism. Studies have associated this protein with controlling expression mechanism of PfEMP1  The interaction of sulfated polysaccharide molecules with merozoite proteins and theIn this study, we profile specific protein-ligand interactions that give the first insight into drug compounds targeting exported proteins. Furthermore, we outline old and novel mutations found within PHISTb/protein sequences from whole blood samples collected from various malaria endemic sites in Kenya. We link the effects of these mutations on protein structure, active sites and interaction with the drug molecules.P. falciparum samples from ongoing approved research, studying the epidemiology of malaria drug resistance patterns in Kenya. Samples were collected from the following sites: Kisumu and Kombewa (endemic regions), Kericho and Kisii (highland epidemic areas), Marigat , and Malindi . The proportion of samples collected was roughly the same between the six collection sites. Samples were selected using non-random method due to time and limited resources. Fifty samples were selected from each site from the laboratory inventory which contained samples from 2008 to 2018. Factors considered were availability of the sample collected at day zero of study participant recruitment.The study was conducted at the Malaria Drug Resistance laboratory in Kisumu, Kenya, under the United States Army Medical Research Directorate-Africa and Kenya Medical Research Institute. The study analyzed a subset of archivedSamples were collected from volunteers consenting to participate in the study, who were aged 6 months. 2.5 ml of venous blood was collected from participants presenting signs of uncomplicated Malaria. 0.5ml of the blood was transferred to the Acid Citrate Dextrose tubes for sterile culture. Participants that tested positive for Malaria were given Artemether lumefantrine and follow up samples taken at day 7, 14 and 28 following treatment. This study analyzed samples collected at day zero before treatment. At least 1ml of the blood was transferred into an ethylene glycol-bis(2-aminoethylether)-N,N,N′,N′tetraaceticacid (EDTA) tube for molecular assays which included DNA extraction. From the remaining blood sample, 3–5 drops of the syringe blood sample was transferred onto Whatman filter paper by blotting on three separate spots and archived at room temperature in a pouch.Written informed consent was provided by participants and/or their legal guardians. The study was carried out in accordance to approved guidelines by the Ethical Review Committee of the Kenya Medical Research Institute (KEMRI), Nairobi, Kenya and Walter Reed Army Institute of Research (WRAIR) Institutional Review Board, Silver Spring, MD. The study was conducted under the approved study protocols KEMRI #1330/WRAIR #1384 and KEMRI #3628/WRAIR #2454.P. falciparum parasite DNA extraction was carried out using Qiagen DNA Mini extraction spin protocol  as per manufacturer’s instructions. Extracted DNA was stored at -20°C for preservation.A total of 251 viable samples were used in this study.Plasmodium parasite. Plasmodium genus was tested with primers F1 and R1 shown inPlasmodium positive samples were then tested forP. falciparum species.P. falciparum species-specific assay was carried out with primers and probes designed with FAM reporter, as reported in previous work  with the following conditions: 95°C for 10 minutes, 40 cycles at 95°C for 15 minutes, and then 60°C for 1 minute, as previously published , as per manufacturer’s instructions.Following successful amplification, PCR amplicons were visualized in 2% agarose gel electrophoresis for confirmation. The 2% agarose gel was prepared using 2.25gm of agarose powder in 150 ml of 10% Tris Acetate EDTA (TAE) buffer. The gel was then heated in the microwave for 3 min to allow mixing. The gel was then cooled until it was lukewarm, then 15µl of gel red was added. Thereafter the gel was poured into the gel tank in which gel combs had been inserted. The gel was incubated at room temperature for 30 to 60 minutes to allow the gel to set. Upon setting, the gel was flooded with TAE running buffer just above the wells for loading of samples. Samples were then loaded into the wells on the gel wells by mixing 4µl of the sample with 4µl of loading dye. Additionally, 1.5µl each of 1kb hyper ladder 1 (Bioline) was loaded into the first and final wells on the gel. After loading all the samples, the gel was completely flooded with running buffer (10% TAE) and was run at 230 voltage for 50 min. The gel was then read on UVI save HD5 - Gel documentation .Amplification products were purified using ExoSAP-IT  enzymatic cleanup procedure. The enzymatic reaction involves an exonuclease enzyme Shrimp Alkaline Phosphatase (SAP) enzyme, which cleaves phosphate group from unincorporated dNTPs. Using 0.2 ml 96 well plates, each well contained 2µl of ExoSAP-IT enzyme mixed with 8µl of PCR product. The plates were incubated at 37°C for 20 minutes to allow the enzymes to work then for a further 20 minutes at 80°C to inactivate the ExoSAP-IT enzymes prior to sequencing. Big dye termination PCR was carried out using primary PCR primers and amplification conditions. Big dye terminator sequencing is a modification of Sanger sequencing where dideoxynucleotides (ddNTPs) labeled with a specific fluorescent dye corresponding to each nucleotide base are added to the reaction. The sequencing products were cleaned using Sephadex supplied by Sigma. 10µl of HiDi formamide (Applied Biosystems) was added into the purified sequence products. The plates were then sealed and heated at 96°C for 3 minutes to denature the DNA and then analyzed using capillary electrophoresis ABI 3130/3500xL Genetic analyzer. Read assembling was done on Qiagen CLC main Workbench version 8.0.1 .MUSCLE)   in Jalvin 7.2.5  was usedab initio multithreading toolLOMETS which is verified to model multidomain protein structures (Protein structures of the PHIST/RLP1 reference (Pf3D7) and mutant proteins structure were predicted via4jle,5ez3 and6d03 all with a normalized Z-score ≥1. The templates selected for the PHISTb/RLP1 mutant structure modelling were4jle,2ziq and6d03, which had a normalized z-score ≥ 1. Two templates were similar for the reference and mutant protein structure threading because of sequence similarities at the starting and ending domains of the the protein sequences. The middle template was different for both protein structure threading of mutant and reference structures due to presence of point mutations in this region of the mutant PHISTb/RLP1 protein sequence.Protein Data Bank (PDB) hits used as templates for the PHISTb/RLP1 reference structure wereModRefiner ; <5 hydrogen bond donors; <10 hydrogen bond acceptors; and molar refractivity should be between 40–130. This was achieved using theLipinski Rule of Five webserver ,MGL tools version 1.5.6 (PyMOL version 2.3.5 (Schrodinger LLC). Specific protein ligand interactions including hydrogen bonding, vander waals forces as well as hydrophobic interactions were analyzed usingLigPlot version 4.5.3 (The Standard Database Format (.SDF) files of the drug compounds obtained were converted to PDB format files usingn 1.5.6 . The outPlasmodium parasite. The 70% positive samples were further tested forP. falciparum species and 63% were positive. The positive samples (n=110) were used for the downstream experiments, i.e. they were used to sequence PHISTb/RLP1 gene.A total of 175 out of 251 (70%) samples tested positive for102 of the 110 samples processed for Sanger sequencing yielded sequences. Of these, 86 samples generated complete sequences; 16 samples had poor sequences.A total of 157 non-synonymous SNPs were observed across the full length of the protein sequences (485 amino acid in length). The SNPs occurred at different frequencies in the total sequenced samples. Only SNPs occurring at a frequency >50% were considered for protein structure analysis (n=20) .PF3D7_0201600). These six mutations were not present in the database; therefore, they could be newly identified mutations for PHISTb/RLP1 gene as observed in our samples. Analyzing the amino acids substitutions further, we observed that mutations at codons N108T, F145L, W209F, Y210N, V269A, V274I and M277L were all substitutions of amino acids within the same group. The F145L, W209F, V269A, V274I and M277L mutations were all substitutions within the non-polar group, while N108T and W209F were substitutions in the polar group. Amino acids within the same group play similar functional roles in the protein sequence and thus these SNPs were not considered for protein function analysis. On the other hand, I129T, T142V, Y147D, E154Q, S156H, T167I, S208L, M211T, L219H, D387N, D390N, and E403K were substitutions of amino acids across different functional groups. We postulated that these point mutations are most likely to change the folding of the protein structure and have an effect on the function of the protein. These codons were modelled in the mutant protein sequence for PHISTb/RLP1 structure. Their effect on the tertiary protein structure as well as effect on interaction with a drug compound were further investigated  files that were visualized in PyMol to visualize the active amino acids in the binding sites clusters for both reference and mutant PHISTb/RLP1 protein structures, as shown inThe PubChem database search yielded the following compounds: beta carrageenan, alpha carrageenan, dextrin sulfate, amylopectin sulfate, zinc sulfate, ghatti sulfate, 2,4-diaminoanisole sulfate, cyclodextrin sulfate, fucoidan, 3-aminophenylboronic acid and 3,6-Di-O-benzoyl-D-galactal. In this group of compounds, dextrin sulfate deviated from the Lipinski Rule of Five. Beta carrageenan has hydrogen bond acceptors of 12, the compound was considered for interaction experiments because the Rule of Five allows a window of one deviation of the physical properties. 3-aminophenylboronic acid PDB file contained atoms that could not be recognized by AutoDock Vina. The compound was left out for docking experiments. All the other drug compounds adhered to Lipinski Rule of Five hence passing the toxicity test of drug likeness and were tested for activity against the exported protein PHISTb/RLP1. Most compounds contain high gastrointestinal absorption while alpha and Beta carrageenan show low absorption rates. The distribution of the compounds was shown by significant score of bioavailability score of 0.55 which is the recommended value for medicinal drugs. The compounds do not inhibit cytochrome isoenzymes and they do not cross the blood brain barrier (BBB). The skin permeability score were negative showing less permeability of the molecules ,Table 7The identified compounds interacted with the target protein. Alpha carrageenan, amylopectin sulfate, cyclodextrin sulfate and Fucoidan exhibited optimum interactions with the PHISTb/RLP1 protein. The amino acids interacting with these compounds were identified in the binding sites. Of these interactions, amino acids S132, K84 and N80 were found within the PHISTb domain. These interactions significantly show the potential of interfering with the function of the exported protein. Beta carrageenan and dextrin sulfate compounds had specific interactions with the target protein. Amino acids K376, E402 and E403 were identified in the binding site clusters. 2, 4-Diaminoanisole sulfate and zinc sulfate interactions with the protein were not specific with the identified binding site clusters. However, we noted that the amino acids interacting with these compounds are within the PHIST domain which spans amino acids 1 to 167 of the protein sequence. Ghatti sulfate showed interaction with the reference protein as well, although not specific.Plasmodium falciparum. The interaction visualization of low molecular weight heparin with PHISTb/RLP1 reference protein reveal specific interactions between the compound and N197, K205 and S161 all predicted with high confidence score in the target binding site as shown inThe same drug compounds were tested on the modelled PHISTb/RLP1 mutant protein structure with the identified non-synonymous SNPs from the sequenced data. The aim was to test whether mutations found within the protein interfere with the interactions or not. The interactions of the drug compounds with mutant protein were not as strong as those of the reference protein. The interactions in the mutant protein occurred at different binding residues because the mutations within the proteins affected the folding of the protein structure. Due to these mutations, there was a shift of the binding sites clusters. Alpha carrageenan, ghatti sulfate and 3,6-Di-O-benzoyl-D-galactal depicted specific interactions with the mutant protein. Residues K35, N90, D17 and N18 were within the PHIST domain. The other compounds were interacting with different residues in the protein domains. The interactions were not optimized to the predicted binding site clusters. However, we noted that some of the interacting residues were within the PHISTb domain, which is the functional region of the protein. The interactions were compared to the positive control low molecular weight heparin which contains predetermined activity againstWhen selecting the poses after docking experiments, we selected the first pose of the AutoDock Vina pdbqt output file. The docking energies of the screened reference and mutant targets were very low. The energies released therefore supported the drug likeness of these compounds. The resulting Root Mean Square Deviation (RMSD) was zero . There was a difference in docking energies of the drug compounds interacting with PHISTb/RLP1 reference and mutant proteins because of the difference in binding site clusters. The interactions with different residues caused by the shifting of binding sites due to mutations resulted in the slight difference in energy released during docking P. falciparum, SNPs have been reported to cluster in subtelomeric regions of the chromosomes. A study comparing synteny of exported proteins inP. vivax andP. falciparum reported a large number of SNPs in chromosome 2 and 10 subtelomeric regions  and the RESA domain, which is a fusion of other domains, namely the DnaJ domain and the DnaJ-associated X domain Pfam accession number PF0987. The distribution of these domains across the protein sequence explains the clustering of the binding sites in the protein structure of the PHISTb/RLP1 protein.Plasmodium parasites   motif that enables the export mechanism of the protein  major virulent factor of the parasite -Sulfated polysaccharides .sdf and .pdbqt files.Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the Thanks for the responses, from my point of view the article can be indexed.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:NAI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I agree with the new version of the manuscript, the authors have addressed most of the reviewers' comments. I have no further comments.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?Not applicableAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?NoAre sufficient details of methods and analysis provided to allow replication by others?NoReviewer Expertise:Molecular modeling including docking and molecular dynamics simulations; QSAR studies, homology modelingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The manuscript describes the modeling of a protein and its mutant from Plasmodium falciparum, and the assessment of some ligands with these models by molecular docking analysis. The mutant was built based on experimental data from several samples. Both protein models were validated using online tools. Although the authors used several online tools for assessing the structural performance of the proteins, the mutant model is lacking confidence in its tridimensional structure. Furthermore, the docking protocol is not fully described. The results are too preliminary to suggest a potential activity of the selected ligands against PHISTb/RLP1 proteins. I would recommend major revision prior to indexing. The modeled mutant protein resulted with a very different tridimensional configuration to the reference protein. For better prediction and assessment of a homology model, it is suggested to perform a molecular dynamics simulation of at least 50 ns in order to let the structure correctly minimize. Otherwise, the predicted protein may have a non-biological relevance. It is not clear why the authors select the fourth model of each structure (reference and mutant) for energy minimization. Does LOMETs give a score? Furthermore, the TM score is significantly high for both structures, meaning a “perfect match” between the initial model and the generated model. It is surprising that the mutant model gets a TM as similar as the reference model, so the mutant has no apparent structural changes despite having a dozen different amino acids. Nevertheless we can see in Fig2a-b big differences among those structures. How do you explain this? Rule of five is not synonymous with toxicity. Rule of five gives an empirical approximation of the pharmacokinetics of a molecule, in terms of absorption. Ro5 is a simple approach intended to select molecules with druggability properties. To examine toxicity of molecules, many other methods can be used  in different online platforms. Docking protocol. Were the ligands used in their salt forms? For better results, the ligands should undergo a “washing” procedure that includes removing contra ions, adjusting charges, and geometric minimizing process. It is not described in the methodology.  Regarding the gridbox, what size does it have? Depending on the size, the ligands (in this case the ligands are large molecules) are allowed to freely rotate or not. This may bias the docking results. On the other hand, if a positive control is not used as a part of the docking experiments, it is difficult to argue that a molecule will have a potent activity, such in the case of the manuscript. It is preferable to suggest only a possible activity.  For completeness of the discussion, a figure depicting the ligand-protein interaction of the ligands with better affinity would be great. Minor Page 6, Section “Preparation of proteins and ligands, …”, line 8: Please change “autodock tolls” for “autodock tools”.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?Not applicableAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?NoAre sufficient details of methods and analysis provided to allow replication by others?NoReviewer Expertise:Molecular modeling including docking and molecular dynamics simulations; QSAR studies, homology modelingI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Comment: The manuscript describes the modeling of a protein and its mutant from Plasmodium falciparum, and the assessment of some ligands with these models by molecular docking analysis. The mutant was built based on experimental data from several samples. Both protein models were validated using online tools. Although the authors used several online tools for assessing the structural performance of the proteins, the mutant model is lacking confidence in its tridimensional structure. Furthermore, the docking protocol is not fully described. The results are too preliminary to suggest a potential activity of the selected ligands against PHISTb/RLP1 proteins. I would recommend major revision prior to indexing.Response: All the tools used in modeling and assessment of the protein structures are open source tools with algorithms that are assessed and published. Furthermore, they have been used and cited in other scientific research prior to this work hence able to provide significant scientific data as documented in the provided links and cited publications for each tool used. The docking protocol details are included; that is preparation of ligand files in MGL tools, grid box parameters and python script used. Comment: The modeled mutant protein resulted with a very different tridimensional configuration to the reference protein. For better prediction and assessment of a homology model, it is suggested to perform a molecular dynamics simulation of at least 50 ns in order to let the structure correctly minimize. Otherwise, the predicted protein may have a non-biological relevance.Response: Energy minimization carried out using ModRefiner an algorithm for atomic-level, high-resolution protein structure refinement, which can start from either C-alpha trace. The models underwent energy minimization using this tool prior to docking experiments. Molecular dynamics simulation of at least 50ns were not possible to carry out due to minimal expertise in a tool that can do that. However, after energy minimization using ModRefiner the models quality improved and were fit for docking simulations. Comment: It is not clear why the authors select the fourth model of each structure (reference and mutant) for energy minimization. Does LOMETs give a score? Furthermore, the TM score is significantly high for both structures, meaning a “perfect match” between the initial model and the generated model. It is surprising that the mutant model gets a TM as similar as the reference model, so the mutant has no apparent structural changes despite having a dozen different amino acids. Nevertheless, we can see in Fig2a-b big differences among those structures. How do you explain this?Response: The fourth model was chosen after screening the Ramachandran score for the five suggested models hence the fourth model had the highest scores. Lomets does not provide scores for models since they are racked from number 1 to 5, but for the template selected. Structures match because the middle template selected by the algorithm was similar in both reference and mutant modelling jobs. The PHIST domain is the only present crystal structure for this protein family in PDB. This is why the TM score matches closely. The difference between the two structures can be seen in the alpha-helix folding which is significantly distinct and postulated to be an effect of the SNPs identified. Comment: Rule of five is not synonymous with toxicity. Rule of five gives an empirical approximation of the pharmacokinetics of a molecule, in terms of absorption. Ro5 is a simple approach intended to select molecules with draggability properties. To examine the toxicity of molecules, many other methods can be used  in different online platforms.Response: ADMET analysis data was included using SwissADME tool as suggested.  Comment: Docking protocol. Were the ligands used in their salt forms? For better results, the ligands should undergo a “washing” procedure that includes removing contra ions, adjusting charges, and geometric minimizing process. It is not described in the methodology. Response: The ligand files were converted using the Open Babel tool and prepared using MGL tools as described in the text. This editing enabled the removal of contradicting s ions prior to docking. Comment: Regarding the grid box, what size does it have? Depending on the size, the ligands (in this case the ligands are large molecules) are allowed to freely rotate or not. This may bias the docking results.Response: grid box dimensions were added in the main text. Comment: On the other hand, if positive control is not used as a part of the docking experiments, it is difficult to argue that a molecule will have a potent activity, such in the case of the manuscript. It is preferable to suggest only a possible activity. Response: Included low molecular weight heparin as the positive control compound since it is a well-researched sulfated polysaccharide with anti-malarial properties.  Comment: For completeness of the discussion, a figure depicting the ligand-protein interaction of the ligands with better affinity would be great.Response: Included a figure in the discussion  My first comment is regarding the selection of the sites, the authors did not mention how the sites were selected. Were the samples collected for this specific study? Or is this study a secondary analysis? If yes, what was the primary study? Second, in the article the authors mentioned that the samples were tested for plasmodium falciparum, so, how were patients that had positive results treated? If that was the case? In the method paragraph, the study design is not clear. How were the samples selected etc.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:I am specialist in public health working mostly in malaria drug and vaccines.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Concerning the comments on sample sites selection and collection: The samples used in this study were not collected specifically for this secondary study but were used on basis of availability. The samples were collected for a bigger project that studies drug resistance patterns from the identified regions. The sampling for this study was hence non-randomly conducted from sample inventory of archived whole blood samples. This study was a sub-study in the Malaria Drug Resistance Laboratory and not the primary study of the institution. The sample sites were selected according to different Malaria endemic zones in Kenya and were guided by the proposal of the main study which studies epidemiology patterns of the disease in Kenya. The patients who tested positive were treated with recommended Artemesinin combined therapies at day zero of testing and follow up samples were collected after 7, 14 and 28 days. These were done according to the objectives of a different study. The current study did not need the treatment data.


Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins. Finely orchestrated protein-protein interactions are at the heart of virtually all fundamental cellular processes. Altering these processes or encoding new functions in proteins has been a long-standing goal in computational protein design. Protein design methods commonly rely on scoring functions that seek to identify amino acid sequences that optimize structural configurations of atoms while minimizing a variety of physics-based and statistical terms. The objectives of the large majority of computational design protocols have been focused on obtaining a predefined structural conformation. However, routinely introducing a functional aspect on designer proteins has been more challenging. Our results suggest that the molecular surface features can be a useful optimization parameter to guide the design process towards functional surfaces that mimic known protein binding sites and interact with their intended targets. Specifically, we demonstrate that our design method can optimize experimental libraries through computational screening, creating a basis for highly specific protein binders, as well as design a potent immunogen that engages with site-specific antibodies. The ability to create proteins with novel functions will be transformative for biomedical applications, providing many opportunities for the design of novel immunogens, protein components for synthetic biology, and other protein-based biotechnologies. Proteins are key components in living cells, performing many functions that commonly rely on physical interactions between molecules. The molecular surface arising from the three-dimensional arrangement of the many atoms that compose a protein is determinant for protein function and is therefore crucial for biological processes . While dIn 1971, Lee and Richards introduced the concept of solvent-accessible surfaces, which in practice are generated by rolling a probe approximating a solvent molecule over the protein atoms . The molC) or shape similarity (SS). Shape complementarity is frequent in the study of molecular recognition, e.g. protein-protein or protein-ligand interactions. In particular, protein-protein interactions (PPIs) have been extensively studied in terms of complementarity, showing that protein-protein interfaces are often highly complementary, both in shape and in charge [Intuitively, the molecular surface forms the boundary of the protein and its surroundings, thus acting as the interface that engages in interactions with other molecules. The study of protein structures as near-continuous molecular surfaces is therefore important to understand structural and functional aspects of proteins, which may not be fully captured by a discrete atomic representation ,16. A win charge ,20. On tn charge ,21.An important extension of such successful applications in analysis and prediction tasks using surface-based representations is that of protein design , where tHere we present a new, surface-centric computational design strategy, termed RosettaSurf, that uses a description of the molecular surface shape and electrostatic properties as objective function for scoring optimization in protein design. Working at the surface-level of protein structures offers a unique approach for the design of proteins and their functions, as molecular surfaces are, to some extent, independent of the underlying sequence ,14. ThisWhile these strategies have been successfully applied to design novel protein topologies –24, the RosettaSurf is a protocol implemented in the Rosetta software package  and we aThe RosettaSurf protocol operates at the solvent-excluded surface level of a protein structure, and its core operation is the comparison between surfaces. To compare molecular surfaces, we defined a score that quantifies the similarity between two surfaces considering both shape and electrostatic features, and incorporated it within the Rosetta software package. The molecular surface is generated from the three-dimensional atomic coordinates of a protein ,4 and stS) score that quantifies the similarity of the geometry of two given molecular surfaces. The SS score ranges from 0 (no similarity) to 1 .To describe the surface geometry, we developed a descriptor based on concepts introduced by Lawrence and Colman  that quaS) of two surfaces is derived from comparing their electrostatic potentials, i.e. ES is assessed by computing the correlations of the two electrostatic fields originating from the target and reference surface, respectively (S range from -1 (highly similar) to +1 (highly complementarity).Electrostatic similarity (Eectively . CorrelaS). Since we were interested in assessing similarity of surface sites that are well maintained and relevant to functional protein design, we focused on interfaces of PPIs and performed logistic regression on a dataset of 2,660 protein complexes to optimize the individual weights of the SurfS score for each component, shape and electrostatics.To accurately capture the relative contributions of both shape and electrostatics on the similarity of molecular surfaces, we combined both scores into a single surface similarity score  by inverting the normal vectors of the reference surface. Complementarity has been typically used to evaluate PPIs and was first demonstrated by Lawrence and Coleman for molecular shapes [The final Surfr shapes .We developed the RosettaSurf design protocol, as part of the RosettaScript software environment , which uS score.With the RosettaSurf design protocol we explicitly optimize molecular surface features during the protein design process. In practice, RosettaSurf performs sequence design in a way where the mutable surface of the design scaffold (target) is optimized to closely match the surface features of a reference protein (reference) . To effiTo reduce computational time spent on rotamer sampling in a combinatorial fashion on the overall surface, we implemented a single amino acid scanning surface-centric protein design approach (RosettaSurf-site). This protocol samples amino acids individually at each position of the design surface, selects the top three rotamers according to the surface and samples those combinatorially with other designable positions, reducing the combinatorial possibilities.In our benchmark studies we sought to compare the developed surface-centric design protocol, RosettaSurf, to state-of-the-art macromolecular design approaches implemented in Rosetta. The Rosetta energy function has been parametrized to evaluate and optimize the energy of many different aspects of molecular interactions ; it contains both statistical and physics-based terms, being calibrated using a discrete atomic representation of the molecules. This type of optimization has been particularly successful for the design of novel sequences that fold into defined protein structures. However, to design proteins that display defined motifs which can perform biological functions has proven to be difficult. By focusing the design process on the areas where molecular interactions occur–the protein surfaces–novel approaches attempting to design function into proteins may represent new routes to address this problem.Two types of metrics in surface comparison are considered: surface complementarity and surface similarity. We show how the surface similarity score captures features of individual residues and demonstrate the ability of the RosettaSurf protocol to recover amino acid sequences of native protein interfaces. Furthermore, we highlight the performance of RosettaSurf for the design of surface patches at protein interfaces.To evaluate the accuracy of our design protocol, we performed a benchmark on the recovery of single amino acids in native protein interfaces. The amino acid of interest is substituted by each of the 19 amino acid identities, excluding cysteine as it can form covalent bonds in the form of disulfide bridges, thus rendering the resulting surface dependent on the interplay of two amino acids, and the surfaces of the substituted amino acids are compared to the surface of the native rotamer . These cIncorporating the electrostatic similarity term into the surface score generally results in a boost in recovery rates over shape similarity alone, in particular for amino acids that have close structural doubles but differ in chemical properties, e.g. glutamine and glutamate, as well as, asparagine and aspartate .The Rosetta energy function shows the best recovery rates for amino acids with unique features on their side-chains, i.e. glycine and proline. However, even for these cases, surface similarity outperforms the recovery by REU. A similar trend can be observed for the unbound complexes, although the general success of retrieving the native amino acid decreases for certain amino acids. This trend is most obvious for polar residues and large side chains  that have access to a variety of different rotamer conformations as the lack of the binding partner allows a larger conformational space for different rotamers to be explored. For these types of amino acids, the sequence recovery rate is higher in the bound conformation as the presence of the binder restricts the possible rotamer space that can be explored, especially for highly exposed residues  Fig. A sMoreover, the surface similarity scores retrieved from an all-against-all amino acid comparison demonstrate the high accuracy by which most amino acids can be identified . Here, wClose inspection of the considered surface of a single amino acid demonstrates the local precision of the surface similarity score . Small ode novo design of protein-protein interactions, it is important for applications in the domain of immunogen design for vaccine development where the surface mimicry of known surfaces  is critical for the biological activity of this type of design [Having shown that the implemented surface similarity score is sufficiently accurate to recover individual amino acids, the following benchmark expands on recovering entire surface patches in protein interfaces. We evaluated the ability of our surface-based sequence design protocols (RosettaSurf and RosettaSurf-site) to recover natural protein interfaces as a gateway for the design of proteins endowed with biochemical function. By focusing on protein interfaces, the correct sequence does not solely depend on minimizing the Rosetta scoring function, but rather needs to represent the surface properties of the interface site. While such design scenario has limited applicability for the f design ,30–32.In this benchmark we considered nine protein-protein complexes forming transient interactions (for details see Standard Rosetta design (FixBB)Surface-centric design (RosettaSurf)Single-site-scanning surface-centric design (RosettaSurf-site)All protocols operate on fixed backbones of the protein complexes that are presented in the native conformation that mediates the interaction. Consequently, the sequence recovery success will largely depend on side-chain placement in a given backbone. The surface-centric design approaches are compared to the standard FixBB Rosetta design protocol, using the ref2015 scoring function . In the S score to recover native amino acids compared to a known ground truth. This is in contrast to the single amino acid recovery benchmark where the reference surface solely served as comparison to evaluate the similarity of the native surface and the surface resulting from the mutation without affecting the amino acid change itself.In a first step, the target protein’s interface is stripped off its native sequence by mutating all interface residues to alanine. Second, the different design protocols were employed on the interface positions with FixBB using the Rosetta energy function to select mutations while RosettaSurf was guided by our surface similarity scoring term . RosettaAll different design protocols were employed for the bound and unbound states of the protein complexes and results are reported by their category as mean sequence recovery rate . OverallS score of greater or equal to 0.9992 as this value marks the lower end of structures with high sequence recovery (> = 70%), leading to one candidate from the RSV epitope-scaffold-Motavizumab complex. When investigating which types of amino acids were common failures, we identified general geometric resemblance as likely reason. Several amino acids are frequently replaced by structurally similar residues, e.g. isoleucine by valine and lysine by arginine.Furthermore, we analyzed more closely outlier decoys, i.e. structures that scored high in surface similarity but showed only little sequence recovery when designing with RosettaSurf. We set a threshold of up to 50% as low recovery and a SurfAs for sequence recovery without the binder, RosettaSurf and RosettaSurf-site performed better for complexes of low shape complementarity with 81% and 80% recovery, respectively. However, sequence recovery obtained for high-complementary interfaces reached 66% for RosettaSurf and 68% for RosettaSurf-site. For antibody/antigen complexes we observed 76% for RosettaSurf and 66% for RosettaSurf-site. All these performances were substantially higher than those of FixBB, that reached 25% and 45% for high-complementary and antibody/antigen complexes, respectively.S score, observing similar results as for the bound benchmark. Four structures, all from the Colicin E9-IM9 complex fulfill these criteria. Again, mainly amino acids with similar geometrical features were common mismatches between recovered and native amino acids, e.g. phenylalanine, tyrosine, and histidine, aspartatic acid and asparagine, as well as valine and threonine.In addition, we investigated possible reasons for structures with low sequence recovery but high surface similarity scores. We adjusted the selecting criteria to structures with < = 30% sequence recovery and > = 0.9996 SurfOverall, similar performance of RosettaSurf and RosettaSurf-site showed that performing the combinatorial sampling of all possible rotamers simultaneously was not necessary. During the RosettaSurf-site pipeline amino acids were sampled individually at each design position and only a subset of the amino acids was used for combinatorial sampling based on the surface similarity score, resulting in similar recovery outcomes. Furthermore, despite small differences in the sequence recovery rate between the three selected categories of protein complexes, overall no strong correlation between the shape complementarity of the considered complexes and the success in sequence recovery could be observed. We considered groupings based on different metrics, namely total interface area, hydrophobic interface area, and number of recoverable positions. No correlation with sequence recovery was observed for these different properties . AdditioOur results have thus implications for the design of functional proteins as the high success of recovering natural protein interfaces may be promising for the design of proteins displaying defined surface properties, as is the case for immunogen design.in silico saturation mutagenesis has been used previously to identify stabilizing mutations or improve binding affinity, these approaches were based on energy functions to identify beneficial mutations [de novo designed interleukin-2/15 antagonists that bind specifically to the IL-2Rcβγ receptor [A common problem in protein design is the optimization of PPIs to generate high affinity protein binders. Experimental screening methods, e.g. site-saturation mutagenesis (SSM) and combinatorial libraries, provide insights into mutations that improve binding interactions but are time and resource-consuming experiments. While utations –40. Howereceptor . The comreceptor ,43.cβγ interface rather than similarity as shape complementarity has been shown to be a key feature of high affinity binding interactions [βC-distance of 7 Å, were excluded from the selection, resulting in a total of 18 positions that can be regarded as interface. From these 18 possible interface residues, four positions were also present in the experimental library. One additional position (residue 39) was included that lies at the boundary of the interface, within 9 Å of the receptor, and was tested in the experimental library, resulting in a total of five positions as design input for our benchmark. In our design protocol, the five selected residues were sampled with RosettaSurf-site and mutations were evaluated based on the shape complementarity to the receptor surface. The computational results were compared to the composition of the combinatorial library that was constructed based on the preceding SSM screening. We analyzed whether RosettaSurf-site was able to identify mutations that are present in the highest affinity binder as well as the performance of the Rosetta energy function to retrieve similar mutations. After performing all possible mutations with RosettaSurf-site, the four top-ranking amino acids for each position were selected (We performed RosettaSurf-site similar to the approach described above (see “Protein Interface Sequence Recovery”), however, with the selection criteria being shape complementarity of the design and IL-2Rractions . Rosettaselected , in lineRosettaSurf-site recovered four out of the five mutations present in the best design, with residue 39 being at the edge of the interface and contributing minimally to the protein-protein interaction, thus making it more challenging for this approach. Four additional residues included in the combinatorial library were recovered, that were observed to improve binding in the SSM but were not present in the highest affinity binder. In contrast, selecting mutations based on Rosetta energy alone recovers only a single mutation observed in the best binder and two residues present in the combinatorial library. These results show the potential of surface-based design of single point mutants as fast and promising approach to select candidates for combinatorial libraries to improve binding interactions without requiring preceding screening experiments like SSM.De novo design methods have been successfully used to build scaffolds from scratch [de novo protein design is critical. The RosettaSurf approach allows to rescue protein scaffolds that only present partial structural matches to the epitope structure motif.In recent years, protein design has shown promising results in the field of immunoengineering, allowing the computational design of epitope-focused immunogens that were shown to elicit functional antibody responses in mice and non-human primates ,30,31,45 scratch , but theAs a demonstration of the use of RosettaSurf to transplant the epitope site into an unrelated scaffold, a two-step strategy was employed: 1) side chain grafting of the α-helical segment onto a small, monomeric protein scaffold ; 2) Roseα RMSD: 0.4 Å) and offers a sufficiently large surface area around the helix to be optimized with RosettaSurf. We generated 760 designs using the RosettaSurf pipeline and the site 0 surface, as observed in complex with site-specific antibody D25 [We identified the NarX histidine kinase receptor (PDB: 3EZI)  as promibody D25 , as a seTwo additional variants of the same protein scaffold were designed to test whether there were advantages in using RosettaSurf. One design containing only the α-helix of site 0 (RSV_helix) and three point mutations that resolve clashes with the side chain grafted epitope helix, similar to Surf_03. The second design was generated with FixBB (RSV_FixBB), starting with side-chain grafting of the epitope α-helix, and allowing to design the same amino acid positions as those of Surf_03. The RosettaSurf design (Surf_03) reaches a surface similarity score of 0.45 compared to the antigenic site in the native viral protein RSVF, while the native scaffold scores 0. The helix-grafted base-design scores 0.06 and the RSV_FixBB design 0.04 in surface resemblance.To test experimentally our predictions, the designs were expressed and purified, and the binding affinities were measured using a panel of monoclonal antibodies that engage the site 0 epitope  31,47,4,447,48. in silico benchmarking and experimental design tasks. The protocol can either be used to optimize surface similarity or surface complementarity depending on the design task at hand.In this work we propose a surface-centric protein design approach and demonstrate its accuracy in several Our ideas to leverage the surface geometrical and chemical features stem from observations that surfaces displaying similar patterns can have similar functional roles , that consists of two structural segments, as an example for structurally challenging sites that remain difficult for computational design approaches. The surface-centric designs show a broader binding profile across a panel of monoclonal antibodies as compared to other design approaches, suggesting that the surface presented is a closer mimic of the native antigenic site.Possible applications for surface-centric design range from the computational design of highly specific protein-protein interactions, focusing on optimizing the surface complementarity both in shape and chemistry. While the herein presented examples work under the premise of a known protein structure to be optimized for binding, an important related problem is the design of a target surface in the absence of a known backbone template. To our knowledge, identifying backbones with compatible target surfaces remains an open research question, however, we envision two approaches that can readily be combined with RosettaSurf. One simplified approach could utilize a database of protein backbone scaffolds that could be used as initial docking candidates and subsequently optimized with RosettaSurf to improve complementarity towards the target. A second potential approach is to apply a recently developed geometric deep learning framework called MaSIF . Due to In the scope of translational applications, surface similarity enables the computational design of proteins that may recapitulate precise surface features of target surfaces, which could then find applications in immunogen design, where surface similarity allows the design of immunogens mimicking an antigenic site of interest that could ultimately be used as probes for antibody isolation or vaccine antigens.Ultimately, we introduce a new conceptual approach in computational protein design where the fine features of molecular surfaces are explicitly optimized. Through this route it will now be possible to explore the tantalizing hypothesis of the existence of a “surface degeneracy code”, which in some cases may allow to represent similar surface patches using very distinct sets of amino acids. Importantly, this capability could also enable the presentation of similar surface patches in proteins with completely different backbone architectures which could again realize many endeavors in functional protein design that are thus far out of reach.S) score is composed of shape and electrostatic similarity components. To evaluate surface shape similarity, two protein structures are first aligned and the closest surface points between target and reference are identified. Next, the normal vectors of the surface points are compared by first computing their dot product to obtain the enclosed angle, followed by distance-based scaling that penalizes points that are far apart ..Fig 1). S score to facilitate accurate description of molecular surfaces. To identify a set of optimal weights that describe the relative contributions of the geometric and biochemical features towards the overall properties of the molecular surface, we aimed to derive weights from native protein surfaces. As our goal was to ultimately apply the SurfS score to design novel protein binders, we focused on surfaces arising at the interface of PPIs.We combined both, the shape and electrostatic similarity scores, into a single SurfThe dataset of protein complexes was compiled from the PDBbind , the SAbS score were subjected to constrained energy minimization in their complexed state using Rosetta to adapt them to the Rosetta energy function and the resulting energy optimized decoy served as input for the subsequent benchmark analysis. The protein complexes were randomly selected from the same pool of transient PPIs used for the logistic regression analysis while ensuring that no duplicated complexes were used for the logistic regression training and the single amino acid recovery benchmark. We divided each of these protein complexes into target proteins, where the interface can be mutated, and binders, the proteins that serve as context for the target. The interface of the target protein is defined as residues that are within 7 Å βC-distance of the binder and have the βC-atom pointing towards the binder. This selection ensures that the amino acid side chains are part of the interface and the contribution of the residue to the binding interaction is not solely due to backbone interactions. The full target interface is converted to alanine, effectively removing any side chain memory of the native structure that would restrict the placement of new rotamers and introduce biases towards the native sequence. Cysteine residues are ignored as they can form chemical linkages in the form of disulfide bonds, thus generating a surface that is not attributable to an individual residue.The dataset of the single amino acid recovery benchmark consists of 1,900 protein complexes, with 100 complexes for each amino acid identity except cysteine. All structures used during the various benchmarks as well as the proteins used to generate the SurfTo test the performance of the surface scoring function we sought to perform a benchmark to evaluate sequence recovery at the interfaces of protein-protein interactions with varying shape complementarity. We assembled a diverse dataset consisting of nine protein complexes capturing different aspects of PPIs and grouped them into three different categories: 1) low-shape complementary interactions which include Enterotoxin G–T-cell receptor complex (PDB: 3MC0) , RibonucData analysis was performed with the help of the rstoolbox Python library .α RMSD of 0.4 Å and provided additional surface area to mimic the entire antigenic site.The structure of antigenic site 0 was extracted from the crystal structure of prefusion stabilized RSVF in complex with antibody D25 (PDB: 4JHW) . The D25In a first step, we transplanted the side chains of the epitope helix contacting the D25 antibody onto the selected scaffold using MotifGraft, followed by the introduction of three mutations on the protein scaffold that were not part of the interface to resolve steric clashes with the transferred side chains, resulting in the design RSV_helix. Subsequently, we performed surface-centric design on 19 residues using the RosettaSurf pipeline to increase surface mimicry of the site 0 around the epitope helix, resulting in 760 design decoys. Surface-centric design was performed in the presence of the D25 antibody and site 0 of RSVF served as a reference to which our designed surface was optimized. We selected the decoy with the highest surface mimicry score, named surf_01, and optimized the design in additional steps. To allow accurate display of the designed surface, we introduced two point mutants in the scaffold adjacent to the optimized surface patch to avoid steric hindrance (surf_02). Lastly, after comparing the similarity of surf_02 and the native antigenic site, we identified a lysine residue at position 18 in our design with suboptimal mimicry. Evaluating the sequence profile of the 20 best scoring decoys sorted by surface mimicry revealed a strong preference for leucine at this position and the residue was incorporated into a new design (Surf_03). Finally, we designed a version of RSV_helix that used Rosetta’s fixed-backbone design (FixBB) to serve as comparison to our surface-optimized designs. We designed 1’000 decoys in the presence of D25 antibody, allowing mutations to occur at the same 19 residues as was the case for RosettaSurf design. All designs converged to an identical sequence which was selected as design RSV_FixBB. Based on Surf_03 we designed a knockout mutant (Surf_03_KO) with a N74Y mutation in the epitope helix.E. coli BL21 (DE3 pLysS)  and grown overnight in LB media at 37°C. Pre-cultures were diluted 1:50 and inoculated to an OD600 of 0.6 in terrific broth  at 37°C and expression was induced by the addition of 1 mM isopropyl-β-D-thiogalactoside (IPTG). Cultures were harvested after 18–20 hours at 20°C. Pellets were resuspended in lysis buffer  and sonicated on ice for a total of 12 minutes, in intervals of 15 s sonication followed by 45 s pause. The lysates were cleared by centrifugation  and purified using a His-Trap FF column on an Äkta pure system , followed by size exclusion on a HiLoad 16/600 Superdex 75 column  in phosphate-buffered saline (PBS). Protein concentrations were determined by measuring the absorbance at 280 nm on a Nanodrop (Thermo Scientific). The designed proteins were concentrated by centrifugation  to 1 mg/ml, snap frozen in liquid nitrogen, and stored at −80°C.Genes for all designs were purchased as DNA fragments from Twist Bioscience, and cloned into pET11 vectors, containing a N-terminal MBP-tag and His-tag as well as a TEV cleavage site, for bacterial expression. Plasmids were transformed into For Fab expression, heavy and light chain DNA sequences were purchased from Twist Biosciences and cloned separately into the pHLSec mammalian expression vector  using AgeI and XhoI restriction sites. Expression plasmids were premixed in a 1:1 stoichiometric ratio, co-transfected into HEK293-F cells, and cultured in FreeStyle medium . Supernatants were harvested after 1 week by centrifugation and purified using a kappa-select column . Elution of bound proteins was conducted using 0.1 M glycine buffer (pH 2.7), and eluates were immediately neutralized by the addition of 1 M Tris ethylamine (pH 9), followed by buffer exchange to PBS (pH 7.4).SPR measurements were performed on a Biacore 8K  with HBS-EP+ as running buffer  at room temperature. Approximately 700 response units (RU) of Fabs were immobilized on a CM5 sensor chip  via amine coupling, and designed monomeric proteins were injected as analyte in two-fold serial dilutions. The flow rate was 30 μl/min with 120 s of contact time followed by 400 s dissociation time. After each injection, surface was regenerated using 0.1 M glycine at pH 3.5. Data were fitted using 1:1 Langmuir binding model within the Biacore 8K analysis software .S1 FigA surface patch is selected on the target protein that will be subjected to mutations for improving surface features. A reference surface is specified and will be used during the design process to guide the introduced mutations. During sequence design, rotamers are sampled in the selected interfaces of the target protein and for each substitution the surface is compared to the reference surface. If mutations improve the surface score, the changes are accepted. During iterative sampling steps of the selected surface patch, the overall surface can be improved.(TIF)Click here for additional data file.S2 FigCase study of the recovery of arginine in difficult and easily recoverable benchmark cases. A) Recovery of the exposed arginine residue is unsuccessful in the unbound test case as a non-native rotamer is placed in the structure. The addition of the binding partner limits the accessible rotameric space and allows successful recovery of the amino acid. B) Successful recovery of arginine independent of the presence or absence of the binder as the native rotamer conformation is less exposed. Overall side chain configurations placed in the bound benchmark cases are closer to the native rotamer (mean full-atom RMSD of ~0.4 Å) as compared to the unbound benchmark cases (mean full-atom RMSD of ~2.3 Å).(TIF)Click here for additional data file.S3 FigS score can discriminate the local changes. The shape similarity score changes by 0.2 and the electrostatic similarity score by 0.5 units, resulting in an overall SurfS score of 0.989 when comparing both rotamers. The differences are specific for the altered region as shown for shape similarity (right) and outline the modified atom positions.A small rotamer change shown for lysine results in only a 0.8 Å all-atom RMSD change (left). When evaluating both rotamers in terms of surface similarity, the Surf(TIF)Click here for additional data file.S4 FigFlexible plot represents the sequence recovery rate of protein complexes whose interfaces consists at least of 40% amino acids with flexible side chains . The Static plot contains complexes with less than 40% flexible amino acids in the interface . Amino acids were considered flexible/static based on Scouras and Daggett’s observations on rotamer dynamics. Accordingly, we assigned R, N, Q, E, H, K, M, and W as flexible and D, I, L, F, P, S, T, Y, V, A, and G as static amino acids. Spearman rank’s correlation analysis shows a weak correlation of sequence recovery rate and rotamer flexibility , indicating that rotamers with higher conformational flexibility are more difficult to recover. B) Sequence recovery of protein interfaces grouped by the median distance of closest surface points into low , medium , and high  groups. C) Sequence recovery of protein interfaces grouped by interface area into low , medium , and high  groups. D) Sequence recovery of protein interfaces grouped by hydrophobic interface area into low , medium , and high  groups. For all benchmark cases, the sequence recovery is reported in the absence (unbound) and presence (bound) of the binder.A) Sequence recovery of interfaces from protein complexes grouped by the amount of flexible amino acid side chains. The plot on the left site shows the overall sequence recovery for all complexes combined under the three different design approaches, i.e. FixBB, RosettaSurf, and RosettaSurf-site. The (TIF)Click here for additional data file.S5 FigThe surface designed protein (Surf_03) demonstrates the broadest binding reactivity, showing measurable interactions with antibodies D25, ADI14496, ADI18900, and ADI19009. In contrast, a design containing solely the helical motif (RSV_helix) of the site 0 epitope interacts only with D25 and ADI19009. Similar results are obtained for a Rosetta-designed variant (RSV_FixBB), binding to D25 and ADI19009. Controls in the form of a KO mutant of design Surf_03 (Surf_03 KO) and the wild type protein (WT) show no measurable binding, indicating that the observed interactions are specific for the designed epitope sites.(TIF)Click here for additional data file.


SARS-CoV-2, a novel betacoronavirus strain, has caused a pandemic that has claimed the lives of nearly 6.7M people worldwide. Vaccines and medicines are being developed around the world to reduce the disease spread, fatality rates, and control the new variants. Understanding the protein-protein interaction mechanism of SARS-CoV-2 in humans, and their comparison with the previous SARS-CoV and MERS strains, is crucial for these efforts. These interactions might be used to assess vaccination effectiveness, diagnose exposure, and produce effective biotherapeutics. Here, we present the HuCoPIA database, which contains approximately 100,000 protein-protein interactions between humans and three strains  of betacoronavirus. The interactions in the database are divided into common interactions between all three strains and those unique to each strain. It also contains relevant functional annotation information of human proteins. The HuCoPIA database contains SARS-CoV-2 , SARS-CoV , and MERS  interactions, with functional annotation of human proteins like subcellular localization, tissue-expression, KEGG pathways, and Gene ontology information. We believe HuCoPIA will serve as an invaluable resource to diverse experimental biologists, and will help to advance the research in better understanding the mechanism of betacoronaviruses.  Coronaviridae family of enveloped RNA viruses has a single-strand genome size ranging from 26 to 32 kilobases, approximately [In late 2019, cases of pneumonia with an unclear cause occurred in the world. The atypical clinical characteristics were reminiscent of viral pneumonia and triggered worldwide concern due to the severity, quick dissemination, and the potential to affect both the lungs and the possibility of a missed diagnosis . The Corximately . They arximately .Most of these mechanisms involve protein-protein interactions (PPIs). In all living cells, PPIs play a significant role in the infection cycle, as well as in initiating a defense response . ComputaIn this study, we examine the use of homology-based (interolog) and domain-based methods to decipher the HPIs between the human and SARS families on a whole-proteome scale. Three different viruses: SARS-CoV-2, SARS-CoV, and MERS, were analyzed against the human proteome. We have compared the common and unique human proteins interacting with these three viruses, and functional characterization of these HPIs may explain the molecular mechanisms for pathogen infections. Through having a detailed functional analysis of the expected interactome to better explain the infections of the illness, we have identified novel protein hubs, enhanced molecular structures, biological processes, tissue expression, and pathways. Furthermore, we have identified the localization of the interacting protein in human cells. Additionally, we have found a difference in HPI patterns between the three viruses. We assume researchers will be able to use the HuCoPIA to search and compare interactions, which could be a good foundation for further experimental validations, and further used in global vaccine development or to provide a deeper understanding of the host of potential virulence factors and drug targets as set out in this analysis. We have compiled all our predictions in a user-friendly database for better visualization.https://www.uniprot.org/facets=reviewed%3Atrue%2Cmodel_organism%3A9606&query=human, accessed on 2 January 2021), SARS-CoV-2 isolates from 30 different countries, 4 isolates of SARS and 1 isolate of MERS were obtained from the NCBI . The human proteome contains 26,526 proteins, whereas SARS-CoV-2, SARS, and MERS isolates contain 44, 34, and 28 proteins, respectively. We used CD-hit, a very widely used program for clustering and comparing protein or nucleotide sequences, to eliminate duplicate proteins. We considered proteins from different strains to be duplicates if they were 100% identical. Similarly, if they were different, we tagged the protein with the country’s identity. For example, the nsp15 protein is different in an isolate from Iran, so we named the nsp15 of Iran as nsp15_Iran. All human proteins were mapped to their corresponding median tissue expression information, available in the GTEX https://www.gtexportal.org/home/datasets, accessed on 15 July 2021) database. Similarly, the subcellular localizations of the human proteins involved in PPIs were mapped from the Uniport  and the Genecard  databases. An overall workflow of the HuCoPIA database is depicted in The human reviewed proteome was obtained from . Apart from these three, we agreed on the common and different interactions of the three viruses.The interolog-based interaction prediction is based on the conservation of protein-protein interactions across comparable systems. There are no standard cutoff evaluation parameters for defining a true homolog in predicting the HPIs. We found that using an e-value of 1e-50, alignment coverage of 80%, and sequence identity of 40% were the best-fit alignment parameters for predicting the human–betacoronavirus PPIs. Domain-based analysis was one of the computational tools we utilized to uncover the host-pathogen interactions of new COVID-19 strains and the human proteome. The goal of this study is to obtain the domain structures of host and pathogen proteins, then utilize these domain pairings to search DDI databases for probable DDIs. Human proteins are filtered with a 1e-23 e-value and 0.2 coverage, whereas viral proteins are filtered with a 1e-1 e-value and default coverage. To reduce the false positive rate, all projected DDIs are compared to the Negatome database. We use in-house python scripts and queries to link each domain in the DDI to matching proteins. Following the introduction of interolog and domain-domain analysis on an individual basis, it is critical that we combine all the expected host-pathogen interactions into a single set of results. We have combined the data from both methodologies, removed redundant host pathogen interactions, and given more weight to interactions predicted by both interolog and domain-based processes. This is how the PPIs for human and the https://www.uniprot.org/facets=reviewed%3Atrue%2Cmodel_organism%3A9606&query=human, accessed on 12 October 2021) and Genecard  datasets to map the subcellular localization for most proteins. The subcellular localization of all human proteins was downloaded from these two sources and then assigned to the proteins involved in PPIs using our in-house Python scripts.Pathogenesis of a wide spectrum of human illnesses is attributed to abnormal protein localization . The undhttps://www.gtexportal.org/home/datasets, accessed on 15 July 2021). Then, the file was filtered using our in-house Python script and the tissue were selected based on the expression for each protein. Finally, we have highly expressed tissue for each human protein.Genes with disease-related mutations are found in all cells of the body, however, genes that are expressed more heavily in certain tissues can cause functional problems and pathophysiological abnormalities. However, it is critical to comprehend the entire disease-tissue network, since other tissues may be impacted but not show any signs of malfunction. The median tissue expression information was downloaded from the GTEX database  , and the front-end React JS code can be accessed at , and finally the database was implemented through PM2, an advanced process manager for JavaScript runtime. The network visualization was implemented using the Cytoscape  JavaScript plugin.To handle the large PPIs dataset, and for faster results, HuCoPIA has been implemented on a High-Performance Computing cluster; HuCoPIA has been built using MERN stack technology. Express JS and a source-available cross-platform document-oriented database program (MongoDB), classified as a NoSQL database, were used for the back-end development. The React JS application was used to develop the front-end of the database. All the back-end server code, in TypeScript, is available at  and between A′ and B′ (in another system) are interologs . The fouDomain-based analysis was one of the computational tools we utilized to uncover the host-pathogen interactions of new COVID-19 strains and the human proteome. The goal of this study was to obtain the domain structures of host and pathogen proteins, then utilize these domain pairings to search DDI databases for probable DDIs. There are a variety of ways for extracting domains from protein sequences; in our case, we used a program called HMMER, which uses hidden Markov models to extract domains from sequence patterns. By comparing our human and viral protein sequences to the HMM-PFAM database, we were able to identify all the domains. The domain pairings between human proteins and viral proteins were then set up in a SQL database named HPD (Human-pathogen domain pairs). To uncover all the potential domain-domain interactions, we brute-forced all the domain pairings in HPD against the three DDI databases . The predicted DDIs were then traced back to PPIs using our in-house python scripts and SQL queries to link each domain in the DDI to matching proteins. Several studies have effectively used the profile-HMM technique to predict host-pathogen interactions. Default e-value and coverage settings were used for all protein sequences at first, and then they were filtered to determine the best combination of parameters. Human proteins were filtered with a 1e-23 e-value and 0.2 coverage, whereas viral proteins were filtered with a 1e-1 e-value and default coverage. To reduce the false positive rate, all projected DDIs were compared to the Negatome database. Human and viral protein sequences were aligned with the Negatome dataset first, using severe query settings such as e-values of 1e-3 and 1e-1, identities of 40 and 20, and coverages of 30 and 20 for human and virus proteins, respectively. All the projected erroneous interactions were deleted from the domain-domain analysis predictions. The total predicted interaction by the domain method was SARS-CoV-2 (4296), SARS-CoV (5864), and MERS (6194).coronaviridae family of viruses were agreed upon. We have three consensus files since we considered three viruses . Apart from these three, we reached an agreement on the three viruses’ shared and distinct interactions. Therefore, we have seven sets of consensus results in the conclusion. One set for each of the three viruses, one set for each of the three in which just the interactions unique to that virus are present, and one set in which all three viruses’ interactions are present. The total predicted interactions in consensus between the interolog and domain-based procedures are SARS-CoV-2 (1399), SARS-CoV (1758), and MERS (1002).Following the introduction of interolog and domain-domain analysis on an individual basis, it was critical that we combined all the expected host-pathogen interactions into a single set of results. To begin, we combined all six interolog result sets (one from each of the six databases), merged them, and deleted duplicate predictions to create a single set of PPIs. Similarly, all three result sets were derived from three DDI databases and blended to retain just the interactions that were similar. We used consensus in the instance of DDA, since the IDDI database projected 30 times more interactions than the other two databases, which is exceedingly unlikely. As a result, we were able to minimize the number of false positives from DDA and arrive at a consistent set of DDIs. In this way we integrated the results from both approaches, deleted the duplicate host pathogen interactions, and given higher weight to the interactions predicted by both interolog and domain-based procedures. This is how the PPIs between human and the For evaluating the predicted PPIs, we downloaded the high confidence, experimentally validated, interactions from . There whttp://bioinfo.usu.edu/hucopia/ accessed on 22 December 2022. The HuCoPIA database contains SARS-CoV-2 ), SARS-CoV ), and MERS ) interactions. Users can provide a comma-separated list of human genes or upload a text file and select a virus , interaction type , interaction type , pathogen proteins, and annotation type . The resulting interactions can be downloaded in a tab-delimited file. Also, users can visualize the PPI interaction network in a Cytoscape environment with further links to public databases  for existing annotation details. Users can also download the network as a PNG or in JSON format, which can be opened in network analyzer software such as Gephi, Cytoscape, or others.HuCoPIA can be accessed freely at http://bioinfo.usu.edu/hucopia/, last accessed on 22 December 2022), select all virus proteins from the dropdown for each virus, and select tissue expression as the annotation type. Upload the genes text file on HuCoPIA  from the human genome, and they want to search for interactions based on tissue expression annotation. We have selected these genes involved in virus replication from the literature. The step-wise procedure of searching genes in HuCoPIA is explained in detail below:coronaviridae family viruses.AP2M1 is reported to be a crucial host factor for SARS-CoV infection. Based on the previously reported discovery of sunitinib, a kinase inhibitor is involved in the regulation of AP2M1. AP2M1 encodes the μ2 component of the AP2 complex, which is a clathrin adapter protein complex. AP2M1, clathrin, and other components form a clathrin-dependent endocytic pathway by which cells absorb metabolites, hormones, proteins, and viruses via inward budding of the plasma membrane ,46. In oHuCoPIA is a database of computationally identified protein-protein interactions between the human proteome and three strains  of betacoronavirus. HuCoPIA provides a user-friendly interface where users can search for protein-protein interactions by selecting different options such as common or unique interactions across the three viruses, and different functional annotations. It provides access to functional annotations of the human proteome, for example, gene ontology, tissue expression, localization, and KEGG pathway. The comparison of these viruses allows us to obtain common or unique interactions between human proteins and these viruses. Moreover, it provides the most highly expressed tissues and subcellular localization in the human genome. Interactions present in HuCOPIA are computationally predicted, and though we have removed the false interactions with the Negatome database, still these interactions need further experimental validation. We will keep updating the protein functional annotation information inside HuCoPIA as per the availability in biological databases, and remove more false positives based on the availability of negative interactions. Researchers will be able to use HuCoPIA to search and compare interactions and then these interactions can be validated experimentally and further used in global  vaccine development or to better understand the pathogenesis mechanisms.


While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. A deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests. However, the methods described thus far are limited in their applicability to the wide range of current protein design challenges, and have not been extensively validated experimentally.The protein sequence design problem is to find, given a protein backbone structure of interest, an amino acid sequence that will fold to this structure. Physically based approaches like Rosetta approach sequence design as an energy optimization problem, searching for the combination of amino acid identities and conformations that have the lowest energy for a given input structure. Recently deep learning approaches have shown considerable promise in rapidly generating plausible amino acid sequences given monomeric protein backbones without need for compute intensive explicit consideration of sidechain rotameric states –6, 6a. HWe set out to develop a deep learning based protein sequence design method broadly applicable to design of monomers, cyclic oligomers, protein nanoparticles, and protein-protein interfaces. We began from a previously described message passing neural network (MPNN) with 3 encoder and 3 decoder layers and 128 hidden dimensions which predicts protein sequences in an autoregressive manner from N to C terminus using protein backbone features – distances between Ca-Ca atoms, relative Ca-Ca-Ca frame orientations and rotations, and backbone dihedral angles–as input  protein To enable application to a broad range of single and multi-chain design problems, we replaced the fixed N to C terminal decoding order with an order agnostic autoregressive model in which the decoding order is randomly sampled from the set of all possible permutations . This alWe took advantage of the flexibility of the decoding order, which enables selection during inference of a decoding order appropriate for the specific task, to enable the fixing of residue identities in sets of corresponding positions (the residues at these positions are decoded at the same time). For example, for a C2 homodimer backbone with two chains A and B with sequence A1, A2, A3,.. and B1, B2, B3,…, the amino acids for chains A and B have to be the same for corresponding indices; we implement this by predicting logits  for A1 and B1 first and then combine these two predictions to construct a normalized probability distribution from which a joint amino acid is sampled . For pseFor a test set of 402 monomer backbones we redesigned sequences using Rosetta fixed backbone combinatorial sequence design , and 98 heteromers. The median overall sequences recoveries were 52% for monomers, 55% for homomers, and 51% for heteromers . In all While recent protein sequence design approaches have focused on maximizing native sequence recovery, this is not necessarily optimal for actual protein design applications. Native sequence recovery is likely highest for models trained on perfect protein backbones, and with stochastic sequence inference carried out at low temperature. We reasoned, however, that improved protein design performance might be achieved by models trained with backbone noise and with inference conducted at higher temperature, as described in the following paragraphs.Robustness to small displacements in atomic coordinates is a desirable feature for sequence design methods in real world applications where the protein backbone geometry is not known at atomic resolution. We found that training models on backbones to which Gaussian noise (std=0.02Å) had been added improved sequence recovery on confident protein structure models generated by AlphaFold (average pLDDT>80.0) from UniRef50, while the sequence recovery on unperturbed PDB structures decreased as expected . CrystalAlphaFold  and RoseWe found that the strength of the single sequence to structure mapping, as assessed by AlphaFold, was higher for models trained with additional backbone noise. As noted above, the average sequence recovery for perfect backbones decreases with increasing amounts of noise added during training  as theseBecause the sequence determinants of protein expression, solubility and function are not perfectly understood, in most protein design applications it is desirable to test multiple designed sequences experimentally. We found that the diversity of sequences generated by MPNN could be considerably increased, with only a very small decrease in average sequence recovery, by carrying out inference at higher temperatures . We alsoE. coli and characterized biochemically and structurally.While in silico native protein sequence recovery is a useful benchmark, the ultimate test of a protein design method is its ability to generate sequences which fold to the desired structure and have the desired function when tested experimentally. We evaluated ProteinMPNN on a representative set of design challenges ranging from protein monomer design, protein nanocage design, and protein function design. In each case, we attempted to rescue previous failed designs with sequences generated using Rosetta or AlphaFold–we kept the backbones of the original designs fixed but discarded the original sequences and generated new ones using ProteinMPNN. Synthetic genes encoding the designs were obtained, and the proteins expressed in Wicky et al.). These calculations generated a very wide range of protein sequences and backbones for both monomers and oligomers that differ considerably from those of native structures. In initial tests, the sequences generated by AF were encoded in synthetic genes, and we attempted to express 150 proteins in E. coli. However, we found that the AF generated sequences were mostly insoluble . Starting from a random sequence, a Monte Carlo trajectory is carried out optimizing the extent to which AF predicts the sequence to fold to a well-defined structure . Thus, ProteinMPNN can robustly and accurately design sequences for both monomers and cyclic oligomers.We were able to solve the X-ray crystal structure of one of the ProteinMPNN monomer designs with a fold more complex (TM-score=0.56 against PDB) than most proteins . The alpproteins . CrystalWe next took advantage of the flexible decoding order of ProteinMPNN to design sequences for proteins containing internal repeats, tying the identities of proteins in equivalent positions. We found that many previously suboptimal Rosetta designs of repeat protein structures could be rescued by ProteinMPNN redesign, an example is shown in We next experimented with enforcing both the cyclic and internal repeat symmetry by tying positions both within and between subunits, as illustrated in E. coli and purification by SEC that 13 designs formed assemblies with the expected MW (~1 MDa)  . Althoug(~1 MDa) . Thus Pr(~1 MDa) –18. SequE. coli , notably the previously described message passing method on which ProteinMPNN is based, but have focused on the monomer design problem, achieve lower native sequence recoveries, and with the exception of a TIM barrel design study  required for physically based approaches such as Rosetta, which carry out large scale sidechain packing calculations, achieves much higher protein sequence recovery on native backbones (52.4% vs 32.9%), and most importantly, rescues previously failed designs made using Rosetta or AlphaFold for protein monomers, assemblies, and protein-protein interfaces. Machine learning sequence design approaches have been developed previously , 6a, notgn study , C and mUnlike Rosetta and other physically based methods, ProteinMPNN requires no expert customization for specific design challenges, and it should thus make protein design more broadly accessible. This robustness reflects fundamental differences in how the sequence design problem is framed. In traditional physically based approaches, sequence design maps to the problem of identifying an amino acid sequence whose lowest energy state is the desired structure. This is, however, computationally intractable as it requires computing energies over all possible structures, including unwanted oligomeric and aggregated states; instead Rosetta and other approaches as a proxy carry out a search for the lowest energy sequence for a given backbone structure, and structure prediction calculations are required in a second step to confirm that there are no other structures in which the sequence has still lower energy. Because of the lack of concordance between the actual design objective and what is being explicitly optimized, considerable customization can be required to generate sequences which actually fold; for example in Rosetta design calculations hydrophobic amino acids are often restricted on the protein surface as they can stabilize undesired multimeric states, and at the boundary region between the protein surface and core there can be considerable ambiguity about the extent to which such restrictions should be applied. While deep learning methods lack the physical transparency of methods like Rosetta, they are trained directly to find the most probable amino acid for a protein backbone given all the examples in the PDB, and hence such ambiguities do not arise, making sequence design more robust and less dependent on the judgement of a human expert.Wicky et al.), ProteinMPNN designs also have a much higher propensity to crystallize, greatly facilitating structure determination of designed proteins. The observation that ProteinMPNN generated sequences are predicted to fold to native protein backbones more confidently and accurately than the original native sequences (using single sequence information in both cases) suggests that ProteinMPNN may be widely useful in improving expression and stability of recombinantly expressed native proteins (residues required for function would clearly have to be kept fixed). We are currently extending ProteinMPNN to protein-nucleic acid design and protein-small molecule design which should increase its utility still further.The high rate of experimental design success of ProteinMPNN, together with the high compute efficiency, applicability to almost any protein sequence design problem, and lack of requirement for customization has made it the standard approach for protein sequence design at the Institute for Protein Design and we expect it to be rapidly adopted throughout the community. As illustrated in the accompanying paper (Supp Methods and S1-S9Supp T1