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De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Graphical abstract, Highlights • Evolutionary design of new SARS-CoV-2 spike proteins by perturbing the core protein sequence without changing the surface conformation. • Computationally designed SARS-CoV-2 spike proteins contain additional MHC-II T cell promiscuous epitopes with enhanced T cell immunity. • Structurally designed SARS-CoV-2 spike proteins preserve function and B cell immunity. • The newly designed SARS-CoV-2 spike proteins can be explored as new COVID-19 vaccine candidates., The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the association of its Spike (S) protein with the human Angiotensin-converting enzyme 2 (ACE2) receptor to invade host cells, we computationally redesigned the S protein sequence to improve its immunogenicity and antigenicity. Toward this purpose, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants that perturb the core protein sequence but keep the surface conformation and B cell epitopes. The T cell epitope content and similarity scores of the perturbed sequences were calculated and evaluated. Out of 22,914 designs with favorable stability energy, 301 candidates contained at least two pre-existing immunity-related epitopes and had promising immunogenic potential. The benchmark tests showed that, although the epitope restraints were not included in the scoring function of EvoDesign, the top S protein design successfully recovered 31 out of the 32 major histocompatibility complex (MHC) -II T cell promiscuous epitopes in the native S protein, where two epitopes were present in all seven human coronaviruses. Moreover, the newly designed S protein introduced nine new MHC-II T cell promiscuous epitopes that do not exist in the wildtype SARS-CoV-2. These results demonstrated a new and effective avenue to enhance a target protein’s immunogenicity using rational protein design, which could be applied for new vaccine design against COVID-19 and other human viruses.

The rapid progress of cryo-electron microscopy (cryo-EM) in structural biology has raised an urgent need for robust methods to create and refine atomic-level structural models using low-resolution EM density maps. We propose a new protocol to create initial models using I-TASSER protein structure prediction, followed by EM density map-based rigid-body structure fitting, flexible fragment adjustment and atomic-level structure refinement simulations. The protocol was tested on a large set of 285 non-homologous proteins and generated structural models with correct folds for 260 proteins, where 28% had RMSDs below 2 A. Compared to other state-of-the-art methods, the major advantage of the proposed pipeline lies in the uniform structure prediction and refinement protocol, as well as the extensive structural re-assembly simulations, which allow for low-to-medium resolution EM density map-guided structure modeling starting from amino acid sequences. Interestingly, the quality of both the image fitting and subsequent structure refinement was found to be strongly correlated with the correctness of the initial I-TASSER models; this is mainly due to the different correlation patterns observed between force field and structural quality for the models with template modeling score (or TM-score, a metric quantifying the similarity of models to the native) above and below a threshold of 0.5. Overall, the results demonstrate a new avenue that is ready to use for large-scale cryo-EM-based structure modeling and atomic-level density map-guided structure refinement.

The outbreak of COVID-19 has now become a global pandemic that has severely impacted lives and economic stability. There is, however, no effective antiviral drug that can be used to treat COVID-19 to date. Built on the fact that SARS-CoV-2 initiates its entry into human cells by the receptor binding domain (RBD) of its spike protein binding to the angiotensin-converting enzyme 2 (hACE2), we extended a recently developed approach, EvoDesign, to design multiple peptide sequences that can competitively bind to the SARS-CoV-2 RBD to inhibit the virus from entering human cells. The protocol starts with the construction of a hybrid peptidic scaffold by linking two fragments grafted from the interface of the hACE2 protein (a.a. 22-44 and 351-357) with a linker glycine, which is followed by the redesign and refinement simulations of the peptide sequence to optimize its binding affinity to the interface of the SARS-CoV-2 RBD. The binding experiment analyses showed that the designed peptides exhibited a significantly stronger binding potency to hACE2 than the wild-type hACE2 receptor (with -53.35 vs. -46.46 EvoEF2 energy unit scores for the top designed and wild-type peptides, respectively). This study demonstrates a new avenue to utilize computationally designed peptide motifs to treat the COVID-19 disease by blocking the critical spike-RBD and hACE2 interactions.

Motivation Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence. Results We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with coevolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2549 proteins and generated correct single- and multi-domain classifications with a Matthew’s correlation coefficient of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method. For proteins with discontinuous domains, the domain boundary detection and normalized domain overlapping scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins. Availability and implementation https://zhanglab.ccmb.med.umich.edu/FUpred. Supplementary information Supplementary data are available at Bioinformatics online.

Despite the immense progress recently witnessed in protein structure prediction, the modeling accuracy for proteins that lack sequence and/or structure homologs remains to be improved. We developed an open-source program, DeepFold, which integrates spatial restraints predicted by multi-task deep residual neural-networks along with a knowledge-based energy function to guide its gradient-descent folding simulations. The results on large-scale benchmark tests showed that DeepFold creates full-length models with accuracy significantly beyond classical folding approaches and other leading deep learning methods. Of particular interest is the modeling performance on the most difficult targets with very few homologous sequences, where DeepFold achieved an average TM-score that was 40.3% higher than trRosetta and 44.9% higher than DMPfold. Furthermore, the folding simulations for DeepFold were 262 times faster than traditional fragment assembly simulations. These results demonstrate the power of accurately predicted deep learning potentials to improve both the accuracy and speed of ab initio protein structure prediction.

Treatment regimens for head and neck squamous cell carcinoma (HNSCC) typically include cisplatin and radiotherapy and are limited by toxicities. We have identified naturally derived withalongolide A triacetate (WGA-TA) from Physalis longifolia as a lead compound for targeting HNSCC. We hypothesized that combining WGA-TA with cisplatin may allow for lower, less toxic cisplatin doses. HNSCC cell lines were treated with WGA-TA and cisplatin. After treatment with the drugs, the cell viability was determined by MTS assay. The combination index was calculated using CompuSyn. The expression of proteins involved in the targeting of translational initiation complex, epithelial to mesenchymal transition (EMT), and apoptosis were measured by western blot. Invasion and migration were measured using the Boyden-chamber assay. Treatment of MDA-1986 and UMSCC-22B cell lines with either WGA-TA or cisplatin alone for 72 h resulted in a dose dependent decrease in cell viability. Cisplatin in combination with WGA-TA resulted in significant synergistic cell death starting from 1.25 μM cisplatin. Combination treatment with WGA-TA resulted in lower cisplatin dosing while maintaining the downregulation of translational initiation complex proteins, the induction of apoptosis, and the blockade of migration, invasion, and EMT transition. These results suggest that combining a low concentration of cisplatin with WGA-TA may provide a safer, more effective therapeutic option for HNSCC that warrants translational validation.

In this study, the major secretome components of Penicillium oxalicum 16 and Trichoderma reesei RUT-C30 under wheat bran (WB) and rice straw (RS) solid-state fermentation were systematically analyzed. The activities of the major components, e.g., cellulase, hemicellulase, and amylase, were consistent with their abundance in the secretomes. P. oxalicum 16 secreted more abundant glycoside hydrolases than T. reesei RUT-C30. The main up-regulated proteins from the induction of WB, compared with that from RS, were amylase, pectinase, and protease, whereas the main down-regulated enzymes were cellulase, hemicellulase, swollenin, and lytic polysaccharide monooxygenase (LPMO). Specifically, WB induced more β-1,4-glucosidases, namely, S8B0F3 (UniProt ID), and A0A024RWA5 than RS, but RS induced more β-1,4-exoglucanases and β-1,4-endoglucanases, namely, A0A024RXP8, A024SH76, S7B6D6, S7ZP52, A024SH20, A024S2H5, S8BGM3, S7ZX22, and S8AIJ2. The P. oxalicum 16 xylanases S8AH74 and S7ZA57 were the major components responsible for degrading soluble xylan, and S8BDN2 probably acted on solid-state hemicellulose instead of soluble xylan. The main hemicellulase component of T. reesei RUT-C30 in RS was the xyloglucanase A0A024S9Z6 with an abundance of 16%, but T. reesei RUT-C30 lacked the hemicellulase mannanase and had a small amount of the hemicellulase xylanase. P. oxalicum 16 produced more amylase than T. reesei RUT-C30, and the results suggest amylase S7Z6T2 may degrade soluble starch. The percentage of the glucoamylase S8B6D7 did not significantly change, and reached an average abundance of 5.5%. The major auxiliary degradation enzymes of P. oxalicum 16 were LPMOs S7Z716 and S7ZPW1, whereas those of T. reesei RUT-C30 were swollenin and LPMOs A0A024SM10, A0A024SFJ2, and A0A024RZP7.

In this article, we report 3D structure prediction results by two of our best server groups (“Zhang-Server” and “QUARK”) in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (i) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (ii) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (iii) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (iv) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.

Sequence-based contact prediction has shown considerable promise in assisting non-homologous structure modeling, but it often requires many homologous sequences and a sufficient number of correct contacts to achieve correct folds. Here, we developed a method, C-QUARK, that integrates multiple deep-learning and coevolution-based contact-maps to guide the replica-exchange Monte Carlo fragment assembly simulations. The method was tested on 247 non-redundant proteins, where C-QUARK could fold 75% of the cases with TM-scores (template-modeling scores) ≥0.5, which was 2.6 times more than that achieved by QUARK. For the 59 cases that had either low contact accuracy or few homologous sequences, C-QUARK correctly folded 6 times more proteins than other contact-based folding methods. C-QUARK was also tested on 64 free-modeling targets from the 13th CASP (critical assessment of protein structure prediction) experiment and had an average GDT_TS (global distance test) score that was 5% higher than the best CASP predictors. These data demonstrate, in a robust manner, the progress in modeling non-homologous protein structures using low-accuracy and sparse contact-map predictions., Predicting protein structure from sequence is still not possible for all proteins. Here, the authors introduce a method that integrates deep learning and information about protein co-evolution to guide the prediction of non-homologous protein structures with greater accuracy.

SUMMARY Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction., Graphical abstract, In brief Zheng et al. develop C-I-TASSER, which integrates interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations, for high-accuracy protein structure prediction. C-I-TASSER folds more than twice the number of proteins without homology than I-TASSER and has successfully folded 50% of Pfam families without solved experimental structures.

Protein rotamers refer to the conformational isomers taken by the side-chains of amino acids to accommodate specific structural folding environments. Since accurate modeling of atomic interactions is difficult, rotamer information collected from experimentally solved protein structures is often used to guide side-chain packing in protein folding and sequence design studies. Many rotamer libraries have been built in the literature but there is little quantitative guidance on which libraries should be chosen for different structural modeling studies. Here, we performed a comparative study of six widely used rotamer libraries and systematically examined their suitability for protein folding and sequence design in four aspects: (1) side-chain match accuracy, (2) side-chain conformation prediction, (3) de novo protein sequence design, and (4) computational time cost. We demonstrated that, compared to the backbone-dependent rotamer libraries (BBDRLs), the backbone-independent rotamer libraries (BBIRLs) generated conformations that more closely matched the native conformations due to the larger number of rotamers in the local rotamer search spaces. However, more practically, using an optimized physical energy function incorporated into a simulated annealing Monte Carlo searching scheme, we showed that utilization of the BBDRLs could result in higher accuracies in side-chain prediction and higher sequence recapitulation rates in protein design experiments. Detailed data analyses showed that the major advantage of BBDRLs lies in the energy term derived from the rotamer probabilities that are associated with the individual backbone torsion angle subspaces. This term is important for distinguishing between amino acid identities as well as the rotamer conformations of an amino acid. Meanwhile, the backbone torsion angle subspace-specific rotamer search drastically speeds up the searching time, despite the significantly larger number of total rotamers in the BBDRLs. These results should provide important guidance for the development and selection of rotamer libraries for practical protein design and structure prediction studies.

We report the results of two fully-automated structure prediction pipelines, “Zhang-Server” and “QUARK”, in CASP13. The pipelines were built upon the C-I-TASSER and C-QUARK programs, which in turn are based on I-TASSER and QUARK but with three new modules: (1) a novel multiple sequence alignment (MSA) generation protocol to construct deep sequence-profiles for contact prediction; (2) an improved meta-method, NeBcon, which combines multiple contact predictors, including ResPRE that predicts contact-maps by coupling precision-matrices with deep residual convolutional neural-networks; and (3) an optimized contact potential to guide structure assembly simulations. For 50 CASP13 FM domains that lacked homologous templates, average TM-scores of the first models produced by C-I-TASSER and C-QUARK were 28% and 56% higher than those constructed by I-TASSER and QUARK respectively. For the first time, contact-map predictions demonstrated usefulness on TBM domains with close homologous templates, where TM-scores of C-I-TASSER models were significantly higher than those of I-TASSER models with a p-value

EvoDesign ( https://zhanglab.ccmb.med.umich.edu/EvoDesign ) is an online server system for protein design. The method uses evolutionary profiles to guide the sequence search simulation and demonstrated significant advantages over physics-based approaches in terms of more accurately designing proteins that adopt desired target folds. Despite the success, the previous EvoDesign program focused only on monomer protein design, which limited its ability and usefulness in terms of designing functional proteins. In this work, we propose a new EvoDesign server, which extends the principles of evolution-based design to design protein–protein interactions. Starting from a two-chain complex structure, structurally similar interfaces are identified from known protein–protein interaction databases. An interface evolutionary profile is then constructed from a multiple sequence alignment of the interface analogies, which is combined with a newly developed, atomic-level physical energy function to guide the replica-exchange Monte Carlo simulation search. The purpose of the server is to redesign the specified complex chain to increase its stability and binding affinity for the other chain in the complex. With the improved scope and accuracy of the methodology, the new EvoDesign pipeline should become a useful online tool for functional protein design and drug discovery studies.

Positively charged groups that mimic arginine or lysine in a natural substrate of trypsin are necessary for drugs to inhibit the trypsin-like serine protease TMPRSS2 that is involved in the viral entry and spread of coronaviruses, including SARS-CoV-2. Based on this assumption, we identified a set of 13 approved or clinically investigational drugs with positively charged guanidinobenzoyl and/or aminidinobenzoyl groups, including the experimentally verified TMPRSS2 inhibitors Camostat and Nafamostat. Molecular docking using the C-I-TASSER-predicted TMPRSS2 catalytic domain model suggested that the guanidinobenzoyl or aminidinobenzoyl group in all the drugs could form putative salt bridge interactions with the side-chain carboxyl group of Asp435 located in the S1 pocket of TMPRSS2. Molecular dynamics simulations further revealed the high stability of the putative salt bridge interactions over long-time (100 ns) simulations. The molecular mechanics/generalized Born surface area-binding free energy assessment and per-residue energy decomposition analysis also supported the strong binding interactions between TMPRSS2 and the proposed drugs. These results suggest that the proposed compounds, in addition to Camostat and Nafamostat, could be effective TMPRSS2 inhibitors for COVID-19 treatment by occupying the S1 pocket with the hallmark positively charged groups.

Cerenkov Emission (CE) during external beam radiation therapy (EBRT) from a linear accelerator (Linac) has been demonstrated as a useful tool for radiotherapy quality assurance and potentially other applications for online tracking of tumors during treatment delivery. However, some of the current challenges that are impacting the potential of CE are related to the limited detection sensitivity and the lack of flexible tools to fit into an already complex treatment delivery environment. Silicon photomultiplier (SiPM) solid-state devices are new promising tools for low light detection due to their extreme sensitivity that mirrors photomultiplier tubes and yet have a form factor that is similar to silicon photodiodes, allowing for improved flexibility in device design that may help in the process of wider clinical applicability. In this paper, we assess the feasibility of using SiPMs to detect CE during EBRT from a Linac and contrast their performance with commercially available silicon photodiodes (PDs). We demonstrate the feasibility of the SiPM-based probes for standard dosimetry measurements. We also demonstrate that CE optical signals can be detected from tissue depths about five times greater than that for standard probes based on PDs, making our SiPM probe an enabling technology of CE measurements, particularly for deep tissue applications.

The rigid-body fitting of predicted structural models into cryo-electron microscopy (cryo-EM) density maps is a necessary procedure for density map-guided protein structure determination and prediction. We proposed a novel multiobjective optimization protocol, MOFIT, which performs a rigid-body density-map fitting based on particle swarm optimization (PSO). MOFIT was tested on a large set of 292 nonhomologous single-domain proteins. Starting from structural models predicted by I-TASSER, MOFIT achieved an average coordinate root-mean-square deviation of 2.46 A, which was 1.57, 2.79, and 3.95 A lower than three leading single-objective function-based methods, where the differences were statistically significant with p-values of 1.65 × 10-6, 6.36 × 10-8, and 6.44 × 10-11 calculated using two-tail Student's t tests. Detailed analyses showed that the major advantages of MOFIT lie in the multiobjective protocol and the extensive PSO search simulations guided by the composite objective functions, which integrates complementary correlation coefficients from the global structure, local fragments, and individual residues with the cryo-EM density maps.

Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.

Despite considerable research progress on SARS-CoV-2, the direct zoonotic origin (intermediate host) of the virus remains ambiguous. The most definitive approach to identify the intermediate host would be the detection of SARS-CoV-2-like coronaviruses in wild animals. However, due to the high number of animal species, it is not feasible to screen all the species in the laboratory. Given that the recognition of the binding ACE2 proteins is the first step for the coronaviruses to invade host cells, we proposed a computational pipeline to identify potential intermediate hosts of SARS-CoV-2 by modeling the binding affinity between the Spike receptor-binding domain (RBD) and host ACE2. Using this pipeline, we systematically examined 285 ACE2 variants from mammals, birds, fish, reptiles, and amphibians, and found that the binding energies calculated on the modeled Spike-RBD/ACE2 complex structures correlate closely with the effectiveness of animal infections as determined by multiple experimental datasets. Built on the optimized binding affinity cutoff, we suggested a set of 96 mammals, including 48 experimentally investigated ones, which are permissive to SARS-CoV-2, with candidates from primates, rodents, and carnivores at the highest risk of infection. Overall, this work not only suggested a limited range of potential intermediate SARS-CoV-2 hosts for further experimental investigation; but more importantly, it proposed a new structure-based approach to general zoonotic origin and susceptibility analyses that are critical for human infectious disease control and wildlife protection.

Motivation Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory. Results We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies. Availability and implementation The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR. Supplementary information Supplementary data are available at Bioinformatics online.

The outbreak of COVID-19 has now become a global pandemic and it continues to spread rapidly worldwide, severely threatening lives and economic stability. Making the problem worse, there is no specific antiviral drug that can be used to treat COVID-19 to date. SARS-CoV-2 initiates its entry into human cells by binding to angiotensin-converting enzyme 2 (hACE2) via the receptor binding domain (RBD) of its spike protein. Therefore, molecules that can block SARS-CoV-2 from binding to hACE2 may potentially prevent the virus from entering human cells and serve as an effective antiviral drug. Based on this idea, we designed a series of peptides that can strongly bind to SARS-CoV-2 RBD in computational experiments. Specifically, we first constructed a 31-mer peptidic scaffold by linking two fragments grafted from hACE2 (a.a. 22-44 and 351-357) with a linker glycine, and then redesigned the peptide sequence to enhance its binding affinity to SARS-CoV-2 RBD. Compared with several computational studies that failed to identify that SARS-CoV-2 shows higher binding affinity for hACE2 than SARS-CoV, our protein design scoring function, EvoEF2, makes a correct identification, which is consistent with the recently reported experimental data, implying its high accuracy. The top designed peptide binders exhibited much stronger binding potency to hACE2 than the wild-type (−53.35 vs. −46.46 EvoEF2 energy unit for design and wild-type, respectively). The extensive and detailed computational analyses support the high reasonability of the designed binders, which not only recapitulated the critical native binding interactions but also introduced new favorable interactions to enhance binding. Due to the urgent situation created by COVID-19, we share these computational data to the community, which should be helpful to develop potential antiviral peptide drugs to combat this pandemic.

Since Anfinsen demonstrated that the information encoded in a protein’s amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.

Motivation Most proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein–protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. Results We present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities. Availability and implementation Web-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe. Supplementary information Supplementary data are available at Bioinformatics online.

Although a growing body of evidence underscores the contributions of community-based participatory research, community coalitions and other community engagement approaches to addressing health equity, one of the most potent forms of engagement—community organizing—has attracted far less attention in our field. Yet, organizing by and for communities, to build power, select issues, develop and use strategies, and take action to address the goals they collectively have set, may offer important lessons for public health professionals in these fraught times. We share, largely in their own words, the experiences and reflections of ~140 grassroots organizers across the United States who attended regional convenings of organizers in 2017, planned and run by four leading community capacity and base-building organizations, and where diverse organizers shared strategies that work, challenges faced, and the deep concerns among their already often disenfranchised communities in the contemporary sociopolitical and cultural context. After briefly reviewing some of community organizing’s core tenets and complexities, we share our qualitative research methods and key findings about the primary cross-regional concerns raised (mass incarceration, voter suppression, and immigrant rights), the themes that emerged (e.g., centering leadership by women of color and of using a health lens to frame community issues), as well as the challenges faced (e.g., the retraumatization often experienced by organizers and the difficulties in building alliances between groups “that have been taught to distrust each other”). We conclude by discussing how many of the promising practices and lessons shared by the community organizers might enhance our own field’s health equity-focused efforts, particularly if we take seriously one of their most bedrock messages: that there can be no health equity without racial equity and social justice, and that to get to health equity, we must first address equity writ large, particularly in troubling times.

Motivation The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs. Results We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein–protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data. Availability and implementation The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF. Supplementary information Supplementary data are available at Bioinformatics online.

Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins., Author summary Despite decades of effort in computational method development, template-based modeling (TBM) still remains the most reliable approach to high-resolution protein structure prediction. Previous studies have shown that the PDB library is complete for single-domain proteins and TBM is in principle sufficient to solve the structure prediction problem if the most similar structure in the PDB could be reliably identified and used as template for model reconstruction. But in reality, the success of TBM depends on the availability of closely-homologous templates, where its accuracy and reliability decrease sharply when the evolutionary relationship between query and template becomes more distant. We developed a new threading approach, CEthreader, which allows for dynamic programing alignments of predicted contact-maps through eigen-decomposition. The large-scale benchmark tests show that the coupling of contact map with profile and secondary structure alignments through the proposed protocol can significantly improve the accuracy of template recognition for distantly-homologous protein targets.

The LOMETS2 server (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is an online meta-threading server system for template-based protein structure prediction. Although the server has been widely used by the community over the last decade, the previous LOMETS server no longer represents the state-of-the-art due to aging of the algorithms and unsatisfactory performance on distant-homology template identification. An extension of the server built on cutting-edge methods, especially techniques developed since the recent CASP experiments, is urgently needed. In this work, we report the recent advancements of the LOMETS2 server, which comprise a number of major new developments, including (i) new state-of-the-art threading programs, including contact-map-based threading approaches, (ii) deep sequence search-based sequence profile construction and (iii) a new web interface design that incorporates structure-based function annotations. Large-scale benchmark tests demonstrated that the integration of the deep profiles and new threading approaches into LOMETS2 significantly improve its structure modeling quality and template detection, where LOMETS2 detected 176% more templates with TM-scores >0.5 than the previous LOMETS server for Hard targets that lacked homologous templates. Meanwhile, the newly incorporated structure-based function prediction helps extend the usefulness of the online server to the broader biological community.