Crystal structure of designed PX domain from cytokine-independent survival kinase and implications on evolution-based protein engineering

Estimating Change in Foldability Due to Multipoint Deletions in Protein Structures

Insertions/deletions of amino acids in the protein backbone potentially result in altered structural/functional specifications. They can either contribute positively to the evolutionary process or can result in disease conditions. Despite being the second most prevalent form of protein modification, there are no databases or computational frameworks that delineate harmful multipoint deletions (MPD) from beneficial ones. We introduce a positive unlabeled learning-based prediction framework (PROFOUND) that utilizes fold-level attributes, environment-specific properties, and deletion site-specific properties to predict the change in foldability arising from such MPDs, both in the non-loop and loop regions of protein structures. In the absence of any protein structure dataset to study MPDs, we introduce a dataset with 153 MPD instances that lead to native-like folded structures and 7650 unlabeled MPD instances whose effect on the foldability of the corresponding proteins is unknown. PROFOUND on 10-fold cross-validation on our newly introduced dataset reports a recall of 82.2% (86.6%) and a fall out rate (FR) of 14.2% (20.6%), corresponding to MPDs in the protein loop (non-loop) region. The low FR suggests that the foldability in proteins subject to MPDs is not random and necessitates unique specifications of the deleted region. In addition, we find that additional evolutionary attributes contribute to higher recall and lower FR. The first of a kind foldability prediction system owing to MPD instances and the newly introduced dataset will potentially aid in novel protein engineering endeavors.

EvoEF2: accurate and fast energy function for computational protein design

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.

De novo design of protein peptides to block association of the SARS-CoV-2 spike protein with human ACE2

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.

Delineation of crosstalk between HSP27 and MMP-2/MMP-9: A synergistic therapeutic avenue for glioblastoma management

BACKGROUND

Epithelial to mesenchymal transition (EMT) and extracellular matrix (ECM) remodeling, are the two elemental processes promoting glioblastoma (GBM). In the present work we propose a mechanistic modelling of GBM and in process establish a hypothesis elucidating critical crosstalk between heat shock proteins (HSPs) and matrix metalloproteinases (MMPs) with synergistic upregulation of EMT-like process and ECM remodeling.

METHODS

The interaction and the precise binding site between the HSP and MMP proteins was assayed computationally, in-vitro and in GBM clinical samples.

RESULTS

A positive crosstalk of HSP27 with MMP-2 and MMP-9 was established in both GBM patient tissues and cell-lines. This association was found to be of prime significance for ECM remodeling and promotion of EMT-like characteristics. In-silico predictions revealed 3 plausible interaction sites of HSP27 interacting with MMP-2 and MMP-9. Site-directed mutagenesis followed by in-vitro immunoprecipitation assay (IP) with 3 mutated recombinant HSP27, confirmed an interface stretch containing residues 29-40 of HSP27 to be a common interaction site for both MMP-2 and MMP-9. This was further validated with in-vitro IP of truncated (sans AA 29-40) recombinant HSP27 with MMP-2 and MMP-9.

CONCLUSION

The association of HSP27 with MMP-2 and MMP-9 proteins along with the identified interacting stretch has the potential to contribute towards drug development to inhibit GBM infiltration and migration.

GENERAL SIGNIFICANCE

Current findings provide a novel therapeutic target for GBM opening a new horizon in the field of GBM management.

EvoDesign: Designing Protein-Protein Binding Interactions Using Evolutionary Interface Profiles in Conjunction with an Optimized Physical Energy Function

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.

Changing the Apoptosis Pathway through Evolutionary Protein Design

One obstacle in de novo protein design is the vast sequence space that needs to be searched through to obtain functional proteins. We developed a new method using structural profiles created from evolutionarily related proteins to constrain the simulation search process, with functions specified by atomic-level ligand-protein binding interactions. The approach was applied to redesigning the BIR3 domain of the X-linked inhibitor of apoptosis protein (XIAP), whose primary function is to suppress the cell death by inhibiting caspase-9 activity; however, the function of the wild-type XIAP can be eliminated by the binding of Smac peptides. Isothermal calorimetry and luminescence assay reveal that the designed XIAP domains can bind strongly with the Smac peptides but do not significantly inhibit the caspase-9 proteolytic activity in vitro compared with the wild-type XIAP protein. Detailed mutation assay experiments suggest that the binding specificity in the designs is essentially determined by the interplay of structural profile and physical interactions, which demonstrates the potential to modify apoptosis pathways through computational design.

An Evolution-Based Approach to De Novo Protein Design

EvoDesign is a computational algorithm that allows the rapid creation of new protein sequences that are compatible with specific protein structures. As such, it can be used to optimize protein stability, to resculpt the protein surface to eliminate undesired protein-protein interactions, and to optimize protein-protein binding. A major distinguishing feature of EvoDesign in comparison to other protein design programs is the use of evolutionary information in the design process to guide the sequence search toward native-like sequences known to adopt structurally similar folds as the target. The observed frequencies of amino acids in specific positions in the structure in the form of structural profiles collected from proteins with similar folds and complexes with similar interfaces can implicitly capture many subtle effects that are essential for correct folding and protein-binding interactions. As a result of the inclusion of evolutionary information, the sequences designed by EvoDesign have native-like folding and binding properties not seen by other physics-based design methods. In this chapter, we describe how EvoDesign can be used to redesign proteins with a focus on the computational and experimental procedures that can be used to validate the designs.