Cold Spots in Protein Binding

Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein-protein interfaces

BACKGROUND

Protein-protein interactions (PPIs) are conveyed through binding interfaces or surface patches on proteins that become buried upon binding. Structural and biophysical analysis of many protein-protein interfaces revealed certain unique features of these surfaces that determine the energetics of interactions and play a critical role in protein evolution. One of the significant aspects of binding interfaces is the presence of binding hot spots, where mutations are highly deleterious for binding. Conversely, binding cold spots are positions occupied by suboptimal amino acids and several mutations in such positions could lead to affinity enhancement. While there are many software programs for identification of hot spot positions, there is currently a lack of software for cold spot detection.

RESULTS

In this paper, we present Cold Spot SCANNER, a Colab Notebook, which scans a PPI binding interface and identifies cold spots resulting from cavities, unfavorable charge-charge, and unfavorable charge-hydrophobic interactions. The software offers a Py3DMOL-based interface that allows users to visualize cold spots in the context of the protein structure and generates a zip file containing the results for easy download.

CONCLUSIONS

Cold spot identification is of great importance to protein engineering studies and provides a useful insight into protein evolution. Cold Spot SCANNER is open to all users without login requirements and can be accessible at: https://colab.

RESEARCH

google.com/github/sagagugit/Cold-Spot-Scanner/blob/main/Cold_Spot_Scanner.ipynb .

Cold spots are universal in protein–protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico‐chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein–protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo‐ and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high‐affinity complexes showing fewer centrally located colds spots than low‐affinity complexes. A larger number of cold spots is also found in non‐cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo‐dimeric complexes compared to hetero‐complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.

Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 Mpro and its evolutionary mutations as a case study

The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.

Climbing Up and Down Binding Landscapes through Deep Mutational Scanning of Three Homologous Protein-Protein Complexes

Protein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and noncognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we study three homologous protease-inhibitor PPIs that span 9 orders of magnitude in binding affinity. Using state-of-the-art methodology that combines protein randomization, affinity sorting, deep sequencing, and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to other biological complexes in the cell.

Protein-protein interactions at a glance: Protocols for the visualization of biomolecular interactions

Protein-protein interactions (PPIs) play a key role in many biological processes and are intriguing targets for drug discovery campaigns. Advancements in experimental and computational techniques are leading to a growth of data accessibility, and, with it, an increased need for the analysis of PPIs. In this respect, visualization tools are essential instruments to represent and analyze biomolecular interactions. In this chapter, we reviewed some of the available tools, highlighting their features, and describing their functions with practical information on their usage.

An ultra-high affinity protein-protein interface displaying sequence-robustness

Protein-protein interactions are crucial in biology and play roles in for example, the immune system, signaling pathways, and enzyme regulation. Ultra-high affinity interactions (K_d <0.1 nM) occur in these systems, however, structures and energetics behind stability of ultra-high affinity protein-protein complexes are not well understood. Regulation of the starch debranching barley limit dextrinase (LD) and its endogenous cereal type inhibitor (LDI) exemplifies an ultra-high affinity complex (K_d of 42 pM). In this study the LD-LDI complex is investigated to unveil how robust the ultra-high affinity is to LDI sequence variation at the protein-protein interface and whether alternative sequences can retain the ultra-high binding affinity. The interface of LD-LDI was engineered using computational protein redesign aiming at identifying LDI variants predicted to retain ultra-high binding affinity. These variants present a very diverse set of mutations going beyond conservative and alanine substitutions typically used to probe interfaces. Surface plasmon resonance analysis of the LDI variants revealed that high affinity of LD-LDI requires interactions of several residues at the rim of the protein interface, unlike the classical hotspot arrangement where key residues are found at the center of the interface. Notably, substitution of interface residues in LDI, including amino acids with functional groups different from the wild-type, could occur without loss of affinity. This demonstrates that ultra-high binding affinity can be conferred without hotspot residues, thus making complexes more robust to mutational drift in evolution. The present mutational analysis also demonstrates how energetic coupling can emerge between residues at large distances at the interface.

Computational design and experimental optimization of protein binders with prospects for biomedical applications

Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.

Quantitative mapping of binding specificity landscapes for homologous targets by using a high-throughput method

To facilitate investigations of protein-protein interactions (PPIs), we developed a novel platform for quantitative mapping of protein binding specificity landscapes, which combines the multi-target screening of a mutagenesis library into high- and low-affinity populations with sophisticated next-generation sequencing analysis. Importantly, this method generates accurate models to predict affinity and specificity values for any mutation within a protein complex, and requires only a few experimental binding affinity measurements using purified proteins for calibration. We demonstrated the utility of the approach by mapping quantitative landscapes for interactions between the N-terminal domain of the tissue inhibitor of metalloproteinase 2 (N-TIMP2) and three matrix metalloproteinases (MMPs) having homologous structures but different affinities (MMP-1, MMP-3, and MMP-14). The binding landscapes for N-TIMP2/MMP-1 and N-TIMP2/MMP-3 showed the PPIs to be almost fully optimized, with most single mutations giving a loss of affinity. In contrast, the non-optimized PPI for N-TIMP2/MMP-14 was reflected in a wide range of binding affinities, where single mutations exhibited a far more attenuated effect on the PPI. Our new platform reliably and comprehensively identified not only hot- and cold-spot residues, but also specificity-switch mutations that shape target affinity and specificity. Thus, our approach provides a methodology giving an unprecedentedly rich quantitative analysis of the binding specificity landscape, which will broaden the understanding of the mechanisms and evolutionary origins of specific PPIs and facilitate the rational design of specific inhibitors for structurally similar target proteins.

Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization

Quantifying the effects of various mutations on binding free energy is crucial for understanding the evolution of protein-protein interactions and would greatly facilitate protein engineering studies. Yet, measuring changes in binding free energy (ΔΔG_bind) remains a tedious task that requires expression of each mutant, its purification, and affinity measurements. We developed an attractive approach that allows us to quantify ΔΔG_bind for thousands of protein mutants in one experiment. Our protocol combines protein randomization, Yeast Surface Display technology, deep sequencing, and a few experimental ΔΔG_bind data points on purified proteins to generate ΔΔG_bind values for the remaining numerous mutants of the same protein complex. Using this methodology, we comprehensively map the single-mutant binding landscape of one of the highest-affinity interaction between BPTI and Bovine Trypsin (BT). We show that ΔΔG_bind for this interaction could be quantified with high accuracy over the range of 12 kcal mol⁻¹ displayed by various BPTI single mutants.

Quantifying the effect of mutations on binding free energy is important to understand protein-protein interaction (PPI). Here the authors develop a method based on yeast display and next-generation sequencing to generate quantitative binding landscapes for any PPI regardless of their Kd value.

Affinity Improvement of a Cancer-Targeted Antibody through Alanine-Induced Adjustment of Antigen-Antibody Interface

To investigate favorable single amino acid substitutions that improve antigen-antibody interactions, alanine (Ala) mutagenesis scanning of the interfacial residues of a cancer-targeted antibody, B5209B, was performed based on X-ray crystallography analysis. Two substitutions were shown to significantly enhance the binding affinity for the antigen, by up to 30-fold. One substitution improved the affinity by a gain of binding enthalpy, whereas the other substitution improved the affinity by a gain of binding entropy. Molecular dynamics simulations showed that the enthalpic improvement could be attributed to the stabilization of distant salt bridges located at the periphery of the antigen-antibody interface. The entropic improvement was due to the release of water molecules that were stably trapped in the antigen-antibody interface of the wild-type antibody. Importantly, these effects of the Ala substitutions were caused by subtle adjustments of the binding interface. These results will be helpful to design high-affinity antibodies with avoiding entropy-enthalpy compensation.

Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries

Characterizing the binding selectivity landscape of interacting proteins is crucial both for elucidating the underlying mechanisms of their interaction and for developing selective inhibitors. However, current mapping methods are laborious and cannot provide a sufficiently comprehensive description of the landscape. Here, we introduce a novel and efficient strategy for comprehensively mapping the binding landscape of proteins using a combination of experimental multi-target selective library screening and in silico next-generation sequencing analysis. We map the binding landscape of a non-selective trypsin inhibitor, the amyloid protein precursor inhibitor (APPI), to each of the four human serine proteases (kallikrein-6, mesotrypsin, and anionic and cationic trypsins). We then use this map to dissect and improve the affinity and selectivity of APPI variants toward each of the four proteases. Our strategy can be used as a platform for the development of a new generation of target-selective probes and therapeutic agents based on selective protein-protein interactions.

A bi-specific inhibitor targeting IL-17A and MMP-9 reduces invasion and motility in MDA-MB-231 cells

The cytokine IL-17A is associated with the progression of various cancers, but little is known about the molecular cross-talk between IL-17A and other tumor-promoting factors. Previous studies have shown that the IL-17A-mediated invasion of breast cancer cells can be inhibited by selective antagonists of the matrix metalloproteinase 9 (MMP-9), suggesting that the cross-talk between IL-17A and MMP-9 may promote cancer invasiveness and metastasis. Here, we present a novel strategy for developing cancer therapeutics, based on the simultaneous binding and inhibition of both IL-17A and MMP-9. To this end, we use a bi-specific heterodimeric fusion protein, comprising a natural inhibitor of MMPs (N-TIMP2) fused with an engineered extracellular domain (V3) of the IL-17A receptor. We show that, as compared with the mono-specific inhibitors of IL-17A (V3) and MMP-9 (N-TIMP2), the engineered bi-specific fusion protein inhibits both MMP-9 activation and IL-17A-induced cytokine secretion from fibroblasts and exhibits a synergistic inhibition of both the migration and invasion of breast cancer cells. Our findings demonstrate, for the first time, that dual targeting of inflammatory (IL-17A) and extracellular matrix remodeling (MMP) pathways can potentially be used as a novel therapeutic approach against cancer. Moreover, the platform developed here for generating the bi-specific IL-17A/MMP-9 inhibitor can be utilized for generating bi-specific inhibitors for other cytokines and MMPs.

Pre-equilibrium competitive library screening for tuning inhibitor association rate and specificity toward serine proteases

High structural and sequence similarity within protein families can pose significant challenges to the development of selective inhibitors, especially toward proteolytic enzymes. Such enzymes usually belong to large families of closely similar proteases and may also hydrolyze, with different rates, protein- or peptide-based inhibitors. To address this challenge, we employed a combinatorial yeast surface display library approach complemented with a novel pre-equilibrium, competitive screening strategy for facile assessment of the effects of multiple mutations on inhibitor association rates and binding specificity. As a proof of principle for this combined approach, we utilized this strategy to alter inhibitor/protease association rates and to tailor the selectivity of the amyloid β-protein precursor Kunitz protease inhibitor domain (APPI) for inhibition of the oncogenic protease mesotrypsin, in the presence of three competing serine proteases, anionic trypsin, cationic trypsin and kallikrein-6. We generated a variant, designated APPI_{P13W/M17G/I18F/F34V}, with up to 30-fold greater specificity relative to the parental APPI_{M17G/I18F/F34V} protein, and 6500- to 230 000-fold improved specificity relative to the wild-type APPI protein in the presence of the other proteases tested. A series of molecular docking simulations suggested a mechanism of interaction that supported the biochemical results. These simulations predicted that the selectivity and specificity are affected by the interaction of the mutated APPI residues with nonconserved enzyme residues located in or near the binding site. Our strategy will facilitate a better understanding of the binding landscape of multispecific proteins and will pave the way for design of new drugs and diagnostic tools targeting proteases and other proteins.

Converting a broad matrix metalloproteinase family inhibitor into a specific inhibitor of MMP-9 and MMP-14

MMP-14 and MMP-9 are two well-established cancer targets for which no specific clinically relevant inhibitor is available. Using a powerful combination of computational design and yeast surface display technology, we engineered such an inhibitor starting from a nonspecific MMP inhibitor, N-TIMP2. The engineered purified N-TIMP2 variants showed enhanced specificity toward MMP-14 and MMP-9 relative to a panel of off-target MMPs. MMP-specific N-TIMP2 sequence signatures were obtained that could be understood from the structural perspective of MMP/N-TIMP2 interactions. Our MMP-9 inhibitor exhibited 1000-fold preference for MMP-9 vs. MMP-14, which is likely to translate into significant differences under physiological conditions. Our results provide new insights regarding evolution of promiscuous proteins and optimization strategies for design of inhibitors with single-target specificities.

Using chirality to probe the conformational dynamics and assembly of intrinsically disordered amyloid proteins

Intrinsically disordered protein (IDP) conformers occupy large regions of conformational space and display relatively flat energy surfaces. Amyloid-forming IDPs, unlike natively folded proteins, have folding trajectories that frequently involve movements up shallow energy gradients prior to the “downhill” folding leading to fibril formation. We suggest that structural perturbations caused by chiral inversions of amino acid side-chains may be especially valuable in elucidating these pathways of IDP folding. Chiral inversions are subtle in that they do not change side-chain size, flexibility, hydropathy, charge, or polarizability. They allow focus to be placed solely on the question of how changes in amino acid side-chain orientation, and the resultant alterations in peptide backbone structure, affect a peptide’s conformational landscape (Ramachandran space). If specific inversions affect folding and assembly, then the sites involved likely are important in mediating these processes. We suggest here a “focused chiral mutant library” approach for the unbiased study of amyloid-forming IDPs.