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.

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Matrix metalloproteinase-9 (MMP-9) is an endopeptidase that remodels the extracellular matrix and has been implicated as a major driver in cancer metastasis. Hence, there is a high demand for MMP-9 inhibitors for therapeutic purposes. For such drug design efforts, large amounts of MMP-9 are required. Yet, the catalytic domain of MMP-9 (MMP-9Cat) is an intrinsically unstable enzyme that tends to auto-cleave within minutes, making it difficult to use in drug design experiments and other biophysical studies. We set our goal to design MMP-9Cat variant that is active but stable to autocleavage. For this purpose, we first identified potential autocleavage sites on MMP-9Cat using mass spectroscopy and then eliminated the autocleavage site by predicting mutations that minimize autocleavage potential without reducing enzyme stability. Four computationally designed MMP-9Cat variants were experimentally constructed and evaluated for auto-cleavage and enzyme activity. Our best variant, Des2, with 2 mutations, was as active as the wild-type enzyme but did not exhibit auto-cleavage after seven days of incubation at 37C. This MMP-9Cat variant, with an identical to MMP-9Cat WT active site, is an ideal candidate for drug design experiments targeting MMP-9 and enzyme crystallization experiments. The developed strategy for MMP-9CAT stabilization could be applied to redesign of other proteases to improve their stability for various biotechnological applications.

Matrix metalloproteinase-9 (MMP-9) is an endopeptidase that remodels the extracellular matrix. MMP-9 has been implicated in several diseases including neurodegeneration, arthritis, cardiovascular diseases, fibrosis and several types of cancer, resulting in a high demand for MMP-9 inhibitors for therapeutic purposes. For such drug design efforts, large amounts of MMP-9 are required. Yet, the catalytic domain of MMP-9 (MMP-9Cat) is an intrinsically unstable enzyme that tends to auto-cleave within minutes, making it difficult to use in drug design experiments and other biophysical studies. We set our goal to design MMP-9Cat variant that is active but stable to autocleavage. For this purpose, we first identified potential autocleavage sites on MMP-9Cat using mass spectroscopy and then eliminated the autocleavage site by predicting mutations that minimize autocleavage potential without reducing enzyme stability. Four computationally designed MMP-9Cat variants were experimentally constructed and evaluated for auto-cleavage and enzyme activity. Our best variant, Des2, with 2 mutations, was as active as the wild-type enzyme but did not exhibit auto-cleavage after seven days of incubation at 37°C. This MMP-9Cat variant, with an identical to MMP-9Cat WT active site, is an ideal candidate for drug design experiments targeting MMP-9 and enzyme crystallization experiments. The developed strategy for MMP-9CAT stabilization could be applied to redesign other proteases to improve their stability for various biotechnological applications.

Matrix metalloproteinases (MMPs) are key drivers of various diseases, including cancer. While several antibodies against MMPs are in development, our goal is to construct therapeutic anti-MMP inhibitors based on a natural broad MMP inhibitor, tissue inhibitor of metalloproteinases-2 (N-TIMP2). To confer high binding specificity toward one MMP type, we extend one of the N-TIMP2 loops, allowing it to interact with the non-conserved MMP surface. Multiple computational designs of the loop were used to design a focused library for yeast surface display, which was sorted for high binding to the target MMP-14 and low binding to off-target MMP-3. Deep sequencing of the two selected populations followed by comparative data analysis was used to identify the most promising variants, which were expressed, purified, and tested for inhibition of MMP-14 and off-target MMPs. Our best N-TIMP2 variant exhibited 29 pM binding affinity to MMP-14 and 2.4 μM affinity to MMP-3, 7500-fold more specific than WT N-TIMP2. Furthermore, the variant inhibited cell invasion with increased potency relative to WT N-TIMP2 in two breast cancer cell lines. We obtained the engineered variant high-accuracy model by including NGS data as input to AlphaFold multiple sequence alignment (MSA). Modeling results together with experimental mutagenesis demonstrate that the loop packs tightly against non-conserved residues on MMP-14 and clashes with MMP-3. This study demonstrates that introduction of loop extensions into inhibitors to stretch to the non-conserved surface of the target proteins is an attractive strategy for conferring high binding specificity in design of MMP inhibitors and other therapeutic proteins.