Computational design of matrix metalloprotenaise-9 (MMP-9) resistant to auto-cleavage.

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 auto-cleavage. For this purpose, we first identified potential auto-cleavage sites on MMP-9Cat using mass spectroscopy and then eliminated the auto-cleavage site by predicting mutations that minimize auto-cleavage 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 7 days of incubation at 37°C. This MMP-9Cat variant, with an identical with 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.

Computational design of Matrix Metalloprotenaise-9 (MMP-9) resistant to auto-cleavage.

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-9 Cat ) 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-9 Cat variant that is active but stable to autocleavage. For this purpose, we first identified potential autocleavage sites on MMP-9 Cat using mass spectroscopy and then eliminated the autocleavage site by predicting mutations that minimize autocleavage potential without reducing enzyme stability. Four computationally designed MMP-9 Cat 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-9 Cat variant, with an identical to MMP- 9 Cat WT active site, is an ideal candidate for drug design experiments targeting MMP-9 and enzyme crystallization experiments. The developed strategy for MMP-9 CAT stabilization could be applied to redesign of other proteases to improve their stability for various biotechnological applications.

Designed Loop Extension Followed by Combinatorial Screening Confers High Specificity to a Broad Matrix MetalloproteinaseInhibitor.

Matrix metalloproteinases (MMPs) are key drivers of various diseases, including cancer. Development of probes and drugs capable of selectively inhibiting the individual members of the large MMP family remains a persistent challenge. The inhibitory N-terminal domain of tissue inhibitor of metalloproteinases-2 (N-TIMP2), a natural broad MMP inhibitor, can provide a scaffold for protein engineering to create more selective MMP inhibitors. Here, we pursued a unique approach harnessing both computational design and combinatorial screening to confer high binding specificity toward a target MMP in preference to an anti-target MMP. We designed a loop extension of N-TIMP2 to allow new interactions with the non-conserved MMP surface and generated an efficient focused library for yeast surface display, which was then screened for high binding to the target MMP-14 and low binding to anti-target MMP-3. Deep sequencing analysis identified the most promising variants, which were expressed, purified, and tested for selectivity of inhibition. Our best N-TIMP2 variant exhibited 29 pM binding affinity to MMP-14 and 2.4 µM affinity to MMP-3, revealing 7500-fold greater specificity than WT N-TIMP2. High-confidence structural models were obtained by including NGS data in the AlphaFold multiple sequence alignment. The modeling together with experimental mutagenesis validated our design predictions, demonstrating that the loop extension packs tightly against non-conserved residues on MMP-14 and clashes with MMP-3. This study demonstrates how introduction of loop extensions in a manner guided by target protein conservation data and loop design can offer an attractive strategy to achieve specificity in design of protein ligands.

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.