Protein Engineering Design & Selection最新文献

Proline dehydrogenase (PRODH) catalyzes the FAD-dependent oxidation of l-proline to Δ1-pyrroline-5-carboxylate and is a target for inhibitor discovery because of its importance in cancer cell metabolism. Because human PRODH is challenging to purify, the PRODH domains of the bacterial bifunctional enzyme proline utilization A (PutA) have been used for inhibitor development. These systems have limitations due to large polypeptide chain length, conformational flexibility and the presence of domains unrelated to PRODH activity. Herein, we report the engineering of minimal PRODH domains for inhibitor discovery. The best designs contain one-third of the 1233-residue PutA from Sinorhizobium meliloti and include a linker that replaces the PutA α-domain. The minimal PRODHs exhibit near wild-type enzymatic activity and are susceptible to known inhibitors and inactivators. Crystal structures of minimal PRODHs inhibited by S-(-)-tetrahydro-2-furoic acid and 2-(furan-2-yl)acetic acid were determined at 1.23 and 1.72 Å resolution. Minimal PRODHs should be useful in chemical probe discovery.

Understanding how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interacts with different mammalian angiotensin-converting enzyme II (ACE2) cell entry receptors elucidates determinants of virus transmission and facilitates development of vaccines for humans and animals. Yeast display-based directed evolution identified conserved ACE2 mutations that increase spike binding across multiple species. Gln42Leu increased ACE2-spike binding for human and four of four other mammalian ACE2s; Leu79Ile had an effect for human and three of three mammalian ACE2s. These residues are highly represented, 83% for Gln42 and 56% for Leu79, among mammalian ACE2s. The above findings can be important in protecting humans and animals from existing and future SARS-CoV-2 variants.

Protein stability, dynamics and function are intricately linked. Accordingly, protein designers leverage dynamics in their designs and gain insight to their successes and failures by analyzing their proteins' dynamics. Molecular dynamics (MD) simulations are a powerful computational tool for quantifying both local and global protein dynamics. This review highlights studies where MD simulations were applied to characterize the stability and dynamics of designed proteins and where dynamics were incorporated into computational protein design. First, we discuss the structural basis underlying the extreme stability and thermostability frequently observed in computationally designed proteins. Next, we discuss examples of designed proteins, where dynamics were not explicitly accounted for in the design process, whose coordinated motions or active site dynamics, as observed by MD simulation, enhanced or detracted from their function. Many protein functions depend on sizeable or subtle conformational changes, so we finally discuss the computational design of proteins to perform a specific function that requires consideration of motion by multi-state design.

Engineering precise substrate specificity of proteases advances the potential to use them in biotechnological and therapeutic applications. Collagen degradation, a physiological process mediated by collagenases, is an integral part of extracellular matrix remodeling and when uncontrolled, implicated in different pathological conditions. Lysosomal cathepsin-K cleaves triple helical collagen fiber, whereas cathepsin-L cannot do so. In this study, we have imparted collagenolytic property to cathepsin-L, by systematically engineering proline-specificity and glycosaminoglycans (GAG)-binding surface in the protease. The proline-specific mutant shows high specificity for prolyl-peptidic substrate but is incapable of cleaving collagen. Engineering a GAG-binding surface on the proline-specific mutant enabled it to degrade type-I collagen in the presence of chondroitin-4-sulfate (C4-S). We also present the crystal structures of proline-specific (1.4 Å) and collagen-specific (1.8 Å) mutants. Finally docking studies with prolyl-peptidic substrate (Ala-Gly-Pro-Arg-Ala) at the active site and a C4-S molecule at the GAG-binding site enable us to identify key structural features responsible for collagenolytic activity of cysteine cathepsins.

Studies have shown that Lig4 syndrome mutations in DNA ligase IV (LigIV) are compromised in its function with residual level of double strand break ligation activity in vivo. It was speculated that Lig4 syndrome mutations adversely affect protein folding and stability. Though there are crystal structures of LigIV, there are no reports of crystal structures of Lig4 syndrome mutants and their biophysical characterization to date. Here, we have examined the conformational states, thermal stability, hydrophobicity and DNA binding efficiency of human DNA LigIV wild type and its hypomorphic mutants by far-UV circular dichroism, tyrosine and tryptophan fluorescence, and 1-anilino-8-naphthalene-sulfonate binding, dynamic light scattering, size exclusion chromatography, multi-angle light scattering and electrophoretic mobility shift assay. We show here that LigIV hypomorphic mutants have reduced DNA-binding efficiency, a shift in secondary structure content from the helical to random coil, marginal reduction in their thermal stability and increased hydrophobicity as compared to the wild-type LigIV.

Theoretical and experimental advances in protein engineering have led to the creation of precisely defined, novel protein assemblies of great size and complexity, with diverse applications. One powerful approach involves designing a new attachment or binding interface between two simpler symmetric oligomeric protein components. The required methods of design, which present both similarities and key differences compared to problems in protein docking, remain challenging and are not yet routine. With the aim of more fully enabling this emerging area of protein material engineering, we developed a computer program, nanohedra, to introduce two key advances. First, we encoded in the program the construction rules (i.e. the search space parameters) that underlie all possible symmetric material constructions. Second, we developed algorithms for rapidly identifying favorable docking/interface arrangements based on tabulations of empirical patterns of known protein fragment-pair associations. As a result, the candidate poses that nanohedra generates for subsequent amino acid interface design appear highly native-like (at the protein backbone level), while simultaneously conforming to the exacting requirements for symmetry-based assembly. A retrospective computational analysis of successful vs failed experimental studies supports the expectation that this should improve the success rate for this challenging area of protein engineering.

Metalloproteins are essential to sustain life. Natural evolution optimized them for intricate structural, regulatory and catalytic functions that cannot be fulfilled by either a protein or a metal ion alone. In order to understand this synergy and the complex design principles behind the natural systems, simpler mimics were engineered from the bottom up by installing de novo metal sites in either natural or fully designed, artificial protein scaffolds. This review focuses on key challenges associated with this approach. We discuss how proteins can be equipped with binding sites that provide an optimal coordination environment for a metal cofactor of choice, which can be a single metal ion or a complex multinuclear cluster. Furthermore, we highlight recent studies in which artificial metalloproteins were engineered towards new functions, including electron transfer and catalysis. In this context, the powerful combination of de novo protein design and directed evolution is emphasized for metalloenzyme development.

Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tools that can identify the function of an enzyme and the site responsible for that function. Then we detail methods for optimizing important experimental properties, such as the enzyme environment and enzyme reactants. We describe recent advances in enzyme systems design and enzyme design itself. Throughout we compare and contrast the data and algorithms used for these tasks to illustrate how the algorithms and data can be best used by future designers.

Proteinase K (PRK) is a proteolytic enzyme that has been widely used in industrial applications. However, poor stability has severely limited the uses of PRK. In this work, we used two structure-guided rational design methods, Rosetta and FoldX, to modify PRK thermostability. Fifty-two single amino acid conversion mutants were constructed based on software predictions of residues that could affect protein stability. Experimental characterization revealed that 46% (21 mutants) exhibited enhanced thermostability. The top four variants, D260V, T4Y, S216Q, and S219Q, showed improved half-lives at 69°C by 12.4-, 2.6-, 2.3-, and 2.2-fold that of the parent enzyme, respectively. We also found that selecting mutations predicted by both methods could increase the predictive accuracy over that of either method alone, with 73% of the shared predicted mutations resulting in higher thermostability. In addition to providing promising new variants of PRK in industrial applications, our findings also show that combining these programs may synergistically improve their predictive accuracy.