Protein Engineering by Combined Computational and In Vitro Evolution Approaches

Deep neural networks for predicting the affinity landscape of protein-protein interactions

Studies determining protein-protein interactions (PPIs) by deep mutational scanning have focused mainly on a narrow range of affinities within complexes and thus include only partial coverage of the mutation space of given proteins. By inserting an affinity-reducing N-terminal alanine in the N-terminal domain of the tissue inhibitor of metalloproteinases-2 (N-TIMP2), we overcame the limitation of its narrow affinity range for matrix metalloproteinase 9 (MMP9CAT). We trained deep neural networks (DNNs) to quantitatively predict the binding affinity of unobserved wild-type variants and variants carrying an N-terminal alanine. Good correlation was obtained between predicted and observed log2 enrichment ratio (ER) values, which also correlated with the affinity of N-TIMP2 variants to MMP9CAT. Our ability to predict affinities of unobserved N-TIMP2 variants was confirmed on an independent dataset of experimentally validated N-TIMP2 proteins. This ability is of significant importance in the field of PPI prediction and for developing therapies targeting these interactions.

The Damietta Server: a comprehensive protein design toolkit

The growing importance of protein design to various research disciplines motivates the development of integrative computational platforms that enhance the accessibility and interoperability of different design tools. To this end, we describe a web-based toolkit that builds on the Damietta protein design engine, which deploys a tensorized energy calculation framework. The Damietta Server seamlessly integrates different design tools, in addition to other tools such as message-passing neural networks and molecular dynamics routines, allowing the user to perform multiple operations on structural models and forward them across tools. The toolkit can be used for tasks such as core or interface design, symmetric design, mutagenic scanning, or conformational sampling, through an intuitive user interface. With the envisioned integration of more tools, the Damietta Server will provide a central resource for protein design and analysis, benefiting basic and applied biomedical research communities. The toolkit is available with no login requirement through https://damietta.de/.

What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks

Identifying residues critical to protein-protein binding and efficient design of stable and specific protein binders are challenging tasks. Extending beyond the direct contacts in a protein-protein binding interface, our study employs computational modeling to reveal the essential network of residue interactions and dihedral angle correlations critical in protein-protein recognition. We hypothesized that mutating residues exhibiting highly correlated dynamic motion within the interaction network could efficiently optimize protein-protein interactions to create tight and selective protein binders. We tested this hypothesis using the ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complex, since Ub is a central player in multiple cellular functions and PLpro is an antiviral drug target. Our designed ubiquitin variant (UbV) hosting three mutated residues displayed a ∼3,500-fold increase in functional inhibition relative to wild-type Ub. Further optimization of two C-terminal residues within the Ub network resulted in a KD of 1.5 nM and IC50 of 9.7 nM for the five-point Ub mutant, eliciting 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interactions, and introduces an effective approach to design high-affinity protein binders for cell biology research and future therapeutics.

Structure and computation-guided yeast surface display for the evolution of TIMP-based matrix metalloproteinase inhibitors

The study of protein-protein interactions (PPIs) and the engineering of protein-based inhibitors often employ two distinct strategies. One approach leverages the power of combinatorial libraries, displaying large ensembles of mutant proteins, for example, on the yeast cell surface, to select binders. Another approach harnesses computational modeling, sifting through an astronomically large number of protein sequences and attempting to predict the impact of mutations on PPI binding energy. Individually, each approach has inherent limitations, but when combined, they generate superior outcomes across diverse protein engineering endeavors. This synergistic integration of approaches aids in identifying novel binders and inhibitors, fine-tuning specificity and affinity for known binding partners, and detailed mapping of binding epitopes. It can also provide insight into the specificity profiles of varied PPIs. Here, we outline strategies for directing the evolution of tissue inhibitors of metalloproteinases (TIMPs), which act as natural inhibitors of matrix metalloproteinases (MMPs). We highlight examples wherein design of combinatorial TIMP libraries using structural and computational insights and screening these libraries of variants using yeast surface display (YSD), has successfully optimized for MMP binding and selectivity, and conferred insight into the PPIs involved.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Assessment of Four Engineered PET Degrading Enzymes Considering Large-Scale Industrial Applications

In recent years, enzymatic recycling of the widely used polyester polyethylene terephthalate (PET) has become a complementary solution to current thermomechanical recycling for colored, opaque, and mixed PET. A large set of promising hydrolases that depolymerize PET have been found and enhanced by worldwide initiatives using various methods of protein engineering. Despite the achievements made in these works, it remains difficult to compare enzymes' performance and their applicability to large-scale reactions due to a lack of homogeneity between the experimental protocols used. Here, we pave the way for a standardized enzymatic PET hydrolysis protocol using reaction conditions relevant for larger scale hydrolysis and apply these parameters to four recently reported PET hydrolases (LCCICCG, FAST-PETase, HotPETase, and PES-H1L92F/Q94Y). We show that FAST-PETase and HotPETase have intrinsic limitations that may not permit their application on larger reaction scales, mainly due to their relatively low depolymerization rates. With 80% PET depolymerization, PES-H1L92F/Q94Y may be a suitable candidate for industrial reaction scales upon further rounds of enzyme evolution. LCCICCG outperforms the other enzymes, converting 98% of PET into the monomeric products terephthalic acid (TPA) and ethylene glycol (EG) in 24 h. In addition, we optimized the reaction conditions of LCCICCG toward economic viability, reducing the required amount of enzyme by a factor of 3 and the temperature of the reaction from 72 to 68 °C. We anticipate our findings to advance enzymatic PET hydrolysis toward a coherent assessment of the enzymes and materialize feasibility at larger reaction scales.

Targeting Ras with protein engineering

Ras proteins are small GTPases that regulate cell growth and division. Mutations in Ras genes are associated with many types of cancer, making them attractive targets for cancer therapy. Despite extensive efforts, targeting Ras proteins with small molecules has been extremely challenging due to Ras's mostly flat surface and lack of small molecule-binding cavities. These challenges were recently overcome by the development of the first covalent small-molecule anti-Ras drug, sotorasib, highlighting the efficacy of Ras inhibition as a therapeutic strategy. However, this drug exclusively inhibits the Ras G12C mutant, which is not a prevalent mutation in most cancer types. Unlike the G12C variant, other Ras oncogenic mutants lack reactive cysteines, rendering them unsuitable for targeting via the same strategy. Protein engineering has emerged as a promising method to target Ras, as engineered proteins have the ability to recognize various surfaces with high affinity and specificity. Over the past few years, scientists have engineered antibodies, natural Ras effectors, and novel binding domains to bind to Ras and counteract its carcinogenic activities via a variety of strategies. These include inhibiting Ras-effector interactions, disrupting Ras dimerization, interrupting Ras nucleotide exchange, stimulating Ras interaction with tumor suppressor genes, and promoting Ras degradation. In parallel, significant advancements have been made in intracellular protein delivery, enabling the delivery of the engineered anti-Ras agents into the cellular cytoplasm. These advances offer a promising path for targeting Ras proteins and other challenging drug targets, opening up new opportunities for drug discovery and development.

Engineered IL13 variants direct specificity of IL13Rα2-targeted CAR T cell therapy

IL13Rα2 is an attractive target due to its overexpression in a variety of cancers and rare expression in healthy tissue, motivating expansion of interleukin 13 (IL13)-based chimeric antigen receptor (CAR) T cell therapy from glioblastoma into systemic malignancies. IL13Rα1, the other binding partner of IL13, is ubiquitously expressed in healthy tissue, raising concerns about the therapeutic window of systemic administration. IL13 mutants with diminished binding affinity to IL13Rα1 were previously generated by structure-guided protein engineering. In this study, two such variants, termed C4 and D7, are characterized for their ability to mediate IL13Rα2-specific response as binding domains for CAR T cells. Despite IL13Rα1 and IL13Rα2 sharing similar binding interfaces on IL13, mutations to IL13 that decrease binding affinity for IL13Rα1 did not drastically change binding affinity for IL13Rα2. Micromolar affinity to IL13Rα1 was sufficient to pacify IL13-mutein CAR T cells in the presence of IL13Rα1-overexpressing cells in vitro. Interestingly, effector activity of D7 CAR T cells, but not C4 CAR T cells, was demonstrated when cocultured with IL13Rα1/IL4Rα-coexpressing cancer cells. While low-affinity interactions with IL13Rα1 did not result in observable toxicities in mice, in vivo biodistribution studies demonstrated that C4 and D7 CAR T cells were better able to traffic away from IL13Rα1+ lung tissue than were wild-type (WT) CAR T cells. These results demonstrate the utility of structure-guided engineering of ligand-based binding domains with appropriate selectivity while validating IL13-mutein CARs with improved selectivity for application to systemic IL13Rα2-expressing malignancies.

Function of CTLGA9 Amino Acid Residue Leucine-6 in Modulating Cry Toxicity

Aedes aegypti is a crucial vector for many arboviral diseases that cause millions of deaths worldwide and thus is of major public health concern. Crystal (Cry) proteins, which are toxins produced by Bacillus thuringiensis, are structurally organized into three-domains, of which domain II is the most variable in terms of binding towards various toxin receptors. The binding of Cry11Aa to putative receptor such as aminopeptidase-N (APN) is explicitly inhibited by midgut C-type lectins (CTLs). The similarity between the domain II fold of Cry11Aa toxin and the carbohydrate recognition domain in the CTLs is a possible structural basis for the involvement of Cry domain II in the recognition of carbohydrates on toxin receptors. In this study, a site-directed point mutation was introduced into the A. aegypti CTLGA9 gene on the basis of molecular docking findings, leading to substitution of the Leucine-6 (Leu-6) residue in the protein with alanine. Subsequently, functional monitoring of the mutated protein was carried out. Unlike the amino acid residues of wild-type CTLGA9, none of the residues of mutant (m) CTLGA9 were competed with Cry11Aa for binding to the APN receptor interface. Additionally, ligand blot analysis showed that both wild-type and mutant CTLGA9 had similar abilities to bind to APN and Cry11Aa. Furthermore, in the competitive ELISA in which labeled mutant CTLGA9 (10 nM) was mixed with increasing concentrations of unlabeled Cry11Aa (0–500 nM), the mutant showed no competition with Cry11Aa for binding to APN., By contrast, in the positive control sample of labeled wild type CTLGA9 mixed with same concentrations of Cry11Aa competition between the two ligands for binding to the APN was evident. These results suggest that Leucine-6 may be the key site involved in the competitive receptor binding between CTLGA9 and Cry11Aa. Moreover, according to the bioassay results, mutant CTLGA9 could in fact enhance the toxicity of Cry11Aa. Our novel findings provide further insights into the mechanism of Cry toxicity as well as a theoretical basis for enhancing the mosquitocidal activity of these toxin through molecular modification strategies.

Match_Motif: A rapid computational tool to assist in protein-protein interaction design

In order to generate protein assemblies with a desired function, the rational design of protein-protein binding interfaces is of significant interest. Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein-protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from https://www.groups.ph.tum.de/t38/downloads/.

Engineered variants of the Ras effector protein RASSF5 (NORE1A) promote anticancer activities in lung adenocarcinoma

Within the superfamily of small GTPases, Ras appears to be the master regulator of such processes as cell cycle progression, cell division, and apoptosis. Several oncogenic Ras mutations at amino acid positions 12, 13, and 61 have been identified that lose their ability to hydrolyze GTP, giving rise to constitutive signaling and eventually development of cancer. While disruption of the Ras/effector interface is an attractive strategy for drug design to prevent this constitutive activity, inhibition of this interaction using small molecules is impractical due to the absence of a cavity to which such molecules could bind. However, proteins and especially natural Ras effectors that bind to the Ras/effector interface with high affinity could disrupt Ras/effector interactions and abolish procancer pathways initiated by Ras oncogene. Using a combination of computational design and in vitro evolution, we engineered high-affinity Ras-binding proteins starting from a natural Ras effector, RASSF5 (NORE1A), which is encoded by a tumor suppressor gene. Unlike previously reported Ras oncogene inhibitors, the proteins we designed not only inhibit Ras-regulated procancer pathways, but also stimulate anticancer pathways initiated by RASSF5. We show that upon introduction into A549 lung carcinoma cells, the engineered RASSF5 mutants decreased cell viability and mobility to a significantly greater extent than WT RASSF5. In addition, these mutant proteins induce cellular senescence by increasing acetylation and decreasing phosphorylation of p53. In conclusion, engineered RASSF5 variants provide an attractive therapeutic strategy able to oppose cancer development by means of inhibiting of procancer pathways and stimulating anticancer processes.

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.

Engineering a Histone Reader Protein by Combining Directed Evolution, Sequencing, and Neural Network Based Ordinal Regression

Directed evolution is a powerful approach for engineering proteins with enhanced affinity or specificity for a ligand of interest but typically requires many rounds of screening/library mutagenesis to obtain mutants with desired properties. Furthermore, mutant libraries generally only cover a small fraction of the available sequence space. Here, for the first time, we use ordinal regression to model protein sequence data generated through successive rounds of sorting and amplification of a protein-ligand system. We show that the ordinal regression model trained on only two sorts successfully predicts chromodomain CBX1 mutants that would have stronger binding affinity with the H3K9me3 peptide. Furthermore, we can extract the predictive features using contextual regression, a method to interpret nonlinear models, which successfully guides identification of strong binders not even present in the original library. We have demonstrated the power of this approach by experimentally confirming that we were able to achieve the same improvement in binding affinity previously achieved through a more laborious directed evolution process. This study presents an approach that reduces the number of rounds of selection required to isolate strong binders and facilitates the identification of strong binders not present in the original library.

Computer-guided binding mode identification and affinity improvement of an LRR protein binder without structure determination

Precise binding mode identification and subsequent affinity improvement without structure determination remain a challenge in the development of therapeutic proteins. However, relevant experimental techniques are generally quite costly, and purely computational methods have been unreliable. Here, we show that integrated computational and experimental epitope localization followed by full-atom energy minimization can yield an accurate complex model structure which ultimately enables effective affinity improvement and redesign of binding specificity. As proof-of-concept, we used a leucine-rich repeat (LRR) protein binder, called a repebody (Rb), that specifically recognizes human IgG1 (hIgG1). We performed computationally-guided identification of the Rb:hIgG1 binding mode and leveraged the resulting model to reengineer the Rb so as to significantly increase its binding affinity for hIgG1 as well as redesign its specificity toward multiple IgGs from other species. Experimental structure determination verified that our Rb:hIgG1 model closely matched the co-crystal structure. Using a benchmark of other LRR protein complexes, we further demonstrated that the present approach may be broadly applicable to proteins undergoing relatively small conformational changes upon target binding.

Focused dengue vaccine development: outwitting nature's design

The four DENV serotypes are mosquito-borne pathogens that belong to the Flavivirus genus. These viruses present a major global health burden, being endemic in over 120 countries, causing ∼390 million reported infections yearly, with clinical symptoms ranging from mild fever to severe and potentially fatal hemorrhagic syndromes. Development of a safe and efficacious DENV vaccine is challenging because of the need to induce immunity against all four serotypes simultaneously, as immunity against one serotype can potentially enhance disease caused by a heterotypic secondary infection. So far, live-virus particle-based vaccine approaches struggle with inducing protective tetravalent immunity, while recombinant subunit approaches that use the envelope protein (E) as the major antigen, are gaining promise in preclinical studies. However, E-based subunits require further development and characterization to be used as effective vaccine antigens against DENV. In this review, we will address the shortcomings of recombinant E-based antigens and will discuss potential solutions to enhance E-based subunit antigen immunogenicity and vaccine efficacy.

An Engineered Variant of the B1 Domain of Protein G Suppresses the Aggregation and Toxicity of Intra- and Extracellular Aβ42

Intra- and extraneuronal deposition of amyloid β (Aβ) peptides have been linked to Alzheimer's disease (AD). While both intra- and extraneuronal Aβ deposits affect neuronal cell viability, the molecular mechanism by which these Aβ structures, especially when intraneuronal, do so is still not entirely understood. This makes the development of inhibitors challenging. To prevent the formation of toxic Aβ structural assemblies so as to prevent neuronal cell death associated with AD, we used a combination of computational and combinatorial-directed evolution approaches to develop a variant of the HTB1 protein (HTB1_M2). HTB1_M2 inhibits in vitro self-assembly of Aβ42 peptide and shifts the Aβ42 aggregation pathway to the formation of oligomers that are nontoxic to neuroblastoma SH-SY5Y cells overexpressing or treated with Aβ42 peptide. This makes HTB1_M2 a potential therapeutic lead in the development of AD-targeted drugs and a tool for elucidating conformational changes in the Aβ42 peptide.

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.

Filling the Void: Introducing Aromatic Interactions into Solvent Tunnels To Enhance Lipase Stability in Methanol

An enhanced stability of enzymes in organic solvents is desirable under industrial conditions. The potential of lipases as biocatalysts is mainly limited by their denaturation in polar alcohols. In this study, we focused on selected solvent tunnels in lipase from Geobacillus stearothermophilus T6 to improve its stability in methanol during biodiesel synthesis. Using rational mutagenesis, bulky aromatic residues were incorporated to occupy solvent channels and induce aromatic interactions leading to a better inner core packing. The chemical and structural characteristics of each solvent tunnel were systematically analyzed. Selected residues were replaced with Phe, Tyr, or Trp. Overall, 16 mutants were generated and screened in 60% methanol, from which 3 variants showed an enhanced stability up to 81-fold compared with that of the wild type. All stabilizing mutations were found in the longest tunnel detected in the "closed-lid" X-ray structure. The combination of Phe substitutions in an A187F/L360F double mutant resulted in an increase in unfolding temperature (T_m ) of 7°C in methanol and a 3-fold increase in biodiesel synthesis yield from waste chicken oil. A kinetic analysis with p-nitrophenyl laurate revealed that all mutants displayed lower hydrolysis rates (k _cat), though their stability properties mostly determined the transesterification capability. Seven crystal structures of different variants were solved, disclosing new π-π or CH/π intramolecular interactions and emphasizing the significance of aromatic interactions for improved solvent stability. This rational approach could be implemented for the stabilization of other enzymes in organic solvents.IMPORTANCE Enzymatic synthesis in organic solvents holds increasing industrial opportunities in many fields; however, one major obstacle is the limited stability of biocatalysts in such a denaturing environment. Aromatic interactions play a major role in protein folding and stability, and we were inspired by this to redesign enzyme voids. The rational protein engineering of solvent tunnels of lipase from Geobacillus stearothermophilus is presented here, offering a promising approach to introduce new aromatic interactions within the enzyme core. We discovered that longer tunnels leading from the surface to the enzyme active site were more beneficial targets for mutagenesis for improving lipase stability in methanol during biodiesel biosynthesis. A structural analysis of the variants confirmed the generation of new interactions involving aromatic residues. This work provides insights into stability-driven enzyme design by targeting the solvent channel void.

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.

Combinatorial engineering of N-TIMP2 variants that selectively inhibit MMP9 and MMP14 function in the cell

Developing selective inhibitors for proteolytic enzymes that share high sequence homology and structural similarity is important for achieving high target affinity and functional specificity. Here, we used a combination of yeast surface display and dual-color selective library screening to obtain selective inhibitors for each of the matrix metalloproteinases (MMPs) MMP14 and MMP9 by modifying the non-specific N-terminal domain of the tissue inhibitor of metalloproteinase-2 (N-TIMP2). We generated inhibitor variants with 30- to 1175-fold improved specificity to each of the proteases, respectively, relative to wild type N-TIMP2. These biochemical results accurately predicted the selectivity and specificity obtained in cell-based assays. In U87MG cells, the activation of MMP2 by MMP14 was inhibited by MMP14-selective blockers but not MMP9-specific inhibitors. Target specificity was also demonstrated in MCF-7 cells stably expressing either MMP14 or MMP9, with only the MMP14-specific inhibitors preventing the mobility of MMP14-expressing cells. Similarly, the mobility of MMP9-expressing cells was inhibited by the MMP9-specific inhibitors, yet was not altered by the MMP14-specific inhibitors. The strategy developed in this study for improving the specificity of an otherwise broad-spectrum inhibitor will likely enhance our understanding of the basis for target specificity of inhibitors to proteolytic enzymes, in general, and to MMPs, in particular. We, moreover, envision that this study could serve as a platform for the development of next-generation, target-specific therapeutic agents. Finally, our methodology can be extended to other classes of proteolytic enzymes and other important target proteins.

Site-Mutation of Hydrophobic Core Residues Synchronically Poise Super Interleukin 2 for Signaling: Identifying Distant Structural Effects through Affordable Computations

A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

A computational combinatorial approach identifies a protein inhibitor of superoxide dismutase 1 misfolding, aggregation, and cytotoxicity

Molecular agents that specifically bind and neutralize misfolded and toxic superoxide dismutase 1 (SOD1) mutant proteins may find application in attenuating the disease progression of familial amyotrophic lateral sclerosis. However, high structural similarities between the wild-type and mutant SOD1 proteins limit the utility of this approach. Here we addressed this challenge by converting a promiscuous natural human IgG-binding domain, the hyperthermophilic variant of protein G (HTB1), into a highly specific aggregation inhibitor (designated HTB1_M) of two familial amyotrophic lateral sclerosis-linked SOD1 mutants, SOD1^G93A and SOD1^G85R We utilized a computational algorithm for mapping protein surfaces predisposed to HTB1 intermolecular interactions to construct a focused HTB1 library, complemented with an experimental platform based on yeast surface display for affinity and specificity screening. HTB1_M displayed high binding specificity toward SOD1 mutants, inhibited their amyloid aggregation in vitro, prevented the accumulation of misfolded proteins in living cells, and reduced the cytotoxicity of SOD1^G93A expressed in motor neuron-like cells. Competition assays and molecular docking simulations suggested that HTB1_M binds to SOD1 via both its α-helical and β-sheet domains at the native dimer interface that becomes exposed upon mutated SOD1 misfolding and monomerization. Our results demonstrate the utility of computational mapping of the protein-protein interaction potential for designing focused protein libraries to be used in directed evolution. They also provide new insight into the mechanism of conversion of broad-spectrum immunoglobulin-binding proteins, such as HTB1, into target-specific proteins, thereby paving the way for the development of new selective drugs targeting the amyloidogenic proteins implicated in a variety of human diseases.

Therapeutic Potential of Matrix Metalloproteinase Inhibition in Breast Cancer

Matrix metalloproteinases (MMPs) are a family of zinc endopeptidases that cleave nearly all components of the extracellular matrix as well as many other soluble and cell-associated proteins. MMPs have been implicated in normal physiological processes, including development, and in the acquisition and progression of the malignant phenotype. Disappointing results from a series of clinical trials testing small molecule, broad spectrum MMP inhibitors as cancer therapeutics led to a re-evaluation of how MMPs function in the tumor microenvironment, and ongoing research continues to reveal that these proteins play complex roles in cancer development and progression. It is now clear that effective targeting of MMPs for therapeutic benefit will require selective inhibition of specific MMPs. Here, we provide an overview of the MMP family and its biological regulators, the tissue inhibitors of metalloproteinases (TIMPs). We then summarize recent research from model systems that elucidate how specific MMPs drive the malignant phenotype of breast cancer cells, including acquisition of cancer stem cell features and induction of the epithelial-mesenchymal transition, and we also outline clinical studies that implicate specific MMPs in breast cancer outcomes. We conclude by discussing ongoing strategies for development of inhibitors with therapeutic potential that are capable of selectively targeting the MMPs most responsible for tumor promotion, with special consideration of the potential of biologics including antibodies and engineered proteins based on the TIMP scaffold. J. Cell. Biochem. 118: 3531-3548, 2017. © 2017 The Authors. Journal of Cellular Biochemistry Published by Wiley Periodicals, Inc.

Computational tools for enzyme improvement: why everyone can - and should - use them

This review presents computational methods that experimentalists can readily use to create smart libraries for enzyme engineering and to obtain insights into protein-substrate complexes. Computational tools have the reputation of being hard to use and inaccurate compared to experimental methods in enzyme engineering, yet they are essential to probe datasets of ever-increasing size and complexity. In recent years, bioinformatics groups have made a huge leap forward in providing user-friendly interfaces and accurate algorithms for experimentalists. These methods guide efficient experimental planning and allow the enzyme engineer to rationalize time and resources. Computational tools nevertheless face challenges in the realm of transient modern technology.

Enzyme engineering: A synthetic biology approach for more effective library generation and automated high-throughput screening

The Golden Gate strategy entails the use of type IIS restriction enzymes, which cut outside of their recognition sequence. It enables unrestricted design of unique DNA fragments that can be readily and seamlessly recombined. Successfully employed in other synthetic biology applications, we demonstrate its advantageous use to engineer a biocatalyst. Hot-spots for mutations were individuated in three distinct regions of Candida antarctica lipase A (Cal-A), the biocatalyst chosen as a target to demonstrate the versatility of this recombination method. The three corresponding gene segments were subjected to the most appropriate method of mutagenesis (targeted or random). Their straightforward reassembly allowed combining products of different mutagenesis methods in a single round for rapid production of a series of diverse libraries, thus facilitating directed evolution. Screening to improve discrimination of short-chain versus long-chain fatty acid substrates was aided by development of a general, automated method for visual discrimination of the hydrolysis of varied substrates by whole cells.

Computational design of an epitope-specific Keap1 binding antibody using hotspot residues grafting and CDR loop swapping

Therapeutic and diagnostic applications of monoclonal antibodies often require careful selection of binders that recognize specific epitopes on the target molecule to exert a desired modulation of biological function. Here we present a proof-of-concept application for the rational design of an epitope-specific antibody binding with the target protein Keap1, by grafting pre-defined structural interaction patterns from the native binding partner protein, Nrf2, onto geometrically matched positions of a set of antibody scaffolds. The designed antibodies bind to Keap1 and block the Keap1-Nrf2 interaction in an epitope-specific way. One resulting antibody is further optimised to achieve low-nanomolar binding affinity by in silico redesign of the CDRH3 sequences. An X-ray co-crystal structure of one resulting design reveals that the actual binding orientation and interface with Keap1 is very close to the design model, despite an unexpected CDRH3 tilt and V_H/V_L interface deviation, which indicates that the modelling precision may be improved by taking into account simultaneous CDR loops conformation and V_H/V_L orientation optimisation upon antibody sequence change. Our study confirms that, given a pre-existing crystal structure of the target protein-protein interaction, hotspots grafting with CDR loop swapping is an attractive route to the rational design of an antibody targeting a pre-selected epitope.

Development of High Affinity and High Specificity Inhibitors of Matrix Metalloproteinase 14 through Computational Design and Directed Evolution

Degradation of the extracellular matrices in the human body is controlled by matrix metalloproteinases (MMPs), a family of more than 20 homologous enzymes. Imbalance in MMP activity can result in many diseases, such as arthritis, cardiovascular diseases, neurological disorders, fibrosis, and cancers. Thus, MMPs present attractive targets for drug design and have been a focus for inhibitor design for as long as 3 decades. Yet, to date, all MMP inhibitors have failed in clinical trials because of their broad activity against numerous MMP family members and the serious side effects of the proposed treatment. In this study, we integrated a computational method and a yeast surface display technique to obtain highly specific inhibitors of MMP-14 by modifying the natural non-specific broad MMP inhibitor protein N-TIMP2 to interact optimally with MMP-14. We identified an N-TIMP2 mutant, with five mutations in its interface, that has an MMP-14 inhibition constant (K_i ) of 0.9 pm, the strongest MMP-14 inhibitor reported so far. Compared with wild-type N-TIMP2, this variant displays ∼900-fold improved affinity toward MMP-14 and up to 16,000-fold greater specificity toward MMP-14 relative to other MMPs. In an in vitro and cell-based model of MMP-dependent breast cancer cellular invasiveness, this N-TIMP2 mutant acted as a functional inhibitor. Thus, our study demonstrates the enormous potential of a combined computational/directed evolution approach to protein engineering. Furthermore, it offers fundamental clues into the molecular basis of MMP regulation by N-TIMP2 and identifies a promising MMP-14 inhibitor as a starting point for the development of protein-based anticancer therapeutics.

Engineering cell signaling modulators from native protein-protein interactions

Recent studies on genome sequencing and genetic screens with RNAi and CRISPR technology have revolutionized our understanding of aberrant signaling networks in human diseases. A strategy combining both genetic and protein-based technologies should be at the heart of modern drug-development efforts, particularly in the era of precision medicine. Thus, there is an urgent need for efficient platforms to develop probes that can modulate protein function in cells to validate drug targets and to develop therapeutic leads. Advanced protein engineering has enabled the rapid production of monoclonal antibodies and small protein scaffold affinity reagents for diverse protein targets. Here, we review the most recent progress on engineering natural protein-protein interactions for modulation of cell signaling.

In vitro Engineering of Novel Bioactivity in the Natural Enzymes

Enzymes catalyze various biochemical functions with high efficiency and specificity. In vitro design of the enzyme leads to novel bioactivity in this natural biomolecule that give answers of some vital questions like crucial residues in binding with substrate, molecular evolution, cofactor specificity etc. Enzyme engineering technology involves directed evolution, rational designing, semi-rational designing, and structure-based designing using chemical modifications. Similarly, combined computational and in vitro evolution approaches together help in artificial designing of novel bioactivity in the natural enzyme. DNA shuffling, error prone PCR and staggered extension process are used to artificially redesign active site of enzyme, which can alter its efficiency and specificity. Modifications of the enzyme can lead to the discovery of new path of molecular evolution, designing of efficient enzymes, locating active sites and crucial residues, shift in substrate, and cofactor specificity. The methods and thermodynamics of in vitro designing of the enzyme are also discussed. Similarly, engineered thermophilic and psychrophilic enzymes attain substrate specificity and activity of mesophilic enzymes that may also be beneficial for industry and therapeutics.