RosettaDesign server for protein design

Preserving structural integrity: fold reproducibility in computational design of proteins non-homologous to wild-type sequences

Even with remarkable accomplishments, designing a protein with a given structure is still a challenging task. There is no general approach that works for all challenges. Protein sequences with higher sequence similarity are usually shown to have similar three dimensional structures. This work is focused on designing non-homologous protein sequences with low sequence similarity to the wild-type sequence while maintaining secondary structure integrity. Basically, the aim of the present study is to check whether or not dissimilar sequences tend to encode a similar structure. In this work, we employ a negative design approach to design protein sequences by optimizing non-native conformational ensembles. Three non-native conformational ensembles are created for each of the three chosen target structures. During the design of protein sequences using the Monte Carlo simulation method and developed Cα distance-based statistical potentials, these ensembles are destabilized along with stabilization of the targets. The structures of the designed sequences are determined using AlphaFold2. Interestingly, the results suggest that secondary structure elements like alpha helices and beta sheets can be conserved even for non-homologous sequences with low sequence similarity. It is also observed that the designed sequences have the ability to reproduce the three target protein's fold viz. all-α, all-β and mixed αβ despite very low sequence similarity to the wild-type sequences. This indicates that the employed design strategy is effective in preserving structural integrity despite low sequence similarity.

In silico design of foldable lasso peptides

Lasso peptides are a unique class of natural products with distinctively threaded structures, conferring exceptional stability against thermal and proteolytic degradation. Despite their promising biotechnological and pharmaceutical applications, reported attempts to prepare them by chemical synthesis result in forming the nonthreaded branched-cyclic isomer, rather than the desired lassoed structure. This is likely due to the entropic challenge of folding a short, threaded motif before chemically mediated cyclization. Accordingly, this study aims to better understand and enhance the relative stability of pre-lasso conformations-the essential precursor to lasso peptide formation-through sequence optimization, chemical modification, and disulfide incorporation. Using Rosetta fixed backbone design, optimal sequences for several class II lasso peptides are identified. Enhanced sampling with well-tempered metadynamics confirmed that designed sequences derived from the lasso structures of rubrivinodin and microcin J25 exhibit a notable improvement in pre-lasso stability relative to the competing nonthreaded conformations. Chemical modifications to the isopeptide bond-forming residues of microcin J25 further increase the probability of pre-lasso formation, highlighting the beneficial role of noncanonical amino acid residues. Counterintuitively, the introduction of a disulfide cross-link decreased pre-lasso stability. Although cross-linking inherently constrains the peptide structure, decreasing the entropic dominance of unfolded phase space, it hinders the requisite wrapping of the N-terminal end around the tail to adopt the pre-lasso conformation. However, combining chemical modifications with the disulfide cross-link results in further pre-lasso stabilization, indicating that the ring modifications counteract the constraints and provide a cooperative benefit with cross-linking. These findings lay the groundwork for further design efforts to enable synthetic access to the lasso peptide scaffold.

Biophysics of SARS-CoV-2 spike protein's receptor-binding domain interaction with ACE2 and neutralizing antibodies: from computation to functional insights

The spike protein encoded by the SARS-CoV-2 has become one of the most studied macromolecules in recent years due to its central role in COVID-19 pathogenesis. The spike protein's receptor-binding domain (RBD) directly interacts with the host-encoded receptor protein, ACE2. This review critically examines computational insights into RBD's interaction with ACE2 and with therapeutic antibodies designed to interfere with this interaction. We begin by summarizing insights from early computational studies on pre-pandemic SARS-CoV-1 RBD interactions and how these early studies shaped the understanding of SARS-CoV-2. Next, we highlight key theoretical contributions that revealed the molecular mechanisms behind the binding affinity of SARS-CoV-2 RBD against ACE2, and the structural changes that have enhanced the infectivity of emerging variants. Special attention is given to the "RBD charge rule", a predictive framework for determining variant infectivity based on the electrostatic properties of the RBD. Towards applying the computational insights to therapy, we discuss a multiscale computational protocol for optimizing monoclonal antibodies to improve binding affinity across multiple spike protein variants, including representatives from the Omicron family. Finally, we explore how these insights can inform the development of future vaccines and therapeutic interventions for combating future coronavirus diseases.

Aminoacyl-tRNA synthetase urzymes optimized by deep learning behave as a quasispecies

Protein design plays a key role in our efforts to work out how genetic coding began. That effort entails urzymes. Urzymes are small, conserved excerpts from full-length aminoacyl-tRNA synthetases that remain active. Urzymes require design to connect disjoint pieces and repair naked nonpolar patches created by removing large domains. Rosetta allowed us to create the first urzymes, but those urzymes were only sparingly soluble. We could measure activity, but it was hard to concentrate those samples to levels required for structural biology. Here, we used the deep learning algorithms ProteinMPNN and AlphaFold2 to redesign a set of optimized LeuAC urzymes derived from leucyl-tRNA synthetase. We select a balanced, representative subset of eight variants for testing using principal component analysis. Most tested variants are much more soluble than the original LeuAC. They also span a range of catalytic proficiency and amino acid specificity. The data enable detailed statistical analyses of the sources of both solubility and specificity. In that way, we show how to begin to unwrap the elements of protein chemistry that were hidden within the neural networks. Deep learning networks have thus helped us surmount several vexing obstacles to further investigations into the nature of ancestral proteins. Finally, we discuss how the eight variants might resemble a sample drawn from a population similar to one subject to natural selection.

Chemical tools to define and manipulate interferon-inducible Ubl protease USP18

Ubiquitin-specific protease 18 (USP18) is a multifunctional cysteine protease primarily responsible for deconjugating the interferon-inducible ubiquitin-like modifier ISG15 from protein substrates. Here, we report the design and synthesis of activity-based probes (ABPs) that incorporate unnatural amino acids into the C-terminal tail of ISG15, enabling the selective detection of USP18 activity over other ISG15 cross-reactive deubiquitinases (DUBs) such as USP5 and USP14. Combined with a ubiquitin-based DUB ABP, the USP18 ABP is employed in a chemoproteomics screening platform to identify and assess inhibitors of DUBs including USP18. We further demonstrate that USP18 ABPs can be utilized to profile differential activities of USP18 in lung cancer cell lines, providing a strategy that will help define the activity-related landscape of USP18 in different disease states and unravel important (de)ISGylation-dependent biological processes.

Tying the Knot: In Silico Design of Foldable Lasso Peptides

Lasso peptides are a unique class of natural products with distinctively threaded structures, conferring exceptional stability against thermal and proteolytic degradation. Despite their promising biotechnological and pharmaceutical applications, reported attempts to prepare them by chemical synthesis result in forming the nonthreaded branched-cyclic isomer, rather than the desired lassoed structure. This is likely due to the entropic challenge of folding a short, threaded motif prior to chemically mediated cyclization. Accordingly, this study aims to better understand and enhance the relative stability of pre-lasso conformations-the essential precursor to lasso peptide formation-through sequence optimization, chemical modification, and disulfide incorporation. Using Rosetta fixed backbone design, optimal sequences for several class II lasso peptides are identified. Enhanced sampling with well-tempered metadynamics confirmed that designed sequences derived from the lasso structures of rubrivinodin and microcin J25 exhibit a notable improvement in pre-lasso stability relative to the competing nonthreaded conformations. Chemical modifications to the isopeptide bond-forming residues of microcin J25 further increase the probability of pre-lasso formation, highlighting the beneficial role of non-canonical amino acid residues. Counterintuitively, the introduction of a disulfide cross-link decreased pre-lasso stability. Although cross-linking inherently constrains the peptide structure, decreasing the entropic dominance of unfolded phase space, it hinders the requisite wrapping of the N-terminal end around the tail to adopt the pre-lasso conformation. However, combining chemical modifications with the disulfide cross-link results in further pre-lasso stabilization, indicating that the ring modifications counteract the constraints and provide a cooperative benefit with cross-linking. These findings lay the groundwork for further design efforts to enable synthetic access to the lasso peptide scaffold.

SIGNIFICANCE

Lasso peptides are a unique class of ribosomally synthesized and post-translationally modified natural products with diverse biological activities and potential for therapeutic applications. Although direct synthesis would facilitate therapeutic design, it has not yet been possible to fold these short sequences to their threaded architecture without the help of biosynthetic enzyme stabilization. Our work explores strategies to enhance the stability of the pre-lasso structure, the essential precursor to de novo lasso peptide formation. We find that sequence design, incorporating non-canonical amino acid residues, and design-guided cross-linking can augment stability to increase the likelihood of lasso motif accessibility. This work presents several strategies for the continued design of foldable lasso peptides.

Enhancing the Thermostability and solubility of a single-domain catalytic antibody

Catalytic antibodies have the ability to bind to and degrade antigens, offering a significant potential for therapeutic use. The light chain of an antibody, UA15-L, can cleave the peptide bond of Helicobacter pylori urease, thus inhibiting the spread of the bacteria. However, the variable domain of UA15-L has a poor thermostability and solubility. In this study, we employed a combined computational and experimental approach to enhance the protein's stability and solubility properties. The protein unfolding hotspots were initially identified using molecular dynamics simulations. Following this, a disulfide bond was designed in an unfolding hotspot to stabilize the protein. Subsequently, protein solubility was enhanced with the assistance of computational methods by introducing polar or charged residues on the protein surface. The combination of multiple mutations resulted in UA15-L variable domain variants with improved thermostability, solubility, expression, and enhanced activity at elevated temperatures. These variants represent promising candidates for further engineering of catalytic activity and specificity.

Insights into the binding recognition and computational design of IL-2 muteins with enhanced predicted binding affinity to the IL-2 receptor α

Interleukin-2 (IL-2) is an immune system regulator that has received approval for cancer treatment. However, high-dose IL-2 therapy has seen restricted use due to its low efficacy and on-target toxicity. To enhance the effectiveness of IL-2 therapy, it is essential to engineer IL-2 molecules to enhance their specificity toward target cell populations. In this study, molecular dynamics (MD) simulations and Rosetta software were utilized to design novel high-affinity IL-2Rα-binding IL-2 muteins. MD simulations were used to identify the target residues of IL-2 for design, and Rosetta software were then employed to predict potential IL-2 muteins with higher binding affinity toward IL-2Rα. Rosetta generated two potential designed IL-2 muteins. The results of the MD validation and MM/GBSA analysis indicated that both designed IL-2 muteins exhibited greater predicted binding affinities toward IL-2Rα than that of the native proteins. RMSF analysis demonstrated that the structural fluctuations of free IL-2 and designed muteins were similar, indicating that the mutations did not alter the intramolecular force responsible for IL-2's stability and folding. These designed IL-2 muteins may have potential benefits for cancer immunotherapy.

Protein Engineering of Nicotinamide Riboside Kinase Based on a Combinatorial Semirational Design Strategy for Efficient Biocatalytic Synthesis of Nicotinamide Mononucleotides

Industrial biosynthesis of β-nicotinamide mononucleotide (β-NMN) lacks a highly active nicotinamide riboside kinase for the phosphorylation process. Cumbersome preprocessing steps and excessive ATP addition contribute to its increased cost. To tackle these challenges, a docking combination simulation (DCS) semirational mutagenesis strategy was designed in this study, combining various modification strategies to obtain a mutant NRK-TRA with 2.9-fold higher enzyme activity. Molecular dynamics simulations and structural analysis demonstrate the enhancement of its structural stability. High-density fermentation was achieved through a 5 L fermentation tank, with a titer reaching 208.3 U/mL, the highest in the current report. An ATP-cycling whole-cell catalytic system was employed and optimized by introducing a polyphosphate kinase 2 (PPK2) recombinant strain, and 15.16 g/L β-NMN was obtained through a series of batch transformation experiments. This study provides a new strategy for the efficient screening of highly active enzyme variants and offers a green and promising biotransformation system for NMN production.

Metals at the Helm: Revolutionizing Protein Assembly and Applications

Protein assembly is an essential process in biological systems, where proteins self-assemble into complex structures with diverse functions. Inspired by the exquisite control over protein assembly in nature, scientists have been exploring ways to design and assemble protein structures with precise control over their topologies and functions. One promising approach for achieving this goal is through metal coordination, which utilizes metal-binding motifs to mediate protein-protein interactions and assemble protein complexes with controlled stoichiometry and geometry. Metal coordination provides a modular and tunable approach for protein assembly and de novo structure design, where the metal ion acts as a molecular glue that holds the protein subunits together in a specific orientation. Metal-coordinated protein assemblies have shown great potential for developing functional metalloproteinase, novel biomaterials and integrated drug delivery systems. In this review, an overview of the recent advances in protein assemblies benefited from metal coordination is provided, focusing on various protein arrangements in different dimensions including protein oligomers, protein nanocage and higher-order protein architectures. Moreover, the key metal-binding motifs and strategies used to assemble protein structures with precise control over their properties are highlighted. The potential applications of metal-mediated protein assemblies in biotechnology and biomedicine are also discussed.

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

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/.

Investigating vulnerability of the conserved SARS-CoV-2 spike's heptad repeat 2 as target for fusion inhibitors using chimeric miniproteins

Inhibition of SARS-CoV-2 membrane fusion is a highly desired target to combat COVID-19. The interaction between the spike's heptad repeat (HR) regions 1 (HR1) and 2 (HR2) is a crucial step during the fusion process and these highly conserved HR regions constitute attractive targets for fusion inhibitors. However, the relative importance of each subregion of the long HR1-HR2 interface for viral inhibition remains unclear. Here, we designed, produced, and characterized a series of chimeric miniproteins that mimic two different half subdomains of HR1. The proteins were designed as single polypeptide chains that spontaneously fold into antiparallel trimeric helical bundles aimed at structurally imitate the molecular surface of each HR1 half subregion. All the miniproteins folded stably as helical structures and could bind complementary HR2 peptides with moderate affinity. However, only the miniproteins mimicking the N-terminal HR1 half subdomain, but not those imitating C-terminal one, could inhibit cell infection by SARS-COV-2 real viruses in cell cultures. Most interestingly, the inhibitory activity of the miniproteins correlated with their structural stability, but not with their relative binding affinity for HR2 peptides. These results are highly relevant for designing more focused and active fusion inhibitors targeting the highly conserved HR2 region of the Spike.

Energy, water, and protein folding: A molecular dynamics-based quantitative inventory of molecular interactions and forces that make proteins stable

Protein folding energetics can be determined experimentally on a case-by-case basis but it is not understood in sufficient detail to provide deep control in protein design. The fundamentals of protein stability have been outlined by calorimetry, protein engineering, and biophysical modeling, but these approaches still face great difficulty in elucidating the specific contributions of the intervening molecules and physical interactions. Recently, we have shown that the enthalpy and heat capacity changes associated to the protein folding reaction can be calculated within experimental error using molecular dynamics simulations of native protein structures and their corresponding unfolded ensembles. Analyzing in depth molecular dynamics simulations of four model proteins (CI2, barnase, SNase, and apoflavodoxin), we dissect here the energy contributions to ΔH (a key component of protein stability) made by the molecular players (polypeptide and solvent molecules) and physical interactions (electrostatic, van der Waals, and bonded) involved. Although the proteins analyzed differ in length, isoelectric point and fold class, their folding energetics is governed by the same quantitative pattern. Relative to the unfolded ensemble, the native conformations are enthalpically stabilized by comparable contributions from protein-protein and solvent-solvent interactions, and almost equally destabilized by interactions between protein and solvent molecules. The native protein surface seems to interact better with water than the unfolded one, but this is outweighed by the unfolded surface being larger. From the perspective of physical interactions, the native conformations are stabilized by van de Waals and Coulomb interactions and destabilized by conformational strain arising from bonded interactions. Also common to the four proteins, the sign of the heat capacity change is set by interactions between protein and solvent molecules or, from the alternative perspective, by Coulomb interactions.

Multi-indicator comparative evaluation for deep learning-based protein sequence design methods

MOTIVATION

Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial role, and numerous successful methods have been developed. Notably, deep learning-based protein sequence design methods have experienced significant advancements in recent years. However, a comprehensive and systematic comparison and evaluation of these methods have been lacking, with indicators provided by different methods often inconsistent or lacking effectiveness.

RESULTS

To address this gap, we have designed a diverse set of indicators that cover several important aspects, including sequence recovery, diversity, root-mean-square deviation of protein structure, secondary structure, and the distribution of polar and nonpolar amino acids. In our evaluation, we have employed an improved weighted inferiority-superiority distance method to comprehensively assess the performance of eight widely used deep learning-based protein sequence design methods. Our evaluation not only provides rankings of these methods but also offers optimization suggestions by analyzing the strengths and weaknesses of each method. Furthermore, we have developed a method to select the best temperature parameter and proposed solutions for the common issue of designing sequences with consecutive repetitive amino acids, which is often encountered in protein design methods. These findings can greatly assist users in selecting suitable protein sequence design methods. Overall, our work contributes to the field of protein sequence design by providing a comprehensive evaluation system and optimization suggestions for different methods.

Rational design engineering of a more thermostable Sulfurihydrogenibium yellowstonense carbonic anhydrase for potential application in carbon dioxide capture technologies

Implementing hyperthermostable carbonic anhydrases into CO2 capture and storage technologies in order to increase the rate of CO2 absorption from the industrial flue gases is of great importance from technical and economical points of view. The present study employed a combination of in silico tools to further improve thermostability of a known thermostable carbonic anhydrase from Sulfurihydrogenibium yellowstonense. Experimental results showed that our rationally engineered K100G mutant not only retained the overall structure and catalytic efficiency but also showed a 3 °C increase in the melting temperature and a two-fold improvement in the enzyme half-life at 85 °C. Based on the molecular dynamics simulation results, rearrangement of salt bridges and hydrogen interactions network causes a reduction in local flexibility of the K100G variant. In conclusion, our study demonstrated that thermostability can be improved through imposing local structural rigidity by engineering a single-point mutation on the surface of the enzyme.

Computational Protein Design - Where it goes?

Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an important task in various fields, including medicine, food, energy, materials, etc. Directed evolution has recently led to significant achievements. Molecular modification of proteins through directed evolution technology has significantly advanced the fields of enzyme engineering, metabolic engineering, medicine, and beyond. However, it is impossible to identify desirable sequences from a large number of synthetic sequences alone. As a result, computational methods, including data-driven machine learning and physics-based molecular modeling, have been introduced to protein engineering to produce more functional proteins. This review focuses on recent advances in computational protein design, highlighting the applicability of different approaches as well as their limitations.

Strategies for modulating transglycosylation activity, substrate specificity, and product polymerization degree of engineered transglycosylases

Glycosides are widely used in many fields due to their favorable biological activity. The traditional plant extractions and chemical methods for glycosides production are limited by environmentally unfriendly, laborious protecting group strategies and low yields. Alternatively, enzymatic glycosylation has drawn special attention due to its mild reaction conditions, high catalytic efficiency, and specific stereo-/regioselectivity. Glycosyltransferases (GTs) and retaining glycoside hydrolases (rGHs) are two major enzymes for the formation of glycosidic linkages. Therein GTs generally use nucleotide phosphate activated donors. In contrast, GHs can use broader simple and affordable glycosyl donors, showing great potential in industrial applications. However, most rGHs mainly show hydrolysis activity and only a few rGHs, namely non-Leloir transglycosylases (TGs), innately present strong transglycosylation activities. To address this problem, various strategies have recently been developed to successfully tailor rGHs to alleviate their hydrolysis activity and obtain the engineered TGs. This review summarizes the current modification strategies in TGs engineering, with a special focus on transglycosylation activity enhancement, substrate specificity modulation, and product polymerization degree distribution, which provides a reference for exploiting the transglycosylation potentials of rGHs.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.

A probabilistic view of protein stability, conformational specificity, and design

Various approaches have used neural networks as probabilistic models for the design of protein sequences. These "inverse folding" models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the [Formula: see text] Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as "BayesDesign", that leverages Bayes' Rule to maximize the [Formula: see text] objective instead of the [Formula: see text] objective common in inverse folding models. The efficacy of BayesDesign is evaluated in the context of two protein model systems, the NanoLuc enzyme and the WW structural motif. Both BayesDesign and the baseline ProteinMPNN algorithm increase the thermostability of NanoLuc and increase the conformational specificity of WW. The possible sources of error in the model are analyzed.

Microcalorimetry reveals multi-state thermal denaturation of G. stearothermophilus tryptophanyl-tRNA synthetase

Mechanistic studies of Geobacillus stearothermophilus tryptophanyl-tRNA synthetase (TrpRS) afford an unusually detailed description-the escapement mechanism-for the distinct steps coupling catalysis to domain motion, efficiently converting the free energy of ATP hydrolysis into biologically useful alternative forms of information and work. Further elucidation of the escapement mechanism requires understanding thermodynamic linkages between domain configuration and conformational stability. To that end, we compare experimental thermal melting of fully liganded and apo TrpRS with a computational simulation of the melting of its fully liganded form. The simulation also provides important structural cameos at successively higher temperatures, enabling more confident interpretation. Experimental and simulated melting both proceed through a succession of three transitions at successively higher temperature. The low-temperature transition occurs at approximately the growth temperature of the organism and so may be functionally relevant but remains too subtle to characterize structurally. Structural metrics from the simulation imply that the two higher-temperature transitions entail forming a molten globular state followed by unfolding of secondary structures. Ligands that stabilize the enzyme in a pre-transition (PreTS) state compress the temperature range over which these transitions occur and sharpen the transitions to the molten globule and fully denatured states, while broadening the low-temperature transition. The experimental enthalpy changes provide a key parameter necessary to convert changes in melting temperature of combinatorial mutants into mutationally induced conformational free energy changes. The TrpRS urzyme, an excerpted model representing an early ancestral form, containing virtually the entire catalytic apparatus, remains largely intact at the highest simulated temperatures.

Designing Potent Anticancer Peptides by Aurein 1.2 Key Residues Mutation and Catenate Cell-Penetrating Peptide

Purpose

Aurein 1.2 (Aur) peptide is known for possessing anticancer characteristics devoid of conventional therapeutics side effects. For improving Aur peptide anticancer functionality, different anticancer peptides were constructed based on Aur peptide through targeting two separate strategies, including (1) sequence-based mutations and (2) adding a cell-penetrating peptide linker.

Methods

The study was approached by designing three different analogs of Aur, including (a) Aur mutant (Aurm), (b) Aur with N-terminal polyarginine linker (R5-Aur), and (c) Aurm with R5 (R5-Aurm). Computational molecular dynamics simulations clearly showed higher structural stability of R5-Aur and R5-Aurm compared to Aur, solely. The α-helical properties of R5-Aur and R5-Aurm were protected during 500 ns simulations in water solution while no such structural conservation was seen for Aur in silico.

Results

The results of the current study highlight response to one of the main challenges of cancer therapy through selective invasion of Aur to cancer cells without significant involvement of normal cells. This issue was confirmed by different assays, including: MTT assay, flow cytometry, qPCR, and nuclei morphological observations. Furthermore, this study intensifies exploiting in silico approaches for adjusting drug delivery. The results of different assessments on designed peptides reveal an anticancer activity pattern rising from Aur toward Aurm, and R5- Aur, consecutively.

Conclusion

The designed structure of Aur shows improved anticancer activity through molecular changes which makes it suggestable for anticancer therapies.

Novel chimeric proteins mimicking SARS-CoV-2 spike epitopes with broad inhibitory activity

SARS-CoV-2 spike (S) protein mediates virus attachment to the cells and fusion between viral and cell membranes. Membrane fusion is driven by mutual interaction between the highly conserved heptad-repeat regions 1 and 2 (HR1 and HR2) of the S2 subunit of the spike. For this reason, these S2 regions are interesting therapeutic targets for COVID-19. Although HR1 and HR2 have been described as transiently exposed during the fusion process, no significant antibody responses against these S2 regions have been reported. Here we designed chimeric proteins that imitate highly stable HR1 helical trimers and strongly bind to HR2. The proteins have broad inhibitory activity against WT B.1 and BA.1 viruses. Sera from COVID-19 convalescent donors showed significant levels of reactive antibodies (IgG and IgA) against the HR1 mimetic proteins, whereas these antibody responses were absent in sera from uninfected donors. Moreover, both inhibitory activity and antigenicity of the proteins correlate positively with their structural stability but not with the number of amino acid changes in their HR1 sequences, indicating a conformational and conserved nature of the involved epitopes. Our results reveal previously undetected spike epitopes that may guide the design of new robust COVID-19 vaccines and therapies.

Thermostability engineering of industrial enzymes through structure modification

Thermostability is an essential requirement of enzymes in the industrial processes to catalyze the reactions at high temperatures; thus, enzyme engineering through directed evolution, semi-rational design and rational design are commonly employed to construct desired thermostable mutants. Several strategies are implemented to fulfill enzymes' thermostability demand including decreasing the entropy of the unfolded state through substitutions Gly → Xxx or Xxx → Pro, hydrogen bond, salt bridge, introducing two different simultaneous interactions through single mutant, hydrophobic interaction, filling the hydrophobic cavity core, decreasing surface hydrophobicity, truncating loop, aromatic-aromatic interaction and introducing positively charged residues to enzyme surface. In the current review, horizons about compatibility between secondary structures and substitutions at preferable structural positions to generate the most desirable thermostability in industrial enzymes are broadened. KEY POINTS: • Protein engineering is a powerful tool for generating thermostable industrial enzymes. • Directed evolution and rational design are practical approaches in enzyme engineering. • Substitutions in preferable structural positions can increase thermostability.

Characterization and computational simulation of human Syx, a RhoGEF implicated in glioblastoma

Structural discovery of guanine nucleotide exchange factor (GEF) protein complexes is likely to become increasingly relevant with the development of new therapeutics targeting small GTPases and development of new classes of small molecules that inhibit protein‐protein interactions. Syx (also known as PLEKHG5 in humans) is a RhoA GEF implicated in the pathology of glioblastoma (GBM). Here we investigated protein expression and purification of ten different human Syx constructs and performed biophysical characterizations and computational studies that provide insights into why expression of this protein was previously intractable. We show that human Syx can be expressed and isolated and Syx is folded as observed by circular dichroism (CD) spectroscopy and actively binds to RhoA as determined by co‐elution during size exclusion chromatography (SEC). This characterization may provide critical insights into the expression and purification of other recalcitrant members of the large class of oncogenic—Diffuse B‐cell lymphoma (Dbl) homology GEF proteins. In addition, we performed detailed homology modeling and molecular dynamics simulations on the surface of a physiologically realistic membrane. These simulations reveal novel insights into GEF activity and allosteric modulation by the plekstrin homology (PH) domain. These newly revealed interactions between the GEF PH domain and the membrane embedded region of RhoA support previously unexplained experimental findings regarding the allosteric effects of the PH domain from numerous activity studies of Dbl homology GEF proteins. This work establishes new hypotheses for structural interactivity and allosteric signal modulation in Dbl homology RhoGEFs.

A Leucyl-tRNA Synthetase Urzyme: Authenticity of tRNA Synthetase Catalytic Activities and Promiscuous Phosphorylation of Leucyl-5'AMP

Aminoacyl-tRNA synthetase (aaRS)/tRNA cognate pairs translate the genetic code by synthesizing specific aminoacyl-tRNAs that are assembled on messenger RNA by the ribosome. Deconstruction of the two distinct aaRS superfamilies (Classes) has provided conceptual and experimental models for their early evolution. Urzymes, containing ~120–130 amino acids excerpted from regions where genetic coding sequence complementarities have been identified, are key experimental models motivated by the proposal of a single bidirectional ancestral gene. Previous reports that Class I and Class II urzymes accelerate both amino acid activation and tRNA aminoacylation have not been extended to other synthetases. We describe a third urzyme (LeuAC) prepared from the Class IA Pyrococcus horikoshii leucyl-tRNA synthetase. We adduce multiple lines of evidence for the authenticity of its catalysis of both canonical reactions, amino acid activation and tRNA^Leu aminoacylation. Mutation of the three active-site lysine residues to alanine causes significant, but modest reduction in both amino acid activation and aminoacylation. LeuAC also catalyzes production of ADP, a non-canonical enzymatic function that has been overlooked since it first was described for several full-length aaRS in the 1970s. Structural data suggest that the LeuAC active site accommodates two ATP conformations that are prominent in water but rarely seen bound to proteins, accounting for successive, in situ phosphorylation of the bound leucyl-5′AMP phosphate, accounting for ADP production. This unusual ATP consumption regenerates the transition state for amino acid activation and suggests, in turn, that in the absence of the editing and anticodon-binding domains, LeuAC releases leu-5′AMP unusually slowly, relative to the two phosphorylation reactions.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

A sequence space search engine for computational protein design to modulate molecular functionality

De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds.Communicated by Ramaswamy H. Sarma.

Protein sequence design with a learned potential

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

Protein Engineering and HDX Identify Structural Regions of G-CSF Critical to Its Stability and Aggregation

The protein engineering and formulation of therapeutic proteins for prolonged shelf-life remain a major challenge in the biopharmaceutical industry. Understanding the influence of mutations and formulations on the protein structure and dynamics could lead to more predictive approaches to their improvement. Previous intrinsic fluorescence analysis of the chemically denatured granulocyte colony-stimulating factor (G-CSF) suggested that loop AB could subtly reorganize to form an aggregation-prone intermediate state. Hydrogen deuterium exchange mass spectrometry (HDX-MS) has also revealed that excipient binding increased the thermal unfolding transition midpoint (T_m) by stabilizing loop AB. Here, we have combined protein engineering with biophysical analyses and HDX-MS to reveal that increased exchange in a core region of the G-CSF comprising loop AB (ABI, a small helix, ABII) and loop CD packed onto helix B and the beginning of loop BC leads to a decrease in T_m and higher aggregation rates. Furthermore, some mutations can increase the population of the aggregation-prone conformation within the native ensemble, as measured by the greater local exchange within this core region.

Modularity-based parallel protein design algorithm with an implementation using shared memory programming

Given a target protein structure, the prime objective of protein design is to find amino acid sequences that will fold/acquire to the given three-dimensional structure. The protein design problem belongs to the non-deterministic polynomial-time-hard class as sequence search space increases exponentially with protein length. To ensure better search space exploration and faster convergence, we propose a protein modularity-based parallel protein design algorithm. The modular architecture of the protein structure is exploited by considering an intermediate structural organization between secondary structure and domain defined as protein unit (PU). Here, we have incorporated a divide-and-conquer approach where a protein is split into PUs and each PU region is explored in a parallel fashion. It has been further analyzed that our shared memory implementation of modularity-based parallel sequence search leads to better search space exploration compared to the case of traditional full protein design. Sequence-based analysis on design sequences depicts an average of 39.7% sequence similarity on the benchmark data set. Structure-based comparison of the modeled structures of the design protein with the target structure exhibited an average root-mean-square deviation of 1.17 Å and an average template modeling score of 0.89. The selected modeled structures of the design protein sequences are validated using 100 ns molecular dynamics simulations where 80% of the proteins have shown better or similar stability to the respective target proteins. Our study informs that our modularity-based protein design algorithm can be extended to protein interaction design as well.

Lattice protein design using Bayesian learning

Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. In general, the computation of the protein design involves a double loop: A loop for amino acid sequence changes and a loop for an exhaustive conformational search for each amino acid sequence. Herein, we propose a novel statistical mechanical design method using Bayesian learning, which can design lattice proteins without the exhaustive conformational search. We consider a thermodynamic hypothesis of the evolution of proteins and apply it to the prior distribution of amino acid sequences. Furthermore, we take the water effect into account in view of the grand canonical picture. As a result, on applying the 2D lattice hydrophobic-polar (HP) model, our design method successfully finds an amino acid sequence for which the target conformation has a unique ground state. However, the performance was not as good for the 3D lattice HP models compared to the 2D models. The performance of the 3D model improves on using a 20-letter lattice proteins. Furthermore, we find a strong linearity between the chemical potential of water and the number of surface residues, thereby revealing the relationship between protein structure and the effect of water molecules. The advantage of our method is that it greatly reduces computation time, because it does not require long calculations for the partition function corresponding to an exhaustive conformational search. As our method uses a general form of Bayesian learning and statistical mechanics and is not limited to lattice proteins, the results presented here elucidate some heuristics used successfully in previous protein design methods.

Designed Artificial Protein Heterodimers With Coupled Functions Constructed Using Bio-Orthogonal Chemistry

The formation of protein complexes is central to biology, with oligomeric proteins more prevalent than monomers. The coupling of functionally and even structurally distinct protein units can lead to new functional properties not accessible by monomeric proteins alone. While such complexes are driven by evolutionally needs in biology, the ability to link normally functionally and structurally disparate proteins can lead to new emergent properties for use in synthetic biology and the nanosciences. Here we demonstrate how two disparate proteins, the haem binding helical bundle protein cytochrome b 562 and the β-barrel green fluorescent protein can be combined to form a heterodimer linked together by an unnatural triazole linkage. The complex was designed using computational docking approaches to predict compatible interfaces between the two proteins. Models of the complexes where then used to engineer residue coupling sites in each protein to link them together. Genetic code expansion was used to incorporate azide chemistry in cytochrome b 562 and alkyne chemistry in GFP so that a permanent triazole covalent linkage can be made between the two proteins. Two linkage sites with respect to GFP were sampled. Spectral analysis of the new heterodimer revealed that haem binding and fluorescent protein chromophore properties were retained. Functional coupling was confirmed through changes in GFP absorbance and fluorescence, with linkage site determining the extent of communication between the two proteins. We have thus shown here that is possible to design and build heterodimeric proteins that couple structurally and functionally disparate proteins to form a new complex with new functional properties.

Amino acid activation analysis of primitive aminoacyl-tRNA synthetases encoded by both strands of a single gene using the malachite green assay

The Rodin-Ohno hypothesis postulates that two classes of aminoacyl-tRNA synthetases were encoded complementary to double-stranded DNA. Particularly, Geobacillus stearothermophilus tryptophanyl-tRNA synthetase (TrpRS, belonging to class I) and Escherichia coli histidyl-tRNA synthetase (HisRS, belonging to class II) show high complementarity of the middle base of the codons in the mRNA sequence encoding each ATP binding site. Here, for the reported 46-residue peptides designed from the three-dimensional structures of TrpRS and HisRS, amino acid activation analysis was performed using the malachite green assay, which detects the pyrophosphate departing from ATP in the forward reaction of the first step of tRNA aminoacylation. A maltose-binding protein fusion with the 46 residues of TrpRS (TrpRS46mer) exhibited high activation capacity for several amino acids in the presence of ATP and amino acids, but the activity of an alanine substitution mutant of the first histidine in the HIGH motif (TrpRS46merH15A) was largely reduced. In contrast, pyrophosphate release by HisRS46mer in the histidine activation step was lower than that in the case of TrpRS46mer. Both HisRS46mer and the alanine mutant at the 113th arginine (HisRS46merR113A) showed slightly higher levels of pyrophosphate release than the maltose-binding protein alone. These results do not rule out the Rodin-Ohno hypothesis, but may suggest the necessity of establishing unique evolutionary models from different perspectives.

Computational optimization of angiotensin-converting enzyme 2 for SARS-CoV-2 Spike molecular recognition

Since the beginning of the Covid19 pandemic, many efforts have been devoted to identifying approaches to neutralize SARS-CoV-2 replication within the host cell. A promising strategy to block the infection consists of using a mutant of the human receptor angiotensin-converting enzyme 2 (ACE2) as a decoy to compete with endogenous ACE2 for the binding to the SARS-CoV-2 Spike protein, which decreases the ability of the virus to enter the host cell. Here, using a computational framework based on the 2D Zernike formalism we investigate details of the molecular binding and evaluate the changes in ACE2-Spike binding compatibility upon mutations occurring in the ACE2 side of the molecular interface. We demonstrate the efficacy of our method by comparing our results with experimental binding affinities changes upon ACE2 mutations, separating ones that increase or decrease binding affinity with an Area Under the ROC curve ranging from 0.66 to 0.93, depending on the magnitude of the effects analyzed. Importantly, the iteration of our approach leads to the identification of a set of ACE2 mutants characterized by an increased shape complementarity with Spike. We investigated the physico-chemical properties of these ACE2 mutants and propose them as bona fide candidates for Spike recognition.

Identifying metal binding amino acids based on backbone geometries as a tool for metalloprotein engineering

Metal cofactors within proteins perform a versatile set of essential cellular functions. In order to take advantage of the diverse functionality of metalloproteins, researchers have been working to design or modify metal binding sites in proteins to rationally tune the function or activity of the metal cofactor. This study has performed an analysis on the backbone atom geometries of metal-binding amino acids among 10 different metal binding sites within the entire protein data bank. A set of 13 geometric parameters (features) was identified that is capable of predicting the presence of a metal cofactor in the protein structure with overall accuracies of up to 97% given only the relative positions of their backbone atoms. The decision tree machine-learning algorithm used can quickly analyze an entire protein structure for the presence of sets of primary metal coordination spheres upon mutagenesis, independent of their original amino acid identities. The methodology was designed for application in the field of metalloprotein engineering. A cluster analysis using the data set was also performed and demonstrated that the features chosen are useful for identifying clusters of structurally similar metal-binding sites.

Design of novel enzyme biocatalysts for industrial bioprocess: Harnessing the power of protein engineering, high throughput screening and synthetic biology

Biocatalysts have wider applications in various industries. Biocatalysts are generating bigger attention among researchers due to their unique catalytic properties like activity, specificity and stability. However the industrial use of many enzymes is hindered by low catalytic efficiency and stability during industrial processes. Properties of enzymes can be altered by protein engineering. Protein engineers are increasingly study the structure-function characteristics, engineering attributes, design of computational tools for enzyme engineering, and functional screening processes to improve the design and applications of enzymes. The potent and innovative techniques of enzyme engineering deliver outstanding opportunities for tailoring industrially important enzymes for the versatile production of biochemicals. An overview of the current trends in enzyme engineering is explored with important representative examples.

An Evolutionary Profile Guided Greedy Parallel Replica-Exchange Monte Carlo Search Algorithm for Rapid Convergence in Protein Design

Protein design, also known as the inverse protein folding problem, is the identification of a protein sequence that folds into a target protein structure. Protein design is proved as an NP-hard problem. While researchers are working on designing heuristics with an emphasis on new scoring functions, we propose a replica-exchange Monte Carlo (REMC) search algorithm that ensures faster convergence using a greedy strategy. Using biological insights, we construct an evolutionary profile to encode the amino acid variability in different positions of the target protein from its structural homologs. The evolutionary profile guides the REMC search, and the greedy approach confirms appreciable exploration and exploitation of the sequence-structure fitness surface. We allow termination of a simulation trajectory once stagnant situation is detected. A series of sequence and structure level validations establish the goodness of our design. On a benchmark dataset, our algorithm reports an average root-mean-square deviation of 1.21Å between the target and the design proteins when modeled with an existing protein folding software. Besides, our algorithm assures 6.16 times overall speedup. In Molecular Dynamics simulations, we observe that four out of selected five design proteins report better to comparable stability to the corresponding target proteins.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

A novel strategy for molecular interfaces optimization: The case of Ferritin-Transferrin receptor interaction

Protein-protein interactions regulate almost all cellular functions and rely on a fine tune of surface amino acids properties involved on both molecular partners. The disruption of a molecular association can be caused even by a single residue mutation, often leading to a pathological modification of a biochemical pathway. Therefore the evaluation of the effects of amino acid substitutions on binding, and the ad hoc design of protein-protein interfaces, is one of the biggest challenges in computational biology. Here, we present a novel strategy for computational mutation and optimization of protein-protein interfaces. Modeling the interaction surface properties using the Zernike polynomials, we describe the shape and electrostatics of binding sites with an ordered set of descriptors, making possible the evaluation of complementarity between interacting surfaces. With a Monte Carlo approach, we obtain protein mutants with controlled molecular complementarities. Applying this strategy to the relevant case of the interaction between Ferritin and Transferrin Receptor, we obtain a set of Ferritin mutants with increased or decreased complementarity. The extensive molecular dynamics validation of the method results confirms its efficacy, showing that this strategy represents a very promising approach in designing correct molecular interfaces.

Development of Ubiquitin Variants with Selectivity for Ubiquitin C-Terminal Hydrolase Deubiquitinases

Ubiquitin (Ub) is a highly conserved protein that is covalently attached to substrate proteins as a post-translational modification to regulate signaling pathways such as proteasomal degradation and cell cycle/transcriptional regulation in the eukaryotic cellular environment. Ub signaling is regulated by the homeostasis of substrate protein ubiquitination/deubiquitination by E3 ligases and deubiquitinating enzymes (DUBs) in healthy eukaryotic systems. One such DUB, ubiquitin C-terminal hydrolase L1 (UCHL1), is endogenously expressed in the central nervous system under normal physiological conditions, but overexpression and/or mutation has been linked to various cancers and neurodegenerative diseases. The lack of UCHL1 probing strategies suggests development of a selective Ub variant (UbV) for probing UCHL1's role in these disease states would be beneficial. We describe a computational design approach to investigate UbVs that lend selectivity, both binding and inhibition, to UCHL1 over the close structural homologue UCHL3 and members of other DUB families. A number of UbVs, mainly those containing Thr9 mutations, displayed appreciable binding and inhibition selectivity for UCHL1 over UCHL3, compared to wild-type Ub in in vitro assays. By appending reactive electrophiles to the C-terminus of the UbVs, we created the first activity-based probe (ABP) with demonstrated reaction selectivity for UCH family DUBs over other families in cell lysates. Further kinetic analysis of covalent inhibition by the UbV-ABP with UCHL1 and UCHL3 offers insight into the future design of UCHL1 selective UbV-ABP. These studies serve as a proof of concept of the viability of the in silico design of ubiquitin variants for UCH family DUBs as a step toward the development of macromolecular UCHL1 inhibitors.

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.

The search of sequence variants using a constrained protein evolution simulation approach

Protein engineering or candidate therapeutic peptide optimization are processes in which the identification of relevant sequence variants is critical. Starting from one amino-acid sequence, the choice of the substitutions must meet the objective of not disrupting the structure of the protein, not impacting the main functional properties of the starting entity, while also meeting the condition to enhance some expected property such as thermal stability, resistance to degradation, … Here, we introduce a new approach of sequence evolution that focuses on the objective of not disrupting the structure of the initial protein by embedding a point to point control on the preservation of the local structure at each position in the sequence. For 6 mini-proteins, we find that, starting from a single sequence, our simple approach intrinsically contains information about site-specific rate heterogeneity of substitution, and that it is able to reproduce sequence diversity as can be observed in the sequences available in the Uniref repository. We show that our approach is able to provide information about positions not to substitute and about substitutions not to perform at a given position to maintain structure integrity. Overall, our results demonstrate that point to point preservation of the local structure along a sequence is an important determinant of sequence evolution.

Bottom-up synthesis of protein-based nanomaterials from engineered β-solenoid proteins

Biomolecular self-assembly is an emerging bottom-up approach for the synthesis of novel nanomaterials. DNA and viruses have both been used to create scaffolds but the former lacks chemical diversity and the latter lack spatial control. To date, the use of protein scaffolds to template materials on the nanoscale has focused on amyloidogenic proteins that are known to form fibrils or two-protein systems where a second protein acts as a cross-linker. We previously developed a unique approach for self-assembly of nanomaterials based on engineering β-solenoid proteins (BSPs) to polymerize into micrometer-length fibrils. BSPs have highly regular geometries, tunable lengths, and flat surfaces that are amenable to engineering and functionalization. Here, we present a newly engineered BSP based on the antifreeze protein of the beetle Rhagium inquisitor (RiAFP-m9), which polymerizes into stable fibrils under benign conditions. Gold nanoparticles were used to functionalize the RiAFP-m9 fibrils as well as those assembled from the previously described SBAFP-m1 protein. Cysteines incorporated into the sequences provide site-specific gold attachment. Additionally, silver was deposited on the gold-labelled fibrils by electroless plating to create nanowires. These results bolster prospects for programable self-assembly of BSPs to create scaffolds for functional nanomaterials.

Creation of (R)-Amine Transaminase Activity within an α-Amino Acid Transaminase Scaffold

The enzymatic transamination of ketones into (R)-amines represents an important route for accessing a range of pharmaceuticals or building blocks. Although many publications have dealt with enzyme discovery, protein engineering, and the application of (R)-selective amine transaminases [(R)-ATA] in biocatalysis, little is known about the actual in vivo role and how these enzymes have evolved from the ubiquitous α-amino acid transaminases (α-AATs). Here, we show the successful introduction of an (R)-transaminase activity in an α-amino acid aminotransferase with one to six amino acid substitutions in the enzyme's active site. Bioinformatic analysis combined with computational redesign of the d-amino acid aminotransferase (DATA) led to the identification of a sextuple variant having a specific activity of 326 milliunits mg^-1 in the conversion of (R)-phenylethylamine and pyruvate to acetophenone and d-alanine. This value is similar to those of natural (R)-ATAs, which typically are in the range of 250 milliunits mg^-1. These results demonstrate that (R)-ATAs can evolve from α-AAT as shown here for the DATA scaffold.