Protein Design Research Articles

Reverse-QTY code design of active human serum albumin self-assembled amphiphilic nanoparticles for effective anti-tumor drug doxorubicin release in mice

Human serum albumin (HSA) is a highly water-soluble protein with 67% alpha-helix content and three distinct domains (I, II, and III). HSA offers a great promise in drug delivery with enhanced permeability and retention effect. But it is hindered by protein denaturation during drug entrapment or conjugation that result in distinct cellular transport pathways and reduction of biological activities. Here we report using a protein design approach named reverse-QTY (rQTY) code to convert specific hydrophilic alpha-helices to hydrophobic to alpha-helices. The designed HSA undergo self-assembly of well-ordered nanoparticles with highly biological actives. The hydrophilic amino acids, asparagine (N), glutamine (Q), threonine (T), and tyrosine (Y) in the helical B-subdomains of HSA were systematically replaced by hydrophobic leucine (L), valine (V), and phenylalanine (F). HSArQTY nanoparticles exhibited efficient cellular internalization through the cell membrane albumin binding protein GP60, or SPARC (secreted protein, acidic and rich in cysteine)-mediated pathways. The designed HSArQTY variants displayed superior biological activities including: i) encapsulation of drug doxorubicin, ii) receptor-mediated cellular transport, iii) tumor cell targeting, and iv) antitumor efficiency compare to denatured HSA nanoparticles. HSArQTY nanoparticles provided superior tumor targeting and antitumor therapeutic effects compared to the albumin nanoparticles fabricated by antisolvent precipitation method. We believe that the rQTY code is a robust platform for specific hydrophobic modification of functional hydrophilic proteins with clear-defined binding interfaces.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters.

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD1 exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD1-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD1. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD1 and the redesigned 1a8uD1-M8 were devised from scratch. More importantly, the designed 1a8uD1-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.

Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins

Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.

Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application.

Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.

PH-dependent self-assembly mechanism of a single repetitive domain from a spider silk protein

Spider silk is self-assembled from full-length silk proteins, and some silk protein fragments can also form silk-like fibers in vitro. However, the mechanism underlying the silk fiber formation is not understood well. In this study, we investigated the fiber formation of a single repetitive domain (RP) from a minor ampullate silk protein (MiSp). Our findings revealed that pH and salt concentration affect not only the stability of MiSp-RP but also its self-assembly into fibers and aggregates. Using nuclear magnetic resonance (NMR) spectroscopy, we solved the three-dimensional (3D) structure of MiSp RP in aqueous solution. On the basis of the structure and mutagenesis, we revealed that charge-dipole interactions are responsible for the pH- and salt-dependent properties of MiSp-RP. Our results indicate that fiber formation is regulated by a delicate balance between intermolecular and intramolecular interactions, rather than by the protein stability alone. These findings have implications for the design of silk proteins for mass production of spider silk.

Structural Dynamics Predominantly Determine the Adaptability of Proteins to Amino Acid Deletions.

The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold.

Basis for Accurate Protein pKa Prediction with Machine Learning.

pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pKa's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pKa prediction is crucial. Here we present a theoretical pKa data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pKa orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pKa prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pKa predictor and thus can be applied immediately to, for example, pKa database construction, protein design, drug discovery, and so on.

Protein Prediction using Dictionary Based Regression Learning

Research Objectives: Molecular genetic data is managed by the information technology known as bioinformatics. Major concept involved in bioinformatics is a protein sequence. Amino acids bonded with peptide bond constitute the sequence of Protein and it is very essential to lead life. To predict sequence of amino acid, primary sequence obtains amino sequence folding and structures prediction. Research Novelty: In this manuscript, dictionary based regression learning and fuzzy genetic algorithm is proposed for protein prediction from structural analysis (DRL-FGA-PD-SA). In this input data are taken from Kaggle domain dataset. The extraction of protein features from given data is made through Kernel Matrix (KM) which extracts composition of amino acids, composition of dipeptide, composition of pseudo-amino-acid, composition of functional domain and distance-based features. Then fuzzy based genetic algorithm (FGA) update the selected features for classification of protein and the features are clustered. Finally, dictionary based regression learning (DRL) predicts the class of protein with conversion of values either 0’s or 1’s. Research Conclusions: The proposed method is executed on MATLAB. Here evaluation metrics as sensitivity, precision, f-measure, specificity, accuracy and error rate are outlined. Then the performance of the proposed DRL-FGA-PD-SA method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precision, compared with the existing systems such assdeep learning methods in protein structure prediction (FFNN-RNN-PD-SA), deep learning technique for protein structure prediction and protein design (DNN-PD-SA) and improved protein structure prediction using potentials from deep learning (DNN-SGDA-PD-SA) respectively.

Score-based generative modeling for de novo protein design.

The generation of de novo protein structures with predefined functions and properties remains a challenging problem in protein design. Diffusion models, also known as score-based generative models (SGMs), have recently exhibited astounding empirical performance in image synthesis. Here we use image-based representations of protein structure to develop ProteinSGM, a score-based generative model that produces realistic de novo proteins. Through unconditional generation, we show that ProteinSGM can generate native-like protein structures, surpassing the performance of previously reported generative models. We experimentally validate some de novo designs and observe secondary structure compositions consistent with generated backbones. Finally, we apply conditional generation to de novo protein design by formulating it as an image inpainting problem, allowing precise and modular design of protein structure.

Engineering transmembrane signal transduction in synthetic membranes using two-component systems

Cells use signal transduction across their membranes to sense and respond to a wide array of chemical and physical signals. Creating synthetic systems which can harness cellular signaling modalities promises to provide a powerful platform for biosensing and therapeutic applications. As a first step toward this goal, we investigated how bacterial two-component systems (TCSs) can be leveraged to enable transmembrane-signaling with synthetic membranes. Specifically, we demonstrate that a bacterial two-component nitrate-sensing system (NarX-NarL) can be reproduced outside of a cell using synthetic membranes and cell-free protein expression systems. We find that performance and sensitivity of the TCS can be tuned by altering the biophysical properties of the membrane in which the histidine kinase (NarX) is integrated. Through protein engineering efforts, we modify the sensing domain of NarX to generate sensors capable of detecting an array of ligands. Finally, we demonstrate that these systems can sense ligands in relevant sample environments. By leveraging membrane and protein design, this work helps reveal how transmembrane sensing can be recapitulated outside of the cell, adding to the arsenal of deployable cell-free systems primed for real world biosensing.

Modification of C20 oxidase in tanshinone biosynthesis pathway

Tanshinones are one of the main effective components of Salvia miltiorrhiza, which play important roles in the treatment of cardiovascular diseases. Microbial heterogony production of tanshinones can provide a large number of raw materials for the production of traditional Chinese medicine(TCM) preparations containing S. miltiorrhiza, reduce the extraction cost, and relieve the pressure of clinical medication. The biosynthetic pathway of tanshinones contains multiple P450 enzymes, and the catalytic element with high efficiency is the basis of microbial production of tanshinones. In this study, the protein modification of CYP76AK1, a key P450-C20 hydroxylase in tanshinone pathway, was researched. The protein modeling methods SWISS-MODEL, Robetta, and AlphaFold2 were used, and the protein model was analyzed to obtain the reliable protein structure. The semi-rational design of mutant protein was carried out by molecular docking and homologous alignment. The key amino acid sites affecting the oxidation activity of CYP76AK1 were identified by molecular docking. The function of the obtained mutations was studied with yeast expression system, and the CYP76AK1 mutations with continuous oxidation function to 11-hydroxysugiol were obtained. Four key amino acid sites that affected the oxidation acti-vity were analyzed, and the reliability of three protein modeling methods was analyzed according to the mutation results. The effective protein modification sites of CYP76AK1 were reported for the first time in this study, which provides a catalytic element for different oxidation activities at C20 site for the study of the synthetic biology of tanshinones and lays a foundation for the analysis of the conti-nuous oxidation mechanism of P450-C20 modification.

Cover Picture: (Mol. Inf. 5/2023)

Molecular InformaticsVolume 42, Issue 5 2380501 Cover PictureFree Access Cover Picture: (Mol. Inf. 5/2023) First published: 11 May 2023 https://doi.org/10.1002/minf.202380501AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Graphical Abstract Molecular Informatics presents highest-quality interdisciplinary research that leads to a deeper understanding of biomolecular complexes on the level of biological systems that are relevant for drug discovery and chemical biology, protein and nucleic acid engineering and design, bio-nanomolecular structures, macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, virtual screening, and novel technologies for the design of biologically active molecules. Volume42, Issue5May 20232380501 RelatedInformation

Recent Advances in NMR Protein Structure Prediction with ROSETTA.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Designed Rubredoxin miniature in a fully artificial electron chain triggered by visible light

Designing metal sites into de novo proteins has significantly improved, recently. However, identifying the minimal coordination spheres, able to encompass the necessary information for metal binding and activity, still represents a great challenge, today. Here, we test our understanding with a benchmark, nevertheless difficult, case. We assemble into a miniature 28-residue protein, the quintessential elements required to fold properly around a FeCys4 redox center, and to function efficiently in electron-transfer. This study addresses a challenge in de novo protein design, as it reports the crystal structure of a designed tetra-thiolate metal-binding protein in sub-Å agreement with the intended design. This allows us to well correlate structure to spectroscopic and electrochemical properties. Given its high reduction potential compared to natural and designed FeCys4-containing proteins, we exploit it as terminal electron acceptor of a fully artificial chain triggered by visible light.

Exploiting conformational dynamics to modulate the function of designed proteins

With the recent success in calculating protein structures from amino acid sequences using artificial intelligence-based algorithms, an important next step is to decipher how dynamics is encoded by the primary protein sequence so as to better predict function. Such dynamics information is critical for protein design, where strategies could then focus not only on sequences that fold into particular structures that perform a given task, but would also include low-lying excited protein states that could influence the function of the designed protein. Herein, we illustrate the importance of dynamics in modulating the function of C34, a designed α/β protein that captures β-strands of target ligands and is a member of a family of proteins designed to sequester β-strands and β hairpins of aggregation-prone molecules that lead to a variety of pathologies. Using a strategy to "see" regions of apo C34 that are invisible to NMR spectroscopy as a result of pervasive conformational exchange, as well as a mutagenesis approach whereby C34 molecules are stabilized into a single conformer, we determine the structures of the predominant conformations that are sampled by C34 and show that these attenuate the affinity for cognate peptide. Subsequently, the observed motion is exploited to develop an allosterically regulated peptide binder whose binding affinity can be controlled through the addition of a second molecule. Our study emphasizes the unique role that NMR can play in directing the design process and in the construction of new molecules with more complex functionality.

Structural insights into thrombolytic activity of destabilase from medicinal leech

Destabilase from the medical leech Hirudo medicinalis belongs to the family of i-type lysozymes. It has two different enzymatic activities: microbial cell walls destruction (muramidase activity), and dissolution of the stabilized fibrin (isopeptidase activity). Both activities are known to be inhibited by sodium chloride at near physiological concentrations, but the structural basis remains unknown. Here we present two crystal structures of destabilase, including a 1.1 Å-resolution structure in complex with sodium ion. Our structures reveal the location of sodium ion between Glu34/Asp46 residues, which were previously recognized as a glycosidase active site. While sodium coordination with these amino acids may explain inhibition of the muramidase activity, its influence on previously suggested Ser49/Lys58 isopeptidase activity dyad is unclear. We revise the Ser49/Lys58 hypothesis and compare sequences of i-type lysozymes with confirmed destabilase activity. We suggest that the general base for the isopeptidase activity is His112 rather than Lys58. pKa calculations of these amino acids, assessed through the 1 μs molecular dynamics simulation, confirm the hypothesis. Our findings highlight the ambiguity of destabilase catalytic residues identification and build foundations for further research of structure–activity relationship of isopeptidase activity as well as structure-based protein design for potential anticoagulant drug development.

Mega-scale experimental analysis of protein folding stability in biology and protein design

Mega-scale experimental analysis of protein folding stability in biology and protein design

Top-down design of protein architectures with reinforcement learning.

As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a "top-down" reinforcement learning-based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo-electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.

Mega-scale experimental analysis of protein folding stability in biology and protein design

Mega-scale experimental analysis of protein folding stability in biology and protein design

Isothermal Compressibility Perturbation as a Protein Design Principle for T1 Lipase Stability-Activity Trade-Off Counteracting.

Given the widely existing stability-activity trade-off in enzyme evolution, it is still a goal to obtain enzymes embracing both high activity and stability. Herein, we employed an isothermal compressibility (βT) perturbation engineering (ICPE) strategy to comprehensively understand the stability-activity seesaw-like mechanism. The stability and activity of mutants derived from ICPE uncovered a high Pearson correlation (r = 0.93) in a prototypical enzyme T1 lipase. The best variant A186L/L188M/A190Y exhibited a high Tm value up to 78.70 °C, catalytic activity of 474.04 U/mg, and a 73.33% increase in dimethyl sulfoxide resistance compared to the wild type, one of the highest comprehensive performances reported to date. The elastic activation mechanism mediated by conformational change with a ΔβT range of -6.81 × 10-6 to -1.90 × 10-6 bar-1 may account for the balancing of stability and activity to achieve better performing enzymes. The ICPE strategy deepens our understanding of stability-activity trade-off and boosts its applications in enzyme engineering.