Latest approaches for efficient protein production in drug discovery

Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta

Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.

A secretory system for extracellular production of spider neurotoxin huwentoxin-I in Escherichia coli

Due to their advantages in structural stability and versatility, cysteine-rich peptides, which are secreted from the venom glands of venomous animals, constitute a naturally occurring pharmaceutical arsenal. However, the correct folding of disulfide bonds is a challenging task in the prokaryotic expression system like Escherichia coli due to the reducing environment. Here, a secretory expression plasmid pSE-G1M5-SUMO-HWTX-I for the spider neurotoxin huwentoxin-I (HWTX-I) with three disulfides as a model of cysteine-rich peptides was constructed. By utilizing the signal peptide G1M5, the fusion protein 6 × His-SUMO-HWTX-I was successfully secreted into extracellular medium of BL21(DE3). After enrichment using cation-exchange chromatography and purification utilizing the Ni-NTA column, 6 × His-SUMO-HWTX-I was digested via Ulp1 kinase to release recombinant HWTX-I (rHWTX-I), which was further purified utilizing RP-HPLC. Finally, both impurities with low and high molecular weights were completely removed. The molecular mass of rHWTX-I was identified as being 3750.8 Da, which was identical to natural HWTX-I with three disulfide bridges. Furthermore, by utilizing whole-cell patch clamp, the sodium currents of hNav1.7 could be inhibited by rHWTX-I and the IC50 value was 419 nmol/L.

Enhanced Molecular Dynamics Method to Efficiently Increase the Discrimination Capability of Computational Protein-Protein Docking

Protein–protein docking typically consists of the generation of putative binding conformations, which are subsequently ranked by fast heuristic scoring functions. The simplicity of these functions allows for computational efficiency but has severe repercussions on their discrimination capabilities. In this work, we show the effectiveness of suitable descriptors calculated along short scaled molecular dynamics runs in recognizing the nearest-native bound conformation among a set of putative structures generated by the HADDOCK tool for eight protein–protein systems.

In Silico Screening of Putative Corynebacterium pseudotuberculosis Antigens and Serological Diagnosis for Caseous Lymphadenitis in Sheep by Enzyme-Linked Immunosorbent Assay

Corynebacterium pseudotuberculosis is the etiologic agent of Caseous Lymphadenitis (CLA), a disease leading to severe damage in sheep and goats farming due to the lack of serological diagnosis, treatment, and effective prophylaxis. In this context, several strategies in an attempt to discover new antigens to compose diagnosis assays or vaccines are fundamental. Therefore, this study aimed to use bioinformatics software to evaluate the critical chemical characteristics of unknown proteins of C. pseudotuberculosis by selecting them for heterologous expression in Escherichia coli. For this purpose, six protein sequences of ascorbate transporter subunit, UPF protein, MMPL family transporter, Ribonuclease, Iron ABC transporter domain-containing permease, and fimbrial subunit were obtained. In silico analyses were performed using amino acid sequences to access immunodominant epitopes and their antigenic and allergenic potential and physicochemical characterization. The expressed proteins were used as an antigen for serological diagnosis by ELISA. All proteins showed distinct immunodominant epitopes and potential antigenic characteristics. The only proteins expressed were PTS and Ribonuclease. In parallel, we expressed CP40 and all were used with ELISA antigen in 49 CLA positive sera and 26 CLA negative sera. The proteins alone showed 100% sensitivity and 96.2%, 92.3%, and 88.5% specificity for rPTS, rRibonuclease, and rCP40, respectively. When proteins were combined, they showed 100% sensitivity and 84.6%, 92.3%, 88.5%, and 92.3% specificity for rPTS/rCp40, rRibonuclease/rCP40, rPTS/rRibonuclease, and rPTS/rRibonuclease/rCP40, respectively. The results of this study show an excellent correlation of sensitivity and specificity with all proteins. None of the specificity values preclude the potential of rPTS, rRibonuclease, or rCP40 for use in ELISA diagnostic assays since the results of this work are superior to those of other studies on CLA diagnosis described in the literature.

Levels of mannose-binding lectin (MBL) associates with sepsis-related in-hospital mortality in women

Background

Mannose-binding lectin (MBL) mediates the innate immune response either through direct opsonisation of microorganisms or through activation of the complement system. There are conflicting data whether MBL deficiency leads to increased susceptibility to infections or not. The aim of this study was to determine if low levels of mannose-binding lectin (MBL) predict sepsis development, sepsis severity and outcome from severe sepsis or septic shock.

Method

Patients aged 18 years or more with documented sepsis within 24 h after admission to the intensive care unit were included if they had participated in a health survey and donated blood samples prior to the sepsis event. A subset of these patients had stored plasma also from the acute phase. Two matched referents free of known sepsis were selected for each case. Plasma levels MBL were determined in stored samples from health surveys (baseline) and from ICU admission (acute phase). The association between MBL and sepsis, sepsis severity and in-hospital mortality were determined with 1300 ng/mL as cut-off for low levels.

Results

We identified 148 patients (61.5% women) with a first-time sepsis event 6.5 years (median with IQR 7.7) after participation in a health survey, of which 122 also had samples from the acute septic phase. Both high MBL levels in the acute phase (odds ratio [95% confidence interval]) (2.84 [1.20-6.26]), and an increase in MBL levels from baseline to the acute phase (3.76 [1.21-11.72]) were associated with increased risk for in-hospital death in women, but not in men (0.47 [0.11-2.06]). Baseline MBL levels did not predict future sepsis, sepsis severity or in-hospital mortality.

Conclusions

An increase from baseline to the acute phase as well as high levels in the acute phase associated with an unfavourable outcome in women.

Two Simple Methods for Optimizing the Production of "Difficult-to-Express" GnRH-DFF40 Chimeric Protein

Purpose: GnRH-DFF40 (gonadotropin releasing hormone - DNA fragmentation factor 40) is a humanized recombinant immunotoxin and serves as a prospective candidate for targeted therapy of gonadotropin releasing hormone receptor (GnRHR) overexpressing malignancies. However, its production in Escherichia coli in a soluble and functional form still remains a challenge. Here we introduce two successful and reproducible conditions for production and purification of “difficult-to-express” GnRH-DFF40 protein.

Methods: A synthetic codon optimized GnRH-DFF40 fusion gene was cloned in pET28a plasmid. Two methods including high cell density IPTG induction (HCDI) and autoinduction method (AIM) with a focus on obtaining high cell density have been investigated to enhance the protein production in (E. coli). Moreover, to obtain higher protein production several factors in the AIM method including carbon sources, incubation time and temperature, plasmid stability and double colony selection, were optimized.

Results: Remarkable amounts of soluble GnRH-DFF40 protein were achieved by both methods. Cell density and protein yields in AIM was about 1.5 fold higher than that what obtained using HCDI. Initial screening showed that 25ºC is better to achieve higher protein production in both methods. pH alterations in AIM were maintained in a more constant level at 25ºC and 37ºC temperatures without any detrimental effects on cell growth during protein production phase up to 21 hours after incubation. Plasmid stability during growth and expression induction phase was maintained at a high level of 98% and 96% for AIM and HCDI methods, respectively. After parameter optimization and double colony selection in AIM, a very high yield of recombinant protein was achieved (528.3 mg/L).

Conclusion: With the optimization of these high cell density expression methods, reproducible manifold enhancement of soluble protein yields can be achieved for “difficult-to-express” GnRH-DFF40 compared to conventional expression methods.

Accelerating the Production of Druggable Targets: Eukaryotic Cell-Free Systems Come into Focus

In the biopharmaceutical pipeline, protein expression systems are of high importance not only for the production of biotherapeutics but also for the discovery of novel drugs. The vast majority of drug targets are proteins, which need to be characterized and validated prior to the screening of potential hit components and molecules. A broad range of protein expression systems is currently available, mostly based on cellular organisms of prokaryotic and eukaryotic origin. Prokaryotic cell-free systems are often the system of choice for drug target protein production due to the simple generation of expression hosts and low cost of preparation. Limitations in the production of complex mammalian proteins appear due to inefficient protein folding and posttranslational modifications. Alternative protein production systems, so-called eukaryotic cell-free protein synthesis systems based on eukaryotic cell-lysates, close the gap between a fast protein generation system and a high quality of complex mammalian proteins. In this study, we show the production of druggable target proteins in eukaryotic cell-free systems. Functional characterization studies demonstrate the bioactivity of the proteins and underline the potential for eukaryotic cell-free systems to significantly improve drug development pipelines.

Optimized Procedure for Recovering HIV-1 Protease (C-SA) from Inclusion Bodies

HIV-1 is an infectious virus that causes acquired immunodeficiency syndrome (AIDS) and it is one of the major causes of deaths worldwide. The production of HIV-1 protease (PR) on a large scale has been a problem for scientists due to its cytotoxicity, low yield, insolubility, and low activity. HIV-1 C-SA protease has been cloned, expressed, and purified previously, however, with low recovery (0.25 mg/L). Herein we report an optimal expression and solubilisation procedure to recover active HIV-1 C-SA protease enzyme from inclusion bodies. The HIV protease was expressed in seven different vectors (pET11b, pET15b, pET28a pET32a, pET39b, pET41b and pGEX 6P-1). The highest expression was achieved when the vector pET32a (Trx tag) was employed. A total of 19.5 mg of fusion protein was refolded of which 5.5 mg of active protease was obtained after cleavage. The free protease had a high specific activity of 2.81 µmoles/min/mg. Interestingly the Trx-fusion protein also showed activity closer (1.24 µmoles/min/mg) to that of the free protease suggesting that the pET32a vector (Trx tag) expressed in BL21(DE3) pLysS provides a more efficient way to obtain HIV-1 protease.

A Dynamic Overview of Antimicrobial Peptides and Their Complexes

In this narrative review, we comprehensively review the available information about the recognition, structure, and dynamics of antimicrobial peptides (AMPs). Their complex behaviors occur across a wide range of time scales and have been challenging to portray. Recent advances in nuclear magnetic resonance and molecular dynamics simulations have revealed the importance of the molecular plasticity of AMPs and their abilities to recognize targets. We also highlight experimental data obtained using nuclear magnetic resonance methodologies, showing that conformational selection is a major mechanism of target interaction in AMP families.

Cold-Shock Expression System in E. coli for Protein NMR Studies

The cold-shock system using the pCold vector is one of the most effective Escherichia coli heterologous protein expression systems. It allows the improvement of the expression level of the protein of interest in a soluble fraction. In this chapter, we describe practical procedures for the overexpression of heterologous protein of interest by using the pCold vector or the single-protein production system. The latter is one of the most advanced pCold technologies for isotope labeling of the target protein and its NMR studies.

Current NMR Techniques for Structure-Based Drug Discovery

A variety of nuclear magnetic resonance (NMR) applications have been developed for structure-based drug discovery (SBDD). NMR provides many advantages over other methods, such as the ability to directly observe chemical compounds and target biomolecules, and to be used for ligand-based and protein-based approaches. NMR can also provide important information about the interactions in a protein-ligand complex, such as structure, dynamics, and affinity, even when the interaction is too weak to be detected by ELISA or fluorescence resonance energy transfer (FRET)-based high-throughput screening (HTS) or to be crystalized. In this study, we reviewed current NMR techniques. We focused on recent progress in NMR measurement and sample preparation techniques that have expanded the potential of NMR-based SBDD, such as fluorine NMR (¹⁹F-NMR) screening, structure modeling of weak complexes, and site-specific isotope labeling of challenging targets.

Modern Technologies of Solution Nuclear Magnetic Resonance Spectroscopy for Three-dimensional Structure Determination of Proteins Open Avenues for Life Scientists

Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for structural studies of chemical compounds and biomolecules such as DNA and proteins. Since the NMR signal sensitively reflects the chemical environment and the dynamics of a nuclear spin, NMR experiments provide a wealth of structural and dynamic information about the molecule of interest at atomic resolution. In general, structural biology studies using NMR spectroscopy still require a reasonable understanding of the theory behind the technique and experience on how to recorded NMR data. Owing to the remarkable progress in the past decade, we can easily access suitable and popular analytical resources for NMR structure determination of proteins with high accuracy. Here, we describe the practical aspects, workflow and key points of modern NMR techniques used for solution structure determination of proteins. This review should aid NMR specialists aiming to develop new methods that accelerate the structure determination process, and open avenues for non-specialist and life scientists interested in using NMR spectroscopy to solve protein structures.

A fully automated procedure for the parallel, multidimensional purification and nucleotide loading of the human GTPases KRas, Rac1 and RalB

Small GTPases regulate many key cellular processes and their role in human disease validates many proteins in this class as desirable targets for therapeutic intervention. Reliable recombinant production of GTPases, often in the active GTP loaded state, is a prerequisite for the prosecution of drug discovery efforts. The preparation of these active forms can be complex and often constricts the supply to the reagent intensive techniques used in structure base drug discovery. We have established a fully automated, multidimensional protein purification strategy for the parallel production of the catalytic G-domains of KRas, Rac1 and RalB GTPases in the active form. This method incorporates a four step chromatography purification with TEV protease-mediated affinity tag cleavage and a conditioning step that achieves the activation of the GTPase by exchanging GDP for the non-hydrolyzable GTP analogue GMPPnP. We also demonstrate that an automated method is efficient at loading of KRas with mantGDP for application in a SOS1 catalysed fluorescent nucleotide exchange assay. In comparison to more conventional manual workflows the automated method offers marked advantages in method run time and operator workload. This reduces the bottleneck in protein production while generating products that are highly purified and effectively loaded with nucleotide analogues.

Simple monitoring of cell leakiness and viability in Escherichia coli bioprocesses-A case study

In a recently published study, we developed a simple methodology to monitor Escherichia coli cell integrity and lysis during bioreactor cultivations, where we intentionally triggered leakiness. In this follow-up study, we used this methodology, comprising the measurement of extracellular alkaline phosphatase to monitor leakiness and flow cytometry to follow viability, to investigate the effect of process parameters on a recombinant E. coli strain producing the highly valuable vascular endothelial growth factor A165 (VEGF-A165) in the periplasm. Since the amount of soluble product was very little (<500 μg/g dry cell weight), we directly linked the effect of the three process parameters temperature, specific uptake rate of the inducer arabinose and specific growth rate (μ) to cell integrity and viability. We found that a low temperature and a high μ were beneficial for cell integrity and that an elevated temperature resulted in reduced viability. We concluded that the recombinant E. coli cells producing VEGF-A165 in the periplasm should be cultivated at low temperature and high μ to reduce leakiness and guarantee high viability. Summarizing, in this follow-up study we demonstrate the usefulness of our simple methodology to monitor leakiness and viability of recombinant E. coli cells during bioreactor cultivations.

A novel toolbox for E. coli lysis monitoring

The bacterium Escherichia coli is a well-studied recombinant host organism with a plethora of applications in biotechnology. Highly valuable biopharmaceuticals, such as antibody fragments and growth factors, are currently being produced in E. coli. However, the high metabolic burden during recombinant protein production can lead to cell death, consequent lysis, and undesired product loss. Thus, fast and precise analyzers to monitor E. coli bioprocesses and to retrieve key process information, such as the optimal time point of harvest, are needed. However, such reliable monitoring tools are still scarce to date. In this study, we cultivated an E. coli strain producing a recombinant single-chain antibody fragment in the cytoplasm. In bioreactor cultivations, we purposely triggered cell lysis by pH ramps. We developed a novel toolbox using UV chromatograms as fingerprints and chemometric techniques to monitor these lysis events and used flow cytometry (FCM) as reference method to quantify viability offline. Summarizing, we were able to show that a novel toolbox comprising HPLC chromatogram fingerprinting and data science tools allowed the identification of E. coli lysis in a fast and reliable manner. We are convinced that this toolbox will not only facilitate E. coli bioprocess monitoring but will also allow enhanced process control in the future.

Putative virulence factors of Corynebacterium pseudotuberculosis FRC41: vaccine potential and protein expression

BACKGROUND

Corynebacterium pseudotuberculosis, a facultative intracellular bacterial pathogen, is the etiological agent of caseous lymphadenitis (CLA), an infectious disease that affects sheep and goats and it is responsible for significant economic losses. The disease is characterized mainly by bacteria-induced caseous necrosis in lymphatic glands. New vaccines are needed for reliable control and management of CLA. Thus, the putative virulence factors SpaC, SodC, NanH, and PknG from C. pseudotuberculosis FRC41 may represent new target proteins for vaccine development and pathogenicity studies.

RESULTS

SpaC, PknG and NanH presented better vaccine potential than SodC after in silico analyses. A total of 136 B and T cell epitopes were predicted from the four putative virulence factors. A cluster analysis was performed to evaluate the redundancy degree among the sequences of the predicted epitopes; 57 clusters were formed, most of them (34) were single clusters. Two clusters from PknG and one from SpaC grouped epitopes for B and T-cell (MHC I and II). These epitopes can thus potentially stimulate a complete immune response (humoral and cellular) against C. pseudotuberculosis. Several other clusters, including two from NanH, grouped B-cell epitopes with either MHC I or II epitopes. The four target proteins were expressed in Escherichia coli. A purification protocol was developed for PknG expression.

CONCLUSIONS

In silico analyses show that the putative virulence factors SpaC, PknG and NanH present good potential for CLA vaccine development. Target proteins were successfully expressed in E. coli. A protocol for PknG purification is described.

Contacts-based prediction of binding affinity in protein-protein complexes

Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

Contacts-based prediction of binding affinity in protein-protein complexes

Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.