Protein Solubility Predictions Using the CamSol Method in the Study of Protein Homeostasis

An in silico vaccinomics strategy to develop multiepitope vaccine using essential hypothetical protein as a target against Brevundimonas subvibrioides: A combined subtractive proteomics and immunoinformatics approach

The genus Brevundimonas, responsible for a spectrum of diseases, encompasses opportunistic pathogens against diverse infections, with B. subvibrioides possessing the largest genome size. Presence of Brevundimonas genus has been found in clinical samples of patients with urinary tract infections (UTIs) and mastitis, specifically, B. subvibrioides. However, former treatment methods using antibiotics have rendered the bacteria resistant to the inhibitory effects of chloramphenicol, azithromycin, etc. as well as alternative chemical treatments. Reckoning with the present scenario, vaccination emerges as the safest and most effective treatment strategy to address the global issue of evolving microbial threats. The growing concern of antibiotic resistance necessitates a shift towards vaccination as the primary treatment strategy. For this study, a total of 15 essential hypothetical proteins (EsHPs) were retrieved from the database of essential genes (DEG) for further multi-server functional annotation, physicochemical characterization, and non-homology analysis. The target antigen for our peptide-based vaccine construct was localized in the extracellular space, exhibited virulence factor and non-homology against human host and gut microflora. The final screened candidate was detected to be antigenic, probable non-allergen, soluble, non-toxic with near absence of transmembrane helices. The linear B-cell lymphocyte (LBL), helper T lymphocyte (HTL), and cytotoxic T lymphocyte (CTL) epitopes present on this protein were predicted and further docked with their respective major histocompatibility complex (MHC) molecules to verify their affinities for favourable antigen presentation. The final vaccine construct was built with human beta-defensin 3 (HBD3) as the adjuvant, 2 LBL epitopes, 3 CTL epitopes, 1 HTL epitope, and the required linkers. The designed construct was found to be immune response-inducing and was therefore cloned into a suitable vector to assess the possibility of appropriate expression. The final construct, when docked with Toll-like receptor 4 (TLR4), followed by molecular dynamics (MD) simulation of unbound and bound complexes, demonstrated a strong generation of immunity, whose safety might be experimentally tested in the near future. This was corroborated by stabilizing root-mean-square deviation (RMSD) curves, increasing hydrogen bonds, and decreasing residual mobility.

Preparation, characterization, and binding mechanism of pH-driven gliadin/soy protein isolate nanoparticles

Protein nanoparticles have attracted significant attention due to their low cost and high bioavailability; however, their poor stability limits their functional applications. To address this challenge, hydrophobic gliadin (G) and hydrophilic soy protein isolate (SPI) were co-assembled using the pH-driven method to evaluate the impact of different G/SPI ratios on their structural and functional properties. The results revealed that at G/SPI ratios between 1:1 and 1:8, the nanoparticles exhibited smaller particle sizes and higher zeta potentials. Spectroscopic analysis showed that protein interactions, primarily hydrogen bonding, hydrophobic interactions, and electrostatic interaction, led to a more compact spatial structure. Functional analysis identified a 1:3 ratio as optimal, offering excellent emulsifying properties (EAI: 28.95 m2/g; ESI: 90.53%) and superior foaming properties (FC: 837.46 %; FS: 87.62 %). Additionally, this ratio significantly enhanced solubility by 75.6 % and improved physical stability compared to gliadin nanoparticles (GNPs). Mechanistic analysis revealed that the assembly of G/SPI nanoparticles was primarily driven by hydrogen bonding, hydrophobic interactions, and electrostatic interactions, with hydrophobic interactions playing a dominant role. Notably, a key turning point in protein folding was identified as the pH shifted from 10 to 9. Molecular docking further pinpointed the binding site, elucidating the assembly process at the molecular level. These findings establish a solid foundation for the development of dual-protein nanoparticles with tailored properties, opening new possibilities for their application in bioactive compound delivery.

Amyloid-forming property of the N-terminal 1-70 residues of human apolipoprotein A-IV

Apolipoprotein A-IV (apoA-IV), the largest member of the exchangeable apolipoprotein family, is a common constituent of amyloid deposits in renal and cardiac amyloidosis. In this study, we characterized the aggregation propensity of the apoA-IV N-terminal fragment to form amyloid fibrils using a variety of biophysical techniques. Thioflavin T fluorescence assay, circular dichroism measurement, and microscopic observations revealed that the N-terminal 1-70 amino acid fragment of apoA-IV readily forms amyloid fibrils by a transition from a random coil to a β-sheet-rich structure. Sequence-based analysis indicated that residues 7-16 and 38-42 are the major aggregation-prone segments within the N-terminal 1-70 residues of apoA-IV. Consistent with this, deletion of these residues strongly inhibited the β-transition and fibril formation of apoA-IV 1-70. Kinetic and thermodynamic analyses of fibril formation by the apoA-IV 1-70 fragment demonstrated that primary nucleation is the dominant step in fibril formation, for which the activation energy barrier is entirely entropic. In addition, we found that the presence of heparin, a representative glycosaminoglycan, accelerated fibril formation kinetics and enhanced the yield of apoA-IV 1-70 fibrils, and the positively charged residues K58-K59 play a critical role in heparin interaction. Overall, our results suggest that the strong amyloid-forming propensity of the N-terminal fragment of apoA-IV may play a key role in amyloid deposition associated with apoA-IV amyloidosis.

Extension of sticholysins N-terminal α-helix signals membrane lipids to acquire curvature for toroidal pore formation

Sticholysin I and II (St I/II) belong to the actinoporins family; these proteins form pores in host cell membranes by binding their N-terminal segment to the membrane, leading to protein-lipid (toroidal) pores. Peptides derived from actinoporins pore-forming domains replicate their folding properties and permeabilizing effects. Despite the advances in understanding how these proteins and peptides mediate pore formation, the role of different N-terminal segments in inducing membrane curvature is still unclear. Here we combine circular dichroism, electron paramagnetic resonance, and small-angle X-ray scattering to investigate how synthetic peptides encompassing the N-terminal segments of St I and II (StI1-31, StII1-30, StI12-31, and StII11-30) interact with lipid bilayers and micelles as mimics of the topography of the initial membrane binding and of the subsequently formed positively curved pore. We investigate both the conformational changes and peptides' effects on membrane organization resulting from these interactions. According to the toroidal pore model, our results support that the actinoporins amphipathic α-helices rest at the membrane interface, forming pore walls with lipid head groups, while the 1-10 segment of St II penetrates the bilayer, acting as an anchor. We relate this ability to the higher hydrophobicity of this segment in St II, compared to St I. This unique feature of St II would contribute to enhanced pore formation, explaining St II's increased activity when compared to other actinoporins. Our results reinforce the notion that pore formation by actinoporins is a highly cooperative process where specific protein segments and the lipid bilayer mutually modulate their conformation and organization.

A comprehensive overview of recent advances in generative models for antibodies

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid development of deep learning methods and the increasing amount of antibody data, antibody generative models have made great progress recently. They aim to solve the antibody space searching problems and are widely incorporated into the antibody development process. Therefore, a comprehensive introduction to the development methods in this field is imperative. Here, we collected 34 representative antibody generative models published recently and all generative models can be divided into three categories: sequence-generating models, structure-generating models, and hybrid models, based on their principles and algorithms. We further studied their performance and contributions to antibody sequence prediction, structure optimization, and affinity enhancement. Our manuscript will provide a comprehensive overview of the status of antibody generative models and also offer guidance for selecting different approaches.

Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aβ2-7 Binding Affinity

Seven treatments are approved for Alzheimer's disease, but five of them only relieve symptoms and do not alter the course of the disease. Aducanumab (Adu) and lecanemab are novel disease-modifying antiamyloid-β (Aβ) human monoclonal antibodies that specifically target the pathophysiology of Alzheimer's disease (AD) and were recently approved for its treatment. However, their administration is associated with serious side effects, and their use is limited to early stages of the disease. Therefore, drug discovery remains of great importance in AD research. To gain new insights into the development of novel drugs for Alzheimer's disease, a combination of techniques was employed, including mutation screening, molecular dynamics, and quantum biochemistry. These were used to outline the interfacial interactions of the Aducanumab::Aβ2-7 complex. Our analysis identified critical stabilizing contacts, revealing up to 40% variation in the affinity of the Adu chains for Aβ2-7 depending on the conformation outlined. Remarkably, two complementarity determining regions (CDRs) of the Adu heavy chain (HCDR3 and HCDR2) and one CDR of the Adu light chain (LCDR3) accounted for approximately 77% of the affinity of Adu for Aβ2-7, confirming their critical role in epitope recognition. A single mutation, originally reported to have the potential to increase the affinity of Adu for Aβ2-7, was shown to decrease its structural stability without increasing the overall binding affinity. Mimetic peptides that have the potential to inhibit Aβ aggregation were designed by using computational outcomes. Our results support the use of these peptides as promising drugs with great potential as inhibitors of Aβ aggregation.

Altered molecular and cellular mechanisms in KIF5A-associated neurodegenerative or neurodevelopmental disorders

Mutations targeting distinct domains of the neuron-specific kinesin KIF5A associate with different neurodegenerative/neurodevelopmental disorders, but the molecular bases of this clinical heterogeneity are unknown. We characterised five key mutants covering the whole spectrum of KIF5A-related phenotypes: spastic paraplegia (SPG, R17Q and R280C), Charcot-Marie-Tooth disease (CMT, R864*), amyotrophic lateral sclerosis (ALS, N999Vfs*40), and neonatal intractable myoclonus (NEIMY, C975Vfs*73) KIF5A mutants. CMT-R864*-KIF5A and ALS-N999Vfs*40-KIF5A showed impaired autoinhibition and peripheral localisation accompanied by altered mitochondrial distribution, suggesting transport competence disruption. ALS-N999Vfs*40-KIF5A formed SQSTM1/p62-positive inclusions sequestering WT-KIF5A, indicating a gain of toxic function. SPG-R17Q-KIF5A and ALS-N999Vfs*40-KIF5A evidenced a shorter half-life compared to WT-KIF5A, and proteasomal blockage determined their accumulation into detergent-insoluble inclusions. Interestingly, SPG-R280C-KIF5A and ALS-N999Vfs*40-KIF5A both competed for degradation with proteasomal substrates. Finally, NEIMY-C975Vfs*73-KIF5A displayed a similar, but more severe aberrant behaviour compared to ALS-N999Vfs*40-KIF5A; these two mutants share an abnormal tail but cause disorders on the opposite end of KIF5A-linked phenotypic spectrum. Thus, our observations support the pathogenicity of novel KIF5A mutants, highlight abnormalities of recurrent variants, and demonstrate that both unique and shared mechanisms underpin KIF5A-related diseases.

Mechanism of phage sensing and restriction by toxin-antitoxin-chaperone systems

Toxin-antitoxins (TAs) are prokaryotic two-gene systems composed of a toxin neutralized by an antitoxin. Toxin-antitoxin-chaperone (TAC) systems additionally include a SecB-like chaperone that stabilizes the antitoxin by recognizing its chaperone addiction (ChAD) element. TACs mediate antiphage defense, but the mechanisms of viral sensing and restriction are unexplored. We identify two Escherichia coli antiphage TAC systems containing host inhibition of growth (HigBA) and CmdTA TA modules, HigBAC and CmdTAC. HigBAC is triggered through recognition of the gpV major tail protein of phage λ. Chaperone HigC recognizes gpV and ChAD via analogous aromatic molecular patterns, with gpV outcompeting ChAD to trigger toxicity. For CmdTAC, the CmdT ADP-ribosyltransferase toxin modifies mRNA to halt protein synthesis and limit phage propagation. Finally, we establish the modularity of TACs by creating a hybrid broad-spectrum antiphage system combining the CmdTA TA warhead with a HigC chaperone phage sensor. Collectively, these findings reveal the potential of TAC systems in broad-spectrum antiphage defense.

Automated optimization of the solubility of a hyper-stable α-amylase

Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm's capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.

ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction

Enhancers play an important role in the process of gene expression regulation. In DNA sequence abundance or absence of enhancers and irregularities in the strength of enhancers affects gene expression process that leads to the initiation and propagation of diverse types of genetic diseases such as hemophilia, bladder cancer, diabetes and congenital disorders. Enhancer identification and strength prediction through experimental approaches is expensive, time-consuming and error-prone. To accelerate and expedite the research related to enhancers identification and strength prediction, around 19 computational frameworks have been proposed. These frameworks used machine and deep learning methods that take raw DNA sequences and predict enhancer's presence and strength. However, these frameworks still lack in performance and are not useful in real time analysis. This paper presents a novel deep learning framework that uses language modeling strategies for transforming DNA sequences into statistical feature space. It applies transfer learning by training a language model in an unsupervised fashion by predicting a group of nucleotides also known as k-mers based on the context of existing k-mers in a sequence. At the classification stage, it presents a novel classifier that reaps the benefits of two different architectures: convolutional neural network and attention mechanism. The proposed framework is evaluated over the enhancer identification benchmark dataset where it outperforms the existing best-performing framework by 5%, and 9% in terms of accuracy and MCC. Similarly, when evaluated over the enhancer strength prediction benchmark dataset, it outperforms the existing best-performing framework by 4%, and 7% in terms of accuracy and MCC.

Widespread amyloid aggregates formation by Zika virus proteins and peptides

Viral pathogenesis typically involves numerous molecular mechanisms. Protein aggregation is a relatively unknown characteristic of viruses, despite the fact that viral proteins have been shown to form terminally misfolded forms. Zika virus (ZIKV) is a neurotropic one with the potential to cause neurodegeneration. Its protein amyloid aggregation may link the neurodegenerative component to the pathogenicity associated with the viral infection. Therefore, we investigated protein aggregation in the ZIKV proteome as a putative pathogenic route and one of the alternate pathways. We discovered that it contains numerous anticipated aggregation-prone regions in this investigation. To validate our prediction, we used a combination of supporting experimental techniques routinely used for morphological characterization and study of amyloid aggregates. Several ZIKV proteins and peptides, including the full-length envelope protein, its domain III (EDIII) and fusion peptide, Pr N-terminal peptide, NS1 β-roll peptide, membrane-embedded signal peptide 2K, and cytosolic region of NS4B protein, were shown to be highly aggregating in our study. Because our findings show that viral proteins can form amyloids in vitro, we need to do a thorough functional study of these anticipated APRs to understand better the role of amyloids in the pathophysiology of ZIKV infection.

Potential of ADAM 17 Signal Peptide To Form Amyloid Aggregates in Vitro

ADAM 17, a disintegrin and metalloproteinase 17 belonging to the adamalysin protein family, is a Zn2+-dependent type-I transmembrane α-secretase protein. As a major sheddase, ADAM 17 acts as an indispensable regulator of chief cellular events and controls diverse cytokines, adhesion molecules, and growth factors. The signal peptide (residues 1-17) of ADAM 17 targets the protein to the secretory pathway and gets cleaved off afterward. No other function is documented for the ADAM 17 signal peptide (ADAM 17-SP) inside the cells. Here, we have taken a reductionist approach to understand the biophysical properties of ADAM 17-SP. Aiming to understand the possibility of aggregation, we found several aggregation-prone segments in the signal peptide. We performed in vitro experiments to show that the signal peptide forms amyloid-like aggregates in buffered conditions. We also studied its aggregation in the presence of sodium tripolyphosphate and heparin to correlate with the cellular conditions, as these biomolecules are naturally present inside cells. Further, we performed seeding experiments to observe the possibility of ADAM 17-SP aggregate interaction with the Aβ42 peptide. The results suggest that its seeds escalate the aggregation kinetics of the Aβ42 peptide and form heteromeric aggregates with it. We believe this finding could further intensify the aggregation studies on other signal peptides and shed light on the potential role of these segments other than signaling.

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

Investigating the aggregation perspective of Dengue virus proteome

Dengue viruses are human pathogens that are transmitted through mosquitoes. Apart from the typical symptoms associated with viral fevers, DENV infections are known to cause several neurological complications such as meningitis, encephalitis, intracranial haemorrhage, retinopathies along with the more severe, and sometimes fatal, vascular leakage and dengue shock syndrome. This study was designed to investigate, in detail, the predicted viral protein aggregation prone regions among all serotypes. Further, in order to understand the cross-talk between viral protein aggregation and aggregation of cellular proteins, cross-seeding experiments between the DENV NS1 (1-30), corresponding to the β-roll domain and the diabetes hallmark protein, amylin, were performed. Various techniques such as fluorescence spectroscopy, circular dichroism, atomic force microscopy and immunoblotting have been employed for this. We observe that the DENV proteomes have many predicted APRs and the NS1 (1-30) of DENV1-3, 2K and capsid anchor of DENV2 and DENV4 are capable of forming amyloids, in vitro. Further, the DENV NS1 (1-30), aggregates are also able to cross-seed and enhance amylin aggregation and vice-versa. This knowledge may lead to an opportunity for designing suitable inhibitors of protein aggregation that may be beneficial for viral infections and comorbidities.

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Sequence-based prediction of pH-dependent protein solubility using CamSol

Solubility is a property of central importance for the use of proteins in research in molecular and cell biology and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are material intensive and time consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of solubility for large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the development of one such method to include in the predictions the effect of the pH on solubility. We illustrate the resulting pH-dependent predictions on a variety of antibodies and other proteins to demonstrate that these predictions achieve an accuracy comparable with that of experimental methods. We make this method publicly available at https://www-cohsoftware.ch.cam.ac.uk/index.php/camsolph, as the version 3.0 of CamSol.

Multidimensional Protein Solubility Optimization with an Ultrahigh-Throughput Microfluidic Platform

Protein-based biologics are highly suitable for drug development as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to elevated concentrations, making insufficient solubility a critical bottleneck in the drug development pipeline. Here, we report an ultrahigh-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 μg of protein (>10-fold improvement), and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation excipients, such as sodium chloride, polysorbate, histidine, arginine, and sucrose. Additionally, we can measure how solubility is affected by the combinatorial effect of multiple additives, find a suitable pH for the formulation, and measure the impact of mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multidimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

The RESP AI model accelerates the identification of tight-binding antibodies

High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the KD of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development.

Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches

With the growing significance of antibodies as a therapeutic class, identifying developability risks early during development is of paramount importance. Several high-throughput in vitro assays and in silico approaches have been proposed to de-risk antibodies during early stages of the discovery process. In this review, we have compiled and collectively analyzed published experimental assessments and computational metrics for clinical antibodies. We show that flags assigned based on in vitro measurements of polyspecificity and hydrophobicity are more predictive of clinical progression than their in silico counterparts. Additionally, we assessed the performance of published models for developability predictions on molecules not used during model training. We find that generalization to data outside of those used for training remains a challenge for models. Finally, we highlight the challenges of reproducibility in computed metrics arising from differences in homology modeling, in vitro assessments relying on complex reagents, as well as curation of experimental data often used to assess the utility of high-throughput approaches. We end with a recommendation to enable assay reproducibility by inclusion of controls with disclosed sequences, as well as sharing of structural models to enable the critical assessment and improvement of in silico predictions.

An antibody scanning method for the detection of α-synuclein oligomers in the serum of Parkinson's disease patients

Misfolded α-synuclein oligomers are closely implicated in the pathology of Parkinson's disease and related synucleinopathies. The elusive nature of these aberrant assemblies makes it challenging to develop quantitative methods to detect them and modify their behavior. Existing detection methods use antibodies to bind α-synuclein aggregates in biofluids, although it remains challenging to raise antibodies against α-synuclein oligomers. To address this problem, we used an antibody scanning approach in which we designed a panel of 9 single-domain epitope-specific antibodies against α-synuclein. We screened these antibodies for their ability to inhibit the aggregation process of α-synuclein, finding that they affected the generation of α-synuclein oligomers to different extents. We then used these antibodies to investigate the size distribution and morphology of soluble α-synuclein aggregates in serum and cerebrospinal fluid samples from Parkinson's disease patients. Our results indicate that the approach that we present offers a promising route for the development of antibodies to characterize soluble α-synuclein aggregates in biofluids.

Structural characterisation of amyloidogenic intrinsically disordered zinc finger protein isoforms DPF3b and DPF3a

Double PHD fingers 3 (DPF3) is a zinc finger protein, found in the BAF chromatin remodelling complex, and is involved in the regulation of gene expression. Two DPF3 isoforms have been identified, respectively named DPF3b and DPF3a. Very limited structural information is available for these isoforms, and their specific functionality still remains poorly studied. In a previous work, we have demonstrated the first evidence of DPF3a being a disordered protein sensitive to amyloid fibrillation. Intrinsically disordered proteins (IDPs) lack a defined tertiary structure, existing as a dynamic conformational ensemble, allowing them to act as hubs in protein-protein interaction networks. In the present study, we have more thoroughly characterised DPF3a in vitro behaviour, as well as unravelled and compared the structural properties of the DPF3b isoform, using an array of predictors and biophysical techniques. Predictions, spectroscopy, and dynamic light scattering have revealed a high content in disorder: prevalence of random coil, aromatic residues partially to fully exposed to the solvent, and large hydrodynamic diameters. DPF3a appears to be more disordered than DPF3b, and exhibits more expanded conformations. Furthermore, we have shown that they both time-dependently aggregate into amyloid fibrils, as revealed by typical circular dichroism, deep-blue autofluorescence, and amyloid-dye binding assay fingerprints. Although spectroscopic and microscopic analyses have unveiled that they share a similar aggregation pathway, DPF3a fibrillates at a faster rate, likely through reordering of its C-terminal domain.

Experimental Study of Potential CD8+ Trivalent Synthetic Peptides for Liver Cancer Vaccine Development Using Sprague Dawley Rat Models

Background. Liver cancer (LC) is the most devastating disease affecting a large set of populations in the world. The mortality due to LC is escalating, indicating the lack of effective therapeutic options. Immunotherapeutic agents may play an important role against cancer cells. As immune cells, especially T lymphocytes, which are part of cancer immunology, the design of vaccine candidates for cytotoxic T lymphocytes may be an effective strategy for curing liver cancer. Results. In our study, based on an immunoinformatics approach, we predicted potential T cell epitopes of MHC class I molecules using integrated steps of data retrieval, screening of antigenic proteins, functional analysis, peptide synthesis, and experimental in vivo investigations. We predicted the binding affinity of epitopes LLECADDRADLAKY, VSEHRIQDKDGLFY, and EYILSLEELVNGMY of LC membrane-bounded extracellular proteins including butyrophilin-like protein-2 (BTNL2), glypican-3 (GPC3), and serum albumin (ALB), respectively, with MHC class I molecules (allele: HLA-A $\ast$ 01:01). These T cell epitopes rely on the level of their binding energy and antigenic properties. We designed and constructed a trivalent immunogenic model by conjugating these epitopes with linkers to activate cytotoxic T cells. For validation, the nonspecific hematological assays showed a significant rise in the count of white blood cells ( $5\times {10}^9/\text{l}$ ), lymphocytes ( $13\times {10}^9/\text{l}$ ), and granulocytes ( $5\times {10}^9/\text{l}$ ) compared to the control after administration of trivalent peptides. Specific immunoassays including granzyme B and IgG ELISA exhibited the significant concentration of these effector molecules in blood serum, indicating the activity of cytotoxic T cells. Granzyme concentration increased to 1050 pg/ml at the second booster dose compared to the control (95 pg/ml), while the concentration of IgG raised to 6 g/l compared to the control (2 g/l). Conclusion. We concluded that a potential therapeutic trivalent vaccine can activate and modulate the immune system to cure liver cancer on the basis of significant outcomes of specific and nonspecific assays.

Cell-Penetrating Peptides with Unexpected Anti-Amyloid Properties

Cell-penetrating peptides (CPPs) with sequences derived originally from a prion protein (PrP) have been shown to exhibit both anti-prion and anti-amyloid properties particularly against prion proteins and the amyloid-β (Aβ) peptide active in Alzheimer's disease. These disease-modifying properties are so far observed in cell cultures and in vitro. The CPP sequences are composed of a hydrophobic signal sequence followed by a highly positively charged hexapeptide segment. The original signal sequence of the prion protein can be changed to the signal sequence of the NCAM1 protein without losing the anti-prion activity. Although the detailed molecular mechanisms of these CPP peptides are not fully understood, they do form amyloid aggregates by themselves, and molecular interactions between the CPPs and PrP/Aβ can be observed in vitro using various spectroscopic techniques. These initial intermolecular interactions appear to re-direct the aggregation pathways for prion/amyloid formation to less cell-toxic molecular structures (i.e., co-aggregates), which likely is why the disease-inducing PrP/Aβ aggregation is counteracted in vivo.

Targeting DNA topoisomerases or checkpoint kinases results in an overload of chaperone systems, triggering aggregation of a metastable subproteome

A loss of the checkpoint kinase ataxia telangiectasia mutated (ATM) leads to impairments in the DNA damage response, and in humans causes cerebellar neurodegeneration, and an increased risk of cancer. A loss of ATM is also associated with increased protein aggregation. The relevance and characteristics of this aggregation are still incompletely understood. Moreover, it is unclear to what extent other genotoxic conditions can trigger protein aggregation as well. Here, we show that targeting ATM, but also ATR or DNA topoisomerases, results in the widespread aggregation of a metastable, disease-associated subfraction of the proteome. Aggregation-prone model substrates, including Huntingtin exon 1 containing an expanded polyglutamine repeat, aggregate faster under these conditions. This increased aggregation results from an overload of chaperone systems, which lowers the cell-intrinsic threshold for proteins to aggregate. In line with this, we find that inhibition of the HSP70 chaperone system further exacerbates the increased protein aggregation. Moreover, we identify the molecular chaperone HSPB5 as a cell-specific suppressor of it. Our findings reveal that various genotoxic conditions trigger widespread protein aggregation in a manner that is highly reminiscent of the aggregation occurring in situations of proteotoxic stress and in proteinopathies.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

Resurrecting Golgi proteins to grasp Golgi ribbon formation and self-association under stress

The Golgi complex is an essential organelle of the eukaryotic exocytic pathway. A subfamily of Golgi matrix proteins, called GRASPs, is central in stress-induced unconventional secretion, Golgi dynamics during mitosis/apoptosis, and Golgi ribbon formation. The Golgi ribbon is vertebrate-specific and correlates with the appearance of two GRASP paralogues and two Golgins (GM130/Golgin45), which form specific GRASP-Golgin pairs. The molecular details of their appearance only in Metazoans are unknown. Moreover, despite new functionalities supported by GRASP paralogy, little is known about their structural and evolutionary differences. Here, we used ancestor sequence reconstruction and biophysical/biochemical approaches to assess the evolution of GRASPs structure/dynamics, fibrillation, and how they started anchoring their Golgin partners. Our data showed that a GRASP ancestor anchored Golgins before gorasp gene duplication in Metazoans. After gene duplication, variations within the GRASP binding pocket determined which paralogue would recruit which Golgin. These interactions are responsible for their specific Golgi location and Golgi ribbon appearance. We also suggest that GRASPs have a long-standing capacity to form supramolecular structures, affecting their participation in stress-induced processes.

An open-source automated PEG precipitation assay to measure the relative solubility of proteins with low material requirement

The solubility of proteins correlates with a variety of their properties, including function, production yield, pharmacokinetics, and formulation at high concentrations. High solubility is therefore a key requirement for the development of protein-based reagents for applications in life sciences, biotechnology, diagnostics, and therapeutics. Accurate solubility measurements, however, remain challenging and resource intensive, which limits their throughput and hence their applicability at the early stages of development pipelines, when long-lists of candidates are typically available in minute amounts. Here, we present an automated method based on the titration of a crowding agent (polyethylene glycol, PEG) to quantitatively assess relative solubility of proteins using about 200 µg of purified material. Our results demonstrate that this method is accurate and economical in material requirement and costs of reagents, which makes it suitable for high-throughput screening. This approach is freely-shared and based on a low cost, open-source liquid-handling robot. We anticipate that this method will facilitate the assessment of the developability of proteins and make it substantially more accessible.

Sequence Determinants of the Aggregation of Proteins Within Condensates Generated by Liquid-liquid Phase Separation

The transition between the native and amyloid states of proteins can proceed via a deposition pathway via oligomeric intermediates or via a condensation pathway involving liquid droplet intermediates generated through liquid-liquid phase separation. While several computational methods are available to perform sequence-based predictions of the propensity of proteins to aggregate via the deposition pathway, much less is known about the physico-chemical principles that underlie aggregation within condensates. Here we investigate the sequence determinants of aggregation via the condensation pathway, and identify three relevant features: droplet-promoting propensity, aggregation-promoting propensity and multimodal interactions quantified by the binding mode entropy. By using this approach, we show that it is possible to predict aggregation-promoting mutations in droplet-forming proteins associated with amyotrophic lateral sclerosis (ALS). This analysis provides insights into the amino acid code for the conversion of proteins between liquid-like and solid-like condensates.

Eradicating mesothelin-positive human gastric and pancreatic tumors in xenograft models with optimized anti-mesothelin antibody-drug conjugates from synthetic antibody libraries

Mesothelin (MSLN) is an attractive candidate of targeted therapy for several cancers, and hence there are increasing needs to develop MSLN-targeting strategies for cancer therapeutics. Antibody–drug conjugates (ADCs) targeting MSLN have been demonstrated to be a viable strategy in treating MSLN-positive cancers. However, developing antibodies as targeting modules in ADCs for toxic payload delivery to the tumor site but not to normal tissues is not a straightforward task with many potential hurdles. In this work, we established a high throughput engineering platform to develop and optimize anti-MSLN ADCs by characterizing more than 300 scFv CDR-variants and more than 50 IgG CDR-variants of a parent anti-MSLN antibody as candidates for ADCs. The results indicate that only a small portion of the complementarity determining region (CDR) residues are indispensable in the MSLN-specific targeting. Also, the enhancement of the hydrophilicity of the rest of the CDR residues could drastically increase the overall solubility of the optimized anti-MSLN antibodies, and thus substantially improve the efficacies of the ADCs in treating human gastric and pancreatic tumor xenograft models in mice. We demonstrated that the in vivo treatments with the optimized ADCs resulted in almost complete eradication of the xenograft tumors at the treatment endpoints, without detectable off-target toxicity because of the ADCs’ high specificity targeting the cell surface tumor-associated MSLN. The technological platform can be applied to optimize the antibody sequences for more effective targeting modules of ADCs, even when the candidate antibodies are not necessarily feasible for the ADC development due to the antibodies’ inferior solubility or affinity/specificity to the target antigen.

Truncated human angiotensin converting enzyme 2; a potential inhibitor of SARS-CoV-2 spike glycoprotein and potent COVID-19 therapeutic agent

The current pandemic of Covid-19 caused by SARS-CoV-2 is continued to spread globally and no potential drug or vaccine against it is available. Spike (S) glycoprotein is the structural protein of SARS-CoV-2 located on the envelope surface, involve in interaction with angiotensin converting enzyme 2 (ACE2), a cell surface receptor, followed by entry into the host cell. Thereby, blocking the S glycoprotein through potential inhibitor may interfere its interaction with ACE2 and impede its entry into the host cell. Here, we present a truncated version of human ACE2 (tACE2), comprising the N terminus region of the intact ACE2 from amino acid position 21-119, involved in binding with receptor binding domain (RBD) of SARS-CoV-2. We analyzed the in-silico potential of tACE2 to compete with intact ACE2 for binding with RBD. The protein-protein docking and molecular dynamic simulation showed that tACE2 has higher binding affinity for RBD and form more stabilized complex with RBD than the intact ACE2. Furthermore, prediction of tACE2 soluble expression in E. coli makes it a suitable candidate to be targeted for Covid-19 therapeutics. This is the first MD simulation based findings to provide a high affinity protein inhibitor for SARS-CoV-2 S glycoprotein, an important target for drug designing against this unprecedented challenge.Communicated by Ramaswamy H. Sarma.

Immunogenomics guided design of immunomodulatory multi-epitope subunit vaccine against the SARS-CoV-2 new variants, and its validation through in silico cloning and immune simulation

Reports of the novel and more contagious strains of SARS-CoV-2 originating in different countries have further aggravated the pandemic situation. The recent substitutions in spike protein may be critical for the virus to evade the host's immune system and therapeutics that have already been developed. Thus, this study has employed an immunoinformatics pipeline to target the spike protein of this novel strain to construct an immunogenic epitope (CTL, HTL, and B cell) vaccine against the new variant. Our investigation revealed that 12 different epitopes imparted a critical role in immune response induction. This was validated by an exploration of physiochemical properties and experimental feasibility. In silico and host immune simulation confirmed the expression and induction of both primary and secondary immune factors such as IL, cytokines, and antibodies. The current study warrants further lab experiments to demonstrate its efficacy and safety.

Intestinal guard: Human CXCL17 modulates protective response against mycotoxins and CXCL17-mimetic peptides development

Mycotoxin contamination is an ongoing and growing issue that can create health risks and even cause death. Unfortunately, there is currently a lack of specific therapy against mycotoxins with few side effects. On the other hand, the strategic expression of CXCL17 in mucosal tissues suggests that it may be involved in immune response when exposed to mycotoxins, but the exact role of CXCL17 remains largely unknown. Using Caco-2 as a cell model of the intestinal epithelial barrier (the first line of defense against mycotoxins), we showed that a strong production of ROS-dependent CXCL17 was triggered by mycotoxins via p38 and JNK pathways. Under the mycotoxins stress, CXCL17 modulated enhanced immuno-protective response with a remission of inflammation and apoptosis through PI3K/AKT/mTOR. Based on our observed feedback of CXCL17 to the mycotoxins, we developed the CXCL17-mimetic peptides in silico (CX1 and CX2) that possessed the safety and the capability to ameliorate mycotoxins-inducible inflammation and apoptosis. In this study, the identification of detoxifying feature of CXCL17 is a prominent addition to the chemokine field, pointing out a new direction for curing the mycotoxins-caused damage.

Quantifying misfolded protein oligomers as drug targets and biomarkers in Alzheimer and Parkinson diseases

Protein misfolding and aggregation are characteristic of a wide range of neurodegenerative disorders, including Alzheimer and Parkinson diseases. A hallmark of these diseases is the aggregation of otherwise soluble and functional proteins into amyloid aggregates. Although for many decades such amyloid deposits have been thought to be responsible for disease progression, it is now increasingly recognized that the misfolded protein oligomers formed during aggregation are, instead, the main agents causing pathological processes. These oligomers are transient and heterogeneous, which makes it difficult to detect and quantify them, generating confusion about their exact role in disease. The lack of suitable methods to address these challenges has hampered efforts to investigate the molecular mechanisms of oligomer toxicity and to develop oligomer-based diagnostic and therapeutic tools to combat protein misfolding diseases. In this Review, we describe methods to quantify misfolded protein oligomers, with particular emphasis on diagnostic applications as disease biomarkers and on therapeutic applications as target biomarkers. The development of these methods is ongoing, and we discuss the challenges that remain to be addressed to establish measurement tools capable of overcoming existing limitations and to meet present needs.

Design of intrinsically disordered proteins that undergo phase transitions with lower critical solution temperatures

Many naturally occurring elastomers are intrinsically disordered proteins (IDPs) built up of repeating units and they can demonstrate two types of thermoresponsive phase behavior. Systems characterized by lower critical solution temperatures (LCST) undergo phase separation above the LCST whereas systems characterized by upper critical solution temperatures (UCST) undergo phase separation below the UCST. There is congruence between thermoresponsive coil-globule transitions and phase behavior whereby the theta temperatures above or below which the IDPs transition from coils to globules serve as useful proxies for the LCST / UCST values. This implies that one can design sequences with desired values for the theta temperature with either increasing or decreasing radii of gyration above the theta temperature. Here, we show that the Monte Carlo simulations performed in the so-called intrinsic solvation (IS) limit version of the temperature-dependent the ABSINTH (self-Assembly of Biomolecules Studied by an Implicit, Novel, Tunable Hamiltonian) implicit solvation model, yields a useful heuristic for discriminating between sequences with known LCST versus UCST phase behavior. Accordingly, we use this heuristic in a supervised approach, integrate it with a genetic algorithm, combine this with IS limit simulations, and demonstrate that novel sequences can be designed with LCST phase behavior. These calculations are aided by direct estimates of temperature dependent free energies of solvation for model compounds that are derived using the polarizable AMOEBA (atomic multipole optimized energetics for biomolecular applications) forcefield. To demonstrate the validity of our designs, we calculate coil-globule transition profiles using the full ABSINTH model and combine these with Gaussian Cluster Theory calculations to establish the LCST phase behavior of designed IDPs.

Truncated human angiotensin converting enzyme 2; a potential inhibitor of SARS-CoV-2 spike glycoprotein and potent COVID-19 therapeutic agent

The current pandemic of Covid-19 caused by SARS-CoV-2 is continued to spread globally and no potential drug or vaccine against it is available. Spike (S) glycoprotein is the structural protein of SARS-CoV-2 located on the envelope surface, involve in interaction with angiotensin converting enzyme 2 (ACE2), a cell surface receptor, followed by entry into the host cell. Thereby, blocking the S glycoprotein through potential inhibitor may interfere its interaction with ACE2 and impede its entry into the host cell. Here, we present a truncated version of human ACE2 (tACE2), comprising the N terminus region of the intact ACE2 from amino acid position 21-119, involved in binding with receptor binding domain (RBD) of SARS-CoV-2. We analyzed the in-silico potential of tACE2 to compete with intact ACE2 for binding with RBD. The protein-protein docking and molecular dynamic simulation showed that tACE2 has higher binding affinity for RBD and form more stabilized complex with RBD than the intact ACE2. Furthermore, prediction of tACE2 soluble expression in E. coli makes it a suitable candidate to be targeted for Covid-19 therapeutics. This is the first MD simulation based findings to provide a high affinity protein inhibitor for SARS-CoV-2 S glycoprotein, an important target for drug designing against this unprecedented challenge.Communicated by Ramaswamy H. Sarma.

Cell-Nonautonomous Regulation of Proteostasis in Aging and Disease

The functional health of the proteome is determined by properties of the proteostasis network (PN) that regulates protein synthesis, folding, macromolecular assembly, translocation, and degradation. In eukaryotes, the PN also integrates protein biogenesis across compartments within the cell and between tissues of metazoans for organismal health and longevity. Additionally, in metazoans, proteome stability and the functional health of proteins is optimized for development and yet declines throughout aging, accelerating the risk for misfolding, aggregation, and cellular dysfunction. Here, I describe the cell-nonautonomous regulation of organismal PN by tissue communication and cell stress-response pathways. These systems are robust from development through reproductive maturity and are genetically programmed to decline abruptly in early adulthood by repression of the heat shock response and other cell-protective stress responses, thus compromising the ability of cells and tissues to properly buffer against the cumulative stress of protein damage during aging. While the failure of multiple protein quality control processes during aging challenges cellular function and tissue health, genetic studies, and the identification of small-molecule proteostasis regulators suggests strategies that can be employed to reset the PN with potential benefit on cellular health and organismal longevity.

Proteome-wide observation of the phenomenon of life on the edge of solubility

To function effectively proteins must avoid aberrant aggregation, and hence they are expected to be expressed at concentrations safely below their solubility limits. By analyzing proteome-wide mass spectrometry data of Caenorhabditis elegans, however, we show that the levels of about three-quarters of the nearly 4,000 proteins analyzed in adult animals are close to their intrinsic solubility limits, indeed exceeding them by about 10% on average. We next asked how aging and functional self-assembly influence these solubility limits. We found that despite the fact that the total quantity of proteins within the cellular environment remains approximately constant during aging, protein aggregation sharply increases between days 6 and 12 of adulthood, after the worms have reproduced, as individual proteins lose their stoichiometric balances and the cellular machinery that maintains solubility undergoes functional decline. These findings reveal that these proteins are highly prone to undergoing concentration-dependent phase separation, which on aging is rationalized in a decrease of their effective solubilities, in particular for proteins associated with translation, growth, reproduction, and the chaperone system.

Protein Abundance Biases the Amino Acid Composition of Disordered Regions to Minimize Non-functional Interactions

In eukaryotes, disordered regions cover up to 50% of proteomes and mediate fundamental cellular processes. In contrast to globular domains, where about half of the amino acids are buried in the protein interior, disordered regions show higher solvent accessibility, which makes them prone to engage in non-functional interactions. Such interactions are exacerbated by the law of mass action, prompting the question of how they are minimized in abundant proteins. We find that interaction propensity or "stickiness" of disordered regions negatively correlates with their cellular abundance, both in yeast and human. Strikingly, considering yeast proteins where a large fraction of the sequence is disordered, the correlation between stickiness and abundance reaches R=-0.55. Beyond this global amino-acid composition bias, we identify three rules by which amino-acid composition of disordered regions adjusts with high abundance. First, lysines are preferred over arginines, consistent with the latter amino acid being stickier than the former. Second, compensatory effects exist, whereby a sticky region can be tolerated if it is compensated by a distal non-sticky region. Third, such compensation requires a lower average stickiness at the same abundance when compared to a scenario where stickiness is homogeneous throughout the sequence. We validate these rules experimentally, employing them as different strategies to rescue an otherwise sticky protein fragment from aggregation. Our results highlight that non-functional interactions represent a significant constraint in cellular systems and reveal simple rules by which protein sequences adapt to that constraint. Data from this work are deposited in Figshare, at https://doi.org/10.6084/m9.figshare.8068937.v3.

Protein Phase Separation as a Stress Survival Strategy

Cells under stress must adjust their physiology, metabolism, and architecture to adapt to the new conditions. Most importantly, they must down-regulate general gene expression, but at the same time induce synthesis of stress-protective factors, such as molecular chaperones. Here, we investigate how the process of phase separation is used by cells to ensure adaptation to stress. We summarize recent findings and propose that the solubility of important translation factors is specifically affected by changes in physical-chemical parameters such temperature or pH and modulated by intrinsically disordered prion-like domains. These stress-triggered changes in protein solubility induce phase separation into condensates that regulate the activity of the translation factors and promote cellular fitness. Prion-like domains play important roles in this process as environmentally regulated stress sensors and modifier sequences that determine protein solubility and phase behavior. We propose that protein phase separation is an evolutionary conserved feature of proteins that cells harness to regulate adaptive stress responses and ensure survival in extreme environmental conditions.