Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy

DOTAD: A Database of Therapeutic Antibody Developability

The development of therapeutic antibodies is an important aspect of new drug discovery pipelines. The assessment of an antibody's developability-its suitability for large-scale production and therapeutic use-is a particularly important step in this process. Given that experimental assays to assess antibody developability in large scale are expensive and time-consuming, computational methods have been a more efficient alternative. However, the antibody research community faces significant challenges due to the scarcity of readily accessible data on antibody developability, which is essential for training and validating computational models. To address this gap, DOTAD (Database Of Therapeutic Antibody Developability) has been built as the first database dedicated exclusively to the curation of therapeutic antibody developability information. DOTAD aggregates all available therapeutic antibody sequence data along with various developability metrics from the scientific literature, offering researchers a robust platform for data storage, retrieval, exploration, and downloading. In addition to serving as a comprehensive repository, DOTAD enhances its utility by integrating a web-based interface that features state-of-the-art tools for the assessment of antibody developability. This ensures that users not only have access to critical data but also have the convenience of analyzing and interpreting this information. The DOTAD database represents a valuable resource for the scientific community, facilitating the advancement of therapeutic antibody research. It is freely accessible at http://i.uestc.edu.cn/DOTAD/ , providing an open data platform that supports the continuous growth and evolution of computational methods in the field of antibody development.

Identification of tyrosine sulfation in the variable region of a bispecific antibody and its effect on stability and biological activity

Despite tyrosine sulfation being a relatively common post-translational modification (PTM) on the secreted proteins of higher eukaryotic organisms, there have been surprisingly few reports of this modification occurring in recombinant monoclonal antibodies (mAbs) expressed by mammalian cell lines and even less information regarding its potential impact on mAb efficacy and stability. This discrepancy is likely due to the extreme lability of this modification using many of the mass spectrometry methods typically used within the biopharmaceutical industry for PTM identification, as well as the possible misidentification as phosphorylation. Here, we identified sulfation on a single tyrosine residue located within the identical variable region sequence of a 2 + 1 bispecific mAbs heavy and heavy-heavy chains using a multi-enzymatic approach in combination with mass spectrometry analysis and examined its impact on binding, efficacy, and physical stability. Unlike previous reports, we found that tyrosine sulfation modestly decreased the mAb cell binding and T cell-mediated killing, primarily by increasing the rate of antigen disassociation as determined from surface plasmon resonance-binding experiments. We also found that, while this acidic modification had no significant impact on the mAb thermal stability, sulfation did modestly increase its rate of aggregation, presumably by lowering the mAb's colloidal stability as indicated by polyethylene glycol induced liquid-liquid phase separation experiments.

A potential antibody repertoire diversification mechanism through tyrosine sulfation for biotherapeutics engineering and production

The diversity of three hypervariable loops in antibody heavy chain and light chain, termed the complementarity-determining regions (CDRs), defines antibody's binding affinity and specificity owing to the direct contact between the CDRs and antigens. These CDR regions typically contain tyrosine (Tyr) residues that are known to engage in both nonpolar and pi stacking interaction with antigens through their complementary aromatic ring side chains. Nearly two decades ago, sulfotyrosine residue (sTyr), a negatively charged Tyr formed by Golgi-localized membrane-bound tyrosylprotein sulfotransferases during protein trafficking, were also found in the CDR regions and shown to play an important role in modulating antibody-antigen interaction. This breakthrough finding demonstrated that antibody repertoire could be further diversified through post-translational modifications, in addition to the conventional genetic recombination. This review article summarizes the current advances in the understanding of the Tyr-sulfation modification mechanism and its application in potentiating protein-protein interaction for antibody engineering and production. Challenges and opportunities are also discussed.

In silico prediction of post-translational modifications in therapeutic antibodies

Monoclonal antibodies are susceptible to chemical and enzymatic modifications during manufacturing, storage, and shipping. Deamidation, isomerization, and oxidation can compromise the potency, efficacy, and safety of therapeutic antibodies. Recently, in silico tools have been used to identify liable residues and engineer antibodies with better chemical stability. Computational approaches for predicting deamidation, isomerization, oxidation, glycation, carbonylation, sulfation, and hydroxylation are reviewed here. Although liable motifs have been used to improve the chemical stability of antibodies, the accuracy of in silico predictions can be improved using machine learning and molecular dynamic simulations. In addition, there are opportunities to improve predictions for specific stress conditions, develop in silico prediction of novel modifications in antibodies, and predict the impact of modifications on physical stability and antigen-binding.

Characterization and prediction of positional 4-hydroxyproline and sulfotyrosine, two post-translational modifications that can occur at substantial levels in CHO cells-expressed biotherapeutics

Biotherapeutics may contain a multitude of different post-translational modifications (PTMs) that need to be assessed and possibly monitored and controlled to ensure reproducible product quality. During early development of biotherapeutics, unexpected PTMs might be prevented by in silico identification and characterization together with further molecular engineering. Mass determinations of a human IgG1 (mAb1) and a bispecific IgG-ligand fusion protein (BsAbA) demonstrated the presence of unusual PTMs resulting in major +80 Da, and +16/+32 Da chain variants, respectively. For mAb1, analytical cation exchange chromatography demonstrated the presence of an acidic peak accounting for 20%. A + 79.957 Da modification was localized within the light chain complementarity-determining region-2 and identified as a sulfation based on accurate mass, isotopic distribution, and a complete neutral loss reaction upon collision-induced dissociation. Top-down ultrahigh resolution MALDI-ISD FT-ICR MS of modified and unmodified Fabs allowed the allocation of the sulfation to a specific Tyr residue. An aspartate in amino-terminal position-3 relative to the affected Tyr was found to play a key role in determining the sulfation. For BsAbA, a + 15.995 Da modification was observed and localized to three specific Pro residues explaining the +16 Da chain A, and +16 Da and +32 Da chain B variants. The BsAbA modifications were verified as 4-hydroxyproline and not 3-hydroxyproline in a tryptic peptide map via co-chromatography with synthetic peptides containing the two isomeric forms. Finally, our approach for an alert system based on in-house in silico predictors is presented. This system is designed to prevent these PTMs by molecular design and engineering during early biotherapeutic development.

Engineering of a sulfotyrosine-recognizing small protein scaffold for the study of protein tyrosine O-sulfation

Protein tyrosine O-sulfation is considered as one of the most common types of posttranslational modification of tyrosine in nature. The introduction of a negatively charged sulfate group plays crucial roles in extracellular biomolecular interactions that dictate various cellular processes, including cell adhesion, leukocyte trafficking, hormone activities, and immune responses. Despite substantial advances in our knowledge about protein tyrosine O-sulfation in recent years, our understanding of its biological significance is still in its infancy. This is largely hindered by a chronic lack of suitable biochemical tools. We seek to meet this challenge by engineering a small protein scaffold that can recognize sulfated tyrosine (sulfotyrosine) residues with high affinity. In this chapter, we describe the directed evolution of a Src Homology 2 (SH2) domain to recognize sulfotyrosine. In the first part, the design strategy for the phage display of SH2 variants is discussed. In the second part, the techniques required for phage propagation and selection are described. The evolved SH2 variants are characterized and validated in vitro through fluorescence polarization assays. Finally, the evolved SH2 domain mutants are applied to the visualization of sulfated proteins on the cell surface.

A Novel Phosphorylation Site-Kinase Network-Based Method for the Accurate Prediction of Kinase-Substrate Relationships

Protein phosphorylation is catalyzed by kinases which regulate many aspects that control death, movement, and cell growth. Identification of the phosphorylation site-specific kinase-substrate relationships (ssKSRs) is important for understanding cellular dynamics and provides a fundamental basis for further disease-related research and drug design. Although several computational methods have been developed, most of these methods mainly use local sequence of phosphorylation sites and protein-protein interactions (PPIs) to construct the prediction model. While phosphorylation presents very complicated processes and is usually involved in various biological mechanisms, the aforementioned information is not sufficient for accurate prediction. In this study, we propose a new and powerful computational approach named KSRPred for ssKSRs prediction, by introducing a novel phosphorylation site-kinase network (pSKN) profiles that can efficiently incorporate the relationships between various protein kinases and phosphorylation sites. The experimental results show that the pSKN profiles can efficiently improve the prediction performance in collaboration with local sequence and PPI information. Furthermore, we compare our method with the existing ssKSRs prediction tools and the results demonstrate that KSRPred can significantly improve the prediction performance compared with existing tools.

Evolution of Src Homology 2 (SH2) Domain to Recognize Sulfotyrosine

Protein tyrosine O-sulfation is considered as the most common type of post-translational tyrosine modification in nature and plays important roles in extracellular biomolecular interactions. To facilitate the mapping, biological study, and medicinal application of this type of post-translational modification, we seek to evolve a small protein scaffold that recognizes sulfotyrosine with high affinity. We focused our efforts on the engineering of the Src Homology 2 (SH2) domain, which represents the largest class of known phosphotyrosine-recognition domain in nature and has a highly evolvable binding pocket. By using phage display, we successfully engineered the SH2 domain to recognize sulfotyrosine with high affinity. The best mutant, SH2-60.1, displayed more than 1700 fold higher sulfotyrosine-binding affinity than that of the wild-type SH2 domain. We also demonstrated that the evolved SH2 domain mutants could be used to detect sulfoprotein levels on the cell surface. These evolved SH2 domain mutants can be potentially applied to the study of protein tyrosine O-sulfation with proper experimental designs.

A novel method for predicting post-translational modifications on serine and threonine sites by using site-modification network profiles

Post-translational modifications (PTMs) regulate many aspects of biological behaviours including protein-protein interactions and cellular processes. Identification of PTM sites is helpful for understanding the PTM regulatory mechanisms. The PTMs on serine and threonine sites include phosphorylation, O-linked glycosylation and acetylation. Although a lot of computational approaches have been developed for PTM site prediction, currently most of them generate the predictive models by employing only local sequence information and few of them consider the relationship between different PTMs. In this paper, by adopting the site-modification network (SMNet) profiles that efficiently incorporate in situ PTM information, we develop a novel method to predict PTM sites on serine and threonine. PTM data are collected from various PTM databases and the SMNet is built to reflect the relationship between multiple PTMs, from which SMNet profiles are extracted to train predictive models based on SVM. Performance analysis of the SVM models shows that the SMNet profiles play an important role in accurately predicting PTM sites on serine and threonine. Furthermore, the proposed method is compared with existing PTM prediction approaches. The results from 10-fold cross-validation demonstrate that the proposed method with SMNet profiles performs remarkably better than existing methods, suggesting the power of SMNet profiles in identifying PTM sites.

Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest

Reversible protein phosphorylation is one of the most important post-translational modifications, which regulates various biological cellular processes. Identification of the kinase-specific phosphorylation sites is helpful for understanding the phosphorylation mechanism and regulation processes. Although a number of computational approaches have been developed, currently few studies are concerned about hierarchical structures of kinases, and most of the existing tools use only local sequence information to construct predictive models. In this work, we conduct a systematic and hierarchy-specific investigation of protein phosphorylation site prediction in which protein kinases are clustered into hierarchical structures with four levels including kinase, subfamily, family and group. To enhance phosphorylation site prediction at all hierarchical levels, functional information of proteins, including gene ontology (GO) and protein-protein interaction (PPI), is adopted in addition to primary sequence to construct prediction models based on random forest. Analysis of selected GO and PPI features shows that functional information is critical in determining protein phosphorylation sites for every hierarchical level. Furthermore, the prediction results of Phospho.ELM and additional testing dataset demonstrate that the proposed method remarkably outperforms existing phosphorylation prediction methods at all hierarchical levels. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/phos_pred/.

Small changes huge impact: the role of protein posttranslational modifications in cellular homeostasis and disease

Posttranslational modifications (PTMs) modulate protein function in most eukaryotes and have a ubiquitous role in diverse range of cellular functions. Identification, characterization, and mapping of these modifications to specific amino acid residues on proteins are critical towards understanding their functional significance in a biological context. The interpretation of proteome data obtained from the high-throughput methods cannot be deciphered unambiguously without a priori knowledge of protein modifications. An in-depth understanding of protein PTMs is important not only for gaining a perception of a wide array of cellular functions but also towards developing drug therapies for many life-threatening diseases like cancer and neurodegenerative disorders. Many of the protein modifications like ubiquitination play a decisive role in various drug response(s) and eventually in disease prognosis. Thus, many commonly observed PTMs are routinely tracked as disease markers while many others are used as molecular targets for developing target-specific therapies. In this paper, we summarize some of the major, well-studied protein alterations and highlight their importance in various chronic diseases and normal development. In addition, other promising minor modifications such as SUMOylation, observed to impact cellular dynamics as well as disease pathology, are mentioned briefly.