Investigation of the impact of PTMs on the protein backbone conformation

FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics

Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.

Analysis of Structural Changes in the Protein near the Phosphorylation Site

Modification of the protein after synthesis (PTM) often affects protein function as supported by numerous studies. However, there is no consensus about the degree of structural protein changes after modification. For phosphorylation of serine, threonine, and tyrosine, which is a common PTM in the biology of living organisms, we consider topical issues related to changes in the geometric parameters of a protein (Rg, RMSD, Cα displacement, SASA). The effect of phosphorylation on protein geometry was studied both for the whole protein and at the local level (i.e., in different neighborhoods of the modification site). Heterogeneity in the degree of protein structural changes after phosphorylation was revealed, which allowed for us to isolate a group of proteins having pronounced local structural changes in the neighborhoods of up to 15 amino acid residues from the modification site. This is a comparative study of protein structural changes in neighborhoods of 3-15 amino acid residues from the modified site. Amino acid phosphorylation in proteins with pronounced local changes caused switching from the inactive functional state to the active one.

Protein diversification through post-translational modifications, alternative splicing, and gene duplication

Proteins provide the basis for cellular function. Having multiple versions of the same protein within a single organism provides a way of regulating its activity or developing novel functions. Post-translational modifications of proteins, by means of adding/removing chemical groups to amino acids, allow for a well-regulated and controlled way of generating functionally distinct protein species. Alternative splicing is another method with which organisms possibly generate new isoforms. Additionally, gene duplication events throughout evolution generate multiple paralogs of the same genes, resulting in multiple versions of the same protein within an organism. In this review, we discuss recent advancements in the study of these three methods of protein diversification and provide illustrative examples of how they affect protein structure and function.

Significance of Type II Collagen Posttranslational Modifications: From Autoantigenesis to Improved Diagnosis and Treatment of Rheumatoid Arthritis

Collagen type II (COL2), the main structural protein of hyaline cartilage, is considerably affected by autoimmune responses associated with the pathogenesis of rheumatoid arthritis (RA). Posttranslational modifications (PTMs) play a significant role in the formation of the COL2 molecule and supramolecular fibril organization, and thus, support COL2 function, which is crucial for normal cartilage structure and physiology. Conversely, the specific PTMs of the protein (carbamylation, glycosylation, citrullination, oxidative modifications and others) have been implicated in RA autoimmunity. The discovery of the anti-citrullinated protein response in RA, which includes anti-citrullinated COL2 reactivity, has led to the development of improved diagnostic assays and classification criteria for the disease. The induction of immunological tolerance using modified COL2 peptides has been highlighted as a potentially effective strategy for RA therapy. Therefore, the aim of this review is to summarize the recent knowledge on COL2 posttranslational modifications with relevance to RA pathophysiology, diagnosis and treatment. The significance of COL2 PTMs as a source of neo-antigens that activate immunity leading to or sustaining RA autoimmunity is discussed.

PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk

Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk. As a challenging question, less attention has been given to PTM dynamics underlying cross-talk residue pairs and structural information underlying protein-protein interaction (PPI) graph, limiting the progress in this PTM functional research. Here we propose a novel integrated deep neural network PPICT (Predictor for PTM Inter-protein Cross-Talk), which predicts PTM cross-talk by combining protein sequence-structure-dynamics information and structural information for PPI graph. We find that cross-talk events preferentially occur among residues with high co-evolution and high potential in allosteric regulation. To make full use of the complex associations between protein evolutionary and biophysical features, and protein pair features, a heterogeneous feature combination net is introduced in the final prediction of PPICT. The comprehensive test results show that the proposed PPICT method significantly improves the prediction performance with an AUC value of 0.869, outperforming the existing state-of-the-art methods. Additionally, the PPICT method can capture the potential PTM cross-talks involved in the functional regulatory PTMs on modifying enzymes and their catalyzed PTM substrates. Therefore, PPICT represents an effective tool for identifying PTM cross-talk between proteins at the proteome level and highlights the hints for cross-talk between different signal pathways introduced by PTMs.

Imidazole-amino acids. Conformational switch under tautomer and pH change

Replacement of the main chain peptide bond by imidazole ring seems to be a promising tool for the peptide-based drug design, due to the specific prototropic tautomeric as well as amphoteric properties. In this study, we present that both tautomer and pH change can cause a conformational switch of the studied residues of alanine (1-4) and dehydroalanine (5-8) with the C-terminal peptide group replaced by imidazole. The DFT methods are applied and an environment of increasing polarity is simulated. The conformational maps (Ramachandram diagrams) are presented and the stability of possible conformations is discussed. The neutral forms, tautomers τ (1) and π (2), adapt the conformations αRτ (φ, ψ = - 75°, - 114°) and C7eq (φ, ψ = - 75°, 66°), respectively. Their torsion angles ψ differ by about 180°, which results in a considerable impact on the peptide chain conformation. The cation form (3) adapts both these conformations, whereas the anion analogue (4) prefers the conformations C5 (φ, ψ = - 165°, - 178°) and β2 (φ, ψ ~ - 165°, - 3°). Dehydroamino acid analogues, the tautomers τ (5) and π (6) as well as the anion form (8), have a strong tendency toward the conformations β2 (φ, ψ = - 179°, 0°) and C5 (φ, ψ = - 180°, 180°). The preferences of the protonated imidazolium form (7) depend on the environment. The imidazole ring, acting as a donor or acceptor of the hydrogen bonds created within the studied residues, has a profound effect on the type of conformation.

Ammonia stress affects the structure and function of hemocyanin in Penaeus vannamei

Anthropogenic factors and climate change have serious effects on the aquatic ecosystem and aquaculture. Among water pollutants, ammonia has the greatest impact on aquaculture organisms such as penaeid shrimp because it makes them more susceptible to infections. In this study, we explored the effects of ammonia stress (0, 50, 100, and 150 mg/L) on the molecular structure and functions of the multifunctional respiratory protein hemocyanin (HMC) in Penaeus vannamei. While the mRNA expression of Penaeus vannamei hemocyanin (PvHMC) was up-regulated after ammonia stress, both plasma hemocyanin protein and oxyhemocyanin (OxyHMC) levels decreased. Moreover, ammonia stress changed the molecular structure of hemocyanin, modulated the expression of protein phosphatase 2 A (PP2A) and casein kinase 2α (CK2α) to regulate the phosphorylation modification of hemocyanin, and enhanced its degradation into fragments by trypsin. Under moderate ammonia stress conditions, hemocyanin also undergoes glycosylation to improve its in vitro antibacterial activity and binding with Gram-negative (Vibrio parahaemolyticus) and Gram-positive (Staphylococcus aureus) bacteria, albeit differently. The current findings indicate that P. vannamei hemocyanin undergoes adaptive molecular modifications under ammonia stress enabling the shrimp to survive and counteract the consequences of the stress.

Mini-review: Recent advances in post-translational modification site prediction based on deep learning

Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.

The pattern of apolipoprotein A-I lysine carbamylation reflects its lipidation state and the chemical environment within human atherosclerotic aorta

Protein lysine carbamylation is an irreversible post-translational modification resulting in generation of homocitrulline (N-ε-carbamyllysine), which no longer possesses a charged ε-amino moiety. Two distinct pathways can promote protein carbamylation. One results from urea decomposition, forming an equilibrium mixture of cyanate (CNO⁻) and the reactive electrophile isocyanate. The second pathway involves myeloperoxidase (MPO)-catalyzed oxidation of thiocyanate (SCN⁻), yielding CNO⁻ and isocyanate. Apolipoprotein A-I (apoA-I), the major protein constituent of high-density lipoprotein (HDL), is a known target for MPO-catalyzed modification in vivo, converting the cardioprotective lipoprotein into a proatherogenic and proapoptotic one. We hypothesized that monitoring site-specific carbamylation patterns of apoA-I recovered from human atherosclerotic aorta could provide insights into the chemical environment within the artery wall. To test this, we first mapped carbamyllysine obtained from in vitro carbamylation of apoA-I by both the urea-driven (nonenzymatic) and inflammatory-driven (enzymatic) pathways in lipid-poor and lipidated apoA-I (reconstituted HDL). Our results suggest that lysine residues within proximity of the known MPO-binding sites on HDL are preferentially targeted by the enzymatic (MPO) carbamylation pathway, whereas the nonenzymatic pathway leads to nearly uniform distribution of carbamylated lysine residues along the apoA-I polypeptide chain. Quantitative proteomic analyses of apoA-I from human aortic atheroma identified 16 of the 21 lysine residues as carbamylated and suggested that the majority of apoA-I carbamylation in vivo occurs on “lipid-poor” apoA-I forms via the nonenzymatic CNO⁻ pathway. Monitoring patterns of apoA-I carbamylation recovered from arterial tissues can provide insights into both apoA-I structure and the chemical environment within human atheroma.

Current status of PTMs structural databases: applications, limitations and prospects

Protein 3D structures, determined by their amino acid sequences, are the support of major crucial biological functions. Post-translational modifications (PTMs) play an essential role in regulating these functions by altering the physicochemical properties of proteins. By virtue of their importance, several PTM databases have been developed and released in decades, but very few of these databases incorporate real 3D structural data. Since PTMs influence the function of the protein and their aberrant states are frequently implicated in human diseases, providing structural insights to understand the influence and dynamics of PTMs is crucial for unraveling the underlying processes. This review is dedicated to the current status of databases providing 3D structural data on PTM sites in proteins. Some of these databases are general, covering multiple types of PTMs in different organisms, while others are specific to one particular type of PTM, class of proteins or organism. The importance of these databases is illustrated with two major types of in silico applications: predicting PTM sites in proteins using machine learning approaches and investigating protein structure-function relationships involving PTMs. Finally, these databases suffer from multiple problems and care must be taken when analyzing the PTMs data.

Pyranose Ring Puckering Thermodynamics for Glycan Monosaccharides Associated with Vertebrate Proteins

The conformational properties of carbohydrates can contribute to protein structure directly through covalent conjugation in the cases of glycoproteins and proteoglycans and indirectly in the case of transmembrane proteins embedded in glycolipid-containing bilayers. However, there continue to be significant challenges associated with experimental structural biology of such carbohydrate-containing systems. All-atom explicit-solvent molecular dynamics simulations provide a direct atomic resolution view of biomolecular dynamics and thermodynamics, but the accuracy of the results depends on the quality of the force field parametrization used in the simulations. A key determinant of the conformational properties of carbohydrates is ring puckering. Here, we applied extended system adaptive biasing force (eABF) all-atom explicit-solvent molecular dynamics simulations to characterize the ring puckering thermodynamics of the ten common pyranose monosaccharides found in vertebrate biology (as represented by the CHARMM carbohydrate force field). The results, along with those for idose, demonstrate that the CHARMM force field reliably models ring puckering across this diverse set of molecules, including accurately capturing the subtle balance between 4C1 and 1C4 chair conformations in the cases of iduronate and of idose. This suggests the broad applicability of the force field for accurate modeling of carbohydrate-containing vertebrate biomolecules such as glycoproteins, proteoglycans, and glycolipids.

Dynamics of Post-Translational Modification Inspires Drug Design in the Kinase Family

Post-translational modification (PTM) on protein plays important roles in the regulation of cellular function and disease pathogenesis. The systematic analysis of PTM dynamics presents great opportunities to enlarge the target space by PTM allosteric regulation. Here, we presented a framework by integrating the sequence, structural topology, and particular dynamics features to characterize the functional context and druggabilities of PTMs in the well-known kinase family. The machine learning models with these biophysical features could successfully predict PTMs. On the other hand, PTMs were identified to be significantly enriched in the reported allosteric pockets and the allosteric potential of PTM pockets were thus proposed through these biophysical features. In the end, the covalent inhibitor DC-Srci-6668 targeting the PTM pocket in c-Src kinase was identified, which inhibited the phosphorylation and locked c-Src in the inactive state. Our findings represent a crucial step toward PTM-inspired drug design in the kinase family.

Machine Learning for Prediction of Drug Targets in Microbe Associated Cardiovascular Diseases by Incorporating Host-pathogen Interaction Network Parameters

Host-pathogen interactions play a crucial role in invasion, infection, and induction of immune response in humans. In this work, four machine learning algorithms, namely Logistic regression, K-nearest neighbor, Support Vector Machine, and Random Forest were implemented for the classification of drug targets. The algorithms were trained using 3400 hosts and 3800 pathogen drug and non-drug target proteins as learning instances. For each protein, 68 pathogen and 73 host features were computed that included sequence, structure, biological and host-pathogen network centrality characteristics. The Random Forest classifier model achieved the best accuracy after 10-fold cross-validation. 99 % accuracy was achieved with a ROC-AUC score of 0.99±0.01 for both pathogen and host training sets. The Eigenvector Centrality of host-pathogen interactions and host-host interactions was the top feature in performing classification of pathogen and host targets respectively. Other features important for classification were the presence of catalytic and binding sites, low instability/aliphatic index, and cellular location. The Random Forest classifier was then used for prediction of drug targets involved in Microbe Associated Cardiovascular Diseases. 331 host and 743 pathogen proteins were predicted as drug targets by the random forest model and can be validated experimentally for therapeutic intervention in Microbe Associated Cardiovascular Diseases.

Drug design targeting active posttranslational modification protein isoforms

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

Analysis of Protein Disorder Predictions in the Light of a Protein Structural Alphabet

Intrinsically-disordered protein (IDP) characterization was an amazing change of paradigm in our classical sequence-structure-function theory. Moreover, IDPs are over-represented in major disease pathways and are now often targeted using small molecules for therapeutic purposes. This has had created a complex continuum from order-that encompasses rigid and flexible regions-to disorder regions; the latter being not accessible through classical crystallographic methodologies. In X-ray structures, the notion of order is dictated by access to resolved atom positions, providing rigidity and flexibility information with low and high experimental B-factors, while disorder is associated with the missing (non-resolved) residues. Nonetheless, some rigid regions can be found in disorder regions. Using ensembles of IDPs, their local conformations were analyzed in the light of a structural alphabet. An entropy index derived from this structural alphabet allowed us to propose a continuum of states from rigidity to flexibility and finally disorder. In this study, the analysis was extended to comparing these results to disorder predictions, underlying a limited correlation, and so opening new ideas to characterize and predict disorder.

Catalytic activity regulation through post-translational modification: the expanding universe of protein diversity

Protein composition is restricted by the genetic code to a relatively small number of natural amino acids. Similarly, the known three-dimensional structures adopt a limited number of protein folds. However, proteins exert a large variety of functions and show a remarkable ability for regulation and immediate response to intracellular and extracellular stimuli. To some degree, the wide variability of protein function can be attributed to the post-translational modifications. Post-translational modifications have been observed in all kingdoms of life and give to proteins a significant degree of chemical and consequently functional and structural diversity. Their importance is partly reflected in the large number of genes dedicated to their regulation. So far, hundreds of post-translational modifications have been observed while it is believed that many more are to be discovered along with the technological advances in sequencing, proteomics, mass spectrometry and structural biology. Indeed, the number of studies which report novel post translational modifications is getting larger supporting the notion that their space is still largely unexplored. In this review we explore the impact of post-translational modifications on protein structure and function with emphasis on catalytic activity regulation. We present examples of proteins and protein families whose catalytic activity is substantially affected by the presence of post translational modifications and we describe the molecular basis which underlies the regulation of the protein function through these modifications. When available, we also summarize the current state of knowledge on the mechanisms which introduce these modifications to protein sites.