Changes In Thermodynamic Stability Research Articles

The lack of trade-off between conformational stability and binding affinity in a nanobody with therapeutic potential for a misfolding disease

To improve protein pharmaceuticals, we need to balance protein stability and binding affinity with in vivo efficiency. We have recently developed a nanobody (NB-AGT-2) against the alanine:glyoxylate aminotransferase with high stability (Tm ~ 85 °C) that may be useful to treat a misfolding disease called primary hyperoxaluria type 1. In this work, we characterize the relationships between protein stability and binding affinity in NB-AGT-2 by generating single and double cavity-creating mutants in its hydrophobic core. These mutations decrease thermal stability by 10–20 °C, reflecting changes in thermodynamic stability of up to 8 kcal·mol−1, hardly affecting their binding affinity for its target. Statistical mechanics analysis support long-range propagation of stability effects due to mutations. Our results thus show that NB stability can be largely challenged without an effect on its binding.

Deep mutational scanning reveals a correlation between degradation and toxicity of thousands of aspartoacylase variants

Unstable proteins are prone to form non-native interactions with other proteins and thereby may become toxic. To mitigate this, destabilized proteins are targeted by the protein quality control network. Here we present systematic studies of the cytosolic aspartoacylase, ASPA, where variants are linked to Canavan disease, a lethal neurological disorder. We determine the abundance of 6152 of the 6260 ( ~ 98%) possible single amino acid substitutions and nonsense ASPA variants in human cells. Most low abundance variants are degraded through the ubiquitin-proteasome pathway and become toxic upon prolonged expression. The data correlates with predicted changes in thermodynamic stability, evolutionary conservation, and separate disease-linked variants from benign variants. Mapping of degradation signals (degrons) shows that these are often buried and the C-terminal region functions as a degron. The data can be used to interpret Canavan disease variants and provide insight into the relationship between protein stability, degradation and cell fitness.

Experimental and Computational Analysis of Newly Identified Pathogenic Mutations in the Creatine Transporter SLC6A8

Creatine is an essential metabolite for the storage and rapid supply of energy in muscle and nerve cells. In humans, impaired metabolism, transport, and distribution of creatine throughout tissues can cause varying forms of mental disability, also known as creatine deficiency syndrome (CDS). So far, 80 mutations in the creatine transporter (SLC6A8) have been associated to CDS. To better understand the effect of human genetic variants on the physiology of SLC6A8 and their possible impact on CDS, we studied 30 missense variants including 15 variants of unknown significance, two of which are reported here for the first time. We expressed these variants in HEK293 cells and explored their subcellular localization and transport activity. We also applied computational methods to predict variant effect and estimate site-specific changes in thermodynamic stability. To explore variants that might have a differential effect on the transporter’s conformers along the transport cycle, we constructed homology models of the inward facing, and outward facing conformations. In addition, we used mass-spectrometry to study proteins that interact with wild type SLC6A8 and five selected variants in HEK293 cells. In silico models of the protein complexes revealed how two variants impact the interaction interface of SLC6A8 with other proteins and how pathogenic variants lead to an enrichment of ER protein partners. Overall, our integrated analysis disambiguates the pathogenicity of 15 variants of unknown significance revealing diverse mechanisms of pathogenicity, including two previously unreported variants obtained from patients suffering from the creatine deficiency syndrome.

Mutation in the Common Docking Domain Affects MAP Kinase ERK2 Catalysis and Stability.

The extracellular-signal-regulated kinase 2 (ERK2), a mitogen-activated protein kinase (MAPK) located downstream of the Ras-Raf-MEK-ERK signal transduction cascade, is involved in the regulation of a large variety of cellular processes. The ERK2, activated by phosphorylation, is the principal effector of a central signaling cascade that converts extracellular stimuli into cells. Deregulation of the ERK2 signaling pathway is related to many human diseases, including cancer. This study reports a comprehensive biophysical analysis of structural, function, and stability data of pure, recombinant human non-phosphorylated (NP-) and phosphorylated (P-) ERK2 wild-type and missense variants in the common docking site (CD-site) found in cancer tissues. Because the CD-site is involved in interaction with protein substrates and regulators, a biophysical characterization of missense variants adds information about the impact of point mutations on the ERK2 structure-function relationship. Most of the P-ERK2 variants in the CD-site display a reduced catalytic efficiency, and for the P-ERK2 D321E, D321N, D321V and E322K, changes in thermodynamic stability are observed. The thermal stability of NP-ERK2 and P-ERK2 D321E, D321G, and E322K is decreased with respect to the wild-type. In general, a single residue mutation in the CD-site may lead to structural local changes that reflects in alterations in the global ERK2 stability and catalysis.

Interpreting the molecular mechanisms of disease variants in human transmembrane proteins

Next-generation sequencing of human genomes reveals millions of missense variants, some of which may lead to loss of protein function and ultimately disease. Here, we investigate missense variants in membrane proteins—key drivers in cell signaling and recognition. We find enrichment of pathogenic variants in the transmembrane region across 19,000 functionally classified variants in human membrane proteins. To accurately predict variant consequences, one fundamentally needs to understand the underlying molecular processes. A key mechanism underlying pathogenicity in missense variants of soluble proteins has been shown to be loss of stability. Membrane proteins, however, are widely understudied. Here, we interpret variant effects on a larger scale by performing structure-based estimations of changes in thermodynamic stability using a membrane-specific energy function and analyses of sequence conservation during evolution of 15 transmembrane proteins. We find evidence for loss of stability being the cause of pathogenicity in more than half of the pathogenic variants, indicating that this is a driving factor also in membrane-protein-associated diseases. Our findings show how computational tools aid in gaining mechanistic insights into variant consequences for membrane proteins. To enable broader analyses of disease-related and population variants, we include variant mappings for the entire human proteome.

Prediction of protein stability changes upon single-point variant using 3D structure profile

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.

Hydrogenated vacancy induced changes in thermodynamic stability and mechanical properties of Re-containing γ-Ni/γ'-Ni3Al phase interface

Re is an important alloying element to improve the high temperature performance of Ni-based single crystal superalloys, and has a segregation tendency to the γ/γ' phase interface. Hydrogen embrittlement introduced by hydrogenated vacancies (H-Vacs) severely reduces the service life of alloys. However, the formation mechanism of hydrogenated vacancy (H-Vac) and its effects on the Re-segregated γ/γ' phase interface are still unclear. In this work, the first-principles simulations are applied to clarify the formation of H-Vac and its effects on the thermodynamic stability and mechanical properties of Re-containing γ/γ' phase interface. We find that at the Re-containing γ/γ' phase interfaces, the hydrogen atom is more likely to dissolve in the first-nearest-neighbor (1st-NN) octahedral interstices of vacancy than in the 2nd-NN octahedral interstices. The Vac-containing γ/γ' phase interface can be stabilized when hydrogen atom dissolves in 1st-NN octahedral interstices to form H-Vac complex. For the Re-containing γ/γ' phase interface, the formation of the H-Vac complex does not change its preferential cleavage plane. Moreover, the fracture strength of the Re-containing γ/γ' phase interface is always higher than that of the Re-free γ/γ' phase interface. Thus, Re can enhance the interfacial fracture strength, which is also verified by the first-principles computational tensile tests.

Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation

This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R2 of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation.

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new Stransitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Effect of combining rules on modeling of asphaltene precipitation

Asphaltene deposition causes serious problems in the oil industry and reduces oil recovery. Deposition happens as a consequence of asphaltene precipitation which is a process as a result of a change in thermodynamic stability. Thus, prediction and preventing of the precipitation condition are the first step of preventing asphaltene precipitation and deposition. In this study, a thermodynamic model for asphaltene precipitation has been developed using Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), and a new modification on SRK [modified-SRK equations of state (EOS)]. To modify EOS for non-pure sample (oil sample), van der Waals mixing rule with three types of combining rule containing conventional, Margules, and van Laar type was used. In addition, to verify the derived model, the experiments were conducted on a live oil sample to investigate the effect of pressure reduction and gas injection [nitrogen (0.1, 0.2, and 0.4 mol fraction) and first stage gas (0.2, 0.4 and 0.6 mol fraction)] on asphaltene precipitation. The results show that at low pressures (pressures below 5000 psia), nitrogen is not soluble in oil and the injection of nitrogen reduces asphaltene precipitation because of the liberation of the light component from crude oil; however, increasing the pressure (pressures above 6000 psia) increases the solubility of nitrogen and increases the asphaltene precipitation. For the first stage gas injection, asphaltene precipitation increases because of its high solubility in crude oil at any pressure. The amount of asphaltene precipitation due to first stage gas injection is higher than nitrogen injection except at nitrogen concentration and pressures near the bubble point (pressure of 7000 psia and nitrogen injection of 0.1 mol fraction). According to the modeling results, van Laar type combining rule in conjugated with modified-SRK-EOS predicts the amount of asphaltene precipitation very well at all situations of pressures and different gas injections, and has the least deviation from experimental data rather than the other two types of combining rules; and using mentioned combining formula, the RMSE value decreases to about 50% of the conventional combining rule. It is because of the accurate and distinct interaction parameters of each pair of components in van Laar equation.

Study of Aqueous Am(III)-Aliphatic Dicarboxylate Complexes: Coordination Mode-Dependent Optical Property and Stability Changes.

The thermodynamics of Am(III) complex formation in natural groundwater systems is one of the major topics of research in the field of high-level radioactive waste management. In this study, we investigate the absorption and luminescence properties of aqueous Am(III) complexes with a series of aliphatic dicarboxylates in order to learn the thermodynamic complexation behaviors in relation to binding geometries. The formation of Am(III) complexes with these carboxylate ligands induced distinct red shifts in the absorption spectra, which enabled chemical speciation. The formation constants determined by deconvolution of the absorption spectra showed a linear decrease for the three ligands (oxalate (Ox), malonate (Mal), and succinate (Suc)) and a mild decrease for the remaining ligands (glutarate (Glu) and adipate (Adi)). Time-resolved laser fluorescence spectroscopy (TRLFS) was used to obtain information about the aqua ligand, which indirectly indicated the bidentate bindings of these dicarboxylate ligands. A complementary attenuated total reflectance Fourier transform infrared (ATR-FTIR) study on Eu(III), which is a nonradioactive analogue of Am(III) ion, showed that the coordination modes differ depending on the alkyl chain length. Ox and Mal bind to Am(III) via side-on bidentate bindings with two carboxylate groups, resulting in the formation of stable 5- and 6-membered ring structures, respectively. On the other hand, Suc, Glu, and Adi form end-on bidentate bindings with a single carboxylate group, resulting in a 4-membered ring structure. Density functional theory calculations provided details about the bonding properties and supported the experimentally proposed coordination geometries. This study demonstrates that coordination mode-dependent changes in optical properties occur along with thermodynamic stability changes in Am(III)-dicarboxylate complexes.

Predicting the stability of mutant proteins by computational approaches: an overview.

A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naïve users in finding the best option for their needs.

Estimating the Effect of Single-Point Mutations on Protein Thermodynamic Stability and Analyzing the Mutation Landscape of the p53 Protein.

Nonsynonymous single-nucleotide polymorphisms often result in altered protein stability while playing crucial roles both in the evolution process and in the development of human diseases. Prediction of change in the thermodynamic stability due to such missense mutations will help in protein engineering endeavors and will contribute to a better understanding of different disease conditions. Here, we develop a machine-learning-based framework, viz., ProTSPoM, to estimate the change in protein thermodynamic stability arising out of single-point mutations (SPMs). ProTSPoM outperforms existing methods on the S2648 and S1925 databases and reports a Pearson correlation coefficient of 0.82 (0.88) and a root-mean-squared-error of 0.92 (1.06) kcal/mol between the predicted and experimental ΔΔG values on the long-established S350 (tumor suppressor p53 protein) data set. Further, we estimate the change in thermodynamic stability for all possible SPMs in the DNA binding domain of the p53 protein. We identify single-nucleotide polymorphisms in p53 which are plausibly detrimental to its structural integrity and interaction affinity with the DNA molecule. ProTSPoM with its reliable estimates and time-efficient prediction is well suited to be integrated with existing protein engineering techniques. The ProTSPoM web server is accessible at http://cosmos.iitkgp.ac.in/ProTSPoM/.

HARP: a database of structural impacts of systematic missense mutations in drug targets of Mycobacterium leprae

Computational Saturation Mutagenesis is an in-silico approach that employs systematic mutagenesis of each amino acid residue in the protein to all other amino acid types, and predicts changes in thermodynamic stability and affinity to the other subunits/protein counterparts, ligands and nucleic acid molecules. The data thus generated are useful in understanding the functional consequences of mutations in antimicrobial resistance phenotypes. In this study, we applied computational saturation mutagenesis to three important drug-targets in Mycobacterium leprae (M. leprae) for the drugs dapsone, rifampin and ofloxacin namely Dihydropteroate Synthase (DHPS), RNA Polymerase (RNAP) and DNA Gyrase (GYR), respectively. M. leprae causes leprosy and is an obligate intracellular bacillus with limited protein structural information associating mutations with phenotypic resistance outcomes in leprosy. Experimentally solved structures of DHPS, RNAP and GYR of M. leprae are not available in the Protein Data Bank, therefore, we modelled the structures of these proteins using template-based comparative modelling and introduced systematic mutations in each model generating 80,902 mutations and mutant structures for all the three proteins. Impacts of mutations on stability and protein-subunit, protein-ligand and protein-nucleic acid affinities were computed using various in-house developed and other published protein stability and affinity prediction software. A consensus impact was estimated for each mutation using qualitative scoring metrics for physicochemical properties and by a categorical grouping of stability and affinity predictions. We developed a web database named HARP (a database of Hansen's Disease Antimicrobial Resistance Profiles), which is accessible at the URL - https://harp-leprosy.org and provides the details to each of these predictions.

Thermodynamic Stability of Human γD-Crystallin Mutants Using Alchemical Free-Energy Calculations.

γD-Crystallin (HγDC) is a key structural protein in the human lens, whose aggregation has been associated with the development of cataracts. Single-point mutations and post-translational modifications destabilize HγDC interactions, forming partially folded intermediates, where hydrophobic residues are exposed and thus triggering its aggregation. In this work, we used alchemical free-energy calculations to predict changes in thermodynamic stability (ΔΔG) of 10 alanine-scanning variants and 12 HγDC mutations associated with the development of congenital cataract. Our results show that W42R is the most destabilizing mutation in HγDC. This has been corroborated through experimental determination of ΔΔG employing differential scanning calorimetry. Calculations of hydration free energies from the HγDC WT and the W42R mutant suggested that the mutant has a higher aggregation propensity. Our combined theoretical and experimental results contribute to understand HγDC destabilization and aggregation mechanisms in age-onset cataracts.

The Mechanistic Role of Tropomyosin Overlap Dysregulation in Early Cardiomyopathic Disease Progression

An oft-noted component of sarcomeric dilated (DCM) and hypertrophic (HCM) cardiomyopathies is that primary mutations can exhibit significant phenotypic variability. This variability extends even to families carrying the same mutation suggesting that independently segregating genetic modifiers exist that differentially affect susceptible individuals. In addition, recent clinical studies have demonstrated the importance of early stage treatment, emphasizing the mechanistic role of the primary biophysical disruption “domain” on pathogenesis. One possible site of primary biophysical disruption is the crucial tropomyosin (Tm)-overlap which provides the stability and flexibility required during cross bridge cycling. We hypothesize that the cardiac thin filament (CTF) has an innate but finite capacity to tolerate changes in these parameters, beyond which cardiomyopathies result. Highly penetrant mutations throughout the CTF were studied using differential scanning calorimetry to assess changes in thermodynamic stability and flexibility of fully reconstituted CTF's. N-terminus proximal Tm mutations (E62Q, D84N) decreased Tm-cTn stability while increasing flexibility, where C-terminus proximal Tm mutations (D219N, D230N) do the opposite. Strikingly these proximal-terminal dependent effects occurred independent of late-stage pathology. Mutations in cardiac troponin T (cTnT), however, only increased thermal stability and flexibility when directly adjacent to the overlap. Furthermore, we investigated the role of developmental cTnT isoform switching on the Tm-overlap to assess its capacity as an independent modifier. Fetal-cTnT alone had little effect, however, when expressed with D230N-Tm, filaments exhibited an additive decrease in flexibility of the Tm-overlap. Fetal-cTnT did not cause additive changes when expressed with D84N-Tm and R92L-cTnT. Similarly, mice expressing D230N-Tm and fetal-cTnT exhibited additively reduced % fractional shortening whereas R92L-cTnT did not display additive changes, suggesting fetal-cTnT may be a mutation-specific modifier. Together these data suggest that the Tm overlap represents a sensitive “domain” of early biophysical dysregulation in cardiomyopathic disease progression.

The Site-Specific Amino Acid Preferences of Homologous Proteins Depend on Sequence Divergence.

The propensity of protein sites to be occupied by any of the 20 amino acids is known as site-specific amino acid preferences (SSAP). Under the assumption that SSAP are conserved among homologs, they can be used to parameterize evolutionary models for the reconstruction of accurate phylogenetic trees. However, simulations and experimental studies have not been able to fully assess the relative conservation of SSAP as a function of sequence divergence between protein homologs. Here, we implement a computational procedure to predict the SSAP of proteins based on the effect of changes in thermodynamic stability upon mutation. An advantage of this computational approach is that it allows us to interrogate a large and unbiased sample of homologous proteins, over the entire spectrum of sequence divergence, and under selection for the same molecular trait. We show that computational predictions have reproducibilities that resemble those obtained in experimental replicates, and can largely recapitulate the SSAP observed in a large-scale mutagenesis experiment. Our results support recent experimental reports on the conservation of SSAP of related homologs, with a slowly increasing fraction of up to 15% of different sites at sequence distances lower than 40%. However, even under the sole contribution of thermodynamic stability, our conservative approach identifies up to 30% of significant different sites between divergent homologs. We show that this relation holds for homologs of diverse sizes and structural classes. Analyses of residue contact networks suggest that an important determinant of these differences is the increasing accumulation of structural deviations that results from sequence divergence.

Histidine substitution in the most flexible fragments of firefly luciferase modifies its thermal stability

Molecular dynamics (MD) at two temperatures of 300 and 340 K identified two histidine residues, His461 and His489, in the most flexible regions of firefly luciferase, a light emitting enzyme. We therefore designed four protein mutants H461D, H489K, H489D and H489M to investigate their enzyme kinetic and thermodynamic stability changes. Substitution of His461 by aspartate (H461D) decreased ATP binding affinity, reduced the melting temperature of protein by around 25 °C and shifted its optimum temperature of activity to 10 °C. In line with the common feature of psychrophilic enzymes, the MD data showed that the overall flexibility of H461D was relatively high at low temperature, probably due to a decrease in the number of salt bridges around the mutation site. On the other hand, substitution of His489 by aspartate (H489D) introduced a new salt bridge between the C-terminal and N-terminal domains and increased protein rigidity but only slightly improved its thermal stability. Similar changes were observed for H489K and, to a lesser degree, H489M mutations. Based on our results we conclude that the MD simulation-based rational substitution of histidines by salt-bridge forming residues can modulate conformational dynamics in luciferase and shift its optimal temperature activity.

The N-Terminal Flanking Region Modulates the Actin Binding Affinity of the Utrophin Tandem Calponin-Homology Domain.

Despite sharing a high degree of sequence similarity, the tandem calponin-homology (CH) domain of utrophin binds to actin 30 times stronger than that of dystrophin. We have previously shown that this difference in actin binding affinity could not be ascribed to the differences in inter-CH-domain linkers [Bandi, S., et al. (2015) Biochemistry 54, 5480-5488]. Here, we examined the role of the N-terminal flanking region. The utrophin tandem CH domain contains a 27-residue flanking region before its CH1 domain. We examined its effect by comparing the structure and function of full-length utrophin tandem CH domain Utr(1-261) and its truncated Utr(28-261) construct. Both full-length and truncated constructs are monomers in solution, with no significant differences in their secondary or tertiary structures. Truncated construct Utr(28-261) binds to actin 30 times weaker than that of the full-length Utr(1-261), similar to that of the dystrophin tandem CH domain with a much shorter flanking region. Deletion of the N-terminal flanking region stabilizes the CH1 domain. The magnitude of the change in binding free energy upon truncation is similar to that of the change in thermodynamic stability. The isolated N-terminal peptide by itself is significantly random coil and does not bind to F-actin in the affinity range of Utr(1-261) and Utr(28-261). These results indicate that the N-terminal flanking region significantly affects the actin binding affinity of tandem CH domains. This observation further stresses that protein regions other than the three actin-binding surfaces identified earlier, irrespective of whether they directly bind to actin, also contribute to the actin binding affinity of tandem CH domains.

Temperature dependent phase stability of Ti(C1−xNx) solid solutions using first-principles calculations

We investigated lattice dynamic and temperature-dependent thermodynamic phase stabilities of Ti(C1−xNx) solid solutions using first-principles calculations within the quasi-harmonic approximation. Phonon dispersion and density of states were obtained by the finite-element method. Special quasi-random structures were used to mimic the random distribution of carbon and nitrogen atoms in the sublattice of Ti(C1−xNx). The reliability of the models and calculations were obtained by comparing the structural and elastic properties of Ti(C1−xNx) with previous results. The random mixing of carbon and nitrogen atoms had a minor effect on the elastic properties, but greatly influenced the dynamic stability. Thermodynamic phase stability was investigated using the formation energy of the solid solutions. The phonon density of states with the quasi-harmonic approximation yielded accurate formation energies at various temperatures. Nitrogen-rich phases showed noticeable changes in thermodynamic stability with increasing temperature, and their phase stability decreased quickly in comparison with the carbon-rich phases. Our results provide fundamental insights into Ti(CN) solid solutions that will help underpin the use and production of Ti(CN) materials.