Effect of single amino acid substitution observed in cancer on Pim-1 kinase thermodynamic stability and structure

Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability

Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearson r = 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.

Biochemical characterization of the Drosophila insulin receptor kinase and longevity-associated mutants

Drosophila melanogaster (fruit fly) insulin receptor (D-IR) is highly homologous to the human counterpart. Like the human pathway, D-IR responds to numerous insulin-like peptides to activate cellular signals that regulate growth, development, and lipid metabolism in fruit flies. Allelic mutations in the D-IR kinase domain elevate life expectancy in fruit flies. We developed a robust heterologous expression system to express and purify wild-type and longevity-associated mutant D-IR kinase domains to investigate enzyme kinetics and substrate specificities. D-IR exhibits remarkable similarities to the human insulin receptor kinase domain but diverges in substrate preferences. We show that longevity-associated mutations reduce D-IR catalytic activity. Deletion of the unique kinase insert domain portion or mutations proximal to activating tyrosines do not influence kinase activity, suggesting their potential role in substrate recruitment and downstream signaling. Through biochemical investigations, this study enhances our comprehension of D-IR's role in Drosophila physiology, complementing genetic studies and expanding our knowledge on the catalytic functions of this conserved signaling pathway.

A systematic review on active sites and functions of PIM-1 protein

The Proviral Integration of Molony murine leukemia virus (PIM)-1 protein contributes to the solid cancers and hematologic malignancies, cell growth, proliferation, differentiation, migration, and other life activities. Many studies have related these functions to its molecular structure, subcellular localization and expression level. However, recognition of specific active sites and their effects on the activity of this constitutively active kinase is still a challenge. Based on the close relationship between its molecular structure and functional activity, this review covers the specific residues involved in the binding of ATP and different substrates in its catalytic domain. This review then elaborates on the relevant changes in protein conformation and cell functions after PIM-1 binds to different substrates. Therefore, this intensive study can improve the understanding of PIM-1-regulated signaling pathways by facilitating the discovery of its potential phosphorylation substrates.

Investigating single amino acid substitutions in PIM1 kinase: A structural genomics approach

PIM1, is a serine/threonine proto-oncogene kinase, involved in many biological functions, including cell survival, proliferation, and differentiation, thus play a key role in oncogenesis. It plays a crucial role in the onset and progression of various hematopoietic and non-hematopoietic malignancies, including acute myeloid leukemia and prostate cancer. Mutations in PIM1, especially in its kinase domain, can induce abnormal structural changes and thus alter functionalities that can lead to disease progression and other complexities. Herein, we have performed an extensive analysis of the PIM1 mutations at sequence and structure level while utilizing state-of-the-art computational approaches. Based on the impact on PIM1, numerous pathogenic and destabilizing mutations were identified and subsequently analyzed in detail. Finally, two amino acid substitutions (W109C and F147C) in the kinase domain of PIM1 were selected to explore their impact on the PIM1 structure in a time evolution manner using all-atom molecular dynamics (MD) simulations for 200 ns. MD results indicate significant conformational altercations in the structure of PIM1, especially upon F147C mutation. This study provides a significant insight into the PIM1 dysfunction upon single amino acid substitutions, which can be utilized to get insights into the molecular basis of PIM1-associated disease progression.

The structure-based cancer-related single amino acid variation prediction

Single amino acid variation (SAV) is an amino acid substitution of the protein sequence that can potentially influence the entire protein structure or function, as well as its binding affinity. Protein destabilization is related to diseases, including several cancers, although using traditional experiments to clarify the relationship between SAVs and cancer uses much time and resources. Some SAV prediction methods use computational approaches, with most predicting SAV-induced changes in protein stability. In this investigation, all SAV characteristics generated from protein sequences, structures and the microenvironment were converted into feature vectors and fed into an integrated predicting system using a support vector machine and genetic algorithm. Critical features were used to estimate the relationship between their properties and cancers caused by SAVs. We describe how we developed a prediction system based on protein sequences and structure that is capable of distinguishing if the SAV is related to cancer or not. The five-fold cross-validation performance of our system is 89.73% for the accuracy, 0.74 for the Matthews correlation coefficient, and 0.81 for the F1 score. We have built an online prediction server, CanSavPre ( http://bioinfo.cmu.edu.tw/CanSavPre/ ), which is expected to become a useful, practical tool for cancer research and precision medicine.

Analysis and Interpretation of the Impact of Missense Variants in Cancer

Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.

In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review

Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.

A genetic predictive model for precision treatment of diffuse large B-cell lymphoma with early progression

Background

Early progression after the first-line R-CHOP treatment leads to a very dismal outcome and necessitates alternative treatment for patients with diffuse large B-cell lymphoma (DLBCL). This study aimed to develop a genetic predictive model for early progression and evaluate its potential in advancing alternative treatment.

Methods

Thirty-two hotspot driver genes were examined in 145 DLBCL patients and 5 DLBCL cell lines using next-generation sequencing. The association of clinical features, cell-of-origin, double expression, positive p53 protein, and gene alterations with early progression was analyzed, and the genetic predictive model was developed based on the related independent variables and assessed by the area under receiver operating characteristic. The potential of novel treatment based on the modeling was investigated in in-vitro DLBCL cell lines and in vivo xenograft mouse models.

Results

The frequency of CD79B (42.86% vs 9.38%, p = 0.000) and PIM1 mutations (38.78% vs 17.71%, p = 0.005) showed a significant increase in patients with early progression. CD79B and PIM1 mutations were associated with complex genetic events, double expression, non-GCB subtype, advance stage and unfavorable prognosis. A powerful genetic predictive model (AUROC = 0.771, 95% CI: 0.689–0.853) incorporating lactate dehydrogenase levels (OR = 2.990, p = 0.018), CD79B mutations (OR = 5.970, p = 0.001), and PIM1 mutations (OR = 3.021, p = 0.026) was created and verified in the other cohort. This modeling for early progression outperformed the prediction accuracy of conventional International Prognostic Index, and new molecular subtypes of MCD and Cluster 5. CD79B and PIM1 mutations indicated a better response to inhibitors of BTK (ibrutinib) and pan-PIM kinase (AZD 1208) through repressing activated oncogenic signaling. Since the two inhibitors failed to decrease BCL2 level, BCL2 inhibitor (venetoclax) was added and demonstrated to enhance their apoptosis-inducing activity in mutant cells with double expression.

Conclusions

The genetic predictive model provides a robust tool to identify early progression and determine precision treatment. These findings warrant the development of optimal alternative treatment in clinical trials.

A serine/threonine protein PIM kinase as a biomarker of cancer and a target for anti-tumor therapy

The PIM Kinases belong to the family of a proto-oncogene that essentially phosphorylates the serine/threonine residues of the target proteins. They are primarily categorized into three types PIM-1, PIM-2, PIM-3 which plays an indispensable regulatory role in signal transduction cascades, by promoting cell survival, proliferation, and drug resistance. These kinases are overexpressed in several solid as well as hematopoietic tumors which supports in vitro and in vivo malignant cell growth along with survival by regulating cell cycle and inhibiting apoptosis. They lack regulatory domain which makes them constitutively active once transcribed. PIM kinases usually appear to be important downstream effectors of oncoproteins which overexpresses and helps in mediating drug resistance to available agents, such as rapamycin. Structural studies of PIM kinases revealed that they have unique hinge regions where two Proline resides and makes ATP binding unique, by offering a target for an increasing number of potent PIM kinase inhibitors. Preclinical studies of those inhibitory compounds in various cancers indicate that these novel agents show promising activity and some of them currently being under examination. In this review, we have outlined PIM kinases molecular mechanism and signaling pathways along with matriculation in various cancer and list of inhibitors often used.

Study of HSA interactions with arachidonic acid using spectroscopic methods revealing molecular dynamics of HSA-AA interactions

The interaction between human serum albumin (HSA) and arachidonic acid (AA) as an unsaturated fatty acid were investigated in the present study using methods including UV-VIS spectrophotometry, fluorescence and circular dichroism (CD) spectroscopy, lifetime measurements, fluorescence anisotropy measurements and visual molecular dynamics (MD). The thermodynamic parameters were assessed from HSA thermal and chemical denaturation in the presence and absence of AA. From the thermal denaturation, the Tm and ΔG˚(298K) magnitudes obtained were 327.7 K and 88 kJ/mol, respectively, for HSA alone, and 323.4 K and 85 kJ/mol, respectively, following treatment with a 10 µM AA concentration. The same manner of reduction in Gibbs free energy as a criterion of protein stability was achieved during chemical denaturation by urea in the presence of AA. The present study investigates HSA binding nature through MD approaches, and the results indicated that the binding affinity of AA to the subdomain IIA of HSA is greater compared with that of subdomain IIIA. Although the HSA regular secondary structure evaluation by CD exhibited a minor change following incubation with AA, its tertiary structure revealed an observable fluctuation. Thus, it appears that the interaction between AA and HSA requires minor instability and partial structural changes.

Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods

Purpose

Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods.

Methods

Relying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations. The differences are quantified according to average difference, trajectory-wise distance or time-vary differences. Machine-learning methods are employed to predict the mutation impacts using the resulting conventional or time-series features. Predictions based on estimation of energy and based on investigation of molecular descriptors were conducted as benchmarks.

Results

Our method (machine-learning techniques using time-series features) outperformed the benchmark methods, especially in terms of the balanced F1 score. Particularly, deep-learning models led to the best prediction performance with distinct improvements in balanced F1 score and a sustained accuracy.

Conclusion

Our work highlights the effectiveness of the characterization of affinity change upon mutations. Furthermore, deep-learning techniques are well designed for handling the extracted time-series features. This study can lead to a deeper understanding of mutation-induced diseases and resistance, and further guide the development of innovative drug design.

Characterization of human frataxin missense variants in cancer tissues

Human frataxin is an iron-binding protein involved in the mitochondrial iron-sulfur (Fe-S) clusters assembly, a process fundamental for the functional activity of mitochondrial proteins. Decreased level of frataxin expression is associated with the neurodegenerative disease Friedreich ataxia. Defective function of frataxin may cause defects in mitochondria, leading to increased tumorigenesis. Tumor-initiating cells show higher iron uptake, a decrease in iron storage and a reduced Fe-S clusters synthesis and utilization. In this study, we selected, from COSMIC database, the somatic human frataxin missense variants found in cancer tissues p.D104G, p.A107V, p.F109L, p.Y123S, p.S161I, p.W173C, p.S181F, and p.S202F to analyze the effect of the single amino acid substitutions on frataxin structure, function, and stability. The spectral properties, the thermodynamic and the kinetic stability, as well as the molecular dynamics of the frataxin missense variants found in cancer tissues point to local changes confined to the environment of the mutated residues. The global fold of the variants is not altered by the amino acid substitutions; however, some of the variants show a decreased stability and a decreased functional activity in comparison with that of the wild-type protein.

Single Amino Acid Variant Discovery in Small Numbers of Cells

We have performed deep proteomic profiling down to as few as 9 Panc-1 cells using sample fractionation, TMT multiplexing, and a carrier/reference strategy. Off line fractionation of the TMT-labeled sample pooled with TMT-labeled carrier Panc-1 whole cell proteome was achieved using alkaline reversed phase spin columns. The fractionation in conjunction with the carrier/reference (C/R) proteome allowed us to detect 47 414 unique peptides derived from 6261 proteins, which provided a sufficient coverage to search for single amino acid variants (SAAVs) related to cancer. This high sample coverage is essential in order to detect a significant number of SAAVs. In order to verify genuine SAAVs versus false SAAVs, we used the SAVControl pipeline and found a total of 79 SAAVs from the 9-cell Panc-1 sample and 174 SAAVs from the 5000-cell Panc-1 C/R proteome. The SAAVs as sorted into high confidence and low confidence SAAVs were checked manually. All the high confidence SAAVs were found to be genuine SAAVs, while half of the low confidence SAAVs were found to be false SAAVs mainly related to PTMs. We identified several cancer-related SAAVs including KRAS, which is an important oncoprotein in pancreatic cancer. In addition, we were able to detect sites involved in loss or gain of glycosylation due to the enhanced coverage available in these experiments where we can detect both sites of loss and gain of glycosylation.

The phosphoglycerate kinase 1 variants found in carcinoma cells display different catalytic activity and conformational stability compared to the native enzyme

Cancer cells are able to survive in difficult conditions, reprogramming their metabolism according to their requirements. Under hypoxic conditions they shift from oxidative phosphorylation to aerobic glycolysis, a behavior known as Warburg effect. In the last years, glycolytic enzymes have been identified as potential targets for alternative anticancer therapies. Recently, phosphoglycerate kinase 1 (PGK1), an ubiquitous enzyme expressed in all somatic cells that catalyzes the seventh step of glycolysis which consists of the reversible phosphotransfer reaction from 1,3-bisphosphoglycerate to ADP, has been discovered to be overexpressed in many cancer types. Moreover, several somatic variants of PGK1 have been identified in tumors. In this study we analyzed the effect of the single nucleotide variants found in cancer tissues on the PGK1 structure and function. Our results clearly show that the variants display a decreased catalytic efficiency and/or thermodynamic stability and an altered local tertiary structure, as shown by the solved X-ray structures. The changes in the catalytic properties and in the stability of the PGK1 variants, mainly due to the local changes evidenced by the X-ray structures, suggest also changes in the functional role of PGK to support the biosynthetic need of the growing and proliferating tumour cells.

Single-Nucleotide Polymorphism of PPARγ, a Protein at the Crossroads of Physiological and Pathological Processes

Genome polymorphisms are responsible for phenotypic differences between humans and for individual susceptibility to genetic diseases and therapeutic responses. Non-synonymous single-nucleotide polymorphisms (nsSNPs) lead to protein variants with a change in the amino acid sequence that may affect the structure and/or function of the protein and may be utilized as efficient structural and functional markers of association to complex diseases. This study is focused on nsSNP variants of the ligand binding domain of PPARγ a nuclear receptor in the superfamily of ligand inducible transcription factors that play an important role in regulating lipid metabolism and in several processes ranging from cellular differentiation and development to carcinogenesis. Here we selected nine nsSNPs variants of the PPARγ ligand binding domain, V290M, R357A, R397C, F360L, P467L, Q286P, R288H, E324K, and E460K, expressed in cancer tissues and/or associated with partial lipodystrophy and insulin resistance. The effects of a single amino acid change on the thermodynamic stability of PPARγ, its spectral properties, and molecular dynamics have been investigated. The nsSNPs PPARγ variants show alteration of dynamics and tertiary contacts that impair the correct reciprocal positioning of helices 3 and 12, crucially important for PPARγ functioning.

Effect of BET Missense Mutations on Bromodomain Function, Inhibitor Binding and Stability

Lysine acetylation is an important epigenetic mark regulating gene transcription and chromatin structure. Acetylated lysine residues are specifically recognized by bromodomains, small protein interaction modules that read these modification in a sequence and acetylation dependent way regulating the recruitment of transcriptional regulators and chromatin remodelling enzymes to acetylated sites in chromatin. Recent studies revealed that bromodomains are highly druggable protein interaction domains resulting in the development of a large number of bromodomain inhibitors. BET bromodomain inhibitors received a lot of attention in the oncology field resulting in the rapid translation of early BET bromodomain inhibitors into clinical studies. Here we investigated the effects of mutations present as polymorphism or found in cancer on BET bromodomain function and stability and the influence of these mutants on inhibitor binding. We found that most BET missense mutations localize to peripheral residues in the two terminal helices. Crystal structures showed that the three dimensional structure is not compromised by these mutations but mutations located in close proximity to the acetyl-lysine binding site modulate acetyl-lysine and inhibitor binding. Most mutations affect significantly protein stability and tertiary structure in solution, suggesting new interactions and an alternative network of protein-protein interconnection as a consequence of single amino acid substitution. To our knowledge this is the first report studying the effect of mutations on bromodomain function and inhibitor binding.

On human disease-causing amino acid variants: statistical study of sequence and structural patterns

Statistical analysis was carried out on large set of naturally occurring human amino acid variations, and it was demonstrated that there is a preference for some amino acid substitutions to be associated with diseases. At an amino acid sequence level, it was shown that the disease-causing variants frequently involve drastic changes in amino acid physicochemical properties of proteins such as charge, hydrophobicity, and geometry. Structural analysis of variants involved in diseases and being frequently observed in human population showed similar trends: disease-causing variants tend to cause more changes in hydrogen bond network and salt bridges as compared with harmless amino acid mutations. Analysis of thermodynamics data reported in the literature, both experimental and computational, indicated that disease-causing variants tend to destabilize proteins and their interactions, which prompted us to investigate the effects of amino acid mutations on large databases of experimentally measured energy changes in unrelated proteins. Although the experimental datasets were linked neither to diseases nor exclusory to human proteins, the observed trends were the same: amino acid mutations tend to destabilize proteins and their interactions. Having in mind that structural and thermodynamics properties are interrelated, it is pointed out that any large change in any of them is anticipated to cause a disease.

Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins

This review emphasizes the effects of naturally occurring mutations on structural features and physico-chemical properties of proteins. The basic protein characteristics considered are stability, dynamics, and the binding of proteins and methods for assessing effects of mutations on these macromolecular characteristics are briefly outlined. It is emphasized that the above entities mostly reflect global characteristics of considered macromolecules, while given mutations may alter the local structural features such as salt bridges and hydrogen bonds without affecting the global ones. Furthermore, it is pointed out that disease-causing mutations frequently involve a drastic change of amino acid physico-chemical properties such as charge, hydrophobicity, and geometry, and are less surface exposed than polymorphic mutations.