Modeling the effect of pathogenic mutations on the conformational landscape of protein kinases

Associations between Radiomics and Genomics in Non-Small Cell Lung Cancer Utilizing Computed Tomography and Next-Generation Sequencing: An Exploratory Study

BACKGROUND

Radiomics, an evolving paradigm in medical imaging, involves the quantitative analysis of tumor features and demonstrates promise in predicting treatment responses and outcomes. This study aims to investigate the predictive capacity of radiomics for genetic alterations in non-small cell lung cancer (NSCLC).

METHODS

This exploratory, observational study integrated radiomic perspectives using computed tomography (CT) and genomic perspectives through next-generation sequencing (NGS) applied to liquid biopsies. Associations between radiomic features and genetic mutations were established using the Area Under the Receiver Operating Characteristic curve (AUC-ROC). Machine learning techniques, including Support Vector Machine (SVM) classification, aim to predict genetic mutations based on radiomic features. The prognostic impact of selected gene variants was assessed using Kaplan-Meier curves and Log-rank tests.

RESULTS

Sixty-six patients underwent screening, with fifty-seven being comprehensively characterized radiomically and genomically. Predominantly males (68.4%), adenocarcinoma was the prevalent histological type (73.7%). Disease staging is distributed across I/II (38.6%), III (31.6%), and IV (29.8%). Significant correlations were identified with mutations of ROS1 p.Thr145Pro (shape_Sphericity), ROS1 p.Arg167Gln (glszm_ZoneEntropy, firstorder_TotalEnergy), ROS1 p.Asp2213Asn (glszm_GrayLevelVariance, firstorder_RootMeanSquared), and ALK p.Asp1529Glu (glcm_Imc1). Patients with the ROS1 p.Thr145Pro variant demonstrated markedly shorter median survival compared to the wild-type group (9.7 months vs. not reached, p = 0.0143; HR: 5.35; 95% CI: 1.39-20.48).

CONCLUSIONS

The exploration of the intersection between radiomics and cancer genetics in NSCLC is not only feasible but also holds the potential to improve genetic predictions and enhance prognostic accuracy.

A novel carcinogenic PI3Kα mutation suggesting the role of helical domain in transmitting nSH2 regulatory signals to kinase domain

AIMS

Mutations in PIK3CA, which encodes p110α subunit of PI3K class IA enzymes, are highly frequent in breast cancer. Here, we aimed to probe mutations in exon 9 of PIK3CA and computationally simulate their function.

MATERIALS AND METHODS

PCR/HRM and PCR/sequencing were used for mutation detection in 40 breast cancer specimens. The identified mutations were queried via in silico algorithms to check the pathogenicity. The molecular dynamics (MD) simulations were utilized to assess the function of mutant proteins.

KEY FINDINGS

Three samples were found to harbor at least one of the E542K, E545K and L551Q mutations of which L551Q has not been reported previously. All mutations were confirmed to be pathogenic and MD simulations revealed their impact on protein function and regulation. The novel L551Q mutant dynamics was similar to that of previously found carcinogenic mutants, E542K and E545K. A functional role for the helical domain was also suggested by which the inhibitory signal of p85α is conducted to kinase domain via helical domain. Helical domain mutations lead to impairment of kinase domain allosteric regulation. Interestingly, our results show that p110α substrate binding pocket of kinase domain in mutants may have differential affinity for enzyme substrates, including anit-p110α drugs.

SIGNIFICANCE

The novel p110α L551Q mutation could have carcinogenic feature similar to previously known helical domain mutations.

A pan-cancer assessment of alterations of the kinase domain of ULK1, an upstream regulator of autophagy

Autophagy is a key clearance process to recycle damaged cellular components. One important upstream regulator of autophagy is ULK1 kinase. Several three-dimensional structures of the ULK1 catalytic domain are available, but a comprehensive study, including molecular dynamics, is missing. Also, an exhaustive description of ULK1 alterations found in cancer samples is presently lacking. We here applied a framework which links -omics data to structural protein ensembles to study ULK1 alterations from genomics data available for more than 30 cancer types. We predicted the effects of mutations on ULK1 function and structural stability, accounting for protein dynamics, and the different layers of changes that a mutation can induce in a protein at the functional and structural level. ULK1 is down-regulated in gynecological tumors. In other cancer types, ULK2 could compensate for ULK1 downregulation and, in the majority of the cases, no marked changes in expression have been found. 36 missense mutations of ULK1, not limited to the catalytic domain, are co-occurring with mutations in a large number of ULK1 interactors or substrates, suggesting a pronounced effect of the upstream steps of autophagy in many cancer types. Moreover, our results pinpoint that more than 50% of the mutations in the kinase domain of ULK1, here investigated, are predicted to affect protein stability. Three mutations (S184F, D102N, and A28V) are predicted with only impact on kinase activity, either modifying the functional dynamics or the capability to exert effects from distal sites to the functional and catalytic regions. The framework here applied could be extended to other protein targets to aid the classification of missense mutations from cancer genomics studies, as well as to prioritize variants for experimental validation, or to select the appropriate biological readouts for experiments.

Insights into pathological mutations in insulin-like growth factor I through in silico screening and molecular dynamics simulation

Insulin-like growth factor I (IGF-I) is an anabolic growth hormone indispensable for cell growth, proliferation, differentiation, and other metabolic processes. Three missense mutations in IGF-I have been identified to be disease-related, while more mutations are waiting for phenotype annotation. However, there is no previous work regarding effective and accurate identification of pathological mutations of IGF-I, neither regarding the effects of mutations on the protein structure and dynamics. In this study, we first predicted potential deleterious mutations present in IGF-I using 16 in silico tools. Then, these mutations were further evaluated through multiple bioinformatics methods including conservation analysis, physicochemical characterization, and molecular dynamics simulation. After rigorous screening, five mutations (T4M, V17M, V44M, R50W, and M59R) were finally selected, of which two have been previously reported to be deleterious. These mutations locate at conserved regions and change the residue size locally. In the conventional simulations, the mutations destabilized the overall IGF-I structure by destroying two important hydrogen bonds within the key region of "C-neck." This finding was further confirmed by the thermal unfolding simulations and the free-energy calculations, where the mutants were associated with faster and greater loss of helix and lower energy barriers in comparison with the wild-type protein. The rigorous phenotype prediction and comprehensive structural analysis of missense mutations will not only pave the way of screening for harmful mutations in IGF-I but also provide new prospects for the rational design of IGF-I analogues and tailored medicine.

Allosteric Regulation of Protein Kinases Downstream of PI3-Kinase Signalling

Allostery is a basic principle that enables proteins to process and transmit cellular information. Protein kinases evolved allosteric mechanisms to transduce cellular signals to downstream signalling components or effector molecules. Protein kinases catalyse the transfer of the terminal phosphate from ATP to protein substrates upon specific stimuli. Protein kinases are targets for the development of small molecule inhibitors for the treatment of human diseases. Drug development has focussed on ATP-binding site, while there is increase interest in the development of drugs targeting alternative sites, i.e. allosteric sites. Here, we review the mechanism of regulation of protein kinases, which often involve the allosteric modulation of the ATP-binding site, enhancing or inhibiting activity. We exemplify the molecular mechanism of allostery in protein kinases downstream of PI3-kinase signalling with a focus on phosphoinositide-dependent protein kinase 1 (PDK1), a model kinase where small compounds can allosterically modulate the conformation of the kinase bidirectionally.

"Infostery" analysis of short molecular dynamics simulations identifies highly sensitive residues and predicts deleterious mutations

Characterizing a protein mutational landscape is a very challenging problem in Biology. Many disease-associated mutations do not seem to produce any effect on the global shape nor motions of the protein. Here, we use relatively short all-atom biomolecular simulations to predict mutational outcomes and we quantitatively assess the predictions on several hundreds of mutants. We perform simulations of the wild type and 175 mutants of PSD95’s third PDZ domain in complex with its cognate ligand. By recording residue displacements correlations and interactions, we identify “communication pathways” and quantify them to predict the severity of the mutations. Moreover, we show that by exploiting simulations of the wild type, one can detect 80% of the positions highly sensitive to mutations with a precision of 89%. Importantly, our analysis describes the role of these positions in the inter-residue communication and dynamical architecture of the complex. We assess our approach on three different systems using data from deep mutational scanning experiments and high-throughput exome sequencing. We refer to our analysis as “infostery”, from “info” - information - and “steric” - arrangement of residues in space. We provide a fully automated tool, COMMA2 (www.lcqb.upmc.fr/COMMA2), that can be used to guide medicinal research by selecting important positions/mutations.

Role of conformational dynamics in the evolution of novel enzyme function

The free energy landscape concept that describes enzymes as an ensemble of differently populated conformational sub-states in dynamic equilibrium is key for evaluating enzyme activity, enantioselectivity, and specificity. Mutations introduced in the enzyme sequence can alter the populations of the pre-existing conformational states, thus strongly modifying the enzyme ability to accommodate alternative substrates, revert its enantiopreferences, and even increase the activity for some residual promiscuous reactions. In this feature article, we present an overview of the current experimental and computational strategies to explore the conformational free energy landscape of enzymes. We provide a series of recent publications that highlight the key role of conformational dynamics for the enzyme evolution towards new functions and substrates, and provide some perspectives on how conformational dynamism should be considered in future computational enzyme design protocols.

Remarkable similarity in Plasmodium falciparum and Plasmodium vivax geranylgeranyl diphosphate synthase dynamics and its implication for antimalarial drug design

Malaria, mainly caused by Plasmodium falciparum and Plasmodium vivax, has been a growing cause of morbidity and mortality. P. falciparum is more lethal than is P. vivax, but there is a vital need for effective drugs against both species. Geranylgeranyl diphosphate synthase (GGPPS) is an enzyme involved in the biosynthesis of quinones and in protein prenylation and has been proposed to be a malaria drug target. However, the structure of P. falciparumGGPPS (PfGGPPS) has not been determined, due to difficulties in crystallization. Here, we created a PfGGPPS model using the homologous P.vivaxGGPPS X-ray structure as a template. We simulated the modeled PfGGPPS as well as PvGGPPS using conventional and Gaussian accelerated molecular dynamics in both apo- and GGPP-bound states. The MD simulations revealed a striking similarity in the dynamics of both enzymes with loop 9-10 controlling access to the active site. We also found that GGPP stabilizes PfGGPPS and PvGGPPS into closed conformations and via similar mechanisms. Shape-based analysis of the binding sites throughout the simulations suggests that the two enzymes will be readily targeted by the same inhibitors. Finally, we produced three MD-validated conformations of PfGGPPS to be used in future virtual screenings for potential new antimalarial drugs acting on both PvGGPPS and PfGGPPS.

A single cysteine post-translational oxidation suffices to compromise globular proteins kinetic stability and promote amyloid formation

Oxidatively modified forms of proteins accumulate during aging. Oxidized protein conformers might act as intermediates in the formation of amyloids in age-related disorders. However, it is not known whether this amyloidogenic conversion requires an extensive protein oxidative damage or it can be promoted just by a discrete, localized post-translational modification of certain residues. Here, we demonstrate that the irreversible oxidation of a single free Cys suffices to severely perturb the folding energy landscape of a stable globular protein, compromise its kinetic stability, and lead to the formation of amyloids under physiological conditions. Experiments and simulations converge to indicate that this specific oxidation-promoted protein aggregation requires only local unfolding. Indeed, a large scale analysis indicates that many cellular proteins are at risk of undergoing this kind of deleterious transition; explaining how oxidative stress can impact cell proteostasis and subsequently lead to the onset of pathological states.

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The population of aggregation-prone states by natural proteins does not require their extensive oxidation.

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A single residue irreversible oxidation suffices to promote the formation of amyloid fibrils.

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Under oxidative stress, many cellular proteins are at risk of aggregating into toxic species.