CAPSID: Computational Algorithms for Protein Structures and Interactions

The choice of the most appropriate unsupervised machine-learning method for "heterogeneous" or "mixed" data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of "ready-to-use" tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

OBJECTIVES: This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes. BACKGROUND: Asymptomatic cardiac dysfunction leads to a high risk of long-term cardiovascular morbidity and mortality; however, better echocardiographic classification of asymptomatic individuals remains a challenge. METHODS: Echocardiographic phenotypes were identified using K-means clustering in the first generation of the STANISLAS (Yearly non-invasive follow-up of Health status of Lorraine insured inhabitants) cohort (N = 827; mean age: 60 ± 5 years; men: 48%), and their associations with vascular function and circulating biomarkers were also assessed. These phenotypes were externally validated in the Malmö Preventive Project cohort (N = 1,394; mean age: 67 ± 6 years; men: 70%), and their associations with the composite of cardiovascular mortality (CVM) or heart failure hospitalization (HFH) were assessed as well. RESULTS: Three echocardiographic phenotypes were identified as "mostly normal (MN)" (n = 334), "diastolic changes (D)" (n = 323), and "diastolic changes with structural remodeling (D/S)" (n = 170). The D and D/S phenotypes had similar ages, body mass indices, cardiovascular risk factors, vascular impairments, and diastolic function changes. The D phenotype consisted mainly of women and featured increased levels of inflammatory biomarkers, whereas the D/S phenotype, consisted predominantly of men, displayed the highest values of left ventricular mass, volume, and remodeling biomarkers. The phenotypes were predicted based on a simple algorithm including e', left ventricular mass and volume (e'VM algorithm). In the Malmö cohort, subgroups derived from e'VM algorithm were significantly associated with a higher risk of CVM and HFH (adjusted HR in the D phenotype = 1.87; 95% CI: 1.04 to 3.37; adjusted HR in the D/S phenotype = 3.02; 95% CI: 1.71 to 5.34). CONCLUSIONS: Among asymptomatic, middle-aged individuals, echocardiographic data-driven classification based on the simple e'VM algorithm identified profiles with different long-term HF risk. (4th Visit at 17 Years of Cohort STANISLAS-Stanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442).

Recent analyses of bacterial genomes have shown that integrated elements that transfer by conjugation play an essential role in horizontal gene transfer. Among these elements, the integrative and mobilizable elements (IMEs) are known to encode their own excision and integration machinery, and to carry all the sequences or genes necessary to hijack the mating pore of a conjugative element for their own transfer. However, knowledge of their prevalence and diversity is still severely lacking. In this work, an extensive analysis of 124 genomes from 27 species of Streptococcus reveals 144 IMEs. These IMEs encode either tyrosine or serine integrases. The identification of IME boundaries shows that 141 are specifically integrated in 17 target sites. The IME-encoded relaxases belong to 9 superfamilies, among which 4 are previously unknown in any mobilizable or conjugative element. A total of 118 IMEs are found to encode a non-canonical relaxase related to rolling circle replication initiators (belonging to the 4 novel families or to MobT). Surprisingly, among these, 83 encode a TcpA protein (i.e. a non-canonical coupling protein that is more closely related to FtsK than VirD4) that was not previously known to be encoded by mobilizable elements. Phylogenetic analyses reveal not only many integration/excision module replacements but also losses, acquisitions or replacements of TcpA genes between IMEs. This glimpse into the still poorly known world of IMEs reveals that mobilizable elements have a very high prevalence. Their diversity is even greater than expected, with most encoding a coupling protein and/or a non-canonical relaxase.

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming. As a first step to address these issues, we propose FAIR-Checker, a web-based tool to assess the FAIRness of metadata presented by digital resources. FAIR-Checker offers two main facets: a "Check" module providing a thorough metadata evaluation and recommendations, and an "Inspect" module which assists users in improving metadata quality and therefore the FAIRness of their resource. FAIR-Checker leverages Semantic Web standards and technologies such as SPARQL queries and SHACL constraints to automatically assess FAIR metrics. Users are notified of missing, necessary, or recommended metadata for various resource categories. We evaluate FAIR-Checker in the context of improving the FAIRification of individual resources, through better metadata, as well as analyzing the FAIRness of more than 25 thousand bioinformatics software descriptions.

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes.Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression.Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12–3.32, p = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism.Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.Clinical significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolismBiomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.

The production of coumarins and furanocoumarins in higher plants is widely considered a model illustration of the adaption of plants to their environment. In this report, we show that the multiplication of cytochrome P450 variants within the CYP71AZ subfamily has contributed to the diversification of these molecules. Multiple copies of genes encoding this enzyme family are found in Apiaceae, and their phylogenetic analysis suggests that they have different functions within these plants. CYP71AZ1 from Ammi majus and CYP71AZ3, 4, and 6 from Pastinaca sativa were functionally characterized. While CYP71AZ3 merely hydroxylated esculetin, the other enzymes accepted both simple coumarins and furanocoumarins. Superimposing in silico models of these enzymes led to the identification of different conformations of three regions in the enzyme active site. These sequences were subsequently utilized to mutate CYP71AZ4 to resemble CYP71AZ3. The swapping of these regions lead to significantly modified substrate specificity. Simultaneous mutations of all three regions shifted the specificity of CYP71AZ4 to that of CYP71AZ3, exclusively accepting esculetin. This approach may explain the evolution of this cytochrome P450 family regarding the appearance of furanocoumarins in parsnip and possibly in the Apiaceae.

Candida infections have become a serious public health problem with high mortality rates, especially in immunocompromised patients, being Candida albicans the major opportunistic pathogen responsible for systemic or invasive candidiasis. Commercially available antifungal agents are restricted and fungal resistance to such drugs has increased; therefore, the development of a more specific antifungal is necessary. Using assays for antifungal activity, here we report that two new compounds of 1,3,4-oxadiazoles class (LMM5 and LMM11), which were discovered by in silico methodologies as possible thioredoxin reductase inhibitors, were effective against C. albicans. Both compounds had in vitro antifungal activity with MIC 32 μg/mL. Cytotoxicity in vitro demonstrated that LMM5 and LMM11 were non-toxic in the cell lines evaluated. The kinetic of time-kill curve suggested a fungistatic profile and showed a inhibitory effect of LMM5 and LMM11 in 12 hours that remained for 24 and 36 hour, being better than fluconazole. In the murine systemic candidiasis model by C. albicans, the two compounds reduced significantly the renal and spleen fungal burden. According to the SEM and TEM images, we hypothesized that the mechanism of action of LMM5 and LMM11 is directly related to the inhibition of the enzyme thioredoxin reductase and they affected internally fungal cell. In view of all in vitro and in vivo results, LMM5 and LMM11 are effective therapeutic candidates for the development of new antifungal drugs addressing the treatment of human infections caused by C. albicans.

Conjugative spread of antibiotic resistance and virulence genes in bacteria constitutes an important threat to public health. Beyond the well-known conjugative plasmids, recent genome analyses have shown that integrative and conjugative elements (ICEs) are the most widespread conjugative elements, even if their transfer mechanism has been little studied until now. The initiator of conjugation is the relaxase, a protein catalyzing a site-specific nick on the origin of transfer ( oriT ) of the ICE. Besides canonical relaxases, recent studies revealed non-canonical ones, such as relaxases of the MOB T family that are related to rolling-circle replication proteins of the Rep_trans family. MOB T relaxases are encoded by ICEs of the ICE St3 /ICE Bs1 /Tn 916 superfamily, a superfamily widespread in Firmicutes, and frequently conferring antibiotic resistance. Here, we present the first biochemical and structural characterization of a MOB T relaxase: the RelSt3 relaxase encoded by ICE St3 from Streptococcus thermophilus . We identified the oriT region of ICE St3 and demonstrated that RelSt3 is required for its conjugative transfer. The purified RelSt3 protein is a stable dimer that provides a Mn 2+ -dependent single-stranded endonuclease activity. Sequence comparisons of MOB T relaxases led to the identification of MOB T conserved motifs. These motifs, together with the construction of a 3D model of the relaxase domain of RelSt3, allowed us to determine conserved residues of the RelSt3 active site. The involvement of these residues in DNA nicking activity was demonstrated by targeted mutagenesis. All together, this work argues in favor of MOB T being a full family of non-canonical relaxases. The biochemical and structural characterization of a MOB T member provides new insights on the molecular mechanism of conjugative transfer mediated by ICEs in Gram-positive bacteria. This could be a first step towards conceiving rational strategies to control gene transfer in these bacteria.

The in silico prediction of unwanted side effects (SEs) caused by the promiscuous behavior of drugs and their targets is highly relevant to the pharmaceutical industry. Considerable effort is now being put into computational and experimental screening of several suspected off-target proteins in the hope that SEs might be identified early, before the cost associated with developing a drug candidate rises steeply. Following this need, we present a new method called GESSE to predict potential SEs of drugs from their physicochemical properties (three-dimensional shape plus chemistry) and to target protein data extracted from predicted drug-target relationships. The GESSE approach uses a canonical correlation analysis of the full drug-target and drug-SE matrices, and it then calculates a probability that each drug in the resulting drug-target matrix will have a given SE using a Bayesian discriminant analysis (DA) technique. The performance of GESSE is quantified using retrospective (external database) analysis and literature examples by means of area under the ROC curve analysis, "top hit rates", misclassification rates, and a χ(2) independence test. Overall, the robust and very promising retrospective statistics obtained and the many SE predictions that have experimental corroboration demonstrate that GESSE can successfully predict potential drug-SE profiles of candidate drug compounds from their predicted drug-target relationships.

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Background Sarcomas are rare mesenchymal malignancies whose pathogenesis is poorly understood; both environmental and genetic risk factors could contribute to their aetiology. Methods and results We performed whole-exome sequencing (WES) in a familial aggregation of three individuals affected with soft-tissue sarcoma (STS) without TP53 mutation (Li-Fraumeni-like, LFL) and found a shared pathogenic mutation in CDKN2A tumour suppressor gene. We searched for individuals with sarcoma among 474 melanoma-prone families with a CDKN2A -/+ genotype and for CDKN2A mutations in 190 TP53 -negative LFL families where the index case was a sarcoma. Including the initial family, eight independent sarcoma cases carried a germline mutation in the CDKN2A /p16 INK4A gene. In five out of seven formalin-fixed paraffin-embedded sarcomas, heterozygosity was lost at germline CDKN2A mutations sites demonstrating complete loss of function. As sarcomas are rare in CDKN2A /p16 INK4A carriers, we searched in constitutional WES of nine carriers for potential modifying rare variants and identified three in platelet-derived growth factor receptor ( PDGFRA ) gene. Molecular modelling showed that two never-described variants could impact the PDGFRA extracellular domain structure. Conclusion Germline mutations in CDKN2A /P16 INK4A , a gene known to predispose to hereditary melanoma, pancreatic cancer and tobacco-related cancers, account also for a subset of hereditary sarcoma. In addition, we identified PDGFRA as a candidate modifier gene.

BACKGROUND: Many patients with heart failure with preserved ejection fraction (HFpEF) are women. Exploring mechanisms underlying the sex differences may improve our understanding of the pathophysiology of HFpEF. Studies focusing on sex differences in circulating proteins in HFpEF patients are scarce. METHODS: A total of 415 proteins were analyzed in 392 HFpEF patients included in The Metabolic Road to Diastolic Heart Failure: Diastolic Heart Failure study (MEDIA-DHF). Sex differences in these proteins were assessed using adjusted logistic regression analyses. The associations between candidate proteins and cardiovascular (CV) death or CV hospitalization (with sex interaction) were assessed using Cox regression models. RESULTS: We found 9 proteins to be differentially expressed between female and male patients. Women expressed more LPL and PLIN1, which are markers of lipid metabolism; more LHB, IGFBP3, and IL1RL2 as markers of transcriptional regulation; and more Ep-CAM as marker of hemostasis. Women expressed less MMP-3, which is a marker associated with extracellular matrix organization; less NRP1, which is associated with developmental processes; and less ACE2, which is related to metabolism. Sex was not associated with the study outcomes (adj. HR 1.48, 95% CI 0.83-2.63), p = 0.18. CONCLUSION: In chronic HFpEF, assessing sex differences in a wide range of circulating proteins led to the identification of 9 proteins that were differentially expressed between female and male patients. These findings may help further investigations into potential pathophysiological processes contributing to HFpEF.

The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many real-world graphs though they are heuristic. On the other hand, graph embedding approaches learn low-dimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This appraisal paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods our aim is also to uncover interesting connections between Graph Neural Network(GNN)-based methods and heuristic ones as a means to alleviate the black-box well-known limitation.

By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected-thanks to the molecular docking results-were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC 50 of 7.2 μ M. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.

MOTIVATION: Protein-protein docking algorithms aim to predict the 3D structure of a binary complex using the structures of the individual proteins. This typically involves searching and scoring in a 6D space. Many docking algorithms use FFT techniques to exhaustively cover the search space and to accelerate the scoring calculation. However, FFT docking results often depend on the initial protein orientations with respect to the Fourier sampling grid. Furthermore, Fourier-transforming a physics-base force field can involve a serious loss of precision. RESULTS: Here, we present EROS-DOCK, an algorithm to rigidly dock two proteins using a series of exhaustive 3D rotational searches in which non-clashing orientations are scored using the ATTRACT coarse-grained force field model. The rotational space is represented as a quaternion 'π-ball', which is systematically sub-divided in a 'branch-and-bound' manner, allowing efficient pruning of rotations that will give steric clashes. The algorithm was tested on 173 Docking Benchmark complexes, and results were compared with those of ATTRACT and ZDOCK. According to the CAPRI quality criteria, EROS-DOCK typically gives more acceptable or medium quality solutions than ATTRACT and ZDOCK. AVAILABILITY AND IMPLEMENTATION: The EROS-DOCK program is available for download at http://erosdock.loria.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OBJECTIVES: Summarize recent research and select the best papers published in 2019 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association Yearbook. METHODS: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the section editors to select a list of 15 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole Yearbook editorial committee was organized to finally decide on the selection of the best papers. RESULTS: Among the 931 retrieved papers covering the various subareas of BTI, the review process selected four best papers. The first paper presents a logical modeling of cancer pathways. Using their tools, the authors are able to identify two known behaviours of tumors. The second paper describes a deep-learning approach to predicting resistance to antibiotics in Mycobacterium tuberculosis. The authors of the third paper introduce a Genomic Global Positioning System (GPS) enabling comparison of genomic data with other individuals or genomics databases while preserving privacy. The fourth paper presents a multi-omics and temporal sequence-based approach to provide a better understanding of the sequence of events leading to Alzheimer's Disease. CONCLUSIONS: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

Integrative and conjugative elements (ICEs) are important drivers of horizontal gene transfer in prokaryotes. They are responsible for antimicrobial resistance spread, a major current health concern. ICEs are initially processed by relaxases that recognize the binding site of oriT sequence and nick at a conserved nic site. The ICESt3/Tn916/ICEBs1 superfamily, which is widespread among Firmicutes, encodes uncanonical relaxases belonging to a recently identified family called MOBT. This family is related to the rolling circle replication initiators of the Rep_trans family. The nic site of these MOBT relaxases is conserved but their DNA binding site is still unknown. Here, we identified the bind site of RelSt3, the MOBT relaxase from ICESt3. Unexpectedly, we found this bind site distantly located from the nic site. We revealed that the binding of the RelSt3 N-terminal HTH domain is required for efficient nicking activity. We also deciphered the role of RelSt3 in the initial and final stages of DNA processing during conjugation. Especially, we demonstrated a strand transfer activity, and the formation of covalent DNA-relaxase intermediate for a MOBT relaxase.

The receptors tyrosine kinases (RTKs) for the colony stimulating factor-1, CSF-1R, and for the stem cell factor, SCFR or KIT, are important mediators of signal transduction. The abnormal function of these receptors, promoted by gain-of-function mutations, leads to their constitutive activation, associated with cancer or other proliferative diseases. A secondary effect of the mutations is the alteration of receptors' sensitivity to tyrosine kinase inhibitors, compromising effectiveness of these molecules in clinical treatment. In particular, the mutation V560G in KIT increases its sensitivity to Imatinib, while the D816V in KIT, and D802V in CSF-1R, triggers resistance to the drug. We analyzed the Imatinib binding affinity to the native and mutated KIT (mutations V560G, S628N and D816V) and CSF-1R (mutation D802V) by using molecular dynamics simulations and energy calculations of Imatinib•target complexes. Further, we evaluated the sensitivity of the studied KIT receptors to Imatinib by measuring the inhibition of KIT phosphorylation. Our study showed that (i) the binding free energy of Imatinib to the targets is highly correlated with their experimentally measured sensitivity; (ii) the electrostatic interactions are a decisive factor affecting the binding energy; (iii) the most deleterious impact to the Imatinib sensitivity is promoted by D802V (CSF-1R) and D816V (KIT) mutations; (iv) the role of the juxtamembrane region, JMR, in the imatinib binding is accessory. These findings contribute to a better description of the mutation-induced effects alternating the targets sensitivity to Imatinib.

BACKGOUND: Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination. RESULTS: In this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation. CONCLUSIONS: This study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers.

Receptor tyrosine kinases (RTKs) are key regulators of normal cellular processes and have a critical role in the development and progression of many diseases. RTK ligand-induced stimulation leads to activation of the cytoplasmic kinase domain that controls the intracellular signalling. Although the kinase domain of RTKs has been extensively studied using X-ray analysis, the kinase insert domain (KID) and the C-terminal are partially or fully missing in all reported structures. We communicate the first structural model of the full-length RTK KIT cytoplasmic domain, a crucial target for cancer therapy. This model was achieved by integration of ab initio KID and C-terminal probe models into an X-ray structure, and by their further exploration through molecular dynamics (MD) simulation. An extended (2-µs) MD simulation of the proper model provided insight into the structure and conformational dynamics of the full-length cytoplasmic domain of KIT, which can be exploited in the description of the KIT transduction processes.