Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline.

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

Polymorphism in F pocket affects peptide selection and stability of type 1 diabetes-associated HLA-B39 allotypes.

HLA-B*39:06, HLA-B*39:01, and HLA-B*38:01 are closely related HLA allotypes differentially associated with type 1 diabetes (T1D) risk and progression. B*39:06 is highly predisposing, while B*39:01 and B*38:01 are weakly predisposing and protective allotypes, respectively. Here, we aimed to decipher molecular mechanisms underlying the differential association of these allotypes with T1D pathogenesis. We addressed peptide binding and conformational stability of HLA-B allotypes using computational and experimental approaches. Computationally, we found that B*39:06 and B*39:01 allotypes had more rigid peptide-binding grooves and were more promiscuous in binding peptides than B*38:01. Peptidomes of B*39:06 and B*39:01 contained fewer strong binders and were of lower affinity than that of B*38:01. Experimentally, we demonstrated that B*39:06 and B*39:01 had a higher capacity to bind peptides and exit to the cell surface but lower surface levels and were degraded faster than B*38:01. In summary, we propose that promiscuous B*39:06 and B*39:01 may bind suboptimal peptides and transport them the cell surface, where such unstable complexes may contribute to the pathogenesis of T1D.

Attenuation of Type IV pili activity by natural products.

The virulence factor Type IV pili (T4P) are surface appendages used by the opportunistic pathogen Pseudomonas aeruginosa for twitching motility and adhesion in the environment and during infection. Additionally, the use of these appendages by P. aeruginosa for biofilm formation increases its virulence and drug resistance. Therefore, attenuation of the activity of T4P would be desirable to control P. aeruginosa infections. Here, a computational approach has been pursued to screen natural products that can be used for this purpose. PilB, the elongation ATPase of the T4P machinery in P. aeruginosa, has been selected as the target subunit and virtual screening of FDA-approved drugs has been conducted. Screening identified two natural compounds, ergoloid and irinotecan, as potential candidates for inhibiting this T4P-associated ATPase in P. aeruginosa. These candidate compounds underwent further rigorous evaluation through molecular dynamics (MD) simulations and then through in vitro twitching motility and biofilm inhibition assays. Notably, ergoloid emerged as a particularly promising candidate for weakening the T4P activity by inhibiting the elongation ATPases associated with T4P. This repurposing study paves the way for the timely discovery of antivirulence drugs as an alternative to classical antibiotic treatments to help combat infections caused by P. aeruginosa and related pathogens.Communicated by Ramaswamy H. Sarma.

Piperidine-based natural products targeting Type IV pili antivirulence: A computational approach.

Type IV (T4) pilus is among the virulence factors with a key role in serious bacterial diseases. Specifically, in Neisseria meningitidis and Pseudomonas aeruginosa, it determines pathogenicity and causes infection. Here, a computational approach has been pursued to find piperidine-based inhibitor molecules against the elongation ATPase of T4 pili in these two selected pathogens. Using the modeled structures of the PilF and PilB ATPases of N. meningitidis and P. aeruginosa, virtual library screening via molecular docking has returned inhibitor molecule candidates. The dynamics of the best three binders have further been investigated in detail via molecular dynamic simulations. Among these, ligands with COCONUT IDs CNP0030078 and CNP0051517 were found to have higher potential in the inhibition of ATPases based on molecular dynamic simulation analysis and biological activity information. The obtained results will guide future efforts in antivirulence drug development against T4 pili of N. meningitidis and P. aeruginosa.

Epistatic Effects Between Amino Acid Insertions and Substitutions Mediate Toxin resistance of Vertebrate Na+,K+-ATPases.

The recurrent evolution of resistance to cardiotonic steroids (CTS) across diverse animals most frequently involves convergent amino acid substitutions in the H1-H2 extracellular loop of Na+,K+-ATPase (NKA). Previous work revealed that hystricognath rodents (e.g., chinchilla) and pterocliform birds (sandgrouse) have convergently evolved amino acid insertions in the H1-H2 loop, but their functional significance was not known. Using protein engineering, we show that these insertions have distinct effects on CTS resistance in homologs of each of the two species that strongly depend on intramolecular interactions with other residues. Removing the insertion in the chinchilla NKA unexpectedly increases CTS resistance and decreases NKA activity. In the sandgrouse NKA, the amino acid insertion and substitution Q111R both contribute to an augmented CTS resistance without compromising ATPase activity levels. Molecular docking simulations provide additional insight into the biophysical mechanisms responsible for the context-specific mutational effects on CTS insensitivity of the enzyme. Our results highlight the diversity of genetic substrates that underlie CTS insensitivity in vertebrate NKA and reveal how amino acid insertions can alter the phenotypic effects of point mutations at key sites in the same protein domain.

How Epstein-Barr virus envelope glycoprotein gp350 tricks the CR2? A molecular dynamics study.

The connection of Epstein Barr virus (EBV) with diseases such as Burkitt Lymphoma, Hodgkin disease, multiple sclerosis, systemic lupus erythematosus and various B-cell lymphomas made EBV glycoproteins one of the most popular vaccine immunogens. As a protein being encoded by EBV, the viral membrane envelope protein gp350 is studied extensively due to its abundancy on the surface and its interaction with complementary receptor, CR2. The binding of CR2 and gp350 not only leads to the entrance of the virus to the B-cells, but also prevents CR2 and C3d protein interactions that are required for immune response. Thus, understanding the inhibition of gp350 activity is crucial for vaccine development. Although, the active residues on gp350 structure were determined by several mutational studies, the exact mechanism of CR2 binding is still not clear. To this end, we have performed molecular docking followed by molecular dynamics simulations and MM-PBSA on wildtype and several mutated gp350 and CR2 structures. Apart from identifying crucial amino acids, the results of per-residue decomposition energy analysis clarified the individual energy contributions of amino acids and were also found to be accurate in differentiating the active site residues in CR2 binding. Here, we highlight the role of binding region residues (linker-1) but more interestingly, the dynamic relation between the distant sites of gp350 (linker-2 and D3 residues) and CR2. These findings can lead further vaccine development strategies by pointing to the importance of computationally found novel regions that can be potentially used to modulate gp350 activity.

Assessment of 13 in silico pathogenicity methods on cancer-related variants.

Single nucleotide variants (SNVs) are single base substitutions that could influence many biological functions in the cell including gene expression, protein folding, and protein-protein interactions among many others. Thus, predictions of functional effects of cancer-related variants are crucial for drug responses and treatment options in clinical oncology. Experimental identification of these effects could be slow, inefficient, and inconvenient, hence in silico methods are gaining popularity in predicting the variants' effects. There are many studies on the cancer variants, however, up to date, none of these have been aimed to assess the performance metrics of in silico pathogenicity methods on functional relevance of cancer variants obtained from ClinVar. To this end, we examined the pathogenicity predictions of cancer-related variant datasets of 8 cancer types (bladder, breast, colon, colorectal, kidney, liver, lung, and pancreas cancer) retrieved from ClinVar using 13 different in silico methods including SIFT, CADD, FATHMM-weighted, FATHMM-unweighted, GERP++, MetaSVM, Mutation Assessor, MutationTaster, MutPred, PolyPhen-2, Provean, Revel and VEST4. A combination of statistical performance metric analysis, prediction distribution frequency data and ROC curve analysis results have suggested that; among all in silico prediction tools, top three tools with the highest discriminatory power were found to be MutPred (AUC = 0.677), MetaSVM (AUC = 0.645) and Revel (AUC = 0.637).

Hydration modulates oxygen channel residues for oxygenation of cysteine dioxygenase: Perspectives from molecular dynamics simulations.

Cysteine dioxygenase (CDO) regulates the concentration of l-cysteine substrate by its oxidation in the body to prevent different diseases, including neurodegenerative and autoimmune diseases. CDO catalyzes the oxidation of thiol group of l-cysteine to l-cysteine sulfinic acid using molecular oxygen. In this study, molecular dynamics simulations were applied to ligand-free CDO, cysteine-bound CDO, and oxygen-bound CDO-cysteine complex which were primarily subjected to the evaluation of their structural and dynamical properties. The simulation data provided significant information not only on the conformational changes of the enzyme after its ligation but also on the co-ligation by sequential binding of l-cysteine and molecular oxygen. It was found that the ligation and co-ligation perturbed the active site region as well as the overall protein dynamics which were analyzed in terms of root mean square deviation, root mean square fluctuation and dynamic cross correlation matrices as well as principal component analysis. Furthermore, oxygen transport pathways were successfully explored by taking various tunnel clusters into account and one of those clusters was given preference based on the throughput value. The bottleneck formed by different amino acid residues was examined to figure out their role in the oxygenation process of the enzyme. The residues forming the tunnel's bottleneck and their dynamics mediated by water molecules were further investigated using radial distribution functions which gave insights into the hydration behavior of these residues. The findings based on the hydration behavior in turn served to explore the water-mediated dynamics of these residues in the modulation of the pathway, including tunnel gating for the oxygen entry and diffusion to the active site, which is essential for the CDO's catalytic function.

Unraveling the Allosteric Communication Mechanisms in T-Cell Receptor-Peptide-Loaded Major Histocompatibility Complex Dynamics Using Molecular Dynamics Simulations: An Approach Based on Dynamic Cross Correlation Maps and Residue Interaction Energy Calculations.

Antigen presentation by major histocompatibility complex (MHC) proteins to T-cell receptors (TCRs) plays a crucial role in triggering the adaptive immune response. Most of our knowledge on TCR-peptide-loaded major histocompatibility complex (pMHC) interaction stemmed from experiments yielding static structures, yet the dynamic aspects of this molecular interaction are equally important to understand the underlying molecular mechanisms and to develop treatment strategies against diseases such as cancer and autoimmune diseases. To this end, computational biophysics studies including all-atom molecular dynamics simulations have provided useful insights; however, we still lack a basic understanding of an overall allosteric mechanism that results in conformational changes in the TCR and subsequent T-cell activation. Previous hydrogen-deuterium exchange and nuclear magnetic resonance studies provided clues regarding these molecular mechanisms, including global rigidification and allosteric effects on the constant domain of TCRs away from the pMHC interaction site. Here, we show that molecular dynamics simulations can be used to identify how this overall rigidification may be related to the allosteric communication within TCRs upon pMHC interaction via essential dynamics and nonbonded residue-residue interaction energy analyses. The residues taking part in the rigidification effect are highlighted with an intricate analysis on residue interaction changes, which lead to a detailed outline of the complex formation event. Our results indicate that residues of the Cß domain of TCRs show significant differences in their nonbonded interactions upon complex formation. Moreover, the dynamic cross correlations between these residues are also increased, in line with their nonbonded interaction energy changes. Altogether, our approach may be valuable for elucidating intramolecular allosteric changes in the TCR structure upon pMHC interaction in molecular dynamics simulations.

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.

Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers.

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

Identification of novel inhibitors of the ABC transporter BmrA.

The resistance of microbes to commonly used antibiotics has become a worldwide health problem. A major underlying mechanism of microbial antibiotic resistance is the export of drugs from bacterial cells. Drug efflux is mediated through the action of multidrug resistance efflux pumps located in the bacterial cell membranes. The critical role of bacterial efflux pumps in antibiotic resistance has directed research efforts to the identification of novel efflux pump inhibitors that can be used alongside antibiotics in clinical settings. Here, we aimed to find potential inhibitors of the archetypical ATP-binding cassette (ABC) efflux pump BmrA of Bacillus subtilis via virtual screening of the Mu.Ta.Lig. Chemotheca small molecule library. Molecular docking calculations targeting the nucleotide-binding domain of BmrA were performed using AutoDock Vina. Following a further drug-likeness filtering step based on Lipinski's Rule of Five, top 25 scorers were identified. These ligands were then clustered into separate groups based on their contact patterns with the BmrA nucleotide-binding domain. Six ligands with distinct contact patterns were used for further in vitro inhibition assays based on intracellular ethidium bromide accumulation. Using this methodology, we identified two novel inhibitors of BmrA from the Chemotheca small molecule library.

Revisiting allostery in CREB-binding protein (CBP) using residue-based interaction energy.

CREB-binding protein (CBP) is a multi-subunit scaffold protein complex in transcription regulation process, binding and interacting with ligands such as mixed-lineage leukemia (MLL) and c-Myb allosterically. Here in this study, we have revisited the concept of allostery in CBP via residue-based interaction energy calculation based on molecular dynamics (MD) simulations. To this end, we conducted MD simulations of KIX:MLL:c-Myb ternary complex, its binary components and kinase-inducible domain (KID) interacting domain (KIX) backbone. Interaction energy profiles and cross correlation analysis were performed and the results indicated that KIX:MLL and KIX:c-Myb:MLL complexes demonstrate significant similarities according to both analysis methods. Two regions in the KIX backbone were apparent from the interaction energy and cross correlation maps that hold a key to allostery phenomena observed in CBP. While one of these regions are related to the ligand binding residues, the other comprises of L12-G2 loop and α3 helix regions that have been found to have a significant role in allosteric signal propagation. All in all, residue-based interaction energy calculation method is demonstrated to be a valuable calculation technique for the detection of allosteric signal propagation and ligand interaction regions.

Sequence-structure-function relationships in class I MHC: A local frustration perspective.

Class I Major Histocompatibility Complex (MHC) binds short antigenic peptides with the help of Peptide Loading Complex (PLC), and presents them to T-cell Receptors (TCRs) of cytotoxic T-cells and Killer-cell Immunglobulin-like Receptors (KIRs) of Natural Killer (NK) cells. With more than 10000 alleles, human MHC (Human Leukocyte Antigen, HLA) is the most polymorphic protein in humans. This allelic diversity provides a wide coverage of peptide sequence space, yet does not affect the three-dimensional structure of the complex. Moreover, TCRs mostly interact with HLA in a common diagonal binding mode, and KIR-HLA interaction is allele-dependent. With the aim of establishing a framework for understanding the relationships between polymorphism (sequence), structure (conserved fold) and function (protein interactions) of the human MHC, we performed here a local frustration analysis on pMHC homology models covering 1436 HLA I alleles. An analysis of local frustration profiles indicated that (1) variations in MHC fold are unlikely due to minimally-frustrated and relatively conserved residues within the HLA peptide-binding groove, (2) high frustration patches on HLA helices are either involved in or near interaction sites of MHC with the TCR, KIR, or tapasin of the PLC, and (3) peptide ligands mainly stabilize the F-pocket of HLA binding groove.

Next-Generation Sequencing Identifies BRCA1 and/or BRCA2 Mutations in Women at High Hereditary Risk for Breast Cancer with Shorter Telomere Length.

Telomeres, and telomere length in particular, have broad significance for genome biology and thus are prime research targets for complex diseases such as cancers. In this context, BRCA1 and BRCA2 gene mutations have been implicated in relationship to telomere length, and breast cancer susceptibility. Yet, the linkages among human genetic variation and telomere length in persons with high hereditary cancer risk are inadequately mapped. We report here original findings in 113 unrelated women at high hereditary risk for breast cancer, who were characterized for the BRCA1 and BRCA2 mutations using next-generation sequencing. Thirty-one BRCA2 and 21 BRCA1 mutations were identified in 47 subjects (41.6%). The women with a mutation in BRCA1 and/or BRCA2 genes had, on average, 12% shorter telomere compared to women with no BRCA1 or BRCA2 mutation (p = 0.0139). Moreover, the association between telomere length and BRCA mutation status held up upon stratified analysis in those with or without a breast cancer diagnosis. We also indentified two rare mutations, c.536_537insT and c.10078A>G, and a novel mutation c.8680C>G in BRCA2 that was studied further by homology modeling of the DNA binding tower domain of BRCA2 and the structure of the protein. These data collectively lend evidence to the idea that BRCA1 and BRCA2 mutations play a role in telomere length in women at high hereditary risk for breast cancer. Further clinical and diagnostics discovery research on BRCA1 and BRCA2 variation, telomere length, and breast cancer mechanistic linkages are called for in larger study samples.

Computational investigation of peptide binding stabilities of HLA-B*27 and HLA-B*44 alleles.

Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that binds to foreign antigens and presents them to T lymphocyte cells on the surface of Antigen Presenting Cells (APCs) for appropriate immune recognition. Recently, studies focusing on peptide-based vaccine design have allowed a better understanding of peptide immunogenicity mechanisms, which is defined as the ability of a peptide to stimulate CTL-mediated immune response. Peptide immunogenicity is also known to be related to the stability of peptide-loaded MHC (pMHC) complex. In this study, ENCoM server was used for structure-based estimation of the impact of single point mutations on pMHC complex stabilities. For this purpose, two human MHC molecules from the HLA-B*27 group (HLA-B*27:05 and HLA-B*27:09) in complex with four different peptides (GRFAAAIAK, RRKWRRWHL, RRRWRRLTV and IRAAPPPLF) and three HLA-B*44 molecules (HLA-B*44:02, HLA-B*44:03 and HLA-B*44:05) in complex with two different peptides (EEYLQAFTY and EEYLKAWTF) were analyzed. We found that the stability of pMHC complexes is dependent on both peptide sequence and MHC allele. Furthermore, we demonstrate that allele-specific peptide-binding preferences can be accurately revealed using structure-based computational methods predicting the effect of mutations on protein stability.

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism.

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

How do mutations and allosteric inhibitors modulate caspase-7 activity? A molecular dynamics study.

Caspases are members of a highly regulated aspartate-cysteine protease family which have important roles in apoptosis. Pharmaceutical studies focused on these molecules since they are involved in diseases such as cancer and neurodegenerative disorders. A small molecule which binds to the dimeric interface away from the binding site induces a conformational change that resembles the pro-caspase form of the molecule by shifting loop positions. The fluctuation mechanisms caused by mutations or binding of a ligand can explain the key mechanism for the function of that molecule. In this study, we performed molecular dynamics simulations on wild-type and mutated structures (C290N, R187M, Y223A, G188L and G188P) as well as allosterically inhibited structure (DICA-bound caspase-7) to observe the effects of the single mutations on intrinsic dynamics. The results show that previously known changes in catalytic activity upon mutations or allosteric ligand binding are reflected in corresponding changes in the global dynamics of caspase-7. Communicated by Ramaswamy H. Sarma.

gRINN: a tool for calculation of residue interaction energies and protein energy network analysis of molecular dynamics simulations.

Atomistic molecular dynamics (MD) simulations generate a wealth of information related to the dynamics of proteins. If properly analyzed, this information can lead to new insights regarding protein function and assist wet-lab experiments. Aiming to identify interactions between individual amino acid residues and the role played by each in the context of MD simulations, we present a stand-alone software called gRINN (get Residue Interaction eNergies and Networks). gRINN features graphical user interfaces (GUIs) and a command-line interface for generating and analyzing pairwise residue interaction energies and energy correlations from protein MD simulation trajectories. gRINN utilizes the features of NAMD or GROMACS MD simulation packages and automatizes the steps necessary to extract residue-residue interaction energies from user-supplied simulation trajectories, greatly simplifying the analysis for the end-user. A GUI, including an embedded molecular viewer, is provided for visualization of interaction energy time-series, distributions, an interaction energy matrix, interaction energy correlations and a residue correlation matrix. gRINN additionally offers construction and analysis of Protein Energy Networks, providing residue-based metrics such as degrees, betweenness-centralities, closeness centralities as well as shortest path analysis. gRINN is free and open to all users without login requirement at http://grinn.readthedocs.io.

Computational characterization of residue couplings and micropolymorphism-induced changes in the dynamics of two differentially disease-associated human MHC class-I alleles.

Human major histocompatibility complex class I (MHC I) - or human leukocyte antigen (HLA) - proteins present intracellularly processed peptides to cytotoxic T lymphocytes in the adaptive immune response to pathogens. A high level of polymorphism in human MHC I proteins defines the peptide-binding specificity of thousands of different MHC alleles. However, polymorphism as well as the peptide ligand can also affect the global dynamics of the complex. In this study, we conducted classical molecular dynamics simulations of two HLA alleles, the ankylosing spondylitis (AS) associated/tapasin-dependent HLA-B*27:05 and nondisease-associated/tapasin-independent HLA-B*27:09, both in peptide-free forms as well as complex with four different peptides ligands. Our results indicate that in peptide-free form, the single amino acid substitution distinguishing the two alleles (D116H), leads to a weaker dynamic coupling of residues in the tapasin-dependent HLA-B*27:05. In peptide-bound form, several residues of the binding-groove, mostly in A and B pockets, show hinge-like behavior in the global motion of the MHC. Moreover, allele-dependent changes are shown in residue interactions, affecting the B-pocket as well as the beta-2-microglobulin (ß2m)-facing residues of the HLA chain.