Molecular interactions between a diphenyl scaffold and PED/PEA15: Implications for type II diabetes therapeutics targeting PED/PEA15 - Phospholipase D1 interaction.

In a recent study, we have identified BPH03 as a promising scaffold for the development of compounds aimed at modulating the interaction between PED/PEA15 (Phosphoprotein Enriched in Diabetes/Phosphoprotein Enriched in Astrocytes 15) and PLD1 (phospholipase D1), with potential applications in type II diabetes therapy. PED/PEA15 is known to be overexpressed in certain forms of diabetes, where it binds to PLD1, thereby reducing insulin-stimulated glucose transport. The inhibition of this interaction reestablishes basal glucose transport, indicating PED as a potential target of ligands capable to recover glucose tolerance and insulin sensitivity. In this study, we employ computational methods to provide a detailed description of BPH03 interaction with PED, evidencing the presence of a hidden druggable pocket within its PLD1 binding surface. We also elucidate the conformational changes that occur during PED interaction with BPH03. Moreover, we report new NMR data supporting the in-silico findings and indicating that BPH03 disrupts the PED/PLD1 interface displacing PLD1 from its interaction with PED. Our study represents a significant advancement toward the development of potential therapeutics for the treatment of type II diabetes.

DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation.

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.

Italian guidelines for the management of irritable bowel syndrome in children and adolescents : Joint Consensus from the Italian Societies of: Gastroenterology, Hepatology and Pediatric Nutrition (SIGENP), Pediatrics (SIP), Gastroenterology and Endoscopy (SIGE) and Neurogastroenterology and Motility (SINGEM).

The irritable bowel syndrome (IBS) is a functional gastrointestinal disorder (FGID), whose prevalence has widely increased in pediatric population during the past two decades. The exact pathophysiological mechanism underlying IBS is still uncertain, thus resulting in challenging diagnosis and management. Experts from 4 Italian Societies participated in a Delphi consensus, searching medical literature and voting process on 22 statements on both diagnosis and management of IBS in children. Recommendations and levels of evidence were evaluated according to the grading of recommendations, assessment, development, and evaluation (GRADE) criteria. Consensus was reached for all statements. These guidelines suggest a positive diagnostic strategy within a symptom-based approach, comprehensive of psychological comorbidities assessment, alarm signs and symptoms' exclusion, testing for celiac disease and, under specific circumstances, fecal calprotectin and C-reactive protein. Consensus also suggests to rule out constipation in case of therapeutic failure. Conversely, routine stool testing for enteric pathogens, testing for food allergy/intolerance or small intestinal bacterial overgrowth are not recommended. Colonoscopy is recommended only in patients with alarm features. Regarding treatment, the consensus strongly suggests a dietary approach, psychologically directed therapies and, in specific conditions, gut-brain neuromodulators, under specialist supervision. Conditional recommendation was provided for both probiotics and specific fibers supplementation. Polyethylene glycol achieved consensus recommendation for specific subtypes of IBS. Secretagogues and 5-HT4 agonists are not recommended in children with IBS-C. Certain complementary alternative therapies, antispasmodics and, in specific IBS subtypes, loperamide and rifaximin could be considered.

Biophysical and Structural Characterization of the Interaction between Human Galectin-3 and the Lipopolysaccharide from Pseudomonas aeruginosa.

Given the significant involvement of galectins in the development of numerous diseases, the aim of the following work is to further study the interaction between galectin-3 (Gal3) and the LPS from Pseudomonas aeruginosa. This manuscript focused on the study of the interaction of the carbohydrate recognition domain of Gal3 with the LPS from Pseudomonas aeruginosa by means of different complementary methodologies, such as circular dichroism; spectrofluorimetry; dynamic and static light scattering and evaluation of the impact of Gal3 on the redox potential membranes of Escherichia coli and P. aeruginosa cells, as well as ITC and NMR studies. This thorough investigation reinforces the hypothesis of an interaction between Gal3 and LPS, unraveling the structural details and providing valuable insights into the formation of these intricate molecular complexes. Taken together, these achievements could potentially prompt the design of therapeutic drugs useful for the development of agonists and/or antagonists for LPS receptors such as galectins as adjunctive therapy for P. aeruginosa.

Study of metalation of thioredoxin by gold(I) therapeutic compounds using combined liquid chromatography/capillary electrophoresis with inductively coupled plasma/electrospray MS/MS detection.

The reactivity of thioredoxin (Trx1) with the Au(I) drug auranofin (AF) and two therapeutic N-heterocyclic carbene (NHC)2-Au(I) complexes (bis [1-methyl-3-acridineimidazolin-2-ylidene]gold(I) tetrafluoroborate (Au3BC) and [1,3-diethyl-4,5-bis(4methoxyphenyl)imidazol-2-ylidene]gold(I) (Au4BC)) was investigated. Direct infusion (DI) electrospray ionization (ESI) mass spectrometry (MS) allowed information on the structure, stoichiometry, and kinetics of formation of Trx-Au adducts. The fragmentation of the formed adducts in the gas phase gave insights into the exact Au binding site within the protein, demonstrating the preference for Trx1 Cys32 or Cys35 of AF or the (NHC)2-Au(I) complex Au3BC, respectively. Reversed-phase HPLC suffered from the difficulty of elution of gold compounds, did not preserve the formed metal-protein adducts, and favored the loss of ligands (phosphine or NHC) from Au(I). These limitations were eliminated by capillary electrophoresis (CE) which enabled the separation of the gold compounds, Trx1, and the formed adducts. The ICP-MS/MS detection allowed the simultaneous quantitative monitoring of the gold and sulfur isotopes and the determination of the metallation extent of the protein. The hyphenation of the mentioned techniques was used for the analysis of Trx1-Au adducts for the first time.

AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis.

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.

Peptoids: Smart and Emerging Candidates for the Diagnosis of Cancer, Neurological and Autoimmune Disorders.

Early detection of fatal and disabling diseases such as cancer, neurological and autoimmune dysfunctions is still desirable yet challenging to improve quality of life and longevity. Peptoids (N-substituted glycine oligomers) are a relatively new class of peptidomimetics, being highly versatile and capable of mimicking the architectures and the activities of the peptides but with a marked resistance to proteases and a propensity to cross the cellular membranes over the peptides themselves. For these properties, they have gained an ever greater interest in applications in bioengineering and biomedical fields. In particular, the present manuscript is to our knowledge the only review focused on peptoids for diagnostic applications and covers the last decade's literature regarding peptoids as tools for early diagnosis of pathologies with a great impact on human health and social behavior. The review indeed provides insights into the peptoid employment in targeted cancer imaging and blood-based screening of neurological and autoimmune diseases, and it aims to attract the scientific community's attention to continuing and sustaining the investigation of these peptidomimetics in the diagnosis field considering their promising peculiarities.

The Biological Action and Structural Characterization of Eryngitin 3 and 4, Ribotoxin-like Proteins from Pleurotus eryngii Fruiting Bodies.

Ribotoxin-like proteins (RL-Ps) are specific ribonucleases found in mushrooms that are able to cleave a single phosphodiester bond located in the sarcin-ricin loop (SRL) of the large rRNA. The cleaved SRL interacts differently with some ribosomal proteins (P-stalk). This action blocks protein synthesis because the damaged ribosomes are unable to interact with elongation factors. Here, the amino acid sequences of eryngitin 3 and 4, RL-Ps isolated from Pleurotus eryngii fruiting bodies, were determined to (i) obtain structural information on this specific ribonuclease family from edible mushrooms and (ii) explore the structural determinants which justify their different biological and antipathogenic activities. Indeed, eryngitin 3 exhibited higher toxicity with respect to eryngitin 4 against tumoral cell lines and model fungi. Structurally, eryngitin 3 and 4 consist of 132 amino acids, most of them identical and exhibiting a single free cysteinyl residue. The amino acidic differences between the two toxins are (i) an additional phenylalanyl residue at the N-terminus of eryngitin 3, not retrieved in eryngitin 4, and (ii) an additional arginyl residue at the C-terminus of eryngitin 4, not retrieved in eryngitin 3. The 3D models of eryngitins show slight differences at the N- and C-terminal regions. In particular, the positive electrostatic surface at the C-terminal of eryngitin 4 is due to the additional arginyl residue not retrieved in eryngitin 3. This additional positive charge could interfere with the binding to the SRL (substrate) or with some ribosomal proteins (P-stalk structure) during substrate recognition.

A conformational rearrangement of the SARS-CoV-2 host protein sigma-1 is required for antiviral activity: insights from a combined in-silico/in-vitro approach.

The development of effective drugs to treat coronavirus infections remains a significant challenge for the scientific community. Recent evidence reports on the sigma-1 receptor (S1R) as a key druggable host protein in the SARS-CoV-1 and SARS-CoV-2 interactomes and shows a potent antiviral activity against SARS-CoV-2 for the S1R antagonist PB28. To improve PB28 activity, we designed and tested a series of its analogues and identified a compound that is fourfold more potent against SARS-CoV-2 than PB28 itself. Interestingly, we found no direct correlation between S1R affinity and SARS-CoV-2 antiviral activity. Building on this, we employed comparative induced fit docking and molecular dynamics simulations to gain insights into the possible mechanism that occurs when specific ligand-protein interactions take place and that may be responsible for the observed antiviral activity. Our findings offer a possible explanation for the experimental observations, provide insights into the S1R conformational changes upon ligand binding and lay the foundation for the rational design of new S1R ligands with potent antiviral activity against SARS-CoV-2 and likely other viruses.

ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators.

The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation ≥0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators.

GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design.

This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. Specifically, GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, which is implicated in many pathological conditions, including COVID-19. The ability of GENERA to de novo design promising candidates for a specific target was assessed using two docking programs, PLANTS and GLIDE. A fitness function based on the Pareto dominance resulting from computed PLANTS and GLIDE scores was applied to demonstrate the algorithm's ability to perform multiobjective optimizations effectively. GENERA can quickly generate focused libraries that produce better scores compared to a starting set of known ACE-2 binders. This study is the first to utilize a DL-based algorithm designed for analogue generation as a mutational operator within a GA framework, representing an innovative approach to target-oriented de novo design.

Disclosing the Preferential Mercury Chelation by SeCys Containing Peptides over Their Cys Analogues.

Methylmercury, mercury (II), and mercury (I) chlorides were found to react with vasopressin, a nonapeptide hormone cyclized by two cysteine residues, and its mono- and diselenium analogues to form several mercury-peptide adducts. The replacement of Cys by SeCys in vasopressin increased the reactivity toward methylmercury, with the predominant formation of -Se/S-Hg-Se-bridged structures and the consequent demethylation of methylmercury. In competitive experiments, CH3HgCl reacted preferentially with the diselenium analogue rather than with vasopressin. The diselenium peptide also showed the capability to displace the CH3Hg moiety bound to S in vasopressin. These results open a promising perspective for the use of selenopeptides for methylmercury chelation and detoxification strategies.

New Insights into the Behavior of NHC-Gold Complexes in Cancer Cells.

Among the non-platinum antitumor agents, gold complexes have received increased attention owing to their strong antiproliferative effects, which generally occur through non-cisplatin-like mechanisms of action. Several studies have revealed that many cytotoxic gold compounds, such as N-heterocyclic carbene (NHC)-gold(I) complexes, are potent thioredoxin reductase (TrxR) inhibitors. Many other pathways have been supposed to be altered by gold coordination to protein targets. Within this frame, we have selected two gold(I) complexes based on aromatic ligands to be tested on cancer cells. Differently from bis [1,3-diethyl-4,5-bis(4-methoxyphenyl)imidazol-2-ylidene]gold(I) bromide (Au4BC), bis [1-methyl-3-acridineimidazolin-2-ylidene]gold(I) tetrafluoroborate (Au3BC) inhibited TrxR1 activity in vitro. Treatment of Huh7 hepatocellular carcinoma (HCC) cells, and MDA-MB-231 triple-negative breast cancer (TNBC) cells, with Au4BC inhibited cell viability, increased reactive oxygen species (ROS) levels, caused DNA damage, and induced autophagy and apoptosis. Notably, we found that, although Au3BC inhibited TrxR1 activity, no effect on the cell viabilities of HCC and BC cells was observed. At the molecular level, Au3BC induced a protective response mechanism in Huh7 and MDA-MB-231 cells, by inducing up-regulation of RAD51 and p62 protein expression, two proteins involved in DNA damage repair and autophagy, respectively. RAD51 gene knock-down in HCC cells increased cell sensitivity to Au3BC by significant reduction of cell viability, induction of DNA damage, and induction of apoptosis and autophagy. All together, these results suggest that the tested NHC-Gold complexes, Au3BC and Au4BC, showed different mechanisms of action, either dependent or independent of TrxR1 inhibition. As a result, Au3BC and Au4BC were found to be promising candidates as anticancer drugs for the treatment of HCC and BC.

Endocannabinoid Degradation Enzyme Inhibitors as Potential Antipsychotics: A Medicinal Chemistry Perspective.

The endocannabinoid system (ECS) plays a very important role in numerous physiological and pharmacological processes, such as those related to the central nervous system (CNS), including learning, memory, emotional processing, as well pain control, inflammatory and immune response, and as a biomarker in certain psychiatric disorders. Unfortunately, the half-life of the natural ligands responsible for these effects is very short. This perspective describes the potential role of the inhibitors of the enzymes fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MGL), which are mainly responsible for the degradation of endogenous ligands in psychic disorders and related pathologies. The examination was carried out considering both the impact that the classical exogenous ligands such as Δ9-tetrahydrocannabinol (THC) and (-)-trans-cannabidiol (CBD) have on the ECS and through an analysis focused on the possibility of predicting the potential toxicity of the inhibitors before they are subjected to clinical studies. In particular, cardiotoxicity (hERG liability), probably the worst early adverse reaction studied during clinical studies focused on acute toxicity, was predicted, and some of the most used and robust metrics available were considered to select which of the analyzed compounds could be repositioned as possible oral antipsychotics.

Development of N-(1-Adamantyl)benzamides as Novel Anti-Inflammatory Multitarget Agents Acting as Dual Modulators of the Cannabinoid CB2 Receptor and Fatty Acid Amide Hydrolase.

Cannabinoid type 2 receptor (CB2R), belonging to the endocannabinoid system, is overexpressed in pathologies characterized by inflammation, and its activation counteracts inflammatory states. Fatty acid amide hydrolase (FAAH) is an enzyme responsible for the degradation of the main endocannabinoid anandamide; thus, the simultaneous CB2R activation and FAAH inhibition may be a synergistic anti-inflammatory strategy. Encouraged by principal component analysis (PCA) data identifying a wide chemical space shared by CB2R and FAAH ligands, we designed a small library of adamantyl-benzamides, as potential dual agents, CB2R agonists, and FAAH inhibitors. The new compounds were tested for their CB2R affinity/selectivity and CB2R and FAAH activity. Derivatives 13, 26, and 27, displaying the best pharmacodynamic profile as CB2R full agonists and FAAH inhibitors, decreased pro-inflammatory and increased anti-inflammatory cytokines production. Molecular docking simulations complemented the experimental findings by providing a molecular rationale behind the observed activities. These multitarget ligands constitute promising anti-inflammatory agents.

Advances in Nucleic Acid Research: Exploring the Potential of Oligonucleotides for Therapeutic Applications and Biological Studies.

In recent years, nucleic acids have emerged as powerful biomaterials, revolutionizing the field of biomedicine. This review explores the multifaceted applications of nucleic acids, focusing on their pivotal role in various biomedical applications. Nucleic acids, including deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), possess unique properties such as molecular recognition ability, programmability, and ease of synthesis, making them versatile tools in biosensing and for gene regulation, drug delivery, and targeted therapy. Their compatibility with chemical modifications enhances their binding affinity and resistance to degradation, elevating their effectiveness in targeted applications. Additionally, nucleic acids have found utility as self-assembling building blocks, leading to the creation of nanostructures whose high order underpins their enhanced biological stability and affects the cellular uptake efficiency. Furthermore, this review delves into the significant role of oligonucleotides (ODNs) as indispensable tools for biological studies and biomarker discovery. ODNs, short sequences of nucleic acids, have been instrumental in unraveling complex biological mechanisms. They serve as probes for studying gene expression, protein interactions, and cellular pathways, providing invaluable insights into fundamental biological processes. By examining the synergistic interplay between nucleic acids as powerful biomaterials and ODNs as indispensable tools for biological studies and biomarkers, this review highlights the transformative impact of these molecules on biomedical research. Their versatile applications not only deepen our understanding of biological systems but also are the driving force for innovation in diagnostics and therapeutics, ultimately advancing the field of biomedicine.

High-Resolution Conformational Analysis of RGDechi-Derived Peptides Based on a Combination of NMR Spectroscopy and MD Simulations.

The crucial role of integrin in pathological processes such as tumor progression and metastasis formation has inspired intense efforts to design novel pharmaceutical agents modulating integrin functions in order to provide new tools for potential therapies. In the past decade, we have investigated the biological proprieties of the chimeric peptide RGDechi, containing a cyclic RGD motif linked to an echistatin C-terminal fragment, able to specifically recognize αvß3 without cross reacting with αvß5 and αIIbß3 integrin. Additionally, we have demonstrated using two RGDechi-derived peptides, called RGDechi1-14 and ψRGDechi, that chemical modifications introduced in the C-terminal part of the peptide alter or abolish the binding to the αvß3 integrin. Here, to shed light on the structural and dynamical determinants involved in the integrin recognition mechanism, we investigate the effects of the chemical modifications by exploring the conformational space sampled by RGDechi1-14 and ψRGDechi using an integrated natural-abundance NMR/MD approach. Our data demonstrate that the flexibility of the RGD-containing cycle is driven by the echistatin C-terminal region of the RGDechi peptide through a coupling mechanism between the N- and C-terminal regions.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques.

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

Exploring the Molecular Interactions of Symmetrical and Unsymmetrical Selenoglycosides with Human Galectin-1 and Galectin-3.

Galectins (Gals) are small cytosolic proteins that bind ß-galactoside residues via their evolutionarily conserved carbohydrate recognition domain. Their dysregulation has been shown to be associated with many diseases. Consequently, targeting galectins for clinical applications has become increasingly relevant to develop tailored inhibitors selectively for one galectin. Accordingly, binding studies providing the molecular details of the interaction between galectin and inhibitor may be useful for the rational design of potent and selective antagonists. Gal-1 and Gal-3 are among the best-studied galectins, mainly for their roles in cancer progression; therefore, the molecular details of their interaction with inhibitors are demanded. This work gains more value by focusing on the interaction between Gal-1 and Gal-3 with the selenylated analogue of the Gal inhibitor thiodigalactose, characterized by a selenoglycoside bond (SeDG), and with unsymmetrical diglycosyl selenides (unsym(Se). Gal-1 and Gal-3 were produced heterologously and biophysically characterized. Interaction studies were performed by ITC, NMR spectroscopy, and MD simulation, and thermodynamic values were discussed and integrated with spectroscopic and computational results. The 3D complexes involving SeDG when interacting with Gal-1 and Gal-3 were depicted. Overall, the collected results will help identify hot spots for the design of new, better performing, and more specific Gal inhibitors.

A Comparative Molecular Dynamics Study of Selected Point Mutations in the Shwachman-Bodian-Diamond Syndrome Protein SBDS.

The Shwachman-Diamond Syndrome (SDS) is an autosomal recessive disease whose majority of patients display mutations in a ribosome assembly protein named Shwachman-Bodian-Diamond Syndrome protein (SBDS). A specific therapy for treating this rare disease is missing, due to the lack of knowledge of the molecular mechanisms responsible for its pathogenesis. Starting from the observation that SBDS single-point mutations, localized in different domains of the proteins, are responsible for an SDS phenotype, we carried out the first comparative Molecular Dynamics simulations on three SBDS mutants, namely R19Q, R126T and I212T. The obtained 450-ns long trajectories were compared with those returned by both the open and closed forms of wild type SBDS and strongly indicated that two distinct conformations (open and closed) are both necessary for the proper SBDS function, in full agreement with recent experimental observations. Our study supports the hypothesis that the SBDS function is governed by an allosteric mechanism involving domains I and III and provides new insights into SDS pathogenesis, thus offering a possible starting point for a specific therapeutic option.