Standardizing macromolecular structure files: further efforts are needed.

Investigating large datasets of biological information by automatic procedures may offer chances of progress in knowledge. Recently, tremendous improvements in structural biology have allowed the number of structures in the Protein Data Bank (PDB) archive to increase rapidly, in particular those for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated proteins. However, their automatic analysis can be hampered by the nonuniform descriptors used by authors in some records of the PDB and PDBx/mmCIF files. In this opinion article we highlight the difficulties encountered in automating the analysis of hundreds of structures, suggesting that further standardization of the description of these molecular entities and of their attributes, generalized to the macromolecular structures contained in the PDB, might generate files more suitable for automatized analyses of a large number of structures.

Computational methods to assist in the discovery of pharmacological chaperones for rare diseases.

Pharmacological chaperones are chemical compounds able to bind proteins and stabilize them against denaturation and following degradation. Some pharmacological chaperones have been approved, or are under investigation, for the treatment of rare inborn errors of metabolism, caused by genetic mutations that often can destabilize the structure of the wild-type proteins expressed by that gene. Given that, for rare diseases, there is a general lack of pharmacological treatments, many expectations are poured out on this type of compounds. However, their discovery is not straightforward. In this review, we would like to focus on the computational methods that can assist and accelerate the search for these compounds, showing also examples in which these methods were successfully applied for the discovery of promising molecules belonging to this new category of pharmacologically active compounds.

Predicting the stability of mutant proteins by computational approaches: an overview.

A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naïve users in finding the best option for their needs.

In Silico Analysis of the Effects of Omicron Spike Amino Acid Changes on the Interactions with Human Proteins.

The SARS-CoV-2 variant Omicron is characterized, among others, by more than 30 amino acid changes occurring on the spike glycoprotein with respect to the original SARS-CoV-2 spike protein. We report a comprehensive analysis of the effects of the Omicron spike amino acid changes in the interaction with human antibodies, obtained by modeling them into selected publicly available resolved 3D structures of spike-antibody complexes and investigating the effects of these mutations at structural level. We predict that the interactions of Omicron spike with human antibodies can be either negatively or positively affected by amino acid changes, with a predicted total loss of interactions only in a few complexes. Moreover, our analysis applied also to the spike-ACE2 interaction predicts that these amino acid changes may increase Omicron transmissibility. Our approach can be used to better understand SARS-CoV-2 transmissibility, detectability, and epidemiology and represents a model to be adopted also in the case of other variants.

Investigating the Effects of Amino Acid Variations in Human Menin.

Human menin is a nuclear protein that participates in many cellular processes, as transcriptional regulation, DNA damage repair, cell signaling, cell division, proliferation, and migration, by interacting with many other proteins. Mutations of the gene encoding menin cause multiple endocrine neoplasia type 1 (MEN1), a rare autosomal dominant disorder associated with tumors of the endocrine glands. In order to characterize the structural and functional effects at protein level of the hundreds of missense variations, we investigated by computational methods the wild-type menin and more than 200 variants, predicting the amino acid variations that change secondary structure, solvent accessibility, salt-bridge and H-bond interactions, protein thermostability, and altering the capability to bind known protein interactors. The structural analyses are freely accessible online by means of a web interface that integrates also a 3D visualization of the structure of the wild-type and variant proteins. The results of the study offer insight into the effects of the amino acid variations in view of a more complete understanding of their pathological role.

Performance of Web tools for predicting changes in protein stability caused by mutations.

BACKGROUND: Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. RESULTS: The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. CONCLUSIONS: We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein.

Analysis of the Structure-Function-Dynamics Relationships of GALT Enzyme and of Its Pathogenic Mutant p.Q188R: A Molecular Dynamics Simulation Study in Different Experimental Conditions.

The third step of the catabolism of galactose in mammals is catalyzed by the enzyme galactose-1-phosphate uridylyltransferase (GALT), a homodimeric enzyme with two active sites located in the proximity of the intersubunit interface. Mutations of this enzyme are associated to the rare inborn error of metabolism known as classic galactosemia; in particular, the most common mutation, associated with the most severe phenotype, is the one that replaces Gln188 in the active site of the enzyme with Arg (p.Gln188Arg). In the past, and more recently, the structural effects of this mutation were deduced on the static structure of the wild-type human enzyme; however, we feel that a dynamic view of the proteins is necessary to deeply understand their behavior and obtain tips for possible therapeutic interventions. Thus, we performed molecular dynamics simulations of both wild-type and p.Gln188Arg GALT proteins in the absence or in the presence of the substrates in different conditions of temperature. Our results suggest the importance of the intersubunit interactions for a correct activity of this enzyme and can be used as a starting point for the search of drugs able to rescue the activity of this enzyme in galactosemic patients.

Simulation of the Interactions of Arginine with Wild-Type GALT Enzyme and the Classic Galactosemia-Related Mutant p.Q188R by a Computational Approach.

Classic galactosemia is an inborn error of metabolism associated with mutations that impair the activity and the stability of galactose-1-phosphate uridylyltransferase (GALT), catalyzing the third step in galactose metabolism. To date, no treatments (including dietary galactose deprivation) are able to prevent or alleviate the long-term complications affecting galactosemic patients. Evidence that arginine is able to improve the activity of the human enzyme expressed in a prokaryotic model of classic galactosemia has induced researchers to suppose that this amino acid could act as a pharmacochaperone, but no effects were detected in four galactosemic patients treated with this amino acid. Given that no molecular characterizations of the possible effects of arginine on GALT have been performed, and given that the samples of patients treated with arginine are extremely limited for drawing definitive conclusions at the clinical level, we performed computational simulations in order to predict the interactions (if any) between this amino acid and the enzyme. Our results do not support the possibility that arginine could function as a pharmacochaperone for GALT, but information obtained by this study could be useful for identifying, in the future, possible pharmacochaperones for this enzyme.

The evolution of a Web resource: The Galactosemia Proteins Database 2.0.

Galactosemia Proteins Database 2.0 is a Web-accessible resource collecting information about the structural and functional effects of the known variations associated to the three different enzymes of the Leloir pathway encoded by the genes GALT, GALE, and GALK1 and involved in the different forms of the genetic disease globally called "galactosemia." It represents an evolution of two available online resources we previously developed, with new data deriving from new structures, new analysis tools, and new interfaces and filters in order to improve the quality and quantity of information available for different categories of users. We propose this new resource both as a landmark for the entire world community of galactosemia and as a model for the development of similar tools for other proteins object of variations and involved in human diseases.

Efficient Fludarabine-Activating PNP From Archaea as a Guidance for Redesign the Active Site of E. Coli PNP.

The combination of the gene of purine nucleoside phosphorylase (PNP) from Escherichia coli and fludarabine represents one of the most promising systems in the gene therapy of solid tumors. The use of fludarabine in gene therapy is limited by the lack of an enzyme that is able to efficiently activate this prodrug which, consequently, has to be administered in high doses that cause serious side effects. In an attempt to identify enzymes with a better catalytic efficiency than E. coli PNP towards fludarabine to be used as a guidance on how to improve the activity of the bacterial enzyme, we have selected 5'-deoxy-5'-methylthioadenosine phosphorylase (SsMTAP) and 5'-deoxy-5'-methylthioadenosine phosphorylase II (SsMTAPII), two PNPs isolated from the hyperthermophilic archaeon Sulfolobus solfataricus. Substrate specificity and catalytic efficiency of SsMTAP and SsMTAPII for fludarabine were analyzed by kinetic studies and compared with E. coli PNP. SsMTAP and SsMTAPII share with E. coli PNP a comparable low affinity for the arabinonucleoside but are better catalysts of fludarabine cleavage with k(cat)/K(m) values that are 12.8-fold and 6-fold higher, respectively, than those reported for the bacterial enzyme. A computational analysis of the interactions of fludarabine in the active sites of E. coli PNP, SsMTAP, and SsMTAPII allowed to identify the crucial residues involved in the binding with this substrate, and provided structural information to improve the catalytic efficiency of E. coli PNP by enzyme redesign.

Sirtuin Inhibitor Cambinol Induces Cell Differentiation and Differently Interferes with SIRT1 and 2 at the Substrate Binding Site.

Epigenetic mechanisms finely regulate gene expression and represent potential therapeutic targets. Cambinol is a synthetic heterocyclic compound that inhibits class III histone deacetylases known as sirtuins (SIRTs). The acetylating action that results could be crucial in modulating cellular functions via epigenetic regulations. The main aim of this research was to investigate the effects of cambinol, and its underlying mechanisms, on cell differentiation by combining wet experiments with bioinformatics analyses and molecular docking simulations. Our in vitro study evidenced the ability of cambinol to induce the differentiation in MCF-7, NB4, and 3T3-L1 cell lines. Interestingly, focusing on the latter that accumulated cytoplasmic lipid droplets, the first promising results related to the action mechanisms of cambinol have shown the induction of cell cycle-related proteins (such as p16 and p27) and modulation of the expression of Rb protein and nuclear receptors related to cell differentiation. Moreover, we explored the inhibitory mechanism of cambinol on human SIRT1 and 2 performing in silico molecular simulations by protein-ligand docking. Cambinol, unlike from other sirtuin inhibitors, is able to better interact with the substrate binding site of SIRT1 than with the inhibition site. Additionally, for SIRT2, cambinol partially interacts with the substrate binding site, although the inhibition site is preferred. Overall, our findings suggest that cambinol might contribute to the development of an alternative to the existing epigenetic therapies that modulate SIRTs.

Resources and tools for rare disease variant interpretation.

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Molecular Docking Simulations on Histone Deacetylases (HDAC)-1 and -2 to Investigate the Flavone Binding.

Histone modifications through acetylation are fundamental for remodelling chromatin and consequently activating gene expression. The imbalance between acetylation and deacetylation activity causes transcriptional dysregulation associated with several disorders. Flavones, small molecules of plant origin, are known to interfere with class I histone deacetylase (HDAC) enzymes and to enhance acetylation, restoring cell homeostasis. To investigate the possible physical interactions of flavones on human HDAC1 and 2, we carried out in silico molecular docking simulations. Our data have revealed how flavone, and other two flavones previously investigated, i.e., apigenin and luteolin, can interact as ligands with HDAC1 and 2 at the active site binding pocket. Regulation of HDAC activity by dietary flavones could have important implications in developing epigenetic therapy to regulate the cell gene expression.

Binding of mycotoxins to proteins involved in neuronal plasticity: a combined in silico/wet investigation.

We have applied a combined computational procedure based on inverse and direct docking in order to identify putative protein targets of a panel of mycotoxins and xenobiotic compounds that can contaminate food and that are known to have several detrimental effects on human health. This procedure allowed us to identify a panel of human proteins as possible targets for aflatoxins, gliotoxin, ochratoxin A and deoxynivalenol. Steady-state fluorescence and microscale thermophoresis experiments allowed us to confirm the binding of some of these mycotoxins to acetylcholinesterase and X-linked neuroligin 4, two proteins involved in synapse activity and, particularly for the second protein, neuronal plasticity and development. Considering the possible involvement of X-linked neuroligin 4 in the etiopathogenesis of autism spectrum syndrome, this finding opens up a new avenue to explore the hypothetical role of these xenobiotic compounds in the onset of this pathology.

A theoretical study on predicted protein targets of apple polyphenols and possible mechanisms of chemoprevention in colorectal cancer.

We investigated the potential role of apple phenolic compounds in human pathologies by integrating chemical characterization of phenolic compounds in three apple varieties, computational approaches to identify potential protein targets of the compounds, bioinformatics analyses on data from public archive of gene expression data, and functional analyses to hypothesize the effects of the selected compounds in molecular pathways. Starting by the analytic characterization of phenolic compounds in three apple varieties, i.e. Annurca, Red Delicious, and Golden Delicious, we used computational approaches to verify by reverse docking the potential protein targets of the identified compounds. Direct docking validation of the potential protein-ligand interactions has generated a short list of human proteins potentially bound by the apple phenolic compounds. By considering the known chemo-preventive role of apple antioxidants' extracts against some human pathologies, we performed a functional analysis by comparison with experimental gene expression data and interaction networks, obtained from public repositories. The results suggest the hypothesis that chemo-preventive effects of apple extracts in human pathologies, in particular for colorectal cancer, may be the interference with the activity of nucleotide metabolism and methylation enzymes, similarly to some classes of anticancer drugs.