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.

An overview of bioinformatics courses delivered at the academic level in Italy: Reflections and recommendations from BITS.

In Italian universities, bioinformatics courses are increasingly being incorporated into different study paths. However, the content of bioinformatics courses is usually selected by the professor teaching the course, in the absence of national guidelines that identify the minimum indispensable knowledge in bioinformatics that undergraduate students from different scientific fields should achieve. The Training&Teaching group of the Bioinformatics Italian Society (BITS) proposed to university professors a survey aimed at portraying the current situation of bioinformatics courses within undergraduate curricula in Italy (i.e., bioinformatics courses activated within both bachelor's and master's degrees). Furthermore, the Training&Teaching group took a cue from the survey outcomes to develop recommendations for the design and the inclusion of bioinformatics courses in academic curricula. Here, we present the outcomes of the survey, as well as the BITS recommendations, with the hope that they may support BITS members in identifying learning outcomes and selecting content for their bioinformatics courses. As we share our effort with the broader international community involved in teaching bioinformatics at academic level, we seek feedback and thoughts on our proposal and hope to start a fruitful debate on the topic, including how to better fulfill the real bioinformatics knowledge needs of the research and the labor market at both the national and international level.

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.

A review on drug repurposing applicable to COVID-19.

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.

MOTIVATION: Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases. RESULTS: By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. AVAILABILITY: The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. SUPPLEMENTARY INFORMATION: A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.

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.

The Odd Couple(s): An Overview of Beta-Lactam Antibiotics Bearing More Than One Pharmacophoric Group.

ß-lactam antibiotics are among the most important and widely used antimicrobials worldwide and are comprised of a large family of compounds, obtained by chemical modifications of the common scaffolds. Usually these modifications include the addition of active groups, but less frequently, molecules were synthesized in which either two ß-lactam rings were joined to create a single bifunctional compound, or the azetidinone ring was joined to another antibiotic scaffold or another molecule with a different activity, in order to create a molecule bearing two different pharmacophoric functions. In this review, we report some examples of these derivatives, highlighting their biological properties and discussing how this strategy can lead to the development of innovative antibiotics that can represent either novel weapons against the rampant increase of antimicrobial resistance, or molecules with a broader spectrum of action.

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.

New compounds for a good old class: Synthesis of two Β-lactam bearing cephalosporins and their evaluation with a multidisciplinary approach.

Antimicrobial resistance is spreading massively in the world and is becoming one of the main health threats of the 21st century. One of the possible strategies to overcome this problem is to modify the known classes of antibiotics in a rational way, with the aim of tuning their efficacy. In this paper, we present the synthesis and the evaluation of the biological activity of a series of two ß-lactam bearing cephalosporin derivatives, in which an additional isolated azetidinone ring, bearing different substituents, is joined to the classical cephalosporanic nucleus by a chain of variable length. A computational approach has been also applied in order to predict the molecular interactions between some representative derivatives and selected penicillin-binding proteins, the natural targets of ß-lactam antibiotics. All these derivatives are active against Gram-positive bacteria, with MIC100 comparable or even better than that of the reference antibiotic ceftriaxone, and show no or very low cytotoxic activity on different cell lines. Overall, these molecules appear to be able to exert their activity in particular against microorganisms belonging to some of the species more involved in the development of multidrug resistance.

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.

Computational analysis of the interactions of a novel cephalosporin derivative with ß-lactamases.

BACKGROUND: One of the main concerns of the modern medicine is the frightening spread of antimicrobial resistance caused mainly by the misuse of antibiotics. The researchers worldwide are actively involved in the search for new classes of antibiotics, and for the modification of known molecules in order to face this threatening problem. We have applied a computational approach to predict the interactions between a new cephalosporin derivative containing an additional ß-lactam ring with different substituents, and several serine ß-lactamases representative of the different classes of this family of enzymes. RESULTS: The results of the simulations, performed by using a covalent docking approach, has shown that this compound, although able to bind the selected ß-lactamases, has a different predicted binding score for the two ß-lactam rings, suggesting that one of them could be more resistant to the attack of these enzymes and stay available to perform its bactericidal activity. CONCLUSIONS: The detailed analysis of the complexes obtained by these simulations suggests possible hints to modulate the affinity of this compound towards these enzymes, in order to develop new derivatives with improved features to escape to degradation.

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.

Interaction of human chymase with ginkgolides, terpene trilactones of Ginkgo biloba investigated by molecular docking simulations.

The search for natural chymase inhibitors has a good potential to provide a novel therapeutic approach against the cardiovascular diseases and other heart ailments. We selected from literature 20 promising Ginkgo biloba compounds, and tested them for their potential ability to bind chymase enzyme using docking and a deep analysis of surface pocket features. Docking results indicated that the compounds may interact with the active site of human chymase, with favorable distinct interactions with important residues Lys40, His57, Lys192, Phe191, Val146, Ser218, Gly216, and Ser195. In particular, proanthocyanidin is the one with the best-predicted binding energy, with seven hydrogen bonds. Interestingly, all active G. biloba compounds have formed the hydrogen bond interactions with the positively charged Lys192 residue at the active site, involved in the mechanism of pH enhancement for the cleavage of angiotensin I site. Ginkgolic acid and proanthocyanidin have better predicted binding energy towards chymase than other serine proteases, i.e kallikrein, tryptase and elastase, suggesting specificity for chymase inhibition. Our study suggests these G. biloba compounds are a promising starting point for developing chymase inhibitors for the potential development of future drugs.

Association Analysis of Noncoding Variants in Neuroligins 3 and 4X Genes with Autism Spectrum Disorder in an Italian Cohort.

Since involved in synaptic transmission and located on X-chromosome, neuroligins 3 and 4X have been studied as good positional and functional candidate genes for autism spectrum disorder pathogenesis, although contradictory results have been reported. Here, we performed a case-control study to assess the association between noncoding genetic variants in NLGN3 and NLGN4X genes and autism, in an Italian cohort of 202 autistic children analyzed by high-resolution melting. The results were first compared with data from 379 European healthy controls (1000 Genomes Project) and then with those from 1061 Italian controls genotyped by Illumina single nucleotide polymorphism (SNP) array 1M-duo. Statistical evaluations were performed using Plink v1.07, with the Omnibus multiple loci approach. According to both the European and the Italian control groups, a 6-marker haplotype on NLGN4X (rs6638575(G), rs3810688(T), rs3810687(G), rs3810686(C), rs5916269(G), rs1882260(T)) was associated with autism (odd ratio = 3.58, p-value = 2.58 × 10-6 for the European controls; odds ratio = 2.42, p-value = 6.33 × 10-3 for the Italian controls). Furthermore, several haplotype blocks at 5-, 4-, 3-, and 2-, including the first 5, 4, 3, and 2 SNPs, respectively, showed a similar association with autism. We provide evidence that noncoding polymorphisms on NLGN4X may be associated to autism, suggesting the key role of NLGN4X in autism pathophysiology and in its male prevalence.

Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics.

This Preface introduces the content of the BioMed Central journal Supplements related to BITS2014 meeting, held in Rome, Italy, from the 26th to the 28th of February, 2014.

Environment, dysbiosis, immunity and sex-specific susceptibility: a translational hypothesis for regressive autism pathogenesis.

BACKGROUND: Autism is an increasing neurodevelopmental disease that appears by 3 years of age, has genetic and/or environmental etiology, and often shows comorbid situations, such as gastrointestinal (GI) disorders. Autism has also a striking sex-bias, not fully genetically explainable. OBJECTIVE: Our goal was to explain how and in which predisposing conditions some compounds can impair neurodevelopment, why this occurs in the first years of age, and, primarily, why more in males than females. METHODS: We reviewed articles regarding the genetic and environmental etiology of autism and toxins effects on animal models selected from PubMed and databases about autism and toxicology. DISCUSSION: Our hypothesis proposes that in the first year of life, the decreasing of maternal immune protection and child immune-system immaturity create an immune vulnerability to infection diseases that, especially if treated with antibiotics, could facilitate dysbiosis and GI disorders. This condition triggers a vicious circle between immune system impairment and increasing dysbiosis that leads to leaky gut and neurochemical compounds and/or neurotoxic xenobiotics production and absorption. This alteration affects the 'gut-brain axis' communication that connects gut with central nervous system via immune system. Thus, metabolic pathways impaired in autistic children can be affected by genetic alterations or by environment-xenobiotics interference. In addition, in animal models many xenobiotics exert their neurotoxicity in a sex-dependent manner. CONCLUSIONS: We integrate fragmented and multi-disciplinary information in a unique hypothesis and first disclose a possible environmental origin for the imbalance of male:female distribution of autism, reinforcing the idea that exogenous factors are related to the recent rise of this disease.