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Protein misfolding and aggregation are hallmarks of many severe neurodegenerative diseases including Alzheimer's, Parkinson's and Huntington's disease. As a supramolecular ligand that binds to lysine and arginine residues, the molecular tweezer CLR01 was found to modify the aggregation pathway of disease-relevant proteins in vitro and in vivo with beneficial effects on toxicity. However, the molecular mechanisms of how tweezers exert these effects remain mainly unknown, hampering further drug development. Here, we investigate the modulation mechanism of unfolding and aggregation pathways of SOD1, which are involved in amyotrophic lateral sclerosis (ALS), by CLR01. Using a truncated version of the wildtype SOD1 protein, SOD1bar , we show that CLR01 acts on the first step of the aggregation pathway, the unfolding of the SOD1 monomer. CLR01 increases, by ∼10 °C, the melting temperatures of the A4V and G41D SOD1 mutants, which are commonly observed mutations in familial ALS. Molecular dynamics simulations and binding free energy calculations as well as native mass spectrometry and mutational studies allowed us to identify K61 and K92 as binding sites for the tweezers to mediate the stability increase. The data suggest that the modulation of SOD1 conformational stability is a promising target for future developments of supramolecular ligands against neurodegenerative diseases.

Virtual screening of protein-protein and protein-peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein-protein complexes have been proposed, methods specifically developed to predict protein-peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein-protein and protein-peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein-protein and protein-peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

The respiratory tract is a major entry site for microbial pathogens. To combat bacterial infections, the immune system has various defense mechanisms at its disposal, including antimicrobial peptides (AMPs). To search for novel AMPs from the respiratory tract, a peptide library from human broncho-alveolar-lavage (BAL) fluid was screened for antimicrobial activity by radial diffusion assays allowing the efficient detection of antibacterial activity within a small sample size. After repeated testing-cycles and subsequent purification, we identified ß-2-microglobulin (B2M) in antibacterially active fractions. B2M belongs to the MHC-1 receptor complex present at the surface of nucleated cells. It is known to inhibit the growth of Listeria monocytogenes and Escherichia coli and to facilitate phagocytosis of Staphylococcus aureus. Using commercially available B2M we confirmed a dose-dependent inhibition of Pseudomonas aeruginosa and L. monocytogenes. To characterize AMP activity within the B2M sequence, peptide fragments of the molecule were tested for antimicrobial activity. Activity could be localized to the C-terminal part of B2M. Investigating pH dependency of the antimicrobial activity of B2M demonstrated an increased activity at pH values of 5.5 and below, a hallmark of infection and inflammation. Sytox green uptake into bacterial cells following the exposure to B2M was determined and revealed a pH-dependent loss of bacterial membrane integrity. TEM analysis showed areas of disrupted bacterial membranes in L. monocytogenes incubated with B2M and high amounts of lysed bacterial cells. In conclusion, B2M as part of a ubiquitous cell surface complex may represent a potent antimicrobial agent by interfering with bacterial membrane integrity. CA extern

Enzyme subunit interfaces have remarkable potential in drug design as both target and scaffold for their own inhibitors. We show an evolution-driven strategy for the de novo design of peptide inhibitors targeting interfaces of the Escherichia coli FoF1-ATP synthase as a case study. The evolutionary algorithm ROSE was applied to generate diversity-oriented peptide libraries by engineering peptide fragments from ATP synthase interfaces. The resulting peptides were scored with PPI-Detect, a sequence-based predictor of protein-protein interactions. Two selected peptides were confirmed by in vitro inhibition and binding tests. The proposed methodology can be widely applied to design peptides targeting relevant interfaces of enzymatic complexes.

The Central Limit Free Energy Perturbation (CL-FEP) approach, based on the Free Energy Perturbation (FEP) theory and the Central Limit (CL) theorem, allows evaluating the FEP identity directly from the energy samples of the end states of a system transformation. In CL-FEP, energies obtained from explicit solvent simulations are used to estimate the absolute free energy change. No fitted parameters are introduced in our implementation, and no stratification is needed to obtain accurate free energy evaluations. We first illustrate the applicability of CL-FEP in four dissimilar benchmark systems from host-guest to proteins-peptide complexes, for which the deviation from the experimental values was below 1 kcal/mol. Next, we extended the validation to three sets of complexes comprising 25 host-guest and protein-ligand systems. There, the mean absolute error of the free energy changes calculated with CL-FEP is between 1.1 and 1.4 kcal/mol among the different sets, which is in the range of accuracy of the most reliable free energy approaches applied to these systems. CL-FEP is an unbiased free energy change estimator that also profits from a bootstrapping algorithm, which makes the evaluation of convergence and confidence an inherent component of the procedure. Finally, we developed CLFEP.pl, an automatic tool to compute free energy changes directly from the energy output of standard molecular dynamics simulations.

Description, DNA nanostructures invert the catalytic response of thrombin to substrates of opposite net charge., DNA-scaffolded enzymes typically show altered kinetic properties; however, the mechanism behind this phenomenon is still poorly understood. We address this question using thrombin, a model of allosterically regulated serine proteases, encaged into DNA origami cavities with distinct structural and electrostatic features. We compare the hydrolysis of substrates that differ only in their net charge due to a terminal residue far from the cleavage site and presumably involved in the allosteric activation of thrombin. Our data show that the reaction rate is affected by DNA/substrate electrostatic interactions, proportionally to the degree of DNA/enzyme tethering. For substrates of opposite net charge, this leads to an inversion of the catalytic response of the DNA-scaffolded thrombin when compared to its freely diffusing counterpart. Hence, by altering the electrostatic environment nearby the encaged enzyme, DNA nanostructures interfere with charge-dependent mechanisms of enzyme-substrate recognition and may offer an alternative tool to regulate allosteric processes through spatial confinement.

Modulation of protein–protein interactions (PPIs) is essential for understanding and tuning biologically relevant processes. Although inhibitors for PPIs are widely used, the field still lacks the targeted design of stabilizers. Here, we report unnatural stabilizers based on the combination of multivalency effects and the artificial building block guanidiniocarbonylpyrrol (GCP), an arginine mimetic. Unlike other GCP-based ligands that modulate PPIs in different protein targets, only a tetrameric design shows potent activity as stabilizer of the 14-3-3ζ/C-Raf and 14-3-3ζ/Tau complexes in the low-micromolar range. This evidences the role of multivalency for achieving higher specificity in the modulation of PPIs.

Targeting specific protein binding sites to interfere with protein-protein interactions (PPIs) is crucial for the rational modulation of biologically relevant processes. Survivin, which is highly overexpressed in most cancer cells and considered to be a key player of carcinogenesis, features two functionally relevant binding sites. Here, we demonstrate selective disruption of the Survivin/Histone H3 or the Survivin/Crm1 interaction using a supramolecular approach. By rational design we identified two structurally related ligands (LNES and LHIS), capable of selectively inhibiting these PPIs, leading to a reduction in cancer cell proliferation.

High-throughput methods in science have created a trend to generate massive amount of data that challenge our ability to mine and search through massive information spaces. Thus more efficient and effective solutions for data analysis and optimization are required continuously. The best solutions for many problems-solving approaches in science could have many sources of inspiration coming from diverse natural phenomena. In this context, most Artificial Intelligence (AI) approaches benefit from emulation natural processes for their information processing strategy. Among the AI protocols, meta-heuristic algorithms for learning model and optimization have exploited a number of biological phenomena leading to highly effective search and learning engines. Examples of these processes are the ant colony organization, brain function and genetics among others. The evolution has turned all these biological events in highly efficient procedures, whose basics principles have then provided an excellent ground of new computational algorithms The aim of this report is pave the way to a new class of nature-based meta-heuristic methods which shall be based on diverse chemical and biomolecular systems. We present five examples from different subjects of Chemistry like Organic Chemistry, Chemical Physics and Biomolecules; and introduce how computational models could be inferred from them. Besides, we develop one of these models, in detail, which is based on protein evolution and folding principles. We consider that the wealth of systems and processes related to Chemistry, as those described in the present communication, might boost the development of relevant meta-heuristic and classification algorithms in upcoming years.

Lipases are ubiquitously used in chemo-enzymatic synthesis and industrial applications. Nevertheless, the modulation of the activity of lipases by organic solvents still is not fully understood at the molecular level. We systematically investigated the activity and structure of lipase A from Bacillus subtilis in binary water-organic solvent mixtures of dimethyl sulfoxide (DMSO), acetonitrile (ACN), and isopropyl alcohol (IPA) using activity assays, fluorescence spectroscopy, molecular dynamics (MD) simulations, and FRET/MD analysis. The enzymatic activity strongly depended on the type and amount of organic solvent in the reaction media. Whereas IPA and ACN reduced the activity of the enzyme, small concentrations of DMSO led to lipase activation via an uncompetitive mechanism. DMSO molecules did not directly interfere with the binding of the substrate in the active site, contrary to what is known for other solvents and enzymes. We propose that the His156-Asp133 interaction, the binding of organic molecules to the active site, and the water accessibility of the substrate are key factors modulating the catalytic activity. Furthermore, we rationalized the role of solvent descriptors on the regulation of enzymatic activity in mixtures with low concentrations of the organic molecule, with prospective implications for the optimization of biocatalytic processes via solvent tuning.

The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us to exploit the information content in PPI datasets. However, the numerical codification of these datasets often influences the performance of data mining approaches. Here, we introduce a procedure for the general-purpose numerical codification of polypeptides. This procedure transforms pairs of amino acid sequences into a machine learning-friendly vector, whose elements represent numerical descriptors of residues in proteins. We used this numerical encoding procedure for the development of a support vector machine model (PPI-Detect), which allows predicting whether two proteins will interact or not. PPI-Detect (https://ppi-detect.zmb.uni-due.de/) outperforms state of the art sequence-based predictors of PPI. We employed PPI-Detect for the analysis of derivatives of EPI-X4, an endogenous peptide inhibitor of CXCR4, a G-protein-coupled receptor. There, we identified with high accuracy those peptides which bind better than EPI-X4 to the receptor. Also using PPI-Detect, we designed a novel peptide and then experimentally established its anti-CXCR4 activity. © 2019 Wiley Periodicals, Inc.

Survivin’s dual function as apoptosis inhibitor and regulator of cell proliferation is mediated via its interaction with the export receptor CRM1. This protein–protein interaction represents an attractive target in cancer research and therapy. Here, we report a sophisticated strategy addressing Survivin’s nuclear export signal (NES), the binding site of CRM1, with advanced supramolecular tweezers for lysine and arginine. These were covalently connected to small peptides resembling the natural, self-complementary dimer interface which largely overlaps with the NES. Several biochemical methods demonstrated sequence-selective NES recognition and interference with the critical receptor interaction. These data were strongly supported by molecular dynamics simulations and multiscale computational studies. Rational design of lysine tweezers equipped with a peptidic recognition element thus allowed to address a previously unapproachable protein surface area. As an experimental proof-of-principle for specific transport signal interference, this concept should be transferable to any protein epitope with a flanking well-accessible lysine., Survivin’s dual function as apoptosis inhibitor and regulator of cell proliferation is mediated via its interaction with the export receptor CRM1. Here authors report a strategy addressing its dimer interface overlapping with the nuclear export signal, the binding site of CRM1, with advanced supramolecular tweezers for lysine and arginine.

Broad-spectrum antivirals are powerful weapons against dangerous viruses where no specific therapy exists, as in the case of the ongoing SARS-CoV-2 pandemic. We discovered that a lysine- and arginine-specific supramolecular ligand (CLR01) destroys enveloped viruses, including HIV, Ebola, and Zika virus, and remodels amyloid fibrils in semen that promote viral infection. Yet, it is unknown how CLR01 exerts these two distinct therapeutic activities. Here, we delineate a novel mechanism of antiviral activity by studying the activity of tweezer variants: the “phosphate tweezer” CLR01, a “carboxylate tweezer” CLR05, and a “phosphate clip” PC. Lysine complexation inside the tweezer cavity is needed to antagonize amyloidogenesis and is only achieved by CLR01. Importantly, CLR01 and CLR05 but not PC form closed inclusion complexes with lipid head groups of viral membranes, thereby altering lipid orientation and increasing surface tension. This process disrupts viral envelopes and diminishes infectivity but leaves cellular membranes intact. Consequently, CLR01 and CLR05 display broad antiviral activity against all enveloped viruses tested, including herpesviruses, Measles virus, influenza, and SARS-CoV-2. Based on our mechanistic insights, we potentiated the antiviral, membrane-disrupting activity of CLR01 by introducing aliphatic ester arms into each phosphate group to act as lipid anchors that promote membrane targeting. The most potent ester modifications harbored unbranched C4 units, which engendered tweezers that were approximately one order of magnitude more effective than CLR01 and nontoxic. Thus, we establish the mechanistic basis of viral envelope disruption by specific tweezers and establish a new class of potential broad-spectrum antivirals with enhanced activity.

Enzyme subunit interfaces have remarkable potential in drug design as both target and scaffold for their own inhibitors. We show an evolution-driven strategy for the de novo design of peptide inhibitors targeting interfaces of the Escherichia coli FoF1-ATP synthase as a case study. The evolutionary algorithm ROSE was applied to generate diversity-oriented peptide libraries by engineering peptide fragments from ATP synthase interfaces. The resulting peptides were scored with PPI-Detect, a sequence-based predictor of protein-protein interactions. Two selected peptides were confirmed by in vitro inhibition and binding tests. The proposed methodology can be widely applied to design peptides targeting relevant interfaces of enzymatic complexes.

Protein Lys methylation plays a critical role in numerous cellular processes, but it is challenging to identify Lys methylation in a systematic manner. Here we present an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We develop MethylSight, a program that predicts Kme events solely on the physicochemical properties of residues surrounding the putative methylation sites, which then requires validation by targeted MS. Using this approach, we identify 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts that Lys methylation is a prevalent post-translational modification in the human proteome. Our work provides a useful resource for guiding systematic exploration of the role of Lys methylation in human health and disease. Biggar et al. develop an algorithm to identify lysine methylation sites and use this resource to provide insight into the potential of the methyllysine proteome. The results also validate 45 new histone methylation sites by targeted mass spectrometry and show that one of these sites, H2B-K43me2, is a substrate of the KDM5B demethylase. CA extern

To date, no effective vaccines or therapies are available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pandemic agent of the coronavirus disease 2019 (COVID-19). Due to their safety, efficacy and specificity, peptide inhibitors hold great promise for the treatment of newly emerging viral pathogens. Based on the known structures of viral proteins and their cellular targets, antiviral peptides can be rationally designed and optimized. The resulting peptides may be highly specific for their respective targets and particular viral pathogens or exert broad antiviral activity. Here, we summarize the current status of peptides inhibiting SARS-CoV-2 entry and outline the strategies used to design peptides targeting the ACE2 receptor or the viral Spike protein and its activating proteases furin, transmembrane serine protease 2 (TMPRSS2), or cathepsin L. In addition, we present approaches used against related viruses such as SARS-CoV-1 that might be implemented for inhibition of SARS-CoV-2 infection., Graphical abstract Unlabelled Image

Biofilms are rigid and largely impenetrable three-dimensional matrices constituting virulence determinants of various pathogenic bacteria. Here, we demonstrate that molecular tweezers, unique supramolecular artificial receptors, modulate biofilm formation of Staphylococcus aureus. In particular, the tweezers affect the structural and assembly properties of phenol-soluble modulin α1 (PSMα1), a biofilm-scaffolding functional amyloid peptide secreted by S. aureus. The data reveal that CLR01, a diphosphate tweezer, exhibits significant S. aureus biofilm inhibition and disrupts PSMα1 self-assembly and fibrillation, likely through inclusion of lysine side chains of the peptide. In comparison, different peptide binding occurs in the case of CLR05, a tweezer containing methylenecarboxylate units, which exhibits lower affinity for the lysine residues yet disrupts S. aureus biofilm more strongly than CLR01. Our study points to a possible role for molecular tweezers as potent biofilm inhibitors and antibacterial agents, particularly against untreatable biofilm-forming and PSM-producing bacteria, such as methicillin-resistant S. aureus.

Computational tools for the analysis of protein data and the prediction of biological properties are essential in life sciences and biomedical research. Here, we introduce ProtDCal‐Suite, a web server comprising a set of machine learning‐based methods for studying proteins. The main module of ProtDCal‐Suite is the ProtDCal software. ProtDCal translates the structural information of proteins into numerical descriptors that serve as input to machine‐learning techniques. The ProtDCal‐Suite server also incorporates a post‐processing optional stage that allows ranking and filtering the obtained descriptors by computing their Shannon entropy values across the input set of proteins. ProtDCal's codification was used in the development of models for the prediction of specific protein properties. Thus, the other modules of ProtDCal‐Suite are protein analysis tools implemented using ProtDCal's descriptors. Among them are PPI‐Detect, for predicting the interaction likelihood of protein–protein and protein–peptide pairs, Enzyme Identifier, for identifying enzymes from amino acid sequences or 3D structures, and Pred‐NGlyco, for predicting N‐glycosylation sites. ProtDCal‐Suite is freely accessible at https://protdcal.zmb.uni‐due.de.

SUMMARYProtein Lys methylation plays a critical role in numerous cellular processes, yet it has been challenging to identify Lys methylation in a systematic manner. We present here an approach combining in silico prediction with targeted mass spectrometry (MS) to identify Lys methylation (Kme) sites at the proteome level. We have developed MethylSight, a program that predicts Kme events solely on physicochemical and biochemical properties of putative methylation sites, which can then be validated by targeted MS. Using this approach, we have identified 70 new histone Kme marks with a 90% validation rate. H2BK43me2, which undergoes dynamic changes during stem cell differentiation, is found to be a substrate of KDM5b. Furthermore, MethylSight predicts ~50,000 Kme sites in non-histone proteins with high confidence, suggesting that Lys methylation is a prevalent post-translational modification. Our work provides a useful resource for systematic exploration of the role of Lys methylation in human health and disease.

Previous studies have indicated the presence of defined interactions between oligo or poly(ethylene glycol) (OEG or PEG) and lysine residues. In these interactions, the OEG or PEG residues “wrap around” the lysine amino group, thereby enabling complexation of the amino group by the ether oxygen residues. The resulting biochemical binding affinity and thus biological relevance of this supramolecular interaction however remains unclear so far. Here, we report that OEG-containing phosphophenol ether inhibitors of 14-3-3 proteins also display such a “lysine-wrapping” binding mode. For better investigating the biochemical relevance of this binding mode, we made use of the dimeric nature of 14-3-3 proteins and designed as well as synthesized a set of bivalent 14-3-3 inhibitors for biochemical and X-ray crystallography-based structural studies. We found that all synthesized derivatives adapted the “lysine-wrapping” binding mode in the crystal structures; in solution, a different binding mode is however observed, most probably as the “lysine-wrapping” binding mode turned out to be a rather weak interaction. Accordingly, our studies demonstrate that structural studies of OEG-lysine interactions are difficult to interpret and their presence in structural studies may not automatically be correlated with a relevant interaction also in solution but requires further biochemical studies.

Electrical forces are the background of all the interactions occurring in biochemical systems. From here and by using a combination of ab-initio and ad-hoc models, we introduce the first description of electric field profiles with intrabond resolution to support a characterization of single bond forces attending to its electrical origin. This fundamental issue has eluded a physical description so far. Our method is applied to describe hydrogen bonds (HB) in DNA base pairs. Numerical results reveal that base pairs in DNA could be equivalent considering HB strength contributions, which challenges previous interpretations of thermodynamic properties of DNA based on the assumption that Adenine/Thymine pairs are weaker than Guanine/Cytosine pairs due to the sole difference in the number of HB. Thus, our methodology provides solid foundations to support the development of extended models intended to go deeper into the molecular mechanisms of DNA functioning., Comment: Full version is available on: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0185638

Background: Computational prediction of protein function constitutes one of the more complex problems in Bioinformatics, because of the diversity of functions and mechanisms in that proteins exert in nature. This issue is reinforced especially for proteins that share very low primary or tertiary structure similarity to existing annotated proteomes. In this sense, new alignment-free (AF) tools are needed to overcome the inherent limitations of classic alignment-based approaches to this issue. We have recently introduced AF protein-numerical-encoding programs (TI2BioP and ProtDCal), whose sequence-based features have been successfully applied to detect remote protein homologs, post-translational modifications and antibacterial peptides. Here we aim to demonstrate the applicability of 4 AF protein descriptor families, implemented in our programs, for the identification enzyme-like proteins. At the same time, the use of our novel family of 3D-structure-based descriptors is introduced for the first time. The Dobson & Doig (D&D) benchmark dataset is used for the evaluation of our AF protein descriptors, because of its proven structural diversity that permits one to emulate an experiment within the twilight zone of alignment-based methods (pair-wise identity

The principles governing protein folding stand as one of the biggest challenges of Biophysics. Modeling the global stability of proteins and predicting their tertiary structure are hard tasks, due in part to the variety and large number of forces involved and the difficulties to describe them with sufficient accuracy. We have developed a fast, physics-based empirical potential, intended to be used in global structure prediction methods. This model considers four main contributions: Two entropic factors, the hydrophobic effect and configurational entropy, and two terms resulting from a decomposition of close-packing interactions, namely the balance of the dispersive interactions of folded and unfolded states and electrostatic interactions between residues. The parameters of the model were fixed from a protein data set whose unfolding free energy has been measured at the >standard> experimental conditions proposed by Maxwell et al. (2005) and a large data set of 1151 monomeric proteins obtained from the PDB. A blind test with proteins taken from ProTherm database, at similar experimental conditions, was carried out. We found a good correlation with the test data set, proving the effectiveness of our model for predicting protein folding free energies in considered standard conditions. Such a prediction compares favorably against estimations made with FoldX's function and the force field GROMOS96. This model constitutes a valuable tool for the fast evaluation of protein structure stability in 3D structure prediction methods. © 2013 Elsevier Ltd., Marrero-Ponce, Y. thanks the Universitat de València for a fellowship (program ‘Estades Temporals per Investigadors Convidats’). The authors acknowledge also the partial financial support from the Spanish MINECO (BFU2010–19118, financed in part by the European Regional Development Fund) and the VLIR (Vlaamse Inter Universitaire Raad, Flemish Interuniversity Council, Belgium) under the IUC Program VLIR-UCLV.

The control of electromechanical responses within bonding regions is essential to face frontier challenges in nanotechnologies, such as molecular electronics and biotechnology. Here, we present Iβ-nanocellulose as a potentially new orthotropic 2D piezoelectric crystal. The predicted in-layer piezoelectricity is originated on a sui-generis hydrogen bonds pattern. Upon this fact and by using a combination of ab-initio and ad-hoc models, we introduce a description of electrical profiles along chemical bonds. Such developments lead to obtain a rationale for modelling the extended piezoelectric effect originated within bond scales. The order of magnitude estimated for the 2D Iβ-nanocellulose piezoelectric response, ∼pm V, ranks this material at the level of currently used piezoelectric energy generators and new artificial 2D designs. Such finding would be crucial for developing alternative materials to drive emerging nanotechnologies., C.M.S.-T. acknowledges the financial support from the Spanish Severo Ochoa Excellence program (Grant SEV-2013-0295), the MINECO project PHENTOM (FIS2015-70682-P) and the EU project NANO-RF (Grant Agreement 318352).

N-Glycosylation is a common post-translational modification that plays an important role in the proper folding and function of many proteins. This modification is largely dependent on the presence of a sequence motif called a “sequon” defined as Asn-Xxx-Ser/Thr. However, evidence has shown that the presence of such a “sequon” is insufficient to determine the occurrence of N-glycosylation with high precision. This study aims to elucidate patterns that can more accurately predict N-glycosylation sites in human proteins. The novel motifs are evaluated using benchmarking data from 188 organisms. Performance is largely sustained compared to the human data, which validates the robustness of the novel extracted “extended sequons”. We, therefore, introduce new knowledge about sequence-related factors that control N-glycosylation.

Nanofibrillated cellulose, a polymer that can be obtained from one of the most abundant biopolymers in Nature, is being increasingly explored due to its outstanding properties for packaging and device applications. Still, open challenges in engineering its intrinsic properties remain to address. The results obtained show the precise determination of significant properties as elastic properties and interactions that are compared with similar works and, moreover, demonstrate that nanofibrillated cellulose properties can be reversibly controlled, supporting the extended potential of nanofibrillated cellulose as a robust platform for green-technology applications, in press in Carbohydrate Polymers (2015)

Most successful structure prediction strategies use knowledge-based functions for global optimization, in spite of their intrinsic limited potential to create new folds, while physics-based approaches are often employed only during structure refinement steps. We here propose a physics-based scoring potential intended to perform global searches of the conformational space. We introduce a dynamic test to evaluate the discrimination power of our function, and compare it with predictions of targets from the CASP-ROLL competition. Results demonstrate that this dynamic test is able to generate 3D models which outrank 59% (according GDT-TS score) of models generated with ab initio structure prediction servers., Yasser B. Ruiz-Blanco, Yamila García and Clivia M. Sotomayor-Torres acknowledge the partial support of the Spanish MINECO grant MAT-2012-31392 (TAPHOR). Yasser B. Ruiz-Blanco acknowledges the ELAP scholarship to conduct research at Carleton University. Yovani Marrero-Ponce thanks the ‘International Professor’ program for a fellowship to work at Cartagena University in 2013–2014.

The interaction between the adapter protein 14-3-3σ and transcription factor p53 is important for preserving the tumor-suppressor functions of p53 in the cell. A phosphorylated motif within the C-terminal domain (CTD) of p53 is key for binding to the amphipathic groove of 14-3-3. This motif is unique among 14-3-3 binding partners, and the precise dynamics of the interaction is not yet fully understood. Here, we investigate this interaction at the molecular level by analyzing the binding of different length p53 CTD peptides to 14-3-3σ using ITC, SPR, NMR, and MD simulations. We observed that the propensity of the p53 peptide to adopt turn-like conformation plays an important role in the binding to the 14-3-3σ protein. Our study contributes to elucidate the molecular mechanism of the 14-3-3-p53 binding and provides useful insight into how conformation properties of a ligand influence protein binding.