Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

CCharPPI web server: computational characterization of protein–protein interactions from structure

The atomic structures of protein–protein interactions are central to understanding their role in biological systems, and a wide variety of biophysical functions and potentials have been developed for their characterization and the construction of predictive models. These tools are scattered across a multitude of stand-alone programs, and are often available only as model parameters requiring reimplementation. This acts as a significant barrier to their widespread adoption. CCharPPI integrates many of these tools into a single web server. It calculates up to 108 parameters, including models of electrostatics, desolvation and hydrogen bonding, as well as interface packing and complementarity scores, empirical potentials at various resolutions, docking potentials and composite scoring functions.The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme (FP7/2007- 2013) under REA grant agreement PIEF-GA-2012-327899 and grant BIO2013-48213-R from Spanish Ministry of Economy and Competitiveness.Peer ReviewedPostprint (published version

The Impact of Procedural Meaning on Second Language Processing: A Study on Connectives

Utterances are minimal ostensive stimuli produced by a speaker for an interlocutor, who interprets them by decoding linguistic input and by carrying out inferential processes (Sperber & Wilson 1995[1986]). Inferencing implies performing computations to obtain and connect mental representations with each other and with the context and obtain cognitive effects from the processed utterances (idem). These inferential processes are often guided by linguistic expressions with procedural meaning, among which are discourse markers. Discourse markers act as instructions during discourse processing by constraining contextual access (Blakemore 1987, 2002; Portolés 2001[1998]; Loureda & Acín 2010). As procedural-meaning expressions, their semantics is rigid and asymmetric as to concepts: instructions encoded in the meaning of a discourse marker must necessarily be executed (Leonetti & Escandell Vidal 2004; Escandell Vidal et al. 2011). In this dissertation, we investigate experimentally how concepts and instructions interact in discourse processing, and how such interaction is managed by speakers with different degrees of competence in an L2, as compared to native speakers. Processing data were gathered in an eye-tracking reading experiment for four discourse-related phenomena and three groups of readers. The two participant groups consisted of speakers an intermediate level (B1 CEFR, n = 58) and with a proficiency level (C1 CEFR, n = 49) in Spanish as an L2; the control group consisted of native speakers of Spanish (n = 102). At the discourse level, we compared processing of different argumentative discourse relations (causality versus counter-argumentation signaled by the Spanish connectives por tanto ‘therefore’ and sin embargo ‘however’, study 1); how the presence of a procedural interpretive guide influences processing of causal relations (implicit versus explicit causality marked by por tanto, study 2); and how congruency between procedural meaning and mind-stored assumptions impacts discourse processing (plausible versus implausible causality marked by por tanto, study 3, and plausible versus implausible causality marked by sin embargo, study 4). In general, results show that discourse relations are approached differently in cognitive terms depending on an individual’s degree of linguistic and pragmatic competence. Most frequently, the patterns obtained point to a direct correlation between proficiency and degree of nativelikeness in L2 performance, both in the strategies deployed, and in the effort allocated in processing of causality and counter-argumentation and in the resolution of pragmatic mismatches. Specifically, feasibility and relevance in discourse overrides discursive differences (the type of discourse relation at issue) from a certain degree of communicative competence on. By contrast, when pragmatic and linguistic competence are not sufficiently developed, relevance and discourse feasibility do not seem to offset the higher cognitive complexity of a certain discourse relation (study 1) and the absence of processing instructions (study 2). Communicative competence is also determinant of whether and how the accommodation strategies needed to process utterances in which procedural meaning leads toward recovering of a communicated assumption that clashes with mind-stored assumptions are performed. Accommodation is cognitively demanding and, therefore, effortful, but only given a certain degree of communicative competence to perform a certain task (studies 3 and 4). In complex and highly complex tasks processing by less proficient language users is shallow (cf. Clahsen & Felser 2006a, 2006b, 2006c) compared to more proficient and native readers. As a result, when the cognitive constraints imposed by the task are very high (study 4), readers fail to carry out the accommodation processes needed to recover the assumption communicated in the utterance. From a theoretical perspective, this study may contribute to the refinement of theories on L2 discourse processing, particularly in respect to how non-native language users cognitively manage discourse marking; from an applied perspective, the experimental evidence provided may serve as a basis for future studies to determine if empirically observed processing strategies in an L2 correlate with the thresholds and the content-sequencing established in frameworks of reference for the teaching and learning of second languages in relation to discourse marking and, in general, to contents at the discourse level for any language skill

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Intrahelical side chain interactions in α-helices: poor correlation between energetics and frequency

AbstractPolypeptide sequences in proteins may increase their tendency to adopt helical conformations in several ways. One is the recruiting of amino acid residues with high helical propensity. Another is the appropriate distribution of residues along the helix to establish stabilising side chain interactions. The first strategy is known to be followed by natural proteins because amino acids with high helical propensity are more frequent in α-helices. If proteins also followed the second strategy, stabilising amino acid pairs should be more frequent than others. To test this possibility we compared empirical energies of side chain interactions in α-helices with statistical energies calculated from a data base of proteins with low homology. We find some correlation between the stability afforded by the pairs and their relative abundance in α-helices but the realisation of energetic preferences into statistical preferences is very low. This indicates that natural α-helices do not regularly use intrahelical side chain interactions to increase their stability

Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.This work was funded by grants number BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness, and grant number EFA086/15 from Interreg POCTEFA. D. Barradas-Bautista was supported by a CONACyT predoctoral fellowship from the Mexican Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer ReviewedPostprint (published version

Dissection and prediction of RNA-binding sites on proteins

RNA-binding proteins are involved in many important regulatory processes in cells and their study is essential for a complete understanding of living organisms. They show a large variability from both structural and functional points of view. However, several recent studies performed on protein-RNA crystal structures have revealed interesting common properties. RNA-binding sites usually constitute patches of positively charged or polar residues that make most of the specific and non-specific contacts with RNA. Negatively charged or aliphatic residues are less frequent at protein-RNA interfaces, although they can also be found either forming aliphatic and positive-negative pairs in protein RNA-binding sites or contacting RNA through their main chains. Aromatic residues found within these interfaces are usually involved in specific base recognition at RNA single-strand regions. This specific recognition, in combination with structural complementarity, represents the key source for specificity in protein-RNA association. From all this knowledge, a variety of computational methods for prediction of RNA-binding sites have been developed based either on protein sequence or on protein structure. Some reported methods are really successful in the identification of RNA-binding proteins or the prediction of RNA-binding sites. Given the growing interest in the field, all these studies and prediction methods will undoubtedly contribute to the identification and comprehension of protein-RNA interactions

Abriendo camino en los entornos digitales de comunicación: algunas propuestas significativas

Los formatos digitales en los medios de comunicación, especialmente en los periódicos, atraviesan por una fase de nuevos modelos comunicativos. Se hace un recorrido por el mundo digital, sus cifras más significativas y algunos estudios que avalan el crecimiento en los entornos digitales, especialmente las referidas a los medios de comunicación. Además, se proponen algunos proyectos de aplicación inmediata en formato digital y con una temática específica que no suelen cubrir los medios tradicionales. Al frente de estos proyectos figuran expertos periodistas, con más de dos décadas en puestos de responsabilidad y analistas e inversores en contenidos digitales