1,720,961 research outputs found
Development of new computational methods for the investigation of the molecular mechanisms underlying the interaction among proteins
No full textUnderstanding protein binding mechanisms is fundamental to molecular biology, with significant implications for protein design and mapping the human interactome and complexome. Despite the important consequences for therapeutic and biotechnological applications, understanding the binding process and the stability of the resulting complexes is still a challenge. Predicting protein-protein interactions is difficult due to environmental factors and the complexity of the involved processes, which rely on geometric and chemical matches. The balance between accuracy and efficiency when choosing the features to consider is a central issue in computational techniques developed to predict complex formation. For example, poses rankings based on the scoring functions of docking servers
frequently lacks precision. To better understand the mechanisms underlying protein complex formation, we developed a compact vector description of protein molecular surfaces based on orthogonal polynomial expansions. When applied to the evaluation of shape complementarity, our protocol allows for an indirect evaluation of van der Waals interactions, which are the predominant forces in protein binding at short distances; to improve the characterization of these van der Waals-dominated regions, we studied the role of electrostatic interactions, whose effect has been previously investigated mainly on long-rage.
We found that binding interfaces exhibit a higher degree of electrostatic complementarity, defined as a spatial match between the signs of surface points facing each other, compared to random surface regions. We expanded the formalism to evaluate this feature: as for shape complementarity, this approach allowed us to quickly compare vectors describing surface regions without having to cal-static interactions not only facilitate the initial recognition and approach of proteins over long distances but also guide the reorientation of the interacting partners at shorter distances. In a second phase, complexes requiring more stable binding enhance their interlock through increased shape complementarity. Integrating these features and other physical and chemical characteristics with a neural network, CIRNet, allowed us to identify core interacting residue and improve docking algorithms by re-ranking proposed poses. CIRNet has demonstrated effectiveness across various types of protein complexes for three popular docking servers, reducing the average RMSD between the refined poses and the native state by up to 58%.
culate the forces between all possible atoms pairings. We observed
that electrostatic complementarity plays a key role in determining
the stability of the binding: transient dimers show the highest elec-
trostatic complementarity, while more stable complexes rely more
heavily on shape complementarity. Interestingly, we noticed that
shape complementarity is higher near the center of the interfaces,
whereas electrostatic complementarity remains consistent across the
entire binding region. These findings could suggest that electrostatic interactions not only facilitate the initial recognition and ap-
proach of proteins over long distances but also guide the reorienta-
tion of the interacting partners at shorter distances. In a second
phase, complexes requiring more stable binding enhance their in-
terlock through increased shape complementarity. Integrating these
features and other physical and chemical characteristics with a neu-
ral network, CIRNet, allowed us to identify core interacting residue
and improve docking algorithms by re-ranking proposed poses. CIR-
Net has demonstrated effectiveness across various types of protein
complexes for three popular docking servers, reducing the average
RMSD between the refined poses and the native state by up to 58%
A novel computational strategy for defining the minimal protein molecular surface representation
Full text linkMost proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure's ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling
Differences in the organization of interface residues tunes the stability of the sars-cov-2 spike-ace2 complex
Full text linkThe continuous emergence of novel variants represents one of the major problems in dealing with the SARS-CoV-2 virus. Indeed, also due to its prolonged circulation, more than ten variants of concern emerged, each time rapidly overgrowing the current viral version due to improved spreading features. As, up to now, all variants carry at least one mutation on the spike Receptor Binding Domain, the stability of the binding between the SARS-CoV-2 spike protein and the human ACE2 receptor seems one of the molecular determinants behind the viral spreading potential. In this framework, a better understanding of the interplay between spike mutations and complex stability can help to assess the impact of novel variants. Here, we characterize the peculiarities of the most representative variants of concern in terms of the molecular interactions taking place between the residues of the spike RBD and those of the ACE2 receptor. To do so, we performed molecular dynamics simulations of the RBD-ACE2 complexes of the seven variants of concern in comparison with a large set of complexes with different single mutations taking place on the RBD solvent-exposed residues and for which the experimental binding affinity was available. Analyzing the strength and spatial organization of the intermolecular interactions of the binding region residues, we found that (i) mutations producing an increase of the complex stability mainly rely on instaurating more favorable van der Waals optimization at the cost of Coulombic ones. In particular, (ii) an anti-correlation is observed between the shape and electrostatic complementarities of the binding regions. Finally, (iii) we showed that combining a set of dynamical descriptors is possible to estimate the outcome of point mutations on the complex binding region with a performance of 0.7. Overall, our results introduce a set of dynamical observables that can be rapidly evaluated to probe the effects of novel isolated variants or different molecular systems
Computational Approaches to Predict Protein–Protein Interactions in Crowded Cellular Environments
Full text linkInvestigating protein–protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein–protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein–protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
