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Abstract Background Identifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model. Results We describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces. Conclusions The integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone.

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

Background: Identifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein's interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins' intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model. Results: We describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces. Conclusions: The integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone. [ABSTRACT FROM AUTHOR]

The double vertical explosive welding process produces two composites in one explosion and improves the energy utilization efficiency. However, the interface morphology at different positions shows obvious unevenness. 2D and 3D models of double vertical explosive welding were established by SPH. The interface morphology and process parameters were analysed. The relationship between the interface morphology and process parameters was discussed, and the pressure condition was the most suitable wave-forming condition in this paper. The interface morphology of the whole plate was predicted by combining the pressure condition with the contours of the flyer plate of the 3D model. A relevant experiment was carried out, and samples at different locations were observed under an OM. The results indicated that the wave interface and jetting were reproduced well in the 2D models. The distribution of the wave interface presented a butterfly shape. A straight interface existed at the detonation point and the boundary areas. The distribution of interface morphology in the experimental results was basically the same as those predicted by the numerical simulations. In addition, a discontinuous convex block was observed in the straight interface. Evidence of a vortex, jetting and an island region existed in the large wave interface.

The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physicochemical and geometrical properties of the protein surface with information coming from complete cross‐docking (CC‐D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET2 algorithm and assessed on a new dataset of 262 protein on which we performed CC‐D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/. [ABSTRACT FROM AUTHOR]

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) are crucial in many biological processes. The first step towards the molecular characterisation of PPIs implies the charting of their interfaces, that is, the surfaces mediating the interaction. To this end, we present here iFrag, a sequence-based computational method that infers possible interacting regions between two proteins by searching minimal common sequence fragments of the interacting protein pairs. By utilising the sequences of two interacting proteins (queries), iFrag derives a two-dimensional matrix computing a score for each pair of residues that relates to the presence of similar regions in interolog protein pairs. The scoring matrix is represented as a heat map reflecting the potential interface regions in both query proteins. Unlike existing approaches, iFrag does not require three-dimensional structural information or multiple sequence alignments and can even predict small interaction sites consisting only of few residues. Thus, predicted interfaces range from short fragments composed of few residues to domains of proteins, depending on available information on PPIs, as we demonstrate in several examples. Moreover, as a proof of concept, we include the experimental validation on the successful prediction of a peptide competing with the aggregation of β-amyloid in Alzheimer's disease. iFrag is freely accessible at http://sbi.imim.es/iFrag . [ABSTRACT FROM AUTHOR]

ABSTRACT Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico-chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross-docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S-index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface-based (ranking) score to discriminate partners from non-interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137-154. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The growing structural coverage of proteomes is making structural comparison a powerful tool for function annotation. Such template-based approaches are based on the observation that structural similarity is often sufficient to infer similar function. However, it seems clear that, in addition to structural similarity, the specific characteristics of a given protein should also be taken into account in predicting function. Here we describe PredUs 2.0, a method to predict regions on a protein surface likely to bind other proteins, that is, interfacial residues. PredUs 2.0 is based on the PredUs method that is entirely template-based and uses known binding sites in structurally similar proteins to predict interfacial residues. PredUs 2.0 uses a Bayesian approach to combine the template-based scoring of PredUs with a score that reflects the propensities of individual amino acids to be in interfaces. PredUs 2.0 includes a novel protein size dependent metric to determine the number of residues that should be reported as interfacial. PredUs 2.0 significantly outperforms PredUs as well as other published interface prediction methods. [ABSTRACT FROM AUTHOR]

Background Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). Results First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between TPioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. Conclusion Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations. [ABSTRACT FROM AUTHOR]

Protein interactions define the homeostatic state of the cell. Our ability to understand these interactions and their role in both health and disease is tied to our knowledge of the 3D atomic structure of the interacting partners and their complexes. Despite advances in experimental method of structure determination, the majority of known protein interactions are still missing an atomic structure. High-resolution methods such as X-ray crystallography and NMR spectroscopy struggle with the high-throughput demand, while low-resolution techniques such as cryo-electron microscopy or small-angle X-ray scattering provide data that are too coarse. Computational structure prediction of protein complexes, or docking, was first developed to complement experimental research and has since blossomed into an independent and lively field of research. Its most successful products are hybrid approaches that combine powerful algorithms with experimental data from various sources to generate high-resolution models of protein complexes. This minireview introduces the concept of docking and docking with the help of experimental data, compares and contrasts the available integrative docking methods, and provides a guide for the experimental researcher for what types of data and which particular software can be used to model a protein complex. [ABSTRACT FROM AUTHOR]

Background: Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations. Results: In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners. Conclusions: An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces. [ABSTRACT FROM AUTHOR]

The G protein Coupled Receptor (GPCR) superfamily is one of the most important pharmaceutical targets. Studies of GPCRs have long been performed under the assumption that GPCRs function as monomers. However, recent studies have revealed that many GPCRs function as homo- and/or hetero-dimers or higher-order oligomeric molecular complexes. As a result, information about GPCR oligomerization is rapidly accumulating, although the molecular mechanisms of oligomerization are not fully understood. A comprehensive collection of information about oligomerization would accelerate investigations of the molecular mechanisms of GPCRs'' oligomerization and involvement in signaling. Hence, we have developed a database, G protein coupled Receptor Interaction Partners DataBase (GRIPDB), which provides information about GPCR oligomerization. The entries in the database are divided into two sections: (I) Experiment Information section and (II) Prediction Information section. The Experiment Information section contains (I-i) experimentally indentified GPCR oligomers and their annotations, and (I-ii) experimentally suggested interfaces for the oligomerization. Since the number of experimentally suggested interfaces is limited, the entries in the Prediction Information section have been introduced to provide information about the oligomerization interfaces predicted by our computational method. The experimentally suggested or computationally predicted interfaces are displayed by 3D graphics, using GPCRs with available coordinates. The information in the GRIPDB, especially that about the interfaces, is useful to investigate the molecular mechanisms of signal transduction via GPCR oligomerization. The GRIPDB is available on the web at the following URL: . [ABSTRACT FROM AUTHOR]

Abstract: Gβ subunits from heterotrimeric G-proteins (guanine nucleotide-binding proteins) directly bind diverse proteins, including effectors and regulators, to modulate a wide array of signaling cascades. These numerous interactions constrained the evolution of the molecular surface of Gβ. Although mammals contain five Gβ genes comprising two classes (Gβ1-like and Gβ5-like), plants and fungi have a single ortholog, and organisms such as Caenorhabditis elegans and Drosophila melanogaster contain one copy from each class. A limited number of crystal structures of complexes containing Gβ subunits and complementary biochemical data highlight specific sites within Gβs needed for protein interactions. It is difficult to determine from these interaction sites what, if any, additional regions of the Gβ molecular surface comprise interaction interfaces essential to Gβ''s role as a nexus in numerous signaling cascades. We used a comparative evolutionary approach to identify five known and eight previously unknown putative interfaces on the surface of Gβ. We show that one such novel interface occurs between Gβ and phospholipase C β2 (PLC-β2), a mammalian Gβ interacting protein. Substitutions of residues within this Gβ–PLC-β2 interface reduce the activation of PLC-β2 by Gβ1, confirming that our de novo comparative evolutionary approach predicts previously unknown Gβ–protein interfaces. Similarly, we hypothesize that the seven remaining untested novel regions contribute to putative interfaces for other Gβ interacting proteins. Finally, this comparative evolutionary approach is suitable for application to any protein involved in a significant number of protein–protein interactions. [Copyright &y& Elsevier]

Abstract: When solving transport equation by the discrete ordinates method, the solution procedure must accord with the moving direction of particle due to the stability restriction. Thus it results in a strong correlation among these unknown variables, and the intrinsic serial nature of the corresponding algorithmic procedure becomes the bottleneck for transport equation calculations on large-scale parallel computers. The conventional parallel sweep method has restrictive but in no means high degree of parallelism for two and three spatial dimensional problems, and it has no spatial parallel degree for one-dimensional problems. In this paper, a spatial domain decomposition method is adopted in the computational domain, and different interface prediction and correction methods are introduced to solve the 1-D spherical geometric transport equation. It is natural to generalize to multidimension problems. In order to avoid producing negative flux, we employ the exponential method for variable discretization. The numerical experiments by using MPI show that our parallel method with explicit prediction and implicit correction has good precision, parallelism and simplicity. [Copyright &y& Elsevier]

One of the known potential effects of disease-causing amino acid substitutions in proteins is to modulate protein-protein interactions (PPIs). To interpret such variants at the molecular level and to obtain useful information for prediction purposes, it is important to determine whether they are located at protein-protein interfaces, which are composed of two main regions, core and rim, with different evolutionary conservation and physicochemical properties. Here we have performed a structural, energetics and computational analysis of interactions between proteins hosting mutations related to diseases detected in newborn screening. Interface residues were classified as core or rim, showing that the core residues contribute the most to the binding free energy of the PPI. Disease-causing variants are more likely to occur at the interface core region rather than at the interface rim (p < 0.0001). In contrast, neutral variants are more often found at the interface rim or at the non-interacting surface rather than at the interface core region. We also found that arginine, tryptophan, and tyrosine are over-represented among mutated residues leading to disease. These results can enhance our understanding of disease at molecular level and thus contribute towards personalized medicine by helping clinicians to provide adequate diagnosis and treatments. [ABSTRACT FROM AUTHOR]

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes., (© 2020 Elsevier Inc. All rights reserved.)

An important aspect of protein functionality is the formation of specific complexes with other proteins, which are involved in the majority of biological processes. The functional characterization of such interactions at molecular level is necessary, not only to understand biological and pathological phenomena but also to design improved, or even new interfaces, or to develop new therapeutic approaches. X-ray crystallography and NMR spectroscopy have increased the number of 3D protein complex structures deposited in the Protein Data Bank (PDB). However, one of the more challenging objectives in biological research is to functionally characterize protein interactions and thus identify residues that significantly contribute to the binding. Considering that the experimental characterization of protein interfaces remains expensive, time-consuming, and labor-intensive, computational approaches represent a significant breakthrough in proteomics, assisting or even replacing experimental efforts. Thanks to the technological advances in computing and data processing, these techniques now cover a vast range of protocols, from the estimation of the evolutionary conservation of amino acid positions in a protein, to the energetic contribution of each residue to the binding affinity. In this chapter, we review several existing computational protocols to model the phylogenetic, structural, and energetic properties of residues within protein-protein interfaces.

The growing structural coverage of proteomes is making structural comparison a powerful tool for function annotation. Such template-based approaches are based on the observation that structural similarity is often sufficient to infer similar function. However, it seems clear that, in addition to structural similarity, the specific characteristics of a given protein should also be taken into account in predicting function. Here we describe PredUs 2.0, a method to predict regions on a protein surface likely to bind other proteins, that is, interfacial residues. PredUs 2.0 is based on the PredUs method that is entirely template-based and uses known binding sites in structurally similar proteins to predict interfacial residues. PredUs 2.0 uses a Bayesian approach to combine the template-based scoring of PredUs with a score that reflects the propensities of individual amino acids to be in interfaces. PredUs 2.0 includes a novel protein size dependent metric to determine the number of residues that should be reported as interfacial. PredUs 2.0 significantly outperforms PredUs as well as other published interface prediction methods., (© 2015 The Protein Society.)