Motivation Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. Results Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein–protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. Availability and implementation The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). Supplementary information Supplementary data are available at Bioinformatics online.

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K-means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K-means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

A wide variety of new synthetic compounds have been screened for chemotherapeutic applications. However, many drug candidates exhibit poor membrane permeability and/or severe side effects caused by their lack of selectivity. These problems can often be overcome by using highly specific chemotherapeutic agents. On the other hand, considerable efforts have been devoted to the creation of delivery systems targeting infected cells (or malignant tumors); these systems improve membrane permeability, therapeutic efficacy, and selectivity.[1] In a recent effort aimed at designing novel systems that release free drugs in a stringently controlled fashion, we have synthesized and characterized carrier-drug conjugates connected by linkers that are only cleaved by an enzyme present in the infected cells. In addition, we have

Protein complexes play a central role in many aspects of biological function. Knowledge of the three-dimensional (3D) structures of protein complexes is critical for gaining insights into the structural basis of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determination of 3D structures of protein complexes, computational docking has evolved as a valuable tool to predict the 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to that of the state-of-the-art scoring functions on independent data sets consisting docking software-specific data sets and the CAPRI score set built from a wide variety of docking approaches. iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary and topological, and physicochemical information for scoring docked conformations. This work represents the first successful demonstration of graph kernel to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. It paves the way for the further development of computational methods for predicting the structure of protein complexes.

RNA‐protein interactions play essential roles in regulating gene expression. While some RNA‐protein interactions are “specific”, that is, the RNA‐binding proteins preferentially bind to particular RNA sequence or structural motifs, others are “non‐RNA specific.” Deciphering the protein‐RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein‐RNA interfaces, there is a need for computational methods to identify RNA‐binding residues in proteins. While most of the existing computational methods for predicting RNA‐binding residues in RNA‐binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner‐specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner‐specific protein‐RNA interface prediction tools, PS‐PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA‐specificity metric (RSM), for quantifying the RNA‐specificity of the RNA binding residues predicted by such tools. Our results show that the RNA‐binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner‐agnostic metrics, RNA partner‐specific methods are outperformed by the state‐of‐the‐art partner‐agnostic methods. We conjecture that either (a) the protein‐RNA complexes in PDB are not representative of the protein‐RNA interactions in nature, or (b) the current methods for partner‐specific prediction of RNA‐binding residues in proteins fail to account for the differences in RNA partner‐specific versus partner‐agnostic protein‐RNA interactions, or both.