A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing.

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.

ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method.

SUMMARY: ChemMapper is an online platform to predict polypharmacology effect and mode of action for small molecules based on 3D similarity computation. ChemMapper collects >350 000 chemical structures with bioactivities and associated target annotations (as well as >3 000 000 non-annotated compounds for virtual screening). Taking the user-provided chemical structure as the query, the top most similar compounds in terms of 3D similarity are returned with associated pharmacology annotations. ChemMapper is designed to provide versatile services in a variety of chemogenomics, drug repurposing, polypharmacology, novel bioactive compounds identification and scaffold hopping studies. AVAILABILITY: http://lilab.ecust.edu.cn/chemmapper/. CONTACT: xfliu@ecust.edu.cn or hlli@ecust.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SimG: an alignment based method for evaluating the similarity of small molecules and binding sites.

In this study, a Gaussian volume overlap and chemical feature based molecular similarity metric was devised, and a downhill simplex searching was carried out to evaluate the corresponding similarity. By representing the shapes of both the candidate small molecules and the binding site with chemical features and comparing the corresponding Gaussian volumes overlaps, the active compounds could be identified. These two aspects compose the proposed method named SimG which supports both structure-based and ligand-based strategies. The validity of the proposed method was examined by analyzing the similarity score variation between actives and decoys as well as correlation among distinct reference methods. A retrospective virtual screening test was carried out on DUD data sets, demonstrating that the performance of structure-based shape matching virtual screening in DUD data sets is substantially dependent on some physical properties, especially the solvent-exposure extent of the binding site: The enrichments of targets with less solvent-exposed binding sites generally exceeds that of the one with more solvent-exposed binding sites and even surpasses the corresponding ligand-based virtual screening.

Predicting the Drug Safety for Traditional Chinese Medicine through a Comparative Analysis of Withdrawn Drugs Using Pharmacological Network.

As the major issue to limit the use of drugs, drug safety leads to the attrition or failure in clinical trials of drugs. Therefore, it would be more efficient to minimize therapeutic risks if it could be predicted before large-scale clinical trials. Here, we integrated a network topology analysis with cheminformatics measurements on drug information from the DrugBank database to detect the discrepancies between approved drugs and withdrawn drugs and give drug safety indications. Thus, 47 approved drugs were unfolded with higher similarity measurements to withdrawn ones by the same target and confirmed to be already withdrawn or discontinued in certain countries or regions in subsequent investigations. Accordingly, with the 2D chemical fingerprint similarity calculation as a medium, the method was applied to predict pharmacovigilance for natural products from an in-house traditional Chinese medicine (TCM) database. Among them, Silibinin was highlighted for the high similarity to the withdrawn drug Plicamycin although it was regarded as a promising drug candidate with a lower toxicity in existing reports. In summary, the network approach integrated with cheminformatics could provide drug safety indications effectively, especially for compounds with unknown targets or mechanisms like natural products. It would be helpful for drug safety surveillance in all phases of drug development.

Performance evaluation of multilayer perceptrons for discriminating and quantifying multiple kinds of odors with an electronic nose.

This paper studies several types and arrangements of perceptron modules to discriminate and quantify multiple odors with an electronic nose. We evaluate the following types of multilayer perceptron. (A) A single multi-output (SMO) perceptron both for discrimination and for quantification. (B) An SMO perceptron for discrimination followed by multiple multi-output (MMO) perceptrons for quantification. (C) An SMO perceptron for discrimination followed by multiple single-output (MSO) perceptrons for quantification. (D) MSO perceptrons for discrimination followed by MSO perceptrons for quantification, called the MSO-MSO perceptron model, under the following conditions: (D1) using a simple one-against-all (OAA) decomposition method; (D2) adopting a simple OAA decomposition method and virtual balance step; and (D3) employing a local OAA decomposition method, virtual balance step and local generalization strategy all together. The experimental results for 12 kinds of volatile organic compounds at 85 concentration levels in the training set and 155 concentration levels in the test set show that the MSO-MSO perceptron model with the D3 learning procedure is the most effective of those tested for discrimination and quantification of many kinds of odors.

A novel, customizable and optimizable parameter method using spherical harmonics for molecular shape similarity comparisons.

A novel molecular shape similarity comparison method, namely SHeMS, derived from spherical harmonic (SH) expansion, is presented in this study. Through weight optimization using genetic algorithms for a customized reference set, the optimal combination of weights for the translationally and rotationally invariant (TRI) SH shape descriptor, which can specifically and effectively distinguish overall and detailed shape features according to the molecular surface, is obtained for each molecule. This method features two key aspects: firstly, the SH expansion coefficients from different bands are weighted to calculate similarity, leading to a distinct contribution of overall and detailed features to the final score, and thus can be better tailored for each specific system under consideration. Secondly, the reference set for optimization can be totally configured by the user, which produces great flexibility, allowing system-specific and customized comparisons. The directory of useful decoys (DUD) database was adopted to validate and test our method, and principal component analysis (PCA) reveals that SH descriptors for shape comparison preserve sufficient information to separate actives from decoys. The results of virtual screening indicate that the proposed method based on optimal SH descriptor weight combinations represents a great improvement in performance over original SH (OSH) and ultra-fast shape recognition (USR) methods, and is comparable to many other popular methods. Through combining efficient shape similarity comparison with SH expansion method, and other aspects such as chemical and pharmacophore features, SHeMS can play a significant role in this field and can be applied practically to virtual screening by means of similarity comparison with 3D shapes of known active compounds or the binding pockets of target proteins.