Computer-aided drug design platform using PyMOL

The understanding and optimization of protein-ligand interactions are instrumental to medicinal chemists investigating potential drug candidates. Over the past couple of decades, many powerful standalone tools for computer-aided drug discovery have been developed in academia providing insight into protein-ligand interactions. As programs are developed by various research groups, a consistent user-friendly graphical working environment combining computational techniques such as docking, scoring, molecular dynamics simulations, and free energy calculations is needed. Utilizing PyMOL we have developed such a graphical user interface incorporating individual academic packages designed for protein preparation (AMBER package and Reduce), molecular mechanics applications (AMBER package), and docking and scoring (AutoDock Vina and SLIDE). In addition to amassing several computational tools under one interface, the computational platform also provides a user-friendly combination of different programs. For example, utilizing a molecular dynamics (MD) simulation performed with AMBER as input for ensemble docking with AutoDock Vina. The overarching goal of this work was to provide a computational platform that facilitates medicinal chemists, many who are not experts in computational methodologies, to utilize several common computational techniques germane to drug discovery. Furthermore, our software is open source and is aimed to initiate collaborative efforts among computational researchers to combine other open source computational methods under a single, easily understandable graphical user interface.     

SCOUT: A new algorithm for the inference of pseudo-time trajectory using single-cell data

Single cell technology is a powerful tool to reveal intercellular heterogeneity and discover cellular developmental processes. When analyzing the complexity of cellular dynamics and variability, it is important to construct a pseudo-time trajectory using single-cell expression data to reflect the process of cellular development. Although a number of computational and statistical methods have been developed recently for single-cell analysis, more effective and efficient methods are still strongly needed. In this work we propose a new method named SCOUT for the inference of single-cell pseudo-time ordering with bifurcation trajectories. We first propose to use the fixed-radius near neighbors algorithms based on cell densities to find landmarks to represent the cell states, and employ the minimum spanning tree (MST) to determine the developmental branches. We then propose to use the projection of Apollonian circle or a weighted distance to determine the pseudo-time trajectories of single cells. The proposed algorithm is applied to one synthetic and two realistic single-cell datasets (including single-branching and multi-branching trajectories) and the cellular developmental dynamics is recovered successfully. Compared with other popular methods, numerical results show that our proposed method is able to generate more robust and accurate pseudo-time trajectories. The code of the method is implemented in Python and available at https://github.com/statway/SCOUT.     

Pyrite: A blender plugin for visualizing molecular dynamics simulations using industry‐standard rendering techniques

Molecular dynamics (MD) simulations provide critical insights into many biological mechanisms. Programs such as VMD, Chimera, and PyMOL can produce impressive simulation visualizations, but they lack many advanced rendering algorithms common in the film and video‐game industries. In contrast, the modeling program Blender includes such algorithms but cannot import MD‐simulation data. MD trajectories often require many gigabytes of memory/disk space, complicating Blender import. We present Pyrite, a Blender plugin that overcomes these limitations. Pyrite allows researchers to visualize MD simulations within Blender, with full access to Blender's cutting‐edge rendering techniques. We expect Pyrite‐generated images to appeal to students and non‐specialists alike. A copy of the plugin is available at http://durrantlab.com/pyrite/ , released under the terms of the GNU General Public License Version 3. © 2017 Wiley Periodicals, Inc.     

ICTC-RAAC: An improved web predictor for identifying the types of ion channel-targeted conotoxins by using reduced amino acid cluster descriptors

Conotoxins are small peptide toxins which are rich in disulfide and have the unique diversity of sequences. It is significant to correctly identify the types of ion channel-targeted conotoxins because that they are considered as the optimal pharmacological candidate medicine in drug design owing to their ability specifically binding to ion channels and interfering with neural transmission. Comparing with other feature extracting methods, the reduced amino acid cluster (RAAC) better resolved in simplifying protein complexity and identifying functional conserved regions. Thus, in our study, 673 RAACs generated from 74 types of reduced amino acid alphabet were comprehensively assessed to establish a state-of-the-art predictor for predicting ion channel-targeted conotoxins. The results showed Type 20, Cluster 9 (T = 20, C = 9) in the tripeptide composition (N = 3) achieved the best accuracy, 89.3%, which was based on the algorithm of amino acids reduction of variance maximization. Further, the ANOVA with incremental feature selection (IFS) was used for feature selection to improve prediction performance. Finally, the cross-validation results showed that the best overall accuracy we calculated was 96.4% and 1.8% higher than the best accuracy of previous studies. Based on the predictor we proposed, a user-friendly webserver was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ictcraac.     

HIVCoR: A sequence-based tool for predicting HIV-1 CRF01_AE coreceptor usage

Determination of HIV-1 coreceptor usage is strongly recommended before starting the coreceptor-specific inhibitors for HIV treatment. Currently, the genotypic assays are the most interesting tools due to they are more feasible than phenotypic assays. However, most of prediction models were developed and validated by data set of HIV-1 subtype B and C. The present study aims to develop a powerful and reliable model to accurately predict HIV-1 coreceptor usage for CRF01_AE subtype called HIVCoR. HIVCoR utilized random forest and support vector machine as the prediction model, together with amino acid compositions, pseudo amino acid compositions and relative synonymous codon usage frequencies as the input feature. The overall success rate of 93.79% was achieved from the external validation test on the objective benchmark dataset. Comparison results indicated that HIVCoR was superior to other bioinformatics tools and genotypic predictors. For the convenience of experimental scientists, a user-friendly webserver has been established at http://codes.bio/hivcor/.     

Efficient utilization on PSSM combining with recurrent neural network for membrane protein types prediction

Position-Specific Scoring Matrix (PSSM) is an excellent feature extraction method that was proposed early in protein classifying prediction, but within the restriction of feature shape in PSSM, researchers make a lot attempts to process it so that PSSM can be input to the traditional machine learning algorithms. These processes drop information provided by PSSM in a way thus the feature representation is limited. Moreover, the high-dimensional feature representation of PSSM makes it incompatible with other feature extraction methods. We use the PSSM as the input of Recurrent Neural Network without any post-processing, the amino acids in protein sequences are regarded as time step in RNN. This way takes full advantage of the information that PSSM provides. In this study, the PSSM is input to the model directly and the internal information of PSSM is fully utilized, we propose an end-to-end solution and achieve state-of-the-art performance. Ultimately, the exploration of how to combine PSSM with traditional feature extraction methods is carried out and achieve slightly improved performance. Our network architecture is implemented in Python and is available at https://github.com/YellowcardD/RNN-for-membrane-protein-types-prediction.     

TTRMDB: A database for structural and functional analysis on the impact of SNPs over transthyretin (TTR) using bioinformatic tools

Hereditary Transthyretin-associated amyloidosis (ATTR) is an autosomal dominant protein-folding disorder with adult-onset caused by mutation of transthyretin (TTR). TTR is characterized by extracellular deposition of amyloid, leading to loss of autonomy and finally, death. More than 100 distinct mutations in TTR gene have been reported from variable age of onset, clinical expression and penetrance data. Besides, the cure for the disease remains still obscure. Further, the prioritizing of mutations concerning the characteristic features governing the stability and pathogenicity of TTR mutant proteins remains unanswered, to date and thus, a complex state of study for researchers. Herein, we provide a full report encompassing the effects of every reported mutant model of TTR protein about the stability, functionality and pathogenicity using various computational tools. In addition, the results obtained from our study were used to create TTRMDB (Transthyretin mutant database), which could be easy access to researchers at http://vit.ac.in/ttrmdb.     

ncRDeep: Non-coding RNA classification with convolutional neural network

A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involved in many biological processes, diseases, and cancers. Numerous ncRNAs have been identified and classified with high throughput sequencing technology. Hence, accurate ncRNAs class prediction is important and necessary for further study of their functions. Several computation techniques have been employed to predict the class of ncRNAs. Recent classification methods used the secondary structure as their primary input. However, the computational tools of RNA secondary structure are not accurate enough which affects the final performance of ncRNAs predictors. In this paper, we propose a simple yet efficient method, called ncRDeep, for ncRNAs prediction. It uses a simple convolutional neural network and RNA sequence information only. The ncRDeep was evaluated on benchmark datasets and the comparison results showed that the ncRDeep outperforms the state-of-the-art methods significantly. More specifically, the average accuracy was improved by 8.32%. Finally, we built a freely accessible web server for the developed tool ncRDeep at http://home.jbnu.ac.kr/NSCL/ncRDeep.htm     

A merged molecular docking,ADME-T and dynamics approaches towards the genus of Arisaema as herpes simplex virus type 1 and type 2 inhibitors

An attempt toward screening of phytoconstituents (Arisaema genus) against herpes viruses (HSV-1 and HSV-2) was carried out using in silico approaches. Human HSV-1 and HSV-2 are accountable for cold sores genital herpes, respectively. Two drug targets, namely thymidine kinase (TK; PDB: 2ki5) serine protease (PDB: 1at3) were selected for HSV-1 and HSV-2. Initially, molecular docking tool was employed to screened apex hits phytoconstituents against herpes infections. ADME-T studies of top ranked were also further highlighted to achieve their effectiveness. Following, molecular dynamics studies were also examined to further optimize the stability of ligands. Glide scores and binding interactions of phytoconstituents were compared with Acyclovir, the main drug used in treatment of HSV, the screened top hits exhibited more glide scores and better binding for both HSV-1 and HSV-2 receptors. Additionally, ADME-T showed an ideal range for top hits while molecular dynamics results also illustrated stability of models. Ultimately, the whole efforts reveal to top three most promising hits for HSV-1 (39, 21, 19) and HSV-2 (20, 51, 19) receptors which can be explored further in wet lab experiments as promising agents against HSV infections.     

Design of (quinolin-4-ylthio)carboxylic acids as new Escherichia coli DNA gyrase B inhibitors: machine learning studies,molecular docking,synthesis and biological testing

Spread of multidrug‐resistant Escherichia coli clinical isolates is a main problem in the treatment of infectious diseases. Therefore, the modern scientific approaches in decision this problem require not only a prevention strategy, but also the development of new effective inhibitory compounds with selective molecular mechanism of action and low toxicity. The goal of this work is to identify more potent molecules active against E. coli strains by using machine learning, docking studies, synthesis and biological evaluation. A set of predictive QSAR models was built with two publicly available structurally diverse data sets, including recent data deposited in PubChem. The predictive ability of these models tested by a 5-fold cross-validation, resulted in balanced accuracies (BA) of 59–98% for the binary classifiers. Test sets validation showed that the models could be instrumental in predicting the antimicrobial activity with an accuracy (with BA = 60–99 %) within the applicability domain. The models were applied to screen a virtual chemical library, which was designed to have activity against resistant E. coli strains. The eight most promising compounds were identified, synthesized and tested. All of them showed the different levels of anti-E. coli activity and acute toxicity. The docking results have shown that all studied compounds are potential DNA gyrase inhibitors through the estimated interactions with amino acid residues and magnesium ion in the enzyme active center The synthesized compounds could be used as an interesting starting point for further development of drugs with low toxicity and selective molecular action mechanism against resistant E. coli strains. The developed QSAR models are freely available online at OCHEM http://ochem.eu/article/112525 and can be used to virtual screening of potential compounds with anti-E. coli activity.     

MDAnalysis: A toolkit for the analysis of molecular dynamics simulations

MDAnalysis is an object‐oriented library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. It is written in the Python language with some performance‐critical code in C. It uses the powerful NumPy package to expose trajectory data as fast and efficient NumPy arrays. It has been tested on systems of millions of particles. Many common file formats of simulation packages including CHARMM, Gromacs, Amber, and NAMD and the Protein Data Bank format can be read and written. Atoms can be selected with a syntax similar to CHARMM's powerful selection commands. MDAnalysis enables both novice and experienced programmers to rapidly write their own analytical tools and access data stored in trajectories in an easily accessible manner that facilitates interactive explorative analysis. MDAnalysis has been tested on and works for most Unix‐based platforms such as Linux and Mac OS X. It is freely available under the GNU General Public License from http://mdanalysis.googlecode.com . © 2011 Wiley Periodicals, Inc. J Comput Chem 2011     

Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment

The amlyoid-β peptide (Aβ) is closely linked to the development of Alzheimer''s disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the atomistic level. General key aspects of MD simulations are the force field used for modeling the peptide and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-μs MD simulations acquired for Aβ40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for the different MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-β peptide. This further allows us to provide answers for pertinent questions, like: which force fields are suitable for modeling Aβ? (a99SB-UCB and a99SB-ILDN/TIP4P-D); what does Aβ peptide really look like? (mostly extended and disordered) and; how long does it take MD simulations of Aβ to attain equilibrium? (at least 20–30 μs). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of Aβ in particular, and by extension other similar disordered proteins.

The convergence of MD simulations is tested using varying measures for the intrinsically disordered amyloid-β peptide (Aβ). Markov state models show that 20–30 μs of MD is needed to reliably reproduce the thermodynamics and kinetics of Aβ.     

dbHDPLS: A database of human disease-related protein-ligand structures

Protein-ligand complexes perform specific functions, most of which are related to human diseases. The database, called as human disease-related protein-ligand structures (dbHDPLS), collected 8833 structures which were extracted from protein data bank (PDB) and other related databases. The database is annotated with comprehensive information involving ligands and drugs, related human diseases and protein-ligand interaction information, with the information of protein structures. The database may be a reliable resource for structure-based drug target discoveries and druggability predictions of protein-ligand binding sites, drug-disease relationships based on protein-ligand complex structures. It can be publicly accessed at the website: http://DeepLearner.ahu.edu.cn/web/dbDPLS/.     

Molecular docking,HOMO-LUMO and quantum chemical computation analysis of anti-glyoximehydrazone derivatives containing pyrazolone moiety and their transition metal complexes

In this study, in order to obtain biologically active compounds, a series of anti-glyoximehydrazone ligands bearing vic-dioxime, hydrazone, and pyrazole moieties and their (O•••H–O) bridged nickel(II), cobalt(II) and copper(II) metal complexes were prepared. Further, the molecular docking studies were carried out on those ligands and their nickel(II), cobalt(II) and copper(II) metal complexes to analyze the interaction with EGFR Kinase domain complexed with tak-285 (PDB ID: 3POZ) and human androgen receptor T877A mutant (PDB ID:2OZ7). In addition, the compounds were optimized by using B3LYP/6-311G+(d,p) level of Density Functional Theory (DFT) to evaluate the HOMO–LUMO contours and quantum chemical parameters. Also, bioactivity analysis were performed.Metal complexes had higher binding affinities against 3POZ and 2OZ7. The most promising compounds for 3POZ were nickel(II) and copper(II) metal complexes. However, for the 2OZ7 target receptor, cobalt(II) and copper(II) metal complexes were the possible hit compounds. Furthermore, cobalt(II) metal complex of ligand two was found to be the most reactive one among others. Moreover, it had the highest ω which is related to a potent higher electrophilic character. It was determined that all the compounds had moderate bioactivity.In conclusion, nickel(II), cobalt(II), and copper(II) complexes could be powerful hit compounds for anti-cancer drug discovery studies.     

THPep: A machine learning-based approach for predicting tumor homing peptides

In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at http://codes.bio/thpep/.     

SPONGE:A GPU-Accelerated Molecular Dynamics Package with Enhanced Sampling and AI-Driven Algorithms

SPONGE(Simulation Package tOward Next GEneration molecular modeling)is a software package for molecular dynamics(MD)simulation of solution and surface molecular systems.In this version of SPONGE,the all-atom potential energy functions used in AMBER MD packages are used by default and other all-atom/coarse-grained potential energy functions are also supported.SPONGE is designed to extend the timescale being approached in MD simulations by utilizing the latest CUDA-enabled graphical processing units(GPU)and adopting highly efficient enhanced sampling algorithms,such as integrated tempering,selective integrated tempering and enhanced sampling of reactive trajectories.It is highly modular and new algorithms and functions can be incorporated con veniently.Particularly,a specialized Python plugin can be easily used to perform the machine learning MD simulation with MindSpore,TensorFlow,PyTorch or other popular machine learning frameworks.Furthermore,a plugin of Finite-Element Method(FEM)is also available to handle metallic surface systems.All these advanced features increase the power of SPONGE for modeling and simulation of complex chemical and biological systems.