scholarly journals Likelihood-based Fits of Folding Transitions (LiFFT) for Biomolecule Mapping Data

2018 ◽  
Author(s):  
Rhiju Das
Keyword(s):  
Small Molecules ◽  
Biological Function ◽  
Visual Assessment ◽  
Supplementary Information ◽  
Chemical Mapping ◽  
Mapping Data ◽  
Modeling Uncertainties ◽  
Multi Wavelength ◽  
Mapping Techniques ◽  
Matlab Package

AbstractSummaryBiomolecules shift their structures as a function of temperature and concentrations of protons, ions, small molecules, proteins, and nucleic acids. These transitions impact or underlie biological function and are being monitored at increasingly high throughput. For example, folding transitions for large collections of RNAs can now be monitored at single residue resolution by chemical mapping techniques. LIkelihood-based Fits of Folding Transitions (LIFFT) quantifies these data through well-defined thermodynamic models. LIFFT implements a Bayesian framework that takes into account data at all measured residues and enables visual assessment of modeling uncertainties that can be overlooked in least-squares fits. The framework is appropriate for multimodal techniques ranging from chemical mapping including multi-wavelength spectroscopy.AvailabilityFreely available MATLAB package at https://ribokit.stanford.edu/LIFFT/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Drug Discovery Targeting Focal Adhesion Kinase (FAK) as a Promising Cancer Therapy

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4250
Author(s):  
Xiao-Jing Pang ◽  
Xiu-Juan Liu ◽  
Yuan Liu ◽  
Wen-Bo Liu ◽  
Yin-Ru Li ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Phase I ◽  
Small Molecules ◽  
Anticancer Agents ◽  
Biological Function ◽  
Human Cancer ◽  
Tumor Cell Growth ◽  
Research Groups ◽  
Human Cancer Cell Lines ◽  
Opening Up

FAK is a nonreceptor intracellular tyrosine kinase which plays an important biological function. Many studies have found that FAK is overexpressed in many human cancer cell lines, which promotes tumor cell growth by controlling cell adhesion, migration, proliferation, and survival. Therefore, targeting FAK is considered to be a promising cancer therapy with small molecules. Many FAK inhibitors have been reported as anticancer agents with various mechanisms. Currently, six FAK inhibitors, including GSK-2256098 (Phase I), VS-6063 (Phase II), CEP-37440 (Phase I), VS-6062 (Phase I), VS-4718 (Phase I), and BI-853520 (Phase I) are undergoing clinical trials in different phases. Up to now, there have been many novel FAK inhibitors with anticancer activity reported by different research groups. In addition, FAK degraders have been successfully developed through “proteolysis targeting chimera” (PROTAC) technology, opening up a new way for FAK-targeted therapy. In this paper, the structure and biological function of FAK are reviewed, and we summarize the design, chemical types, and activity of FAK inhibitors according to the development of FAK drugs, which provided the reference for the discovery of new anticancer agents.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

AlphaFill: enriching the AlphaFold models with ligands and co-factors

2021 ◽  
Author(s):  
Maarten L Hekkelman ◽  
Ida de de Vries ◽  
Robbie P Joosten ◽  
Anastassis Perrakis
Keyword(s):  
Small Molecules ◽  
Structural Integrity ◽  
Biological Function ◽  
Protein Structures ◽  
Molecular Function ◽  
Structure Database ◽  
Zinc Finger Motifs ◽  
Structure Similarity ◽  
Protein Models ◽  
Design Experiments

Artificial intelligence (AI) methods for constructing structural models of proteins on the basis of their sequence are having a transformative effect in biomolecular sciences. The AlphaFold protein structure database makes available hundreds of thousands of protein structures. However, all these structures lack cofactors essential for their structural integrity and molecular function (e.g. hemoglobin lacks a bound heme), key ions essential for structural integrity (e.g. zinc-finger motifs) or catalysis (e.g. Ca2+ or Zn2+ in metalloproteases), and ligands that are important for biological function (e.g. kinase structures lack ADP or ATP). Here, we present AlphaFill, an algorithm based on sequence and structure similarity, to "transplant" such "missing" small molecules and ions from experimentally determined structures to predicted protein models. These publicly available structural annotations are mapped to predicted protein models, to help scientists interpret biological function and design experiments.

scholarly journals COVID-19 Docking Server: a meta server for docking small molecules, peptides and antibodies against potential targets of COVID-19

2020 ◽  
Vol 36 (20) ◽  
pp. 5109-5111 ◽  
Author(s):  
Ren Kong ◽  
Guangbo Yang ◽  
Rui Xue ◽  
Ming Liu ◽  
Feng Wang ◽  
...  
Keyword(s):  
Life Cycle ◽  
Drug Discovery ◽  
Small Molecules ◽  
Web Server ◽  
Supplementary Information ◽  
Binding Modes ◽  
Supplementary Data ◽  
Virus Life Cycle ◽  
Ligand Interactions ◽  
New Type

Abstract Motivation The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019. Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target–ligand interactions represents a key challenge. Herein, we introduce COVID-19 Docking Server, a web server that predicts the binding modes between COVID-19 targets and the ligands including small molecules, peptides and antibodies. Results Structures of proteins involved in the virus life cycle were collected or constructed based on the homologs of coronavirus, and prepared ready for docking. The meta-platform provides a free and interactive tool for the prediction of COVID-19 target–ligand interactions and following drug discovery for COVID-19. Availability and implementation http://ncov.schanglab.org.cn. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SARS2020: an integrated platform for identification of novel coronavirus by a consensus sequence-function model

2020 ◽  
Author(s):  
Dachuan Zhang ◽  
Tong Zhang ◽  
Sheng Liu ◽  
Dandan Sun ◽  
Shaozhen Ding ◽  
...  
Keyword(s):  
Biological Function ◽  
Consensus Sequence ◽  
Rapid Identification ◽  
Data Driven ◽  
Supplementary Information ◽  
Respiratory Syndrome Virus ◽  
The Novel ◽  
Function Model ◽  
Catalytic Function ◽  
Novel Coronavirus

Abstract Motivation The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation. Results We built a comprehensive scientific resource, SARS2020, which integrates coronavirus-related research, genomic sequences and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the severe acute respiratory syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics. Availabilityand implementation SARS2020 is available at http://design.rxnfinder.org/sars2020/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MetumpX—a metabolomics support package for untargeted mass spectrometry

2019 ◽  
Vol 36 (5) ◽  
pp. 1647-1648 ◽  
Author(s):  
Bilal Wajid ◽  
Hasan Iqbal ◽  
Momina Jamil ◽  
Hafsa Rafique ◽  
Faria Anwar
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Small Molecules ◽  
Software Package ◽  
Life Sciences ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Software Packages ◽  
Develop Software ◽  
User Friendly

Abstract Motivation Metabolomics is a data analysis and interpretation field aiming to study functions of small molecules within the organism. Consequently Metabolomics requires researchers in life sciences to be comfortable in downloading, installing and scripting of software that are mostly not user friendly and lack basic GUIs. As the researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines truly speeding up the learning curve to build software workstations. Therefore, this paper aims to provide MetumpX, a software package that eases in the installation of 103 software by automatically resolving their individual dependencies and also allowing the users to choose which software works best for them. Results MetumpX is a Ubuntu-based software package that facilitate easy download and installation of 103 tools spread across the standard metabolomics pipeline. As far as the authors know MetumpX is the only solution of its kind where the focus lies on automating development of software workstations. Availability and implementation https://github.com/hasaniqbal777/MetumpX-bin. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Heparin Binding Proteins as Therapeutic Target: An Historical Account and Current Trends

Medicines ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 80 ◽  
Author(s):  
Giancarlo Ghiselli
Keyword(s):  
Small Molecules ◽  
Biological Function ◽  
Antithrombin Iii ◽  
Biological Effects ◽  
Structural Flexibility ◽  
Heparin Binding ◽  
New Class ◽  
High Affinity ◽  
Current Trends ◽  
High Degree

The polyanionic nature and the ability to interact with proteins with different affinities are properties of sulfated glycosaminoglycans (GAGs) that determine their biological function. In designing drugs affecting the interaction of proteins with GAGs the challenge has been to generate agents with high binding specificity. The example to emulated has been a heparin-derived pentasaccharide that binds to antithrombin-III with high affinity. However, the portability of this model to other biological situations is questioned on several accounts. Because of their structural flexibility, oligosaccharides with different sulfation and uronic acid conformation can display the same binding proficiency to different proteins and produce comparable biological effects. This circumstance represents a formidable obstacle to the design of drugs based on the heparin scaffold. The conceptual framework discussed in this article is that through a direct intervention on the heparin-binding functionality of proteins is possible to achieve a high degree of action specificity. This objective is currently pursued through two strategies. The first makes use of small molecules for which in the text we provide examples from past and present literature concerning angiogenic factors and enzymes. The second approach entails the mutagenesis of the GAG-binding site of proteins as a means to generate a new class of biologics of therapeutic interest.

Diffusion and Interdiffusion in Multilayered Semiconductor Systems

1989 ◽  
Vol 163 ◽  
Author(s):  
A. Ourmazd ◽  
Y. Kim ◽  
M. Bode
Keyword(s):  
Thermal Treatment ◽  
Ion Implantation ◽  
Point Defects ◽  
Atomic Level ◽  
Chemical Mapping ◽  
Initial Concentration ◽  
Subsequent Thermal Treatment ◽  
Native Point Defects ◽  
Mapping Techniques ◽  
Quantitative Chemical

AbstractWe apply quantitative chemical mapping techniques to study thermal interdiffusion and ion-implantation induced intermixing at single heterointerfaces at the atomic level. Our results show thermal interdiffusion to be strongly depth dependent. This is related to the need for the presence of native point defects (interstitials and vacancies) to bring about interdiffusion. Since their initial concentration in the bulk is negligible, the point defects must be injected at the surface and transported to the interface for interdiffusion to occur. In the case of ion-implanted samples, we find the passage of a single energetic ion through a sample at 77 K causes significant intermixing, even when the sample receives no subsequent thermal treatment.

scholarly journals Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Jan Lebert ◽  
Namita Ravi ◽  
Flavio H. Fenton ◽  
Jan Christoph
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optical Mapping ◽  
Simulated Data ◽  
Mapping Data ◽  
Phase Singularities ◽  
Rotor Core ◽  
Phase Mapping ◽  
Spatio Temporal ◽  
Mapping Techniques

The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.

scholarly journals Evidence of an Unusual Poly(A) RNA Signature Detected by High-throughput Chemical Mapping

2018 ◽  
Author(s):  
Roger Wellington-Oguri ◽  
Eli Fisker ◽  
Mat Zada ◽  
Michelle Wiley ◽  
Keyword(s):  
High Throughput ◽  
Genetic Material ◽  
Dimethyl Sulfate ◽  
Primer Extension ◽  
Random Coil ◽  
Chemical Mapping ◽  
Mapping Data ◽  
Physiological Conditions ◽  
Chemical Probing ◽  
Sequence Substitution

ABSTRACTHomopolymeric adenosine RNA plays numerous roles in both cells and non-cellular genetic material, and for lack of evidence to the contrary, it is generally accepted to form a random coil under physiological conditions. However, chemical mapping data generated by the Eterna Massive Open Laboratory indicates that a poly (A) sequence of length seven or more, at pH 8.0 and MgCl concentrations of 10 mM, develops unexpected protection to selective 2’-hydroxyl acylation read out by primer extension (SHAPE) and dimethyl sulfate (DMS) chemical probing. This protection first appears in poly(A) sequences of length 7 and grows to its maximum strength at length ~10. In a long poly(A) sequence, substitution of a single A by any other nucleotide disrupts the protection, but only for the 6 or so nucleotides on the 5’ side of the substitution. The authors are grateful for pre-publication comments; please use https://docs.google.com/document/d/14972Q36IDTYMglwMXTOrqd4P9orQ6-P3bPbCuITdv6A.

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