scholarly journals Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein–ligand interactions based on a study on caspase 3 inhibitors

2015 ◽  
Vol 30 (5) ◽  
pp. 809-815 ◽  
Author(s):  
Ozlem Erdas ◽  
Cenk. A. Andac ◽  
A. Selen Gurkan-Alp ◽  
Ferda Nur Alpaslan ◽  
Erdem Buyukbingol
Keyword(s):  
Machine Learning ◽  
Caspase 3 ◽  
Affinity Prediction ◽  
Compressed Images ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods

2021 ◽  
Vol 28 ◽  
Author(s):  
Yu-He Yang ◽  
Jia-Shu Wang ◽  
Shi-Shi Yuan ◽  
Meng-Lu Liu ◽  
Wei Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Vital Role ◽  
Atp Binding ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Protein Ligand Interactions ◽  
Protein Functions ◽  
Ligand Interactions ◽  
Binding Residues

: Protein-ligand interactions are necessary for majority protein functions. Adenosine-5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

scholarly journals Post hoc support vector machine learning for impedimetric biosensors based on weak protein–ligand interactions

The Analyst ◽  
2018 ◽  
Vol 143 (9) ◽  
pp. 2066-2075 ◽  
Author(s):  
Y. Rong ◽  
A. V. Padron ◽  
K. J. Hagerty ◽  
N. Nelson ◽  
S. Chi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mobile Phone ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Impedimetric Biosensor ◽  
Post Hoc

We develop a simple, open source machine learning algorithm for analyzing impedimetric biosensor data using a mobile phone.

scholarly journals Phytoconstituents from Markhamia Tomentosa Bind To HPV Oncoprotein with Apoptogenic Potential: A Molecular Modeling Approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mutiat B. Ibrahim ◽  
Adeola T. Kola-Mustapha ◽  
Niyi S. Adelakun ◽  
Neil A. Koorbanally
Keyword(s):  
Drug Targets ◽  
Active Sites ◽  
Caspase 3 ◽  
Cancer Cell Line ◽  
Hpv 16 ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Potent Growth ◽  
The Stability

Abstract Markhamia tomentosa crude extract and fractions exhibited potent growth inhibitory effects capable to induce apoptosis in cervical (HeLa) cancer cell line via in vitro model. Presently, interaction of M. tomentosa phytoconstituents with molecular drug targets to exert its anticancer property is evaluated via in silico study. Identified phytoconstituents from M. tomentosa were retrieved from PubChem database and docked in active sites of HPV 16 E6, caspase -3 and caspase -8 targets using AutoDockVina from PyRx software. Screening for druglikeness; and absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions was carried out with the use of SwissADME and pkCSM web servers. Standard melphalan and co-crystallized ligands of caspases -3 and -8 enzymes were used to validate protein-ligand interactions. Molecular dynamic simulation was used to validate the stability of the hit molecules complexed with caspases -3 and -8. All identified phytoconstituents from M. tomentosa showed binding affinity for HPV with docking scores range of - 5.4 to -2.6 kcal/mol. Ajugol, carnosol, luteolin and phytol showed good docking energy range of -6.8 to -3.6 kcal/mol; and -4.8 to -1.9 kcal/mol for the active sites of caspases -3 and -8 targets respectively. Based on docking scores; drug-likeliness; and ADMET predictions; luteolin and carnosol were selected as hit compounds. These molecules were found to be stable within the binding site of caspase -3 target throughout the 40ns simulation time. These findings identified hit ligands from M. tomentosa phytoconstituents that inhibit HPV 16 E6 oncogene expression with stimulation of caspases -3 and -8 targets.

scholarly journals Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

2021 ◽  
Author(s):  
H. Tomas Rube ◽  
Chaitanya Rastogi ◽  
Siqian Feng ◽  
Judith Franziska Kribelbauer ◽  
Allyson Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Biological Networks ◽  
Specific Protein ◽  
Biophysical Parameters ◽  
Kinase Substrate ◽  
Protein Ligand Interactions ◽  
Sequence Recognition ◽  
Ligand Interactions ◽  
The Impact

Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene regulation and signal transduction. Next-generation sequencing (NGS) based assays are increasingly being used to profile these interactions with high-throughput. However, these assays do not provide the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to define sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it determines the absolute affinity of protein-ligand interactions. It can also profile the kinetics of kinase-substrate interactions. By constructing a biophysically robust foundation for profiling sequence recognition, ProBound opens up new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

2018 ◽  
Author(s):  
Maciej Wójcikowski ◽  
Michał Kukiełka ◽  
Marta Stepniewska-Dziubinska ◽  
Pawel Siedlecki
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Linear Model ◽  
Binding Affinity ◽  
Small Molecule ◽  
3D Structure ◽  
Protein Ligand Interactions ◽  
Core Set ◽  
Ligand Interactions ◽  
Extended Connectivity

<div>Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of fingerprints, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions. Here, we present a Protein-Ligand Extended Connectivity (PLEC) fingerprint that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC fingerprints were used to construct different machine learning (ML) models tailored for predicting protein-ligand affinities (pK<sub>i/d</sub>). Even the simplest linear model built on the PLEC fingerprint achieved R<sub>p</sub>=0.83 on the PDBbind v2016 "core set”, demonstrating its descriptive power. The PLEC fingerprint has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).</div>

Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

2018 ◽  
Author(s):  
Maciej Wójcikowski ◽  
Michał Kukiełka ◽  
Marta Stepniewska-Dziubinska ◽  
Pawel Siedlecki
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Linear Model ◽  
Binding Affinity ◽  
Small Molecule ◽  
3D Structure ◽  
Protein Ligand Interactions ◽  
Core Set ◽  
Ligand Interactions ◽  
Extended Connectivity

<div>Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of fingerprints, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions. Here, we present a Protein-Ligand Extended Connectivity (PLEC) fingerprint that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC fingerprints were used to construct different machine learning (ML) models tailored for predicting protein-ligand affinities (pK<sub>i/d</sub>). Even the simplest linear model built on the PLEC fingerprint achieved R<sub>p</sub>=0.83 on the PDBbind v2016 "core set”, demonstrating its descriptive power. The PLEC fingerprint has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).</div>

19F-NMR in Target-based Drug Discovery

2019 ◽  
Vol 26 (26) ◽  
pp. 4964-4983 ◽  
Author(s):  
CongBao Kang
Keyword(s):  
Drug Discovery ◽  
Conformational Changes ◽  
Protein Structures ◽  
19F Nmr ◽  
Binding Affinities ◽  
Protein Ligand Interactions ◽  
Compound Libraries ◽  
Ligand Interactions ◽  
Reference Ligand ◽  
Hit Identification

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.

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