scholarly journals Integrated Computational Approaches and Tools for Allosteric Drug Discovery

2020 ◽  
Vol 21 (3) ◽  
pp. 847 ◽  
Author(s):  
Olivier Sheik Amamuddy ◽  
Wayde Veldman ◽  
Colleen Manyumwa ◽  
Afrah Khairallah ◽  
Steve Agajanian ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
In Silico ◽  
Molecular Mechanisms ◽  
Pathogen Resistance ◽  
Allosteric Modulators ◽  
Computational Approaches ◽  
Allosteric Sites ◽  
Computer Based ◽  
Allosteric Mechanisms

Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

scholarly journals PASSer: Prediction of Allosteric Sites Server

2020 ◽  
Author(s):  
Hao Tian ◽  
Xi Jiang ◽  
Peng Tao
Keyword(s):  
Drug Discovery ◽  
Prior Information ◽  
Gradient Boosting ◽  
Command Line ◽  
Allosteric Site ◽  
Command Line Interface ◽  
And Topology ◽  
Extreme Gradient Boosting ◽  
Allosteric Sites ◽  
Allosteric Mechanisms

Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and dynamics information. Here, we provide a novel ensembled model, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN) to predict allosteric sites. Our model can learn both physical properties and topology structure without any prior information and exhibited good performance under several indicators. Prediction results have shown that 84.9% of allosteric pockets in the testing proteins appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (CLI, https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery

2020 ◽  
pp. 25-47 ◽  
Author(s):  
Vishnupriya Kanakaveti ◽  
Anusuya Shanmugam ◽  
C. Ramakrishnan ◽  
P. Anoosha ◽  
R. Sakthivel ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Computational Approaches ◽  
Potential Inhibitors

scholarly journals An integration of deep learning with feature embedding for protein–protein interaction prediction

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7126 ◽  
Author(s):  
Yu Yao ◽  
Xiuquan Du ◽  
Yanyu Diao ◽  
Huaixu Zhu
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Function ◽  
Molecular Mechanisms ◽  
Protein Protein Interactions ◽  
Model Combining ◽  
Representation Method ◽  
Structure Knowledge ◽  
Matthew’S Correlation Coefficient

Protein–protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein–protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein–protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models. In this work, a new residue representation method named Res2vec is designed for protein sequence representation. Residue representations obtained by Res2vec describe more precisely residue-residue interactions from raw sequence and supply more effective inputs for the downstream deep learning model. Combining effective feature embedding with powerful deep learning techniques, our method provides a general computational pipeline to infer protein–protein interactions, even when protein structure knowledge is entirely unknown. The proposed method DeepFE-PPI is evaluated on the S. Cerevisiae and human datasets. The experimental results show that DeepFE-PPI achieves 94.78% (accuracy), 92.99% (recall), 96.45% (precision), 89.62% (Matthew’s correlation coefficient, MCC) and 98.71% (accuracy), 98.54% (recall), 98.77% (precision), 97.43% (MCC), respectively. In addition, we also evaluate the performance of DeepFE-PPI on five independent species datasets and all the results are superior to the existing methods. The comparisons show that DeepFE-PPI is capable of predicting protein–protein interactions by a novel residue representation method and a deep learning classification framework in an acceptable level of accuracy. The codes along with instructions to reproduce this work are available from https://github.com/xal2019/DeepFE-PPI.

scholarly journals New machine learning and physics-based scoring functions for drug discovery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Isabella A. Guedes ◽  
André M. S. Barreto ◽  
Diogo Marinho ◽  
Eduardo Krempser ◽  
Mélaine A. Kuenemann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Protein Interactions ◽  
In Silico ◽  
Drug Targets ◽  
Support Vector ◽  
Scoring Functions ◽  
Protein Protein Interactions ◽  
Energy Prediction ◽  
Original Class

AbstractScoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.

scholarly journals Multilayer view of pathogenic SNVs in human interactome throughin-silicoedgetic profiling

2018 ◽  
Author(s):  
Hongzhu Cui ◽  
Nan Zhao ◽  
Dmitry Korkin
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Molecular Mechanisms ◽  
Synergistic Effects ◽  
Patient Specific ◽  
Single Nucleotide Variants ◽  
Human Interactome ◽  
Frameshift Mutations ◽  
Next Generation Sequencing Technology ◽  
Synonymous Mutations

ABSTRACTNon-synonymous mutations linked to the complex diseases often have a global impact on a biological system, affecting large biomolecular networks and pathways. However, the magnitude of the mutation-driven effects on the macromolecular network is yet to be fully explored. In this work, we present an systematic multi-level characterization of human mutations associated with genetic disorders by determining their individual and combined interaction-rewiring, “edgetic”, effects on the human interactome. Ourin-silicoanalysis highlights the intrinsic differences and important similarities between the pathogenic single nucleotide variants (SNVs) and frameshift mutations. We show that pathogenic SNVs are more likely to cause gene pleiotropy than pathogenic frameshift mutations and are enriched on the protein interaction interfaces. Functional profiling of SNVs indicates widespread disruption of the protein-protein interactions and synergistic effects of SNVs. The coverage of our approach is several times greater than the recently published experimental study and has the minimal overlap with it, while the distributions of determined edgotypes between the two sets of profiled mutations are remarkably similar. Case studies reveal the central role of interaction-disrupting mutations in type 2 diabetes mellitus, and suggest the importance of studying mutations that abnormally strengthen the protein interactions in cancer. With the advancement of next-generation sequencing technology that drives precision medicine, there is an increasing demand in understanding the changes in molecular mechanisms caused by the patient-specific genetic variation. The current and futurein-silicoedgotyping tools present a cheap and fast solution to deal with the rapidly growing datasets of discovered mutations.

scholarly journals In-Silico Methodologies for Cancer Multidrug Optimization

2018 ◽  
Vol 17 (2) ◽  
pp. 7186-7205
Author(s):  
Doaa Mohamed Hasan ◽  
Ahmed Sharaf Eldin ◽  
Ayman Elsayed Khedr ◽  
Hanan Fahmy
Keyword(s):  
In Silico ◽  
Molecular Mechanisms ◽  
Complex Diseases ◽  
Search Space ◽  
Drug Combinations ◽  
Network Robustness ◽  
Molecular Pathways ◽  
Computational Approaches ◽  
Adaptive Resistance ◽  
Control Complex

Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear, which makes it not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer, and discuss the limitations and challenges of these methods.

PASSer: Prediction of Allosteric Sites Server

2020 ◽  
Author(s):  
Hao Tian ◽  
Xi Jiang ◽  
Peng Tao
Keyword(s):  
Drug Discovery ◽  
Prior Information ◽  
Gradient Boosting ◽  
Command Line ◽  
Allosteric Site ◽  
Command Line Interface ◽  
And Topology ◽  
Extreme Gradient Boosting ◽  
Allosteric Sites ◽  
Allosteric Mechanisms

Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and dynamics information. Here, we provide a novel ensembled model, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN) to predict allosteric sites. Our model can learn both physical properties and topology structure without any prior information and exhibited good performance under several indicators. Prediction results have shown that 84.9% of allosteric pockets in the testing proteins appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (CLI, https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

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