scholarly journals Computational design of peptide therapeutics: how useful are sequence-based protein-protein interaction predictors?

2021 ◽  
Author(s):  
Francois Charih ◽  
Kyle K. Biggar ◽  
James R. Green
Keyword(s):  
Protein Interaction ◽  
In Silico ◽  
De Novo ◽  
Cost Effective ◽  
Computational Design ◽  
Training Data ◽  
Protein Protein Interaction ◽  
Peptide Engineering ◽  
Cost Effective Alternative ◽  
High Throughput Experiments

Engineering peptides to achieve a desired therapeutic effect through the inhibition of a specific target activity or protein interaction is a non-trivial task. Few of the existing in silico peptide design algorithms generate target-specific peptides. Instead, many methods produce peptides that achieve a desired effect through an unknown mechanism. In contrast with resource-intensive high-throughput experiments, in silico screening is a cost-effective alternative that can prune the space of candidates when engineering target-specific peptides. Using a set of FDA-approved peptides we curated specifically for this task, we assess the applicability of several sequence-based protein-protein interaction predictors as a screening tool within the context of peptide therapeutic engineering. We show that similarity-based protein-protein interaction predictors are more suitable for this purpose than the current state-of-the-art deep learning methods. We also show that this approach is mostly useful when designing new peptides against targets for which naturally-occurring interactors are already known, and that deploying it for de novo peptide engineering tasks may require gathering additional target-specific training data. Taken together, this work offers evidence that supports the use of similarity-based protein-protein interaction predictors for peptide therapeutic engineering, especially peptide analogs.

scholarly journals X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis

2020 ◽  
Author(s):  
Dongyu Xue ◽  
Han Zhang ◽  
Dongling Xiao ◽  
Yukang Gong ◽  
Guohui Chuai ◽  
...  
Keyword(s):  
Molecular Analysis ◽  
In Silico ◽  
Large Scale ◽  
De Novo ◽  
Representation Learning ◽  
Training Data ◽  
Fine Tuning ◽  
Model Interpretation ◽  
Unlabelled Data ◽  
Super Computing

AbstractIn silico modelling and analysis of small molecules substantially accelerates the process of drug development. Representing and understanding molecules is the fundamental step for various in silico molecular analysis tasks. Traditionally, these molecular analysis tasks have been investigated individually and separately. In this study, we presented X-MOL, which applies large-scale pre-training technology on 1.1 billion molecules for molecular understanding and representation, and then, carefully designed fine-tuning was performed to accommodate diverse downstream molecular analysis tasks, including molecular property prediction, chemical reaction analysis, drug-drug interaction prediction, de novo generation of molecules and molecule optimization. As a result, X-MOL was proven to achieve state-of-the-art results on all these molecular analysis tasks with good model interpretation ability. Collectively, taking advantage of super large-scale pre-training data and super-computing power, our study practically demonstrated the utility of the idea of “mass makes miracles” in molecular representation learning and downstream in silico molecular analysis, indicating the great potential of using large-scale unlabelled data with carefully designed pre-training and fine-tuning strategies to unify existing molecular analysis tasks and substantially enhance the performance of each task.

scholarly journals Better Network Modeling For Link Prediction In Protein-Protein Interaction Networks

2021 ◽  
Author(s):  
Ho Yin Yuen ◽  
Jesper Jansson
Keyword(s):  
Protein Interaction ◽  
Link Prediction ◽  
Network Modeling ◽  
Computation Time ◽  
General Purpose ◽  
Computational Techniques ◽  
Full Potential ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks ◽  
High Throughput Experiments

Abstract Background: Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, inaccuracies in high-throughput experiments often result in incomplete PPI data. Computational techniques are thus used to infer missing data and to evaluate confidence scores, with link prediction being one such approach that uses the structure of the network of PPIs known so far to find good candidates for missing PPIs. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. However, the previously developed L3 principle-based link predictors are only an approximate implementation of the L3 principle. As such, not only is the full potential of the L3 principle not realized, they may even lead to candidate PPIs that otherwise fit the L3 principle being penalized. Result: In this article, we propose a formulation of link predictors without approximation that we call ExactL3 (L3E) by addressing missing elements within L3 predictors in the perspective of network modeling. Through statistical and biological metrics, we show that in general, L3E predictors perform better than the previously proposed methods on seven datasets across two organisms (human and yeast) using a reasonable amount of computation time. In addition to L3E being able to rank the PPIs more accurately, we also found that L3-based predictors, including L3E, predicted a different pool of real PPIs than the general-purpose link predictors. This suggests that different types of PPIs can be predicted based on different topological assumptions and that even better PPI link predictors may be obtained in the future by improved network modeling.

Construction and Analysis of Protein-Protein Interaction Network

2019 ◽  
pp. 204-220
Author(s):  
Divya Dasagrandhi ◽  
Arul Salomee Kamalabai Ravindran ◽  
Anusuyadevi Muthuswamy ◽  
Jayachandran K. S.
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Data Sets ◽  
Protein Protein Interaction ◽  
Huge Data ◽  
Associated Proteins ◽  
High Throughput Experiments ◽  
Generation Sequencing ◽  
Protein Protein Interaction Network

Understanding the mechanisms of a disease is highly complicated due to the complex pathways involved in the disease progression. Despite several decades of research, the occurrence and prognosis of the diseases is not completely understood even with high throughput experiments like DNA microarray and next-generation sequencing. This is due to challenges in analysis of huge data sets. Systems biology is one of the major divisions of bioinformatics and has laid cutting edge techniques for the better understanding of these pathways. Construction of protein-protein interaction network (PPIN) guides the modern scientists to identify vital proteins through protein-protein interaction network, which facilitates the identification of new drug target and associated proteins. The chapter is focused on PPI databases, construction of PPINs, and its analysis.

Discovering Interaction Motifs from Protein Interaction Networks

2009 ◽  
pp. 99-116 ◽  
Author(s):  
Hugo Willy
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Patterns ◽  
Protein Protein Interaction ◽  
Family Based ◽  
High Throughput Experiments

Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction data. However, most of the experiments could only answer the question of whether two proteins interact but not the question on the mechanisms by which proteins interact. Such understanding is crucial for understanding the protein interaction of an organism as a whole (the interactome) and even predicting novel protein interactions. Protein interaction usually occurs at some specific sites on the proteins and, given their importance, they are usually well conserved throughout the evolution of the proteins of the same family. Based on this observation, a number of works on finding protein patterns/motifs conserved in interacting proteins have emerged in the last few years. Such motifs are collectively termed as the interaction motifs. This chapter provides a review on the different approaches on finding interaction motifs with a discussion on their implications, potentials and possible areas of improvements in the future.

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