GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy Changes

Protein-protein interactions (PPIs) are critical for various biological processes, and understanding their dynamics is essential for decoding molecular mechanisms and advancing fields such as cancer research and drug discovery. Mutations in PPIs can disrupt protein binding affinity and lead to functional changes and disease. Predicting the impact of mutations on binding affinity is valuable but experimentally challenging. Computational methods, including physics-based and machine learning-based approaches, have been developed to address this challenge. Machine learning-based methods, fueled by extensive PPI datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in predicting binding affinity changes. However, accurate predictions and generalization of these models across different datasets remain challenging. Geometric graph learning has emerged as a powerful approach, combining graph theory and machine learning, to capture structural features of biomolecules. We present GGL-PPI, a novel method that integrates geometric graph learning and machine learning to predict mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored geometric subgraphs to extract informative features, demonstrating superior performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased predictions of GGL-PPI for both direct and reverse mutations. The findings underscore the potential of GGL-PPI in accurately predicting binding free energy changes, contributing to our understanding of PPIs and aiding drug design efforts

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures

Evolution and Impact of High Content Imaging

Abstract/outline: The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990′s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:• Evolution and impact of high content imaging: An academic perspective• Evolution and impact of high content imaging: An industry perspective• Evolution of high content image analysis• Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications• The role of data integration and multiomics• The role and evolution of image data repositories and sharing standards• Future perspective of high content imaging hardware and softwar

A survey on computational taste predictors

Taste is a sensory modality crucial for nutrition and survival, since it allows the discrimination between healthy foods and toxic substances thanks to five tastes, i.e., sweet, bitter, umami, salty, and sour, associated with distinct nutritional or physiological needs. Today, taste prediction plays a key role in several fields, e.g., medical, industrial, or pharmaceutical, but the complexity of the taste perception process, its multidisciplinary nature, and the high number of potentially relevant players and features at the basis of the taste sensation make taste prediction a very complex task. In this context, the emerging capabilities of machine learning have provided fruitful insights in this field of research, allowing to consider and integrate a very large number of variables and identifying hidden correlations underlying the perception of a particular taste. This review aims at summarizing the latest advances in taste prediction, analyzing available food-related databases and taste prediction tools developed in recent years.Politecnico di Torino within the CRUI-CARE AgreementEuropean Union's Horizon 2020 research and innovation program 87218

Machine Learning for Kinase Drug Discovery

Cancer is one of the major public health issues, causing several million losses every year. Although anti-cancer drugs have been developed and are globally administered, mild to severe side effects are known to occur during treatment. Computer-aided drug discovery has become a cornerstone for unveiling treatments of existing as well as emerging diseases. Computational methods aim to not only speed up the drug design process, but to also reduce time-consuming, costly experiments, as well as in vivo animal testing. In this context, over the last decade especially, deep learning began to play a prominent role in the prediction of molecular activity, property and toxicity. However, there are still major challenges when applying deep learning models in drug discovery. Those challenges include data scarcity for physicochemical tasks, the difficulty of interpreting the prediction made by deep neural networks, and the necessity of open-source and robust workflows to ensure reproducibility and reusability. In this thesis, after reviewing the state-of-the-art in deep learning applied to virtual screening, we address the previously mentioned challenges as follows: Regarding data scarcity in the context of deep learning applied to small molecules, we developed data augmentation techniques based on the SMILES encoding. This linear string notation enumerates the atoms present in a compound by following a path along the molecule graph. Multiplicity of SMILES for a single compound can be reached by traversing the graph using different paths. We applied the developed augmentation techniques to three different deep learning models, including convolutional and recurrent neural networks, and to four property and activity data sets. The results show that augmentation improves the model accuracy independently of the deep learning model, as well as of the data set size. Moreover, we computed the uncertainty of a model by using augmentation at inference time. In this regard, we have shown that the more confident the model is in its prediction, the smaller is the error, implying that a given prediction can be trusted and is close to the target value. The software and associated documentation allows making predictions for novel compounds and have been made freely available. Trusting predictions blindly from algorithms may have serious consequences in areas of healthcare. In this context, better understanding how a neural network classifies a compound based on its input features is highly beneficial by helping to de-risk and optimize compounds. In this research project, we decomposed the inner layers of a deep neural network to identify the toxic substructures, the toxicophores, of a compound that led to the toxicity classification. Using molecular fingerprints —vectors that indicate the presence or absence of a particular atomic environment —we were able to map a toxicity score to each of these substructures. Moreover, we developed a method to visualize in 2D the toxicophores within a compound, the so- called cytotoxicity maps, which could be of great use to medicinal chemists in identifying ways to modify molecules to eliminate toxicity. Not only does the deep learning model reach state-of-the-art results, but the identified toxicophores confirm known toxic substructures, as well as expand new potential candidates. In order to speed up the drug discovery process, the accessibility to robust and modular workflows is extremely advantageous. In this context, the fully open-source TeachOpenCADD project was developed. Significant tasks in both cheminformatics and bioinformatics are implemented in a pedagogical fashion, allowing the material to be used for teaching as well as the starting point for novel research. In this framework, a special pipeline is dedicated to kinases, a family of proteins which are known to be involved in diseases such as cancer. The aim is to gain insights into off-targets, i.e. proteins that are unintentionally affected by a compound, and that can cause adverse effects in treatments. Four measures of kinase similarity are implemented, taking into account sequence, and structural information, as well as protein-ligand interaction, and ligand profiling data. The workflow provides clustering of a set of kinases, which can be further analyzed to understand off-target effects of inhibitors. Results show that analyzing kinases using several perspectives is crucial for the insight into off-target prediction, and gaining a global perspective of the kinome. These novel methods can be exploited in the discovery of new drugs, and more specifically diseases involved in the dysregulation of kinases, such as cancer

Applications of bioinformatics and machine learning in the analysis of proteomics data

In chapter one, a general introduction to the basic principles and techniques of MS-based proteomics, quantification strategies, and a generalized shotgun proteomics workflow are given. Moreover, I also outline how to analyze proteomics data from a bioinformatics perspective including normalization, dealing with missing values, differential analysis, functional annotation, as well as how to reveal the biology from post-translational modification data. Furthermore, I generalized the basics of machine learning algorithms from the perspective of supervised and unsupervised machine learning, along with that the application of machine learning algorithms to the identification of protein complexes. In chapter two, we are seeking to explore the drug addiction mechanism in melanoma cells that carry BRAF mutation. We present a proteomics and phosphoproteomics study of BRAFi-addicted melanoma cells (i.e., 451Lu cell line) in response to BRAFi withdrawal, in which ERK1, ERK2, and JUNB were genetically silenced separately using CRISPR-Cas9. We show that inactivation of ERK2 and, to a lesser extent, JUNB prevents drug addiction in these melanoma cells, while, conversely, knockout of ERK1 fails to reverse this phenotype, showing a response similar to that of control cells. Our data indicate that ERK2 and JUNB share comparable proteome responses dominated by the reactivation of cell division. Importantly, we find that EMT activation in drug-addicted melanoma cells upon drug withdrawal is affected by silencing ERK2 but not ERK1. Moreover, we reveal that PIR acts as an effector of ERK2, and phosphoproteome analysis reveals that silencing of ERK2 but not ERK1 leads to the amplification of GSK3 kinase activity. Our results depict possible mechanisms of drug addiction in melanoma, which may provide a guide for therapeutic strategies in drug-resistant melanoma. In chapter three, we are dedicated to exploring the role of PD-1 in T cell activation by comparing the proteome and phosphoproteome profiles in resting and activated CD8+ T cells, in which PD-1 was silenced using CRISPR–Cas9. Our data reveal that the activated T cells reprogrammed their proteome and phosphoproteome marked by activating of mTORC1 pathway. Moreover, we find that silencing of PD-1 altered the expression of E3 ubiquitin-- protein ligases, and increased glucose and lactate transporters. On the phosphoproteomics level, it evokes phosphorylation events in the mTORC1 pathway and activates the epidermal growth factor and its downstream MAPK pathway. Therefore, the data presented in this chapter depicts mechanisms of PD-1 in response to TCR stimulation in CD8+ T cells, which may provide a guide in immune homeostasis and immune checkpoint therapy. In chapter four, we construct a comprehensive map of human protein complexes through the integration of protein-protein interactions and protein abundance features. A deep learning framework was built to predict protein-protein interactions (PPIs), followed by a two-stage clustering to identify protein complexes. Our deep learning technique-based classifier significantly outperformed recently published machine learning prediction models with an F1-measure of 0.68 and captured in the process 5,010 complexes containing over 9,000 unique proteins. Moreover, this deep learning model enables us to capture poorly characterized interactions and the co-expressed protein involved interactions

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine