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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
portfolio
Protviz: Protein Annotation Visualiser
Python package
publications
Ultra-rapid and low-cost fabrication of centrifugal microfluidic platforms with active mechanical valves
Published in RSC Advances, 2017
Onsite fabrication of centrifugal microfluidic cartridges is one way to provide laboratory and diagnostics platforms for extreme point-of-care (EPOC) settings. This paper presents a rapid fabrication process of centrifugal microfluidic cartridges (discs) using only a cutter plotter that is as low-cost, portable and rapid as conventional printers. Moreover, we devised an active valving mechanism to enable the development of complex sequential fluidic processes. The valves are engraved during disc manufacture itself by the cutter plotter. These embedded valves prevent the need for additional fabrication equipment and materials for the inserts that are usually required for other active valves. The valves are actuated by an external mechanical force, and are called active mechanical valves (M-valve). The M-valve is robust over a wide range of spinning speeds (e.g. up to 7000 rpm or 2470 rcf) and can be actuated manually, or automatically by a robotic arm. To demonstrate our approach, we developed two fluidic systems for immunoassay and chromatography. The first microfluidic platform was developed to automate a fluidic protocol usually required for immunoassays from whole blood. In this microfluidic disc, non-biological liquids were used to demonstrate the application of M-valves for robust control over retention and release of reagents. The chromatography microfluidic cartridge is a miniaturized experimental system for testing the capability of a modified resin (Sepharose 6B-PEG5000) for the isolation of monoPEGylated ribonuclease (RNase). The fabrication of microfluidic discs and M-valves by a simple cutter plotter is the fastest and least expensive method for the onsite development and onsite manufacturing of diagnostic kits for research and actual use even in EPOC settings.
Recommended citation: M. M. Aeinehvand. et al. (2017) ‘Ultra-rapid and low-cost fabrication of centrifugal microfluidic platforms with active mechanical valves’, RSC Adv., 2017,7, 55400-55407
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Pegylated species separation through an innovative PEG-grafted agarose-based resin, association quantified by microcalorimetry
Published in Separation and Purification Technology, 2020
The study of thermodynamic parameters of hydrophobic interaction chromatography (HIC) leads to a better understanding of protein-stationary phase interactions to design and validate new separation systems. In previous works, we have developed an innovative polyethylene(glycol) (PEG)-grafted resin, which was highly efficient in the separation of PEGylated ribonuclease A (RNase A) species in a single step. Moreover, we have described its electrokinetic profile. However, new platforms for other proteins need to be developed and microcalorimetric analysis needs to be performed. The aim of this work was to design a new platform for lysozyme PEGylated species separation using the PEG-grafted resin, and to quantify the changes of energy caused by reversible interactions between the PEG-grafted resin and PEGylated proteins (RNase A and lysozyme) by isothermal titration calorimetry (ITC). The grafted resin and the PEGylated proteins were characterized by ATR-FTIR. Titrations were carried out in potassium phosphate buffer with 1.5 M ammonium sulfate at 25 °C. ATR-FTIR spectra demonstrated the chemical modification of the resin. Resolution of native from mono-PEGylated lysozyme was 0.93 whereas resolution of mono-PEGylated from di-PEGylated form was 1.92. The specific enthalpy was exothermic for both proteins. Mono-PEGylated proteins had a negative entropy, related to the enhanced hydrophobic interaction between PEG5000 molecules from the resin and PEGylated proteins.
Recommended citation: Magaña, P. et al. (2020) ‘Pegylated species separation through an innovative PEG-grafted agarose-based resin, association quantified by microcalorimetry’, Separation and Purification Technology.
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AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences
Published in Nucleic Acids Research, 2023
The AlphaFold Protein Structure Database (AlphaFold DB) is a massive digital library of predicted protein structures, with over 214 million entries, marking a 500-times expansion in size since its initial release in 2021. The structures are predicted using Google DeepMind’s AlphaFold 2 artificial intelligence (AI) system. Our new report highlights the latest updates we have made to this database. We have added more data on specific organisms and proteins related to global health and expanded to cover almost the complete UniProt database, a primary data resource of protein sequences. We also made it easier for our users to access the data by directly downloading files or using advanced cloud-based tools. Finally, we have also improved how users view and search through these protein structures, making the user experience smoother and more informative. In short, AlphaFold DB has been growing rapidly and has become more user-friendly and robust to support the broader scientific community.
Recommended citation: Varadi, Mihaly, et al. “Alphafold protein structure database in 2024: Providing structure coverage for over 214 million protein sequences.” Nucleic Acids Research, 2023.
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Harnessing the 3D-Beacons Network: A Comprehensive Guide to Accessing and Displaying Protein Structure Data
Published in Current Protocols, 2024
Recent advancements in protein structure determination and especially in protein structure prediction techniques have led to the availability of vast amounts of macromolecular structures. However, the accessibility and integration of these structures into scientific workflows are hindered by the lack of standardization among publicly available data resources. To address this issue, we introduced the 3D-Beacons Network, a unified platform that aims to establish a standardized framework for accessing and displaying protein structure data. In this article, we highlight the importance of standardized approaches for accessing protein structure data and showcase the capabilities of 3D-Beacons. We describe four protocols for finding and accessing macromolecular structures from various specialist data resources via 3D-Beacons. First, we describe three scenarios for programmatically accessing and retrieving data using the 3D-Beacons API. Next, we show how to perform sequence-based searches to find structures from model providers. Then, we demonstrate how to search for structures and fetch them directly into a workflow using JalView. Finally, we outline the process of facilitating access to data from providers interested in contributing their structures to the 3D-Beacons Network.
Recommended citation: Magaña, P. et al. (2024) ‘Harnessing the 3D‐Beacons Network: A comprehensive guide to accessing and displaying protein structure data’, Current Protocols, 4(5).
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AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities
Published in Journal of Molecular Biology (JMB), 2025
The AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/) has made significant strides in enhancing its utility and accessibility for the life science research community. The recent integration of AlphaMissense predictions enables access to the pathogenicity of human protein missense variants, with an innovative and interactive heatmap and 3D visualisation that display variant data at the residue level. Users can now toggle between structure model quality (pLDDT) and average pathogenicity scores, providing insights into the implications of specific residue changes. The Foldseek integration offers a rapid and accurate method for protein structure searches and comparisons. Bulk data download options further facilitate comprehensive data analysis and integration with other computational tools. The 3D-Beacons framework (https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/) has also been enhanced with detailed annotation endpoints (such as AlphaMissense data) and integrates LevyLab’s dataset of homomeric AlphaFold 2 models. These advancements significantly improve the functionality and accessibility of these resources, enabling discoveries using structure data.
Recommended citation: Fleming, J. et al. (2025) ‘Alphafold protein structure database and 3D-Beacons: New data and capabilities’, Journal of Molecular Biology, p. 168967.
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talks
Accessing and interpreting predicted protein structures from AlphaFold database
Published:
AlphaFold database (AlphaFold DB) provides open access to over 200 million protein structure predictions to accelerate scientific research. This webinar aims to provide a comprehensive introduction to AlphaFold DB. Participants will gain a clear understanding of the fundamental concepts, principles, and functionalities of the database. They will learn how to access and evaluate predicted protein structures, explore the underlying technologies and algorithms used in AlphaFold, and identify the potential applications of AlphaFold DB in their research
Cambridge-Google AlphaFold Workshop
Published:
I was thrilled to contribute to the Cambridge-Google Workshop: Learning from Experts in Computational Biology and AI last April. As part of a dynamic program, I joined forces with Hariprasad Radhakrishnan (Google Cloud) and Oleg Kovalevskiy (Google DeepMind) to lead a hands-on session. My presentation delved into the AlphaFold database, illustrating its immense value for researchers with concrete case studies and applications. We were delighted by the enthusiastic participation and the thoughtful questions from the 120+ delegates, making it a truly interactive and successful part of a fantastic day organised by the Milner Institute and Google.
training
AlphaFold: A practical guide
Online course, EMBL-EBI training site, 2024
This tutorial is aimed at researchers who are interested in using AlphaFold2 to predict protein structures and integrate these predictions into their projects. An undergraduate-level knowledge of protein structure and structural biology would be an advantage.
Exploring and analysing the protein universe with 3D-Beacons and AlphaFold DB
Workshop, 23rd European Conference on Computational Biology, 2024
Emerging structural predictions, including over 200 million protein sequences via the AlphaFold Database, are transforming our biological insights. The 3D-Beacons network enhances this revolution by providing straightforward access to a wide array of protein structures, both experimentally determined and computationally predicted.
Structural bioinformatics 2024
Workshop, EMBL-EBI (virtual), 2024
This EMBL-EBI course delves into the methods and tools vital for structural bioinformatics. Participants explore how to leverage 3D structural data – whether experimentally determined or predicted by AI – to gain insights into how macromolecules function. It covers key bioinformatics resources, techniques for analysis and interpretation, methods for predicting function, and approaches to exploring molecular interactions.
AlphaFold: A Case Study in Deep Learning for Protein Structure Prediction
Workshop, Institut d'Études Scientifiques de Cargèse, 2024
The Algorithms in Structural Bioinformatics (AlgoSB) school provided an intensive learning environment, and I was honoured to contribute as an instructor for the 2024 edition in Cargèse. On Tuesday, November 19th, my co-lecturer Maxim Tsenkov and I delivered a session titled “AlphaFold: A Case Study in Deep Learning for Protein Structure Prediction.” We explored its foundational aspects, its impact, limitations, and future potential. The afternoon was dedicated to practical application, where I led the hands-on session, focusing on tangible skills: sampling predictions, identifying remote homologous proteins, and effectively evaluating and enriching structures sourced from the AlphaFold Database.
AlphaFold Education Summit
Event, Wellcome Genome Campus, 2025
In January 2025, I had the rewarding experience of coordinating the AlphaFold Education Summit (AFES), a key collaboration with Google DeepMind. Our global mission was to empower researchers and educators worldwide. At its core, AFES implemented a ‘train-the-trainer’ model, featuring dedicated workshops on both AlphaFold 2 and the capabilities of AlphaFold 3. This approach was designed to scale expertise globally, enabling participants to integrate these tools into research and teaching, with a crucial focus on enhancing accessibility for those in under-resourced regions and tackling significant scientific challenges.
From sequences to structures: Protein characterisation using EMBL-EBI APIs
Virtual course, EMBL-EBI training site, 2025
Proteins are fundamental to biological processes, and understanding their sequence, structure, and function is crucial for various applications in research. This one-day, hands-on virtual workshop guided participants through a structured, programmatic workflow using EMBL-EBI resources.