Biology subjects

Stam, M. J.

Publications and source records attributed to Stam, M. J..

5 recordsLinked to original sources

bioRxiv2025

Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition

In this report, we summarize and analyze the 2024 Adaptyv protein design competition. Participants used computational and Machine Learning (ML) methods of their choice to design proteins that bind the Epidermal Growth Factor Receptor (EGFR), a key drug target involved in cell growth, differentiation, and cancer development. Over 1,800 designs were submitted across two rounds. Of these, 601 proteins were selected and characterized for expression and binding affinity to EGFR, with competitors both optimizing existing binders (KD = 1.21 nM) and creating de novo binders (KD = 82 nM). All selected designs were experimentally validated using Adaptyvs automated Bio-Layer Interferometry (BLI) pipeline. This competition illustrates the potential of crowdsourcing to drive creativity and innovation in protein design. However, it also exposed key challenges, such as the lack of standardized benchmarks, experimental design targets, and robust computational metrics for method comparison. We anticipate that future competitions will address these gaps and further motivate progress in computational protein design.

bioengineering↗

bioRxiv2024

The Protein Design Archive (PDA): insights from 40 years of protein design

The field of protein design has changed dramatically over the last 40 years, with a range of methods developing from rational design to more recent data-driven approaches. While considerable insight could be gained from analysing designed proteins, there is no single resource that brings together all the relevant data, making it difficult to identify current challenges and opportunities within the field. Here we present the Protein Design Archive, a website and database of designed proteins. Using the database, we performed systematic analysis that reveals a rapid increase in the number and complexity of designs over time and uncovers biases in their amino-acid usage and secondary structure content. Finally, sequence- and structure-based analysis demonstrates the breadth and novelty of designs within the archive. The PDA will be a valuable resource for guiding development of protein design, helping the field reach its potential. The PDA is available freely and without registration at https://pragmaticproteindesign.bio.ed.ac.uk/pda/.

bioinformatics↗

bioRxiv2024

Large scale analysis of predicted protein structures links model features to in vivo behaviour

Rapid advancements in protein structure prediction methods have ushered in a new era of abundant and accurate structural data, providing opportunities to analyse proteins at a scale that has not been possible before. Here we show that features derived solely from predicted structures can be used to understand in vivo protein behaviour using data-driven methods. We found that these features were predictive of in vivo protein production for a set of designed antibodies, enabling identification of high-quality designs. Following on from this result, we calculated these features for a diverse set of {approx}500,000 predicted structures, and our analysis showed systematic variation between proteins from different organisms to such an extent that the tree of life could be recapitulated from these data. Given the high degree of functional constraint around the chemistry of proteins, this result is surprising, and could have important implications for the design and engineering of novel proteins.

bioinformatics↗

bioRxiv2022

A comparison of methods to suppress electrocardiographic artifacts in local field potential recordings

ObjectiveSensing-enabled neurostimulators for deep brain stimulation (DBS) therapy record neural activity directly from the stimulating electrodes in the form of local field potentials (LFPs). However, these LFPs are often contaminated with electrocardiographic (ECG) artifacts that impede the detection of physiomarkers for adaptive DBS research. This study systematically compared the ability of different ECG suppression methods to recover disease-specific electrical brain activity from ECG-contaminated LFPs. ApproachThree ECG suppression methods were evaluated: (1) QRS interpolation of the Perceive toolbox, (2) four variants of a template subtraction method, and (3) sixteen variants of a singular value decomposition (SVD) method. The performance of these methods was examined using LFPs recorded with the Medtronic PerceptTM PC system from the subthalamic nucleus in nine patients with Parkinsons disease while stimulation was turned off ("OFF-DBS"; anode disconnected) and while stimulation was turned on at 0 mA ("ON-DBS 0 mA"; anode connected). In addition, ECG-contaminated LFPs were simulated by scaling a co-recorded external ECG signal and adding it to the OFF-DBS LFPs. Main ResultsECG artifacts were present in 10 out of 18 ON-DBS 0 mA recordings. All ECG suppression methods were able to drastically reduce the percent difference of beta band (13 - 35 Hz) spectral power and at least partly recover the beta peak and beta burst dynamics. Using predetermined R-peaks improved the performance of the ECG suppression methods. Lengthening the time window around the R-peaks resulted in stronger reduction in artifact-induced beta band power but at an increased risk of flattening the beta peak and loss of beta burst dynamics. SignificanceThe SVD method formed the preferred trade-off between artifact cleaning and signal loss, as long as its parameter settings (time window around the R-peaks; number of components) are adequately chosen.

neuroscience↗

bioRxiv2021

DE-STRESS: A user-friendly web application for the evaluation of protein designs

De novo protein design is a rapidly growing field and there are now many interesting and useful examples of designed proteins in the literature. However, most designs could be classed as failures when characterised in the lab, usually as a result of low expression, misfolding, aggregation or lack of function. This high attrition rate makes protein design unreliable and costly. It is possible that some of these failures could be caught earlier in the design process if it were quick and easy to generate information and a set of high-quality metrics regarding designs, which could be used to make reproducible and data-driven decisions about which designs to characterise experimentally. We present DE-STRESS (DEsigned STRucture Evaluation ServiceS), a web application for evaluating structural models of designed and engineered proteins. DE-STRESS has been designed to be simple, intuitive to use and responsive. It provides a wealth of information regarding designs, as well as tools to help contextualise the results and formally describe the properties that a design requires to be fit for purpose. AvailabilityDE-STRESS is available for non-commercial use, without registration, through the following website: https://pragmaticproteindesign.bio.ed.ac.uk/de-stress/. Source code for the application is available on GitHub: https://github.com/wells-wood-research/de-stress. The data used to generate reference sets is available through a GraphQL API, with the following URL: https://pragmaticproteindesign.bio.ed.ac.uk/big-structure/graphql.

bioinformatics↗