Bioinformatic- and machine-learning-based search for new β-catenin peptide binders

Offer Description

Helmholtz Zentrum Munich

Project Title: Bioinformatic- and machine-learning-based search for new β-catenin peptide binders

Objectives:

1. Quantify common and overrepresented elements in IDR sequences known to bind to β-catenin.
2. i) Use the elements as features to query the proteome; ii) Develop a protocol to model IDR/β-catenin complexes.
3. Improve and update the models i) and ii) based on experimental data.

Project Overview: This project builds on our established expertise in discovering functionally relevant features of human IDRs and using those features for IDR classification proteome-wide. Bioinformatics strategies will be used to determine and quantify common and overrepresented elements of IDR sequences known to bind to β-catenin. Given the limited initial training set, in the first instance an expectation-maximization algorithm coupled with regularized logistic regression (FAIDR) will be used to isolate sequence features that distinguish known β-catenin IDR binders from the rest of the proteome. Once trained, the FAIDR model will be used to predict putative β-catenin IDR binders proteome-wide for screening in WP2 (Maric, JMU). Due to the expected scarcity of the initial training data, precision of the initial model is expected to be low. The initial model will be subsequently retrained using experimentally validated binders as positive examples to refine the model weights and improve its predictive performance. Structural modeling of known and putative binders will be undertaken using AlphaFold Multimer and related models., as well as foundation models Boltz-1 and Chai-1, and if needed further refined by e.g. Rosetta FlexPepDock. A subset of known beta-catenin peptide interface structures will be used to template AF2-Multimer to produce initial models. High-scoring structural models (evaluated using actifPTM developed by the Schueler-Furman group (HJU)) are expected to capture key interface features, enhancing definition of features and improving FAIDR model optimization and improved proteome-wide predictions. Contribution to the overall research program: Couples in-silico screening for putative novel β-catenin IDR binders with experimental validation (WP2) and informs design of novel binders (WP5).

Salary: According to the DFG scheme

Planned secondment: 1. Host: AMC; Supervisor: Dr. Nicolas Leveille; Length: 4 months.

Purpose: Bioinformatics and machine learning with cell-based data.

Enrolment in Doctoral degree(s): Helmholtz Munich Grad. School HELENA & TUM (Pritisanac)

How to apply: Applications are exclusively accepted through the Recruitment Portal (https://phd-recruiting.medunigraz.at/). The required information is specified in the recruitment portal and comprises:

• Motivation statement for selected DC projects
• Detailed CV; publication list if applicable
• BSc/MSc diplomas and academic transcripts/supplements
• Contact details for two referees

Applications will be assessed for eligibility and scientific/academic quality. Shortlisted applicants will be invited to interview. The final selection will follow the open, transparent and merit-based recruitment principles of MSCA.

Key dates:

• Applications open: 15 September 2026
• Applications close: 31 October 2026
• Online pre-interviews: early November 2026
• Final interviews: late November/early December 2026
• Decisions and offers: mid-December 2026
• Start: January 2027 – March 2027

Data protection statement

The personal data you provide as part of your application will be processed for the purposes necessary to administer the recruitment and selection process for the Doctoral Candidate position(s) for which you apply within the FlexCAT Marie Skłodowska-Curie Doctoral Network (MSCA-DN). Access to your application will be restricted to individuals directly involved in the recruitment and selection process, including the recruiting beneficiary, members of the selection committee, and, where necessary, authorised representatives of the FlexCAT consortium. Personal data will be processed in accordance with the applicable data-protection legislation, including the General Data Protection Regulation (EU) 2016/679 (GDPR), and the privacy policies of the recruiting beneficiary. Statistics for project reporting will be provided in anonymised or appropriately aggregated form.

Where to apply

Website: https://phd-recruiting.medunigraz.at/

Requirements

Research Field: Chemistry

Education Level: Master Degree or equivalent

Skills/Qualifications

Applicants of any nationality are welcome to apply. To be eligible for recruitment as an MSCA Doctoral Candidate, applicants must fulfil the following criteria at the date of recruitment:

• Education – you do not hold a doctoral degree and hold (or will shortly complete) a science Master’s degree (see individual project requirements), with excellent results, qualifying you for admission to the doctoral program of the respective host institution. The successful candidate must fulfil the admission requirements of the respective doctoral programme and will be enrolled in a doctoral programme during the project.
• Mobility - you must not have resided or carried out your main activity (work, studies, etc.) in recruiting beneficiary’s country for more than 12 months during the 36 months before your recruitment. Compulsory national service, holidays/short stays and time spent obtaining refugee status under the Geneva Convention4 are not considered for this purpose.
• Language & communication – excellent command of spoken and written English (min. B2 level). Ability to communicate results clearly to diverse audiences, both in writing and verbally.
• Motivation – motivation for multidisciplinary, international doctoral research, hands-on experimental work, mandatory academic/industy secondment(s), to publish research findings in international journals, present results at conferences and contribute to project deliverables. Willingness to contribute to project network-wide activities, communication, training events, and dissemination efforts.
• Technical skills – specific to the individual DC project (see individual project descriptions).

Specific Requirements

Contribution to the overall research program: Couples in-silico screening for putative novel β-catenin IDR binders with experimental validation (WP2) and informs design of novel binders (WP5).

Languages: ENGLISH

Level: Excellent

Research Field: Chemistry

Years of Research Experience: 1 - 4

Additional Information

Selection process

Applications will be assessed for eligibility and scientific/academic quality. Shortlisted applicants will be invited to interview. The final selection will follow the open, transparent and merit-based recruitment principles of MSCA.

Website for additional job details: https://phd-recruiting.medunigraz.at/

Work Location(s)

Number of offers available: 1

Company/Institute: Helmholtz Zentrum Munich

Country: Germany

Contact

City: Munich

Website: https://phd-recruiting.medunigraz.at/