Computational Structural Biologist Contractor

Responsibilities 

• Develop and implement machine learning-based protein design models to understand the structure, function, and dynamics of proteins and protein-ligand complexes associated with various diseases.
• Develop and execute computational strategies for the de novo design of targeting ligands or delivery vectors such as AAVs, peptides and antibodies, to improve the delivery of RNA therapeutics and gene therapies.
• Develop protein structure prediction and relevant machine learning models to evaluate the functional effects of exon skipping and genetic mutations, addressing critical research questions to support therapeutic programs.
• Support ongoing pre-clinical and clinical research in phenotype-to-genotype studies using structural biology methods.
• Utilize, support, and enhance the capabilities of in-house tools and resources for protein structure prediction and structural biology initiatives.

Requirements     

• PhD in Structural Biology, Bioinformatics, Biochemistry, Protein Chemistry, or a related field, with 3 years of relevant experience, ideally in the biotech or pharmaceutical industry. A background in gene therapy, gene editing, or RNA therapeutics is highly preferred.
• Extensive experience with protein structure design applications, including Rosetta, AlphaFold, ESMFold, RFdiffusion, and ProteinMPNN is a must.
• Proficiency in developing machine learning models for proteins, including probabilistic learning and deep learning models such as Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), Transformers, Protein Language Models, Natural Language Processing models, and Generative AI.
• Experience in conducting molecular dynamics simulations using GROMACS and AmberTools, as well as performing molecular docking studies with tools like AutoDock to analyze biomolecular interactions and predict binding affinities.
• A deep understanding of protein structure and function, alongside expertise in antibody and nanobody structures and their applications in drug discovery.
• Expertise in using visualization and analytical tools like PyMOL for interpreting biomolecular structures.
• Experience in protein crystallography or cryo-EM techniques, with the ability to analyze and interpret structural data, is a plus.
• Advanced programming and scripting skills in languages such as Python, R, and SQL, along with hands-on experience using modern deep learning frameworks like PyTorch, TensorFlow, scikit-learn, or JAX.
• Experience working with large-scale computing and cloud infrastructures, along with database systems and development tools in a production environment.
• Ability to programmatically interface with databases through APIs, FTPs, or remote server connections.
• A strong team player, capable of effectively communicating and collaborating with a multidisciplinary team.