Computational Structural Biology
Reveal your protein's structure and function
Structural biology uses experimentation and computation to understand biological systems at the molecular level. Just like all fundamental understanding of biology, the applied fields of protein biotechnology, protein engineering and drug design are all rooted in chemistry and biophysics, hence rely on computational and experimental structural biology techniques at their cores.
Cost efficiency with expert visualization plus molecular modeling and simulations
Expert visualization, analysis, modeling and simulation of molecular structures saves time and money in guiding your research, interpreting your data, and advancing your biotechnological developments.
Our structural biology experts have been assessors at CASP, the competition on protein structure prediction from which Deepmind’s AlphaFold came out. They have years of experience handling, visualizing and interpreting molecular structures, predicting structures with various methods, understanding dynamics, and relating all these cues to molecular function, regulation and design.
Efficiently linking experiments and computational modeling
Our expert spectroscopists, biophysicists and molecular modelers bridge experimental and computational approaches to structural biology, to advise you how to optimize your efforts and optimize costs while minimizing risks due to suboptimal experimental designs.
Protein engineering and design
Our experts can analyze structures and data deep enough to design mutations that modulate protein properties such as thermal stability. Key for this are their decades of experience creating protein chimeras, designing and testing mutants, and working in experimental and computational protein evolution. Besides, they master the most modern AI methods for protein design, as well as the most important structural and functional databases.
Expert- and AI-based tuning of protein properties
Our expert partners leverage on their state-of-the-art AI methods for protein design to create new protein sequences that fold into functional forms with high success rates. These proteins are usually highly stable and substantially divergent from natural proteins, thus facilitating subsequent directed evolution to improve specific functions as well as IP-related issues such as patenting and for-profit applications.
Design new functional proteins
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