Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …

The OPEP coarse-grained protein model has been applied to a wide range of applications
since its first release 15 years ago. The model, which combines energetic and structural …

Fragment assembly using structural motifs excised from other solved proteins has shown to
be an efficient method for ab initio protein‐structure prediction. However, how to construct …

In mammalian cells, much of signal transduction is mediated by weak protein–protein
interactions between globular peptide-binding domains (PBDs) and unstructured peptidic …

Background As molecular biology is creating an increasing amount of sequence and
structure data, the multitude of software to analyze this data is also rising. Most of the …

Understanding and predicting protein structures depends on the complexity and the
accuracy of the models used to represent them. We have set up a hidden Markov model that …

Reduced computer modeling of proteins now has a history of about 30 years. In spite of the
enormous increase in computing abilities, reduced models are still very important tools for …

As more and more structures of macromolecular complexes get solved in different
conditions, it has become apparent that flexibility is an inherent part of their biological …

Protein structures are valuable tools to understand protein function. Nonetheless, proteins
are often considered as rigid macromolecules while their structures exhibit specific flexibility …

The prediction of protein structure from sequence remains a major unsolved problem in
biology. The most successful protein structure prediction methods make use of a divide-and …