Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

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The present invention relates to a computer-implemented method for engineering the interaction between a protein and a cognate peptide that are capable of forming a molecular complex, wherein the method comprises (a) preparing in silico a library of test p ...

Protein-based therapeutics, such as monoclonal antibodies and cytokines, are important therapies for various pathophysiological conditions such as oncology, autoimmune disorders, and viral infections. However, the wide application of such protein therapeut ...

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ...

Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel str ...

Otoliths are calcium carbonate components of the stato-acoustical organ responsible for hearing and maintenance of the body balance in teleost fish. During their formation, control over, e.g., morphology and carbonate polymorph is influenced by complex ins ...

High-throughput generation of large and consistent ab initio data combined with advanced machine-learning techniques are enabling the creation of interatomic potentials of near ab initio quality. This capability has the potential of dramatically impacting ...

Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein's ph ...

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference ...

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

Solving native structures of such large molecules, like biomolecules, is often challenging, particularly due to the potentially infinite number of non-covalent interactions with water. In this thesis, we report the use of cold ion gas-phase action spectros ...

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

Chattez avec Graph Search

Computer-implemented design of peptide:receptor signaling complexes for enhanced chemotaxis

Rational Design of Chemically Controlled Antibodies and Protein Therapeutics

Artificial intelligence for natural product drug discovery

Deep learning approaches for conformational flexibility and switching properties in protein design

First paleoproteome study of fossil fish otoliths and the pristine preservation of the biomineral crystal host

Machine-learned interatomic potentials: Recent developments and prospective applications

Extracting phylogenetic dimensions of coevolution reveals hidden functional signals

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Bridging Native and Intrinsic Structures of Microhydrated Biomolecules by Cold Ion Spectroscopy