Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins

Graphormer supervised de novo protein design method and function validation

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

Conformational Preferences at the Glycosidic Linkage of Saccharides in Solution as Deduced from NMR Experiments and MD Simulations: Comparison to Crystal Structures

Glycans are central to information content and regulation in biological systems. These carbohydrate molecules are active either as oligo- or polysaccharides, often in the form of glycoconjugates. The monosaccharide entities are joined by glycosidic linkages and stereochemical arrangements are of utmost importance in determining conformation and flexibility of saccharides. The conformational preferences and population distributions at the glycosidic torsion angles φ and ψ have been investigated for O-methyl glycosides of three disaccharides where the substitution takes place at a secondary alcohol, viz., in α-l-Fucp-(1→3)-β-d-Glcp-OMe, α-l-Fucp-(1→3)-α-d-Galp-OMe and α-d-Glcp-(1→4)-α-d-Galp-OMe, corresponding to disaccharide structural elements present in bacterial polysaccharides. Stereochemical differences at or adjacent to the glycosidic linkage were explored by solution state NMR spectroscopy using one-dimensional 1 H,1 H-NOESY NMR experiments to obtain transglycosidic proton-proton distances and one- and two-dimensional heteronuclear NMR experiments to obtain 3 JCH transglycosidic coupling constants related to torsion angles φ and ψ. Computed effective proton-proton distances from molecular dynamics (MD) simulations showed excellent agreement to experimentally derived distances for the α-(1→3)-linked disaccharides and revealed that for the bimodal distribution at the ψ torsion angle for the α-(1→4)-linked disaccharide experiment and simulation were at variance with each other, calling for further force field developments. The MD simulations disclosed a highly intricate inter-residue hydrogen bonding pattern for the α-(1→4)-linked disaccharide, including a nonconventional hydrogen bond between H5' in the glucosyl residue and O3 in the galactosyl residue, supported by a large downfield 1 H NMR chemical shift displacement compared to α-d-Glcp-OMe. Comparison of population distributions of the glycosidic torsion angles φ and ψ in the disaccharide entities to those of corresponding crystal structures highlighted the potential importance of solvation on the preferred conformation.

Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling

The biological function of proteins is determined not only by their static structures but also by the dynamic properties of their conformational ensembles. Numerous high-accuracy static structure prediction tools have been recently developed based on deep learning; however, there remains a lack of efficient and accurate methods for exploring protein dynamic conformations. Traditionally, studies concerning protein dynamics have relied on molecular dynamics (MD) simulations, which incur significant computational costs for all-atom precision and struggle to adequately sample conformational spaces with high energy barriers. To overcome these limitations, various enhanced sampling techniques have been developed to accelerate sampling in MD. Traditional enhanced sampling approaches like replica exchange molecular dynamics (REMD) and frontier expansion sampling (FEXS) often follow the MD simulation approach and still cost a lot of computational resources and time. Variational autoencoders (VAEs), as a classic deep generative model, are not restricted by potential energy landscapes and can explore conformational spaces more efficiently than traditional methods. However, VAEs often face challenges in generating reasonable conformations for complex proteins, especially intrinsically disordered proteins (IDPs), which limits their application as an enhanced sampling method. In this study, we presented a novel deep learning model (named Phanto-IDP) that utilizes a graph-based encoder to extract protein features and a transformer-based decoder combined with variational sampling to generate highly accurate protein backbones. Ten IDPs and four structured proteins were used to evaluate the sampling ability of Phanto-IDP. The results demonstrate that Phanto-IDP has high fidelity and diversity in the generated conformation ensembles, making it a suitable tool for enhancing the efficiency of MD simulation, generating broader protein conformational space and a continuous protein transition path.