TMH Stab-pred: Predicting the stability of α-helical membrane proteins using sequence and structural features

TMB Stab-pred: Predicting the stability of transmembrane β-barrel proteins using their sequence and structural signatures

Understanding the folding and stability of transmembrane β-barrel proteins (TMBs) provides insights into their structural integrity, functional mechanisms, and implications for disease states. In this work, we have characterized the important features that influence the folding and stability of TMBs. Our results showed that lipid accessible surface area and transition energy are important for understanding the stability of TMBs. Further, this information was utilized to develop a linear regression-based method for predicting the stability of TMBs. Our method achieved a correlation and mean absolute error (MAE) of 0.96 and 0.94 kcal/mol on the jack-knife test. Moreover, we compared the stability of TMBs with globular all-β proteins and observed that long-range interactions and energetic properties are crucial for maintaining the stability of both β-barrel membrane and all-β globular proteins. On the other hand, side-chain - side-chain hydrogen bonds and lipid accessible surface area are specific to membrane proteins. These features are critical for membrane proteins because they influence a protein to embed within the membrane environment. Further, we have developed a web server, TMB Stab-pred for predicting the stability of TMBs, and it is accessible at https://web.iitm.ac.in/bioinfo2/TMBB/index.html.

AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network

The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, while pursuing high thermodynamic stability and rigidity of proteins, inevitably sacrifices biological functions closely related to protein flexibility. The thermodynamic stability of proteins is not always optimal when they are highest to perfectly perform their biological functions. Extensive theoretical and experimental screening is often required to obtain stable protein structures. Thus, it becomes critically important to develop a stability prediction model based on the balance between protein stability and bioactivity. To design protein drugs with better functionality in a broader structural space, a novel protein structural stability predictor called PSSP has been developed in this study. PSSP is a mean pooled dual graph convolutional network (GCN) model based on sequence characteristics and secondary structure, distance matrix, graph, and residue properties of a nanoprotein to provide rapid prediction and judgment. This model exhibits excellent robustness in predicting the structural stability of nanoproteins. Comparing with previous artificial intelligence algorithms, the results indicate this model can provide a rapid and accurate assessment of the structural stability of artificially designed proteins, which shows the great promises for promoting the robust development of protein design.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.