New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly

Growing ecosystem of deep learning methods for modeling protein-protein interactions

Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.

Identification of key genes involved in calcific aortic valve disease based on integrated bioinformatics analysis

The calcific aortic valve disease (CAVD) develops as an aortic valve sclerosis and progresses to an advanced form of stenosis. In many biological fields, bioinformatics becomes a fundamental component. The key mechanisms involved in CAVD are discovered with the use of bioinformatics to investigate gene function and pathways. We downloaded the original data (GSE51472) from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). After standardization, 2978 differentially expressed genes (DEGs) were identified from the data sets GSE51472 containing samples from normal, calcified, and sclerotic aortic valves. Analysis of DEGs based on the series test of clusters (STCs) revealed the two most significant patterns. Based on the result of the STC, the functional enrichment analysis of gene ontology (GO) was conducted to investigate the molecular function (MF), biological process (BP), and cell compound (CC) of the DEGs. With a p value of 0.01, DEGs associated with "chronic inflammation," "T-cell receptor complexes," and "antigen binding" had the highest significance within BP, CC, and MF. DEG enrichment in signaling pathways was analyzed using KEGG pathway enrichment. Using a p < 0.05 level of significance, the most enriched biological pathways related to CAVD were "Chemokine signaling pathway," "Cytokine-cytokine receptor interaction," "Tuberculosis," "PI3K-Akt signaling pathway," and "Transcriptional misregulation in cancer." Finally, the construction of gene co-expression networks and pathway networks illustrated the pathogensis of CAVD. TLR2, CD86, and TYROBP were identified as hub genes for the development of CAVD. Moreover, "MAPK signaling pathway," "Apoptosis," and "Pathways in cancer" were regarded as the core pathways among the samples of normal, sclerotic and calcified aortic valve samples.

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

SimGen: A General Simulation Method for Large Systems

SimGen is a stand-alone computer program that reads a script of commands to represent complex macromolecules, including proteins and nucleic acids, in a structural hierarchy that can then be viewed using an integral graphical viewer or animated through a high-level application programming interface in C++. Structural levels in the hierarchy range from α-carbon or phosphate backbones through secondary structure to domains, molecules, and multimers with each level represented in an identical data structure that can be manipulated using the application programming interface. Unlike most coarse-grained simulation approaches, the higher-level objects represented in SimGen can be soft, allowing the lower-level objects that they contain to interact directly. The default motion simulated by SimGen is a Brownian-like diffusion that can be set to occur across all levels of representation in the hierarchy. Links can also be defined between objects, which, when combined with large high-level random movements, result in an effective search strategy for constraint satisfaction, including structure prediction from predicted pairwise distances. The implementation of SimGen makes use of the hierarchic data structure to avoid unnecessary calculation, especially for collision detection, allowing it to be simultaneously run and viewed on a laptop computer while simulating large systems of over 20,000 objects. It has been used previously to model complex molecular interactions including the motion of a myosin-V dimer "walking" on an actin fibre, RNA stem-loop packing, and the simulation of cell motion and aggregation. Several extensions to this original functionality are described.

The importance of dynamics in integrative modeling of supramolecular assemblies

Revealing the atomistic architecture of supramolecular complexes is a fundamental step toward a deeper understanding of cellular functioning. To date, this formidable task is facilitated by an emerging array of integrative modeling approaches that combine experimental data from different sources. One major challenge these methods have to face is the treatment of the dynamic rearrangements of the individual subunits upon assembly. While this flexibility can be sampled at different levels, integrating native dynamic determinants with available experimental inputs can provide an effective way to reveal the molecular recognition mechanisms at the basis of supramolecular assembly.