Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Exploring chemical space for "druglike" small molecules in the age of AI

The announcement of 2024 Nobel Prize in Chemistry to Alphafold has reiterated the role of AI in biology and mainly in the domain of "drug discovery". Till few years ago, structure-based drug design (SBDD) has been the preferred experimental design in many academic and pharmaceutical R and D divisions for developing novel therapeutics. However, with the advent of AI, the drug design field especially has seen a paradigm shift in its R&D across platforms. If "drug design" is a game, there are two main players, the small molecule drug and its target biomolecule, and the rules governing the game are mainly based on the interactions between these two players. In this brief review, we will be discussing our efforts in improving the state-of-the-art technology with respect to small molecules as well as in understanding the rules of the game. The review is broadly divided into five sections with the first section introducing the field and the challenges faced and the role of AI in this domain. In the second section, we describe some of the existing small molecule libraries developed in our labs and follow-up this section with a more recent knowledge-based resource available for public use. In section four, we describe some of the screening tools developed in our laboratories and are available for public use. Finally, section five delves into how domain knowledge is improving the utilization of AI in drug design. We provide three case studies from our work to illustrate this work. Finally, we conclude with our thoughts on the future scope of AI in drug design.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

Application of ethanol alleviates heat damage to leaf growth and yield in tomato

Chemical priming has emerged as a promising area in agricultural research. Our previous studies have demonstrated that pretreatment with a low concentration of ethanol enhances abiotic stress tolerance in Arabidopsis and cassava. Here, we show that ethanol treatment induces heat stress tolerance in tomato (Solanum lycopersicon L.) plants. Seedlings of the tomato cultivar 'Micro-Tom' were pretreated with ethanol solution and then subjected to heat stress. The survival rates of the ethanol-pretreated plants were significantly higher than those of the water-treated control plants. Similarly, the fruit numbers of the ethanol-pretreated plants were greater than those of the water-treated ones. Transcriptome analysis identified sets of genes that were differentially expressed in shoots and roots of seedlings and in mature green fruits of ethanol-pretreated plants compared with those in water-treated plants. Gene ontology analysis using these genes showed that stress-related gene ontology terms were found in the set of ethanol-induced genes. Metabolome analysis revealed that the contents of a wide range of metabolites differed between water- and ethanol-treated samples. They included sugars such as trehalose, sucrose, glucose, and fructose. From our results, we speculate that ethanol-induced heat stress tolerance in tomato is mainly the result of increased expression of stress-related genes encoding late embryogenesis abundant (LEA) proteins, reactive oxygen species (ROS) elimination enzymes, and activated gluconeogenesis. Our results will be useful for establishing ethanol-based chemical priming technology to reduce heat stress damage in crops, especially in Solanaceae.

The role of intrinsic disorder in binding of plant microtubule-associated proteins to the cytoskeleton

Microtubules (MTs) represent one of the main components of the eukaryotic cytoskeleton and support numerous critical cellular functions. MTs are in principle tube-like structures that can grow and shrink in a highly dynamic manner; a process largely controlled by microtubule-associated proteins (MAPs). Plant MAPs are a phylogenetically diverse group of proteins that nonetheless share many common biophysical characteristics and often contain large stretches of intrinsic protein disorder. These intrinsically disordered regions are determinants of many MAP-MT interactions, in which structural flexibility enables low-affinity protein-protein interactions that enable a fine-tuned regulation of MT cytoskeleton dynamics. Notably, intrinsic disorder is one of the major obstacles in functional and structural studies of MAPs and represents the principal present-day challenge to decipher how MAPs interact with MTs. Here, we review plant MAPs from an intrinsic protein disorder perspective, by providing a complete and up-to-date summary of all currently known members, and address the current and future challenges in functional and structural characterization of MAPs.