An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.

Application of molecular dynamics simulation for exploring the roles of plant biomolecules in promoting environmental health

Understanding the dynamic changes of plant biomolecules is vital for exploring their mechanisms in the environment. Molecular dynamics (MD) simulation has been widely used to study structural evolution and corresponding properties of plant biomolecules at the microscopic scale. Here, this review (i) outlines structural properties of plant biomolecules, and the crucial role of MD simulation in advancing studies of the biomolecules; (ii) describes the development of MD simulation in plant biomolecules, determinants of simulation, and analysis parameters; (iii) introduces the applications of MD simulation in plant biomolecules, including the response of the biomolecules to multiple stresses, their roles in corrosive environments, and their contributions in improving environmental health; (iv) reviews techniques integrated with MD simulation, such as molecular biology, quantum mechanics, molecular docking, and machine learning modeling, which bridge gaps in MD simulation. Finally, we make suggestions on determination of force field types, investigation of plant biomolecule mechanisms, and use of MD simulation in combination with other techniques. This review provides comprehensive summaries of the mechanisms of plant biomolecules in the environment revealed by MD simulation and validates it as an applicable tool for bridging gaps between macroscopic and microscopic behavior, providing insights into the wide application of MD simulation in plant biomolecules.

Clustered tree regression to learn protein energy change with mutated amino acid

Accurate and effective prediction of mutation-induced protein energy change remains a great challenge and of great interest in computational biology. However, high resource consumption and insufficient structural information of proteins severely limit the experimental techniques and structure-based prediction methods. Here, we design a structure-independent protocol to accurately and effectively predict the mutation-induced protein folding free energy change with only sequence, physicochemical and evolutionary features. The proposed clustered tree regression protocol is capable of effectively exploiting the inherent data patterns by integrating unsupervised feature clustering by K-means and supervised tree regression using XGBoost, and thus enabling fast and accurate protein predictions with different mutations, with an average Pearson correlation coefficient of 0.83 and an average root-mean-square error of 0.94kcal/mol. The proposed sequence-based method not only eliminates the dependence on protein structures, but also has potential applications in protein predictions with rare structural information.