Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. But despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics and genetics, and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination - by varying degrees of flexibility and shape/chemistry complementarity - but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.
Computational Insights into Mechanostability and Dissociation Dynamics of the Dengue Virus Envelope Protein Ectodomain Dimer Across pH and Temperature Gradients
São Paulo State University (UNESP)（圣保罗州立大学）；Nicolaus Copernicus University in Torun（托伦哥白尼大学）；Istituto Italiano di Tecnologia (IIT)（意大利理工学院）
Evolution of the genetic code. Why are there strong and weak letter doublets? The first gene, the first protein. Early (ancient) biosynthesis of protein
Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this paper, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept we use peptides that generate MRI con…
Proteomics is the large-scale analysis of the proteins. The common method for identifying proteins and characterising their amino acid sequences is to digest the proteins into peptides, analyse the peptides using mass spectrometry and assign the resulting tandem mass spectra (MS/MS) to peptides using database search tools. However, database search algorithms are highly dependent on a reference protein database and they cannot identify peptides and proteins not included in the database. Therefore, de novo sequencing algorithms are developed to overcome the problem by directly reconstructing the…
Modeling Protein Evolution via Generative Inference From Monte Carlo Chains to Population Genetics
Politecnico di Torino（都灵理工大学）；Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Laboratory of Computational and Quantitative Biology（计算与定量生物学实验室）；École Normale Supérieure（巴黎高等师范学院）；PSL Université（巴黎文理研究大学）；Université de Paris（巴黎大学）；The James Franck Institute（詹姆斯·弗兰克研究所）；The University of Chicago（芝加哥大学）；Department of Physics, The University of Chicago（芝加哥大学物理系）；INFN, Sezione di Torino（意大利国家核物理研究所都灵分部）；Italian Institute for Genomic Medicine, IRCCS Candiolo（意大利基因组医学研究所坎迪奥洛医院）；Dipartimento di Fisica, Sapienza Università di Roma（罗马大学物理系）
Improving homology-directed repair by small molecule agents for genetic engineering in unconventional yeast? -- Learning from the engineering of mammalian systems
