Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts

Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein-protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein-protein docking. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected]

The Wild Oats Pilot Project

In 1982, wild oats cost North Dakota $160 to$260 million dollars as it infested small grain crops. Wild oats are deemed a weed. Wild oats not only infest small grain crops but sugarbeets, soybean, peas, dry beans, lentils, corn, sunflowers and grass seeds. Complete control of emergenced wild oat plants in one season will not eliminate a wild oats infestation as many seeds may remain viable and dormant in the soil. The Wild Oats Pilot Project was a four year program conducted under the cooperative agreement between the Agricultural Experiment Station of North Dakota State University and the Agricultural Research Service of the United States Department of Agriculture. The goals of the project were to evaluate the cumulative effects of four years of mechanical, cultural and chemical wild oat practices on the population of wild oats seed in the soil and develop economical rotation herbicide systems which would minimize crop loss due to wild oats. The article goes into the materials, method, results and discussion of this project's findings. Wild oat control systems should be determined by the level of infestation

Understanding the fabric of protein crystals: Computational classification of biological interfaces and crystal contacts

Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein–protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein–protein docking. © 2021 Oxford University PressISSN:1367-4803ISSN:1460-205