Abstract: A method and apparatus are provided for designing sequence-defined biomolecules, such as proteins using a data-driven, evolution-based process. To design proteins, an iterative method founded on a combination of an unsupervised sequence-based model with a supervised functionality-based model can select candidate amino acid sequences that are likely to have a desired functionality. Feedback from measuring the candidate proteins using a high-throughput gene-synthesis and a protein screening process is used to refine and improve the models guiding the candidate selection to the most promising regions of the very large amino acid sequence search space.

Abstract: Statistical algorithms that are useful to aid in the identification of evolutionarily conserved amino acid positions within a family of proteins, and in the identification of interacting amino acid positions within a protein sequence are disclosed. The algorithms may also be useful in the analysis of other polymer sequences such as polysaccharides, lipids, deoxyribonucleic acid (DNA), and ribonucleic acid sequences (RNA), and, more specifically, in the analysis of DNA microarray data. Algorithms useful for analyzing the structural changes of perturbations to determine the mechanisms by which positions are coupled are also disclosed.

Abstract: Methods of using biological sequence data. Evolved biological sequences may be used to identify the defining biological characteristics of the sequences—the three-dimensional structure and biochemical function. Some of the present methods extract such information, use such information to predict functional mechanism, and/or use such information in the design of artificial biological sequences. Other methods are included, as are related computer readable media and computer systems.

Abstract: Statistical algorithms that are useful to aid in the identification of evolutionarily conserved amino acid positions within a family of proteins, and in the identification of interacting amino acid positions within a protein sequence are disclosed. The algorithms may also be useful in the analysis of other polymer sequences such as polysaccharides, lipids, deoxyribonucleic acid (DNA), and ribonucleic acid sequences (RNA), and, more specifically, in the analysis of DNA microarray data. Algorithms useful for analyzing the structural changes of perturbations to determine the mechanisms by which positions are coupled are also disclosed.

Abstract: Statistical algorithms that are useful to aid in the identification of evolutionarily conserved amino acid positions within a family of proteins, and in the identification of interacting amino acid positions within a protein sequence are disclosed. The algorithms may also be useful in the analysis of other polymer sequences such as polysaccharides, lipids, deoxyribonucleic acid (DNA), and ribonucleic acid sequences (RNA), and, more specifically, in the analysis of DNA microarray data. Algorithms useful for analyzing the structural changes of perturbations to determine the mechanisms by which positions are coupled are also disclosed.