Rama Ranganathan

The design and synthesis of novel genes and deoxyribonucleic acid (DNA) sequences is a central technique in synthetic biology. Current methods of high throughput gene synthesis use pooled oligonucleotides obtained from custom-designed DNA microarray chips, and rely on orthogonal (non-interacting) polymerase chain reaction primers to specifically de-multiplex, by amplification, the precise subset of oligonucleotides necessary to assemble a full length gene. The availability of a large validated set of mutually orthogonal primers is therefore a crucial reagent for high-throughput gene synthesis. Here, we present a set of 166 20-nucleotide primers that are experimentally verified to be non-interacting, capable of specifying 13 695 unique genes. These primers represent a valuable resource to the synthetic biology community for specifying genetic components that can be assembled through a scalable and modular architecture.

Ras proteins are highly conserved signaling molecules that exhibit regulated, nucleotide-dependent switching between active and inactive states. The high conservation of Ras requires mechanistic explanation, especially given the general mutational tolerance of proteins. Here, we use deep mutational scanning, biochemical analysis and molecular simulations to understand constraints on Ras sequence. Ras exhibits global sensitivity to mutation when regulated by a GTPase activating protein and a nucleotide exchange factor. Removing the regulators shifts the distribution of mutational effects to be largely neutral, and reveals hotspots of activating mutations in residues that restrain Ras dynamics and promote the inactive state. Evolutionary analysis, combined with structural and mutational data, argue that Ras has co-evolved with its regulators in the vertebrate lineage. Overall, our results show that sequence conservation in Ras depends strongly on the biochemical network in which it operates, providing a framework for understanding the origin of global selection pressures on proteins.

The internal mechanics of proteins—the coordinated motions of amino acids and the pattern of forces constraining these motions—connects protein structure to function. Here we describe a new method combining the application of strong electric field pulses to protein crystals with time-resolved X-ray crystallography to observe conformational changes in spatial and temporal detail. Using a human PDZ domain (LNX2^PDZ2) as a model system, we show that protein crystals tolerate electric field pulses strong enough to drive concerted motions on the sub-microsecond timescale. The induced motions are subtle, involve diverse physical mechanisms, and occur throughout the protein structure. The global pattern of electric-field-induced motions is consistent with both local and allosteric conformational changes naturally induced by ligand binding, including at conserved functional sites in the PDZ domain family. This work lays the foundation for comprehensive experimental study of the mechanical basis of protein function.

We present a protocol for the functional assessment of comprehensive single-site saturation mutagenesis libraries of proteins utilizing ...

Defining the extent of epistasis - the non-independence of the effects of mutations - is essential for understanding the relationship of genotype, phenotype, and fitness in biological systems. The applications cover many areas of biological research, including biochemistry, genomics, protein and systems engineering, medicine, and evolutionary biology. However, the quantitative definitions of epistasis vary among fields, and its analysis beyond just pairwise effects remains obscure in general. Here, we show that different definitions of epistasis are versions of a single mathematical formalism - the weighted Walsh-Hadamard transform. We discuss that one of the definitions, the backgound-averaged epistasis, is the most informative when the goal is to uncover the general epistatic structure of a biological system, a description that can be rather different from the local epistatic structure of specific model systems. Key issues are the choice of effective ensembles for averaging and to practically contend with the vast combinatorial complexity of mutations. In this regard, we discuss possible approaches for optimally learning the epistatic structure of biological systems.Comment: 6 pages, 3 figures, supplementary informatio

The essential biological properties of proteins—folding, biochemical activities, and the capacity to adapt—arise from the global pattern of interactions between amino acid residues. The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids termed sectors. This evolution-based decomposition has potential for new understandings of the structural basis for protein function. To facilitate its usage, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package (pySCA). We show that the pattern of amino acid interactions within sectors is linked to the divergence of functional lineages in a multiple sequence alignment—a model for how sector properties might be differentially tuned in members of a protein family. This work provides new tools for studying proteins and for generally testing the concept of sectors as the principal units of function and adaptive variation. Proteins display the ability to fold, to carry out complex biochemical reactions, and to be adaptive to changing conditions of selection—the essential characteristics contributing to organismal fitness. A major goal is to understand how these properties emerge from the global pattern of interactions between amino acid residues. Here, we describe the principles and implementation of the statistical coupling analysis (SCA), a method to reveal this pattern through analysis of coevolution between amino acids in an ensemble of homologous sequences. The basic result is a decomposition of protein structures into groups of contiguous amino acids called “sectors” which have been linked to conserved functional properties. This work provides conceptual and practical tools for sector analysis in any sufficiently well-represented protein family, and represents a necessary basis for broadly testing the concept of protein sectors.

The essential biological properties of proteins - folding, biochemical activities, and the capacity to adapt - arise from the global pattern of interactions between amino acid residues. The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids termed sectors. This evolution-based decomposition has potential for new understandings of the structural basis for protein function, but requires broad further testing by the scientific community. To facilitate this, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package. We show that the pattern of amino acid interactions within sectors is linked to the divergence of functional lineages in a multiple sequence alignment - a model for how sector properties might be differentially tuned in members of a protein family. This work provides new tools for understanding the structural basis for protein function and for generally testing the concept of sectors as the principal functional units of proteins.

Defining the extent of epistasis - the non-independence of the effects of mutations - is essential for understanding the relationship of genotype, phenotype, and fitness in biological systems. The applications cover many areas of biological research, including biochemistry, genomics, protein and systems engineering, medicine, and evolutionary biology. However, the quantitative definitions of epistasis vary among fields, and its analysis beyond just pairwise effects remains obscure in general. Here, we show that different definitions of epistasis are versions of a single mathematical formalism - the weighted Walsh-Hadamard transform. We discuss that one of the definitions, the backgound-averaged epistasis, is the most informative when the goal is to uncover the general epistatic structure of a biological system, a description that can be rather different from the local epistatic structure of specific model systems. Key issues are the choice of effective ensembles for averaging and to practically contend with the vast combinatorial complexity of mutations. In this regard, we discuss possible approaches for optimally learning the epistatic structure of biological systems.