Mispacking and the Fitness Landscape of the Green Fluorescent Protein Chromophore Milieu.

The autocatalytic maturation of the chromophore in green fluorescent protein (GFP) was thought to require the precise positioning of the side chains surrounding it in the core of the protein, many of which are strongly conserved among homologous fluorescent proteins. In this study, we screened for green fluorescence in an exhaustive set of point mutations of seven residues that make up the chromophore microenvironment, excluding R96 and E222 because mutations at these positions have been previously characterized. Contrary to expectations, nearly all amino acids were tolerated at all seven positions. Only four point mutations knocked out fluorescence entirely. However, chromophore maturation was found to be slower and/or fluorescence reduced in several cases. Selected combinations of mutations showed nonadditive effects, including cooperativity and rescue. The results provide guidelines for the computational engineering of GFPs.

Protein backbone ensemble generation explores the local structural space of unseen natural homologs.

MOTIVATION: Mutations in homologous proteins affect changes in the backbone conformation that involve a complex interplay of forces which are difficult to predict. Protein design algorithms need to anticipate these backbone changes in order to accurately calculate the energy of the structure given an amino acid sequence, without knowledge of the final, designed sequence. This is related to the problem of predicting small changes in the backbone between highly similar sequences. RESULTS: We explored the ability of the Rosetta suite of protein design tools to move the backbone from its position in one structure (template) to its position in a close homologous structure (target) as a function of the diversity of a backbone ensemble constructed using the template structure, the percent sequence identity between the template and target, and the size of local zone being considered in the ensemble. We describe a pareto front in the likelihood of moving the backbone toward the target as a function of ensemble diversity and zone size. The equations and protocols presented here will be useful for protein design. AVAILABILITY AND IMPLEMENTATION: PyRosetta scripts available at www.bioinfo.rpi.edu/bystrc/downloads.html#ensemble CONTACT: bystrc@rpi.edu.

Toward Computationally Designed Self-Reporting Biosensors Using Leave-One-Out Green Fluorescent Protein.

Leave-one-out green fluorescent protein (LOOn-GFP) is a circularly permuted and truncated GFP lacking the nth ß-strand element. LOO7-GFP derived from the wild-type sequence (LOO7-WT) folds and reconstitutes fluorescence upon addition of ß-strand 7 (S7) as an exogenous peptide. Computational protein design may be used to modify the sequence of LOO7-GFP to fit a different peptide sequence, while retaining the reconstitution activity. Here we present a computationally designed leave-one-out GFP in which wild-type strand 7 has been replaced by a 12-residue peptide (HA) from the H5 antigenic region of the Thailand strain of H5N1 influenza virus hemagglutinin. The DEEdesign software was used to generate a sequence library with mutations at 13 positions around the peptide, coding for approximately 3 × 10(5) sequence combinations. The library was coexpressed with the HA peptide in E. coli and colonies were screened for in vivo fluorescence. Glowing colonies were sequenced, and one (LOO7-HA4) with 7 mutations was purified and characterized. LOO7-HA4 folds, fluoresces in vivo and in vitro, and binds HA. However, binding results in a decrease in fluorescence instead of the expected increase, caused by the peptide-induced dissociation of a novel, glowing oligomeric complex instead of the reconstitution of the native structure. Efforts to improve binding and recover reconstitution using in vitro evolution produced colonies that glowed brighter and matured faster. Two of these were characterized. One lost all affinity for the HA peptide but glowed more brightly in the unbound oligomeric state. The other increased in affinity to the HA peptide but still did not reconstitute the fully folded state. Despite failing to fold completely, peptide binding by computational design was observed and was improved by directed evolution. The ratio of HA to S7 binding increased from 0.0 for the wild-type sequence (no binding) to 0.01 after computational design (weak binding) and to 0.48 (comparable binding) after in vitro evolution. The novel oligomeric state is composed of an open barrel.

InteractiveROSETTA: a graphical user interface for the PyRosetta protein modeling suite.

UNLABELLED: Modern biotechnical research is becoming increasingly reliant on computational structural modeling programs to develop novel solutions to scientific questions. Rosetta is one such protein modeling suite that has already demonstrated wide applicability to a number of diverse research projects. Unfortunately, Rosetta is largely a command-line-driven software package which restricts its use among non-computational researchers. Some graphical interfaces for Rosetta exist, but typically are not as sophisticated as commercial software. Here, we present InteractiveROSETTA, a graphical interface for the PyRosetta framework that presents easy-to-use controls for several of the most widely used Rosetta protocols alongside a sophisticated selection system utilizing PyMOL as a visualizer. InteractiveROSETTA is also capable of interacting with remote Rosetta servers, facilitating sophisticated protocols that are not accessible in PyRosetta or which require greater computational resources. AVAILABILITY AND IMPLEMENTATION: InteractiveROSETTA is freely available at https://github.com/schenc3/InteractiveROSETTA/releases and relies upon a separate download of PyRosetta which is available at http://www.pyrosetta.org after obtaining a license (free for academic use).

Improving computational efficiency and tractability of protein design using a piecemeal approach. A strategy for parallel and distributed protein design.

MOTIVATION: Accuracy in protein design requires a fine-grained rotamer search, multiple backbone conformations, and a detailed energy function, creating a burden in runtime and memory requirements. A design task may be split into manageable pieces in both three-dimensional space and in the rotamer search space to produce small, fast jobs that are easily distributed. However, these jobs must overlap, presenting a problem in resolving conflicting solutions in the overlap regions. RESULTS: Piecemeal design, in which the design space is split into overlapping regions and rotamer search spaces, accelerates the design process whether jobs are run in series or in parallel. Large jobs that cannot fit in memory were made possible by splitting. Accepting the consensus amino acid selection in conflict regions led to non-optimal choices. Instead, conflicts were resolved using a second pass, in which the split regions were re-combined and designed as one, producing results that were closer to optimal with a minimal increase in runtime over the consensus strategy. Splitting the search space at the rotamer level instead of at the amino acid level further improved the efficiency by reducing the search space in the second pass. AVAILABILITY AND IMPLEMENTATION: Programs for splitting protein design expressions are available at www.bioinfo.rpi.edu/tools/piecemeal.html CONTACT: bystrc@rpi.edu Supplementary information: Supplementary data are available at Bioinformatics online.