Becoming a de novo gene

A computational exploration of resilience and evolvability of protein-protein interaction networks

Protein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype's PPI network's resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks' resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms-random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

CoLiDe: Combinatorial Library Design tool for probing protein sequence space

MOTIVATION

Current techniques of protein engineering focus mostly on re-designing small targeted regions or defined structural scaffolds rather than constructing combinatorial libraries of versatile compositions and lengths. This is a missed opportunity because combinatorial libraries are emerging as a vital source of novel functional proteins and are of interest in diverse research areas.

RESULTS

Here, we present a computational tool for Combinatorial Library Design (CoLiDe) offering precise control over protein sequence composition, length and diversity. The algorithm uses evolutionary approach to provide solutions to combinatorial libraries of degenerate DNA templates. We demonstrate its performance and precision using four different input alphabet distribution on different sequence lengths. In addition, a model design and experimental pipeline for protein library expression and purification is presented, providing a proof-of-concept that our protocol can be used to prepare purified protein library samples of up to 1011-1012 unique sequences. CoLiDe presents a composition-centric approach to protein design towards different functional phenomena.

AVAILABILITYAND IMPLEMENTATION

CoLiDe is implemented in Python and freely available at https://github.com/voracva1/CoLiDe.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.