Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here we introduce several improvements to indel modeling: (1) While previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here we propose a richer model that explicitly distinguishes between the two; (2) We introduce numerous summary statistics that allow Approximate Bayesian Computation (ABC) based parameter estimation; (3) We develop a method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical datasets; (4) Using a model-selection scheme we test whether the richer model better fits biological data compared to the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed richer model better fits a large number of empirical datasets and that, for the majority of these datasets, the deletion rate is higher than the insertion rate.

Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here we introduce several improvements to indel modeling: (1) while previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here, we propose a richer model that explicitly distinguishes between the two; (2) We introduce numerous summary statistics that allow Approximate Bayesian Computation (ABC) based parameter estimation; (3) We develop a neural-network model-selection scheme to test whether the richer model better fits biological data compared to the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed indel model better fits a large number of empirical datasets and that, for the majority of these datasets, the deletion rate is higher than the insertion rate. Finally, we demonstrate that indel rates are negatively correlated to the effective population size across various phylogenomic clades.

Aim: To investigate the potential of an ultrashort aromatic peptide hydrogelator integrated with hyaluronic acid (HA) to serve as a scaffold for bone regeneration.Materials and Methods: Fluorenylmethyloxycarbonyl-diphenylalanine (FmocFF)/HA hydrogel was prepared and characterized using microscopy and rheology. Osteogenic differentiation of MC3T3-E1 preosteoblasts was investigated using Alizarin red, alkaline phosphatase and calcium deposition assays. In vivo, 5-mm-diameter calvarial critical-sized defects were prepared in 20 Sprague-Dawley rats and filled with either FmocFF/HA hydrogel, deproteinized bovine bone mineral, FmocFF/Alginate hydrogel or left unfilled. Eight weeks after implantation, histology and micro-computed tomography analyses were performed. Immunohistochemistry was performed in six rats to assess the hydrogel's immunomodulatory effect.Results: A nanofibrous FmocFF/HA hydrogel with a high storage modulus of 46 KPa was prepared. It supported osteogenic differentiation of MC3T3-E1 preosteoblasts and facilitated calcium deposition. In vivo, the hydrogel implantation resulted in approximately 93% bone restoration. It induced bone deposition not only around the margins, but also generated bony islets along the defect. Elongated M2 macrophages lining at the periosteum-hydrogel interface were observed 1 week after implantation.After 3 weeks, these macrophages were dispersed through the regenerating tissue surrounding the newly formed bone.

Motivation Ancestral sequence reconstruction (ASR) is widely used to understand protein evolution, structure and function. Current ASR methodologies do not fully consider differences in evolutionary constraints among positions imposed by the three-dimensional (3D) structure of the protein. Here, we developed an ASR algorithm that allows different protein sites to evolve according to different mixtures of replacement matrices. We show that assigning replacement matrices to protein positions based on their solvent accessibility leads to ASR with higher log-likelihoods compared to naïve models that assume a single replacement matrix for all sites. Improved ASR log-likelihoods are also demonstrated when solvent accessibility is predicted from protein sequences rather than inferred from a known 3D structure. Finally, we show that using such structure-aware mixture models results in substantial differences in the inferred ancestral sequences. Availability and implementation http://fastml.tau.ac.il. Supplementary information Supplementary data are available at Bioinformatics online.

The inference of genome rearrangement events has been extensively studied, as they play a major role in molecular evolution. However, probabilistic evolutionary models that explicitly imitate the evolutionary dynamics of such events, as well as methods to infer model parameters, are yet to be fully utilized. Here, we developed a probabilistic approach to infer genome rearrangement rate parameters using an Approximate Bayesian Computation (ABC) framework. We developed two genome rearrangement models, a basic model, which accounts for genomic changes in gene order, and a more sophisticated one which also accounts for changes in chromosome number. We characterized the ABC inference accuracy using simulations and applied our methodology to both prokaryotic and eukaryotic empirical datasets. Knowledge of genome-rearrangement rates can help elucidate their role in evolution as well as help simulate genomes with evolutionary dynamics that reflect empirical genomes.