Biology subjects

Sormanni, P.

Publications and source records attributed to Sormanni, P..

26 records · Page 2Linked to original sources

bioRxiv2022

Multi-dimensional protein solubility optimization with an ultra-high-throughput microfluidic platform

Protein-based biologics are highly suitable for drug development, as they exhibit low toxicity and high specificity for their targets. However, for therapeutic applications, biologics must often be formulated to very high concentrations, making insufficient solubility a critical bottleneck in drug development pipelines. Here, we report an ultra-high-throughput microfluidic platform for protein solubility screening. In comparison with previous methods, this microfluidic platform can make, incubate, and measure samples in a few minutes, uses just 20 micrograms of protein (> 10-fold improvement) and yields 10,000 data points (1000-fold improvement). This allows quantitative comparison of formulation additives, such as salt, polysorbate, histidine, arginine and sucrose. Additionally, we can measure how solubility is affected by different concentrations of multiple additives, find a suitable pH for the formulation, and measure the impact of single mutations on solubility, thus enabling the screening of large libraries. By reducing material and time costs, this approach makes detailed multi-dimensional solubility optimization experiments possible, streamlining drug development and increasing our understanding of biotherapeutic solubility and the effects of excipients.

biophysics↗

bioRxiv2022

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and polyspecificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk

biochemistry↗

bioRxiv2022

Sequence-based pH-dependent prediction of protein solubility using CamSol

Solubility is a property of central importance for the use of proteins in research and in applications in biotechnology and medicine. Since experimental methods for measuring protein solubility are resource-intensive and time-consuming, computational methods have recently emerged to enable the rapid and inexpensive screening of large libraries of proteins, as it is routinely required in development pipelines. Here, we describe the extension of one of such methods, CamSol, to include in the predictions the effect of the pH of the solubility. We illustrate the accuracy of the pH-dependent predictions on a variety of antibodies and other proteins.

biophysics↗

bioRxiv2021

Fragment-based computational design of antibodies targeting structured epitopes

De novo design methods hold the promise of reducing the time and cost of antibody discovery, while enabling the facile and precise targeting of predetermined epitopes. Here we describe a fragment-based method for the combinatorial design of antibody binding loops and their grafting onto antibody scaffolds. We designed and tested six single-domain antibodies targeting different epitopes on three antigens, including the receptor-binding domain of the SARS-CoV-2 spike protein. Biophysical characterisation showed that all designs are highly stable, and bind their intended targets with affinities in the nanomolar range without any in vitro affinity maturation. We further discuss how a high-resolution input antigen structure is not required, as our method yields similar predictions when the input is a crystal structure or a computer-generated model. This computational procedure, which readily runs on a laptop, provides a starting point for the rapid generation of lead antibodies binding to pre-selected epitopes. summaryA combinatorial method can rapidly design nanobodies for predetermined epitopes, which bind with KDs in the nanomolar range.

bioinformatics↗

bioRxiv2020

Pairs of amino acids at the P- and A-sites of the ribosome predictably and causally modulate translation-elongation rates

Variation in translation-elongation kinetics along a transcripts coding sequence plays an important role in the maintenance of cellular protein homeostasis by regulating co-translational protein folding, localization, and maturation. Translation-elongation speed is influenced by molecular factors within mRNA and protein sequences. For example, the presence of proline in the ribosomes P- or A-site slows down translation, but the effect of other pairs of amino acids, in the context of all 400 possible pairs, has not been characterized. Here, we study Saccharomyces cerevisiae using a combination of bioinformatics, mutational experiments, and evolutionary analyses, and show that many different pairs of amino acids and their associated tRNA molecules predictably and causally encode translation rate information when these pairs are present in the A- and P-sites of the ribosome independent of other factors known to influence translation speed including mRNA structure, wobble base pairing, tripeptide motifs, positively charged upstream nascent chain residues, and cognate tRNA concentration. The fast-translating pairs of amino acids that we identify are enriched four-fold relative to the slow-translating pairs across Saccharomyces cerevisiaes proteome, while the slow-translating pairs are enriched downstream of domain boundaries. Thus, the chemical identity of amino acid pairs contributes to variability in translation rates, elongation kinetics are causally encoded in the primary structure of proteins, and signatures of evolutionary selection indicate their potential role in co-translational processes.

molecular biology↗

bioRxiv2018

Identifying A- and P-site locations on ribosome-protected mRNA fragments using Integer Programming

Identifying the A- and P-site locations on ribosome-protected mRNA fragments from Ribo-Seq experiments is a fundamental step in the quantitative analysis of transcriptome-wide translation properties at the codon level. Many analyses of Ribo-Seq data have utilized heuristic approaches applied to a narrow range of fragment sizes to identify the A-site. In this study, we use Integer Programming to identify A-site by maximizing an objective function that reflects the fact that the ribosomes A-site on ribosome-protected fragments must reside between the second and stop codons of an mRNA. This identifies the A-site location as a function of the fragments size and its 5{square} end reading frame in Ribo-Seq data generated from S. cerevisiae and mouse embryonic stem cells. The correctness of the identified A-site locations is demonstrated by showing that this method, as compared to others, yields the largest ribosome density at established stalling sites. By providing greater accuracy and utilization of a wider range of fragment sizes, our approach increases the signal-to-noise ratio of underlying biological signals associated with translation elongation at the codon length scale.

bioinformatics↗

bioRxiv2018

A Chemical Kinetic Basis for Measuring Translation Initiation and Elongation Rates from Ribosome Profiling data

Analysis methods based on simulations and optimization have been previously developed to estimate relative translation rates from next-generation sequencing data. Translation involves molecules and chemical reactions; hence, bioinformatics methods consistent with the laws of chemistry and physics are more likely to produce accurate results. Here, we derive simple equations based on chemical kinetic principles to measure the translation-initiation rate, transcriptome-wide elongation rate, and individual codon translation rates from ribosome profiling experiments. Our methods reproduce the known rates from ribosome profiles generated from detailed simulations of translation. Applying our methods to data from S. cerevisiae and mouse embryonic stem cells we find that the extracted rates reproduce expected correlations with various molecular properties. A codon can exhibit up to 26-fold variability in its translation rate depending upon its context within a transcript. This broad distribution means that the average translation rate of a codon is not representative of the rate at which most instances of that codon are translated. We also find that mouse embryonic stem cells have a global translation speed of 5.2 AA/s, is similar to what has been previous reported using another analysis method. This large variability in translation rates suggests that translational regulation might be used by cells to a greater degree than previously thought.

bioinformatics↗

bioRxiv2017

Paratope Prediction using Convolutional and Recurrent Neural Networks

Antibodies play an essential role in the immune system of vertebrates and are vital tools in research and diagnostics. While hypervariable regions of antibodies, which are responsible for binding, can be readily identified from their amino acid sequence, it remains challenging to accurately pinpoint which amino acids will be in contact with the antigen (the paratope). In this work, we present a sequence-based probabilistic machine learning algorithm for paratope prediction, named Parapred. Parapred uses a deep-learning architecture to leverage features from both local residue neighbourhoods and across the entire sequence. The method outperforms the current state-of-the-art methodology, and only requires a stretch of amino acid sequence corresponding to a hypervariable region as an input, without any information about the antigen. We further show that our predictions can be used to improve both speed and accuracy of a rigid docking algorithm. The Parapred method is freely available at https://github.com/eliberis/parapred for download.

bioinformatics↗