Biology subjects

Caceres-Delpiano, J.

Publications and source records attributed to Caceres-Delpiano, J..

6 recordsLinked to original sources

bioRxiv2024

Developing machine-learning-based amyloid predictors with Cross-Beta DB

Due to shifts in environmental conditions, mutations, or interactions with other biomolecules, some proteins that would normally be soluble can undergo aggregation, resulting in the formation of clumps of amyloid fibrils. Understanding of this phenomenon is of paramount importance due not only to its association with various diseases (including Alzheimers disease), but also due to increasingly abundant evidence for its functional roles. Numerous studies have demonstrated that the propensity to form amyloids is coded by the amino acid sequence and this finding has paved the way for the development of several computational predictors of amyloidogenicity. The ultimate objective of computational methods is to accurately predict the formation of disease-related and functionally relevant amyloids that occur in vivo. These amyloid fibrils are known to form very specific "cross-{beta}" structures of protein regions longer than about 15 residues. Remarkably, despite the significance of the naturally occurring amyloids, there has been a lack of datasets specifically dedicated to them. Hence, we built Cross-Beta DB, a database composed of cross-{beta} amyloids formed in natural conditions. This database is expected to be indispensable for benchmarking amyloid predictors. We used the Cross-Beta DB to train and benchmark several such algorithms, using machine learning. The best-performing of these, the random-forest-based Cross-Beta RF Predictor, demonstrated superior performance over the other existing methods, fostering high expectations for an improved prediction of naturally occurring amyloids.

bioinformatics↗

bioRxiv2022

ExpressUrself: A spatial model for predicting recombinant expression from mRNA sequence

Maximising the yield of recombinantly expressed proteins is a critical part of any protein engineering pipeline. In most cases, the expression of a given protein can be tuned by adjusting its DNA coding sequence, however finding coding sequences that optimise expression is a nontrivial task. The 3-dimensional structure of mRNA is known to strongly influence the expression levels of proteins, due to its effect on the efficiency of ribosome attachment. While correlations between mRNA structure and expression are well established, no model to date has succeeded in effectively utilising this information to accurately predict expression levels. Here we present ExpressUrself, a model designed to capture spatial characteristics of the sequence surrounding the start codon of an mRNA transcript, and intended to be used for optimising protein expression. The model is trained and tested on a large data set of variant DNA sequences and is able to predict the expression of previously unseen transcripts to a high degree of accuracy.

bioengineering↗

bioRxiv2021

Graph convolutional network for protein sequence sampling and prediction using structural data

The increasing integration between protein engineering and machine learning has led to many interesting results. A problem still to solve is to evaluate the likelihood that a sequence will fold into a target structure. This problem can be also viewed as sequence prediction from a known structure. In the current work, we propose improvements in the recent architecture of Geometric Vector Perceptrons [1] in order to optimize the sampling of sequences from a known backbone structure. The proposed model differs from the original in that there is: (i) no updating in the vectorial embedding, only in the scalar one, (ii) only one layer of decoding. The first aspect improves the accuracy of the model and reduces the use of memory, the second allows for training of the model with several tasks without incurring data leakage. We treat the trained classifier as an Energy-Based Model and sample sequences by sampling amino acids in a non-autoreggresive manner in the empty positions of the sequence using energy-guided criteria and followed by random mutation optimization. We improve the median identity of samples from 40.2% to 44.7%. An additional question worth investigating is whether sampled and original sequences fold into similar structures independent of their identity. We chose proteins in our test set whose sampled sequences show low identity (under 30%) but for which our model predicted favorable energies. We used AlphaFold [2, 3] and observed that the predicted structures for sampled sequences highly resemble the predicted structures for original sequences, with an average TM-score of 0.84.

bioinformatics↗

bioRxiv2021

Deep learning model for the prediction and classification of protein toxins across all domains of life

Toxins are widely produced by different organisms to disrupt the physiology of other organisms, and support their own existence. Their study is useful to understand protein evolution, environmental adaptation and survival competition. In-silico predictions of toxic proteins can support empirical frameworks, and help in the safety measurements needed for various industrial related processes. Some in-silico methods are slow, hard to implement or lack taxa representation in their training datasets. Here we present a deep learning model to classify protein toxins, through the use of Convolutional Neural Networks (ConvTOX). ConvTOX is able to accurately identify toxic proteins across the domains of life, with accuracies over 80% for animal and plant toxins, and over 50% for bacterial toxins. Moreover, ConvTOX is able to generalize the identification of differences among toxin types, such as neurotoxins and myotoxins, and to accurately identify structural similarities between different protein toxins. ConvTOX overcomes limitations from previous models by being able to predict toxin proteins from across all domains of life, and by not being limited to only short toxin peptides. Limitations are still clear in terms of lower accuracies for specific phylogenetic groups (such as bacterial toxins), but still this works presents itself as a one step forward for the universal use, classification and study of toxic proteins.

synthetic biology↗

bioRxiv2020

Deep learning enables the design of functional de novo antimicrobial proteins

Protein sequences are highly dimensional and present one of the main problems for the optimization and study of sequence-structure relations. The intrinsic degeneration of protein sequences is hard to follow, but the continued discovery of new protein structures has shown that there is convergence in terms of the possible folds that proteins can adopt, such that proteins with sequence identities lower than 30% may still fold into similar structures. Given that proteins share a set of conserved structural motifs, machine-learning algorithms can play an essential role in the study of sequence-structure relations. Deep-learning neural networks are becoming an important tool in the development of new techniques, such as protein modeling and design, and they continue to gain power as new algorithms are developed and as increasing amounts of data are released every day. Here, we trained a deep-learning model based on previous recurrent neural networks to design analog protein structures using representations learning based on the evolutionary and structural information of proteins. We test the capabilities of this model by creating de novo variants of an antifungal peptide, with sequence identities of 50% or lower relative to the wild-type (WT) peptide. We show by in silico approximations, such as molecular dynamics, that the new variants and the WT peptide can successfully bind to a chitin surface with comparable relative binding energies. These results are supported by in vitro assays, where the de novo designed peptides showed antifungal activity that equaled or exceeded the WT peptide.

bioengineering↗

bioRxiv2020

The Automated Optimisation of a Coarse-Grained Force Field Using Free Energy Data

Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment.

biophysics↗