Biology subjects

Naef, L.

Publications and source records attributed to Naef, L..

4 recordsLinked to original sources

bioRxiv2024

PINDER: The protein interaction dataset and evaluation resource

Protein-protein interactions (PPIs) are fundamental to understanding biological processes and play a key role in therapeutic advancements. As deep-learning docking methods for PPIs gain traction, benchmarking protocols and datasets tailored for effective training and evaluation of their generalization capabilities and performance across real-world scenarios become imperative. Aiming to overcome limitations of existing approaches, we introduce PINDER, a comprehensive annotated dataset that uses structural clustering to derive non-redundant interface-based data splits and includes holo (bound), apo (unbound), and computationally predicted structures. PINDER consists of 2,319,564 dimeric PPI systems (and up to 25 million augmented PPIs) and 1,955 high-quality test PPIs with interface data leakage removed. Additionally, PINDER provides a test subset with 180 dimers for comparison to AlphaFold-Multimer without any interface leakage with respect to its training set. Unsurprisingly, the PINDER benchmark reveals that the performance of existing docking models is highly overestimated when evaluated on leaky test sets. Most importantly, by retraining DiffDock-PP on PINDER interface-clustered splits, we show that interface cluster-based sampling of the training split, along with the diverse and less leaky validation split, leads to strong generalization improvements.

bioinformatics↗

bioRxiv2024

PLINDER: The protein-ligand interactions dataset and evaluation resource

Protein-ligand interactions (PLI) are foundational to small molecule drug design. With computational methods striving towards experimental accuracy, there is a critical demand for a well-curated and diverse PLI dataset. Existing datasets are often limited in size and diversity, and commonly used evaluation sets suffer from training information leakage, hindering the realistic assessment of method generalization capabilities. To address these shortcomings, we present PLIN-DER, the largest and most annotated dataset to date, comprising 449,383 PLI systems, each with over 500 annotations, similarity metrics at protein, pocket, interaction and ligand levels, and paired unbound (apo) and predicted structures. We propose an approach to generate training and evaluation splits that minimizes task-specific leakage and maximizes test set quality, and compare the resulting performance of DiffDock when retrained with different kinds of splits.

biochemistry↗

bioRxiv2022

From sequence to function through structure: deep learning for protein design

The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be leveraged to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field. AvailabilitypLM generated and UniRef50 sampled sequence sets and predictions are available at http://data.bioembeddings.com/public/design. Code-base and Notebooks for analysis are available at https://github.com/hefeda/PGP. An online version of Table 1 can be found at https://github.com/hefeda/design_tools. O_TBL View this table: org.highwire.dtl.DTLVardef@1823c4borg.highwire.dtl.DTLVardef@1449504org.highwire.dtl.DTLVardef@193458borg.highwire.dtl.DTLVardef@1bab4c4org.highwire.dtl.DTLVardef@b1d39f_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 1:C_FLOATNO O_TABLECAPTIONmachine-learning-based protein design methods. Methods ordered by their release date, accounting for the date of pre-prints when available. Class (1-4) captured in Fig. 1. Expanded method detail in main text. Unnamed methods are referenced by the first name of the first author. Online version at https://github.com/hefeda/design_tools. Legend: Architecture: The architecture of the deep learning model; E2E: an end-to-end differentiable solution; Input: the input to run (infer from) the model, e.g. a contact map; Output: the output, e.g. a protein sequence; Dataset: the number and type of samples used to train the method; Params: the exact or estimated number of parameters of the model; 1: the 3D recovery was performed in an external, second step; 2: conditioned generation; -: no input required for generation. C_TABLECAPTION C_TBL

bioinformatics↗

bioRxiv2020

Disinhibition of the orbitofrontal cortex biases goal-directed behaviour in obesity

The lateral orbitofrontal cortex (lOFC) receives sensory information about food and integrates these signals with expected outcomes to guide future actions, and thus may play a key role in a distributed network of neural circuits that regulate feeding behaviour. Here, we reveal a novel role for the lOFC in the cognitive control of behaviour in obesity. Food-seeking behaviour is biased in obesity such that in male obese mice, behaviours are less flexible to changes in the perceived value of the outcome. Obesity is associated with reduced lOFC inhibitory drive, and chemogenetic reduction in GABAergic neurotransmission in the lOFC induces obesity-like impairments in goal-directed behaviour. Conversely, pharmacological or optogenetic restoration of inhibitory neurotransmission in the lOFC of obese mice reinstates flexible behaviour. Our results indicate that obesity-induced disinhibition of the lOFC leads to a failure to update changes in the value of food with satiety, which in turn may influence how individuals make decisions in an obesogenic environment.

neuroscience↗