A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Researchers identify critical protein residues using advanced graph theory.
Generative model designs highly designable proteins using geometric algebra.
ProtTrans models predict protein features without evolutionary info.
Motivation. Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains very challenging to predict contact map using only sequence information. Most existing methods pr…
Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling (EC) analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, resp…
GEFA predicts drug-target affinity using graph neural networks.
DiAMoNDBack models protein backmapping from coarse-grained Cα traces.
Artificial neural networks (ANNs) have gained a well-deserved popularity among machine learning tools upon their recent successful applications in image- and sound processing and classification problems. ANNs have also been applied for predicting the family or function of a protein, knowing its residue sequence. Here w…
Recent developments in specialized computer hardware have greatly accelerated atomic level Molecular Dynamics (MD) simulations. A single GPU-attached cluster is capable of producing microsecond-length trajectories in reasonable amounts of time. Multiple protein states and a large number of microstates associated with f…
A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy. Amino acid side chain conformation prediction is essential for protein homology modeling and protein design. Current widely-adopted methods use physics-based energy functions to evaluate s…
Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between prot…
Motivation: Proteins are known to undergo conformational changes in the course of their functions. The changes in conformation are often attributable to a small fraction of residues within the protein. Therefore identification of these variable regions is important for an understanding of protein function. Results: We …
Persistent homology provides a new, efficient molecular descriptor for protein dynamics.
New method learns diverse protein scaffolds for motif design.
Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.
Amino acid sequence portrays most intrinsic form of a protein and expresses primary structure of protein. The order of amino acids in a sequence enables a protein to acquire a particular stable conformation that is responsible for the functions of the protein. This relationship between a sequence and its function motiv…
A new method estimates protein evolutionary fields and couplings from alignments.
Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolution…
Paper improves Tm prediction of protein fragments using sparsity and probabilistic models.
Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise. The maximum likelihood solution for the model is an eigenvalue problem on the sample covariance matrix. In this paper we consider the situation where the data v…
ESS-Flow guides flow models without retraining, using Bayesian inference in source space.
The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand base…
A new framework uses text descriptions to improve protein design.
New methods avoid spectral pollution in transfer operators for accurate analysis.
Deep learning models optimize protein sequences.
Mathematical pipeline identifies structural homology of knotted proteins.
ProGen models protein sequences for synthetic biology.
We equip a knot with a set of colored bonds, that is, colored intervals properly embedded into . Such a construction can be viewed as a structure that topologically models a closed protein chain including any type of bridges connecting the backbone residues. We introduce an invariant of su…
New 3D protein analysis methods improve accuracy.
New method detects and compares folding pathways of knotted proteins.
Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins' properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability are necessary to facilitate efficient protein design. However, accuracy of predic…
PANDA predicts protein binding affinity changes from sequences, outperforming existing methods.
A new model explains protein interactions via electron delocalization.
EBM predicts protein conformations at atomic scale using crystallized data.
Knot theory applied to proteins, distinguishing folded linear chains.
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on thei…
Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is essential to understand the principles of cellular organization and explore the causes of various diseases. It is critical to integrate multipl…
Mathematician summarizes protein geometry and mutation effects.
Machine learning predicts protein structures and simulates dynamics.
EGR refines and assesses protein complex structures.
We introduce a new model of proteins, which extends and enhances the traditional graphical representation by associating a combinatorial object called a fatgraph to any protein based upon its intrinsic geometry. Fatgraphs can easily be stored and manipulated as triples of permutations, and these methods are therefore a…
Two proteins are homologous if they have a common evolutionary origin, and the binary classification problem is to identify proteins in a candidate set that are homologous to a particular native protein. The feature (explanatory) variables available for classification are various measures of similarity of proteins. The…
As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem of protein structure classification, which computationally classifies the structures of proteins into pre-defined groups. We develop a weight…
Flexible Kernels for Protein Property Prediction
New method steers protein design towards desired properties.
A new diffusion model generates novel protein backbones without relying on pretrained networks.
PLUS pre-trains protein sequences with structural info, improving performance.
Study improves LLMs for PPI analysis by addressing uncertainty.