HDXRank: A Deep Learning Framework for Ranking Protein Complex Predictions with Hydrogen-Deuterium Exchange Data.

Accurate modeling of protein-protein complex structures is essential for understanding biological mechanisms. Hydrogen-deuterium exchange (HDX) experiments provide valuable insights into binding interfaces. Incorporating HDX data into protein complex modeling workflows offers a promising approach to improve prediction accuracy.

Balancing G protein selectivity and efficacy in the adenosine A receptor.

The adenosine A receptor (AR) engages several G proteins, notably G and its cognate G protein. This coupling promiscuity is facilitated by a dynamic ensemble, revealed by F nuclear magnetic resonance imaging of AR and G protein. Two transmembrane helix 6 (TM6) activation states, formerly associated with partial and full agonism, accommodate the differing volumes of G and G.

Extensive antibody search with whole spectrum black-box optimization.

In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from.

Quantum Annealing Designs Nonhemolytic Antimicrobial Peptides in a Discrete Latent Space.

Increasing the variety of antimicrobial peptides is crucial in meeting the global challenge of multi-drug-resistant bacterial pathogens. While several deep-learning-based peptide design pipelines are reported, they may not be optimal in data efficiency. High efficiency requires a well-compressed latent space, where optimization is likely to fail due to numerous local minima.

Probing Conformational Dynamics of Antibodies with Geometric Simulations.

This chapter describes the application of constrained geometric simulations for prediction of antibody structural dynamics. We utilize constrained geometric simulations method FRODAN, which is a low computational complexity alternative to molecular dynamics (MD) simulations that can rapidly explore flexible motions in protein structures. FRODAN is highly suited for conformational dynamics analysis of large proteins, complexes, intrinsically disordered proteins, and dynamics that occurs on longer biologically relevant time scales that are normally inaccessible to classical MD simulations.

Low pH structure of heliorhodopsin reveals chloride binding site and intramolecular signaling pathway.

Within the microbial rhodopsin family, heliorhodopsins (HeRs) form a phylogenetically distinct group of light-harvesting retinal proteins with largely unknown functions. We have determined the 1.97 Å resolution X-ray crystal structure of Thermoplasmatales archaeon SG8-52-1 heliorhodopsin (TaHeR) in the presence of NaCl under acidic conditions (pH 4.

Site Density Functional Theory and Structural Bioinformatics Analysis of the SARS-CoV Spike Protein and hACE2 Complex.

The entry of the SARS-CoV-2, a causative agent of COVID-19, into human host cells is mediated by the SARS-CoV-2 spike (S) glycoprotein, which critically depends on the formation of complexes involving the spike protein receptor-binding domain (RBD) and the human cellular membrane receptor angiotensin-converting enzyme 2 (hACE2). Using classical site density functional theory (SDFT) and structural bioinformatics methods, we investigate binding and conformational properties of these complexes and study the overlooked role of water-mediated interactions. Analysis of the three-dimensional reference interaction site model (3DRISM) of SDFT indicates that water mediated interactions in the form of additional water bridges strongly increases the binding between SARS-CoV-2 spike protein and hACE2 compared to SARS-CoV-1-hACE2 complex.

Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks.

Antimicrobial peptides are a potential solution to the threat of multidrug-resistant bacterial pathogens. Recently, deep generative models including generative adversarial networks (GANs) have been shown to be capable of designing new antimicrobial peptides. Intuitively, a GAN controls the probability distribution of generated sequences to cover active peptides as much as possible.