Here, we introduce the third release of Kalium database (http://kaliumdb.org/), a manually curated comprehensive depository that accumulates data on polypeptide ligands of potassium channels. The major goal of this amplitudinous update is to summarize findings for natural polypeptide ligands of K+ channels, as well as data for the artificial derivatives of these substances obtained over the decades of exploration. We manually analyzed more than 700 original manuscripts and systematized the information on mutagenesis, production of radio- and fluorescently labeled derivatives, and the molecular pharmacology of K+ channel ligands. In result, data on more than 1200 substances were processed and added enriching the database content fivefold. We also included the electrophysiological data obtained on the understudied and neglected K+ channels including the heteromeric and concatenated channels. We associated target channels in Kalium with corresponding entries in the official database of the International Union of Basic and Clinical Pharmacology (IUPHAR). Kalium was supplemented with an adaptive Statistics page, where users are able to obtain actual data output. Several other improvements were introduced, such as a color code to distinguish the range of ligand activity concentrations and advanced tools for filtration and sorting. Kalium is a fully open-access database, crosslinked to other databases of interest. It can be utilized as a convenient resource containing ample up-to-date information about polypeptide ligands of K+ channels. This article is protected by copyright. All rights reserved.

We have applied our advanced computational and experimental methodologies to investigate the complex structure and binding mechanism of a modified Wilms' Tumor 1 (mWT1) protein epitope to the understudied Asian-dominant allele HLA-A*24:02 (HLA-A24) in aqueous solution. We have applied our developed multicanonical molecular dynamics (McMD)-based dynamic docking method to analyze the binding pathway and mechanism, which we verified by comparing the highest probability structures from simulation with our experimentally solved X-ray crystal structure. Subsequent path sampling MD simulations elucidated the atomic details of the binding process and indicated that first an encounter complex is formed between the N-terminal's positive charge of the 9-residue mWT1 fragment peptide and a cluster of negative residues on the surface of HLA-A24, with the MHC molecule preferring a predominantly closed conformation. The peptide first binds to this closed MHC conformation, forming an encounter complex, after which the binding site opens due to increased entropy of the binding site, allowing the peptide to bind to form the native complex structure. Further sequence and structure analyses also suggest that although the peptide loading complex would help with stabilizing the MHC molecule, the binding depends in a large part on the intrinsic affinity between the MHC molecule and the antigen peptide. Finally, our computational tools and analyses can be of great benefit to study the binding mechanism of different MHC types to their antigens, where it could also be useful in the development of higher affinity variant peptides and for personalized medicine. This article is protected by copyright. All rights reserved.

Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RFjoint , for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RFjoint to both another zero-shot model (MSA Transformer) and a model which requires specific training on a particular protein family for mutation effect prediction (DeepSequence). Thus, although the architecture of RFjoint was developed to address the protein design problem of scaffolding functional motifs, RFjoint acquired an understanding of the mutational landscapes of proteins during model training that is equivalent to that of recently developed large protein language models. The ability to simultaneously reason over protein structure and sequence could enable even more precise mutation effect predictions following supervised training on the task. These results suggest that RFjoint has a quite broad understanding of protein sequence-structure landscapes, and can be viewed as a joint model for protein sequence and structure which could be broadly useful for protein modeling. This article is protected by copyright. All rights reserved.

In 2023, the Antimicrobial Peptide Database (currently available at https://aps.unmc.edu) is 20 years old. The timeline for the APD expansion in peptide entries, classification methods, search functions, post-translational modifications, binding targets, and mechanisms of action of antimicrobial peptides (AMPs) has been summarized in our previous Protein Science paper. This article highlights new database additions and findings. To facilitate antimicrobial development to combat drug-resistant pathogens, the APD has been re-annotating the data for antibacterial activity (active, inactive, and uncertain), toxicity (hemolytic and non-hemolytic AMPs), and salt tolerance (salt sensitive and insensitive). Comparison of the respective desired and undesired AMP groups produces new knowledge for peptide design. Our unification of AMPs from the six life kingdoms into "natural AMPs" enabled the first comparison with globular or transmembrane proteins. Due to the dominance of amphipathic helical and disulfide-linked peptides, cysteine, glycine, and lysine in natural AMPs are much more abundant than those in globular proteins. To include peptides predicted by machine learning, a new "predicted" group has been created. Remarkably, the averaged amino acid composition of predicted peptides is located between the lower bound of natural AMPs and the upper bound of synthetic peptides. Synthetic peptides in the current APD, with the highest cationic and hydrophobic amino acid percentages, are mostly designed with varying degrees of optimization. Hence, natural AMPs accumulated in the APD over 20 years have laid the foundation for machine learning prediction. We discuss future directions for peptide discovery. It is anticipated that the APD will continue to play a role in research and education. This article is protected by copyright. All rights reserved.

Malate (2-hydroxysuccinic acid) and tartrate (2,3-dihydroxysuccinic acid) are chiral substrates; the former existing in two enantiomeric forms (R and S) while the latter exists as three stereoisomers (R,R; S,S; and R,S). Dehydration by stereospecific hydrogen abstraction and anti-elimination of the hydroxyl group yield the achiral products fumarate and oxaloacetate, respectively. Class-I fumarate hydratase (FH) and L-tartrate dehydratase (L-TTD) are two highly conserved enzymes belonging to the iron-sulfur cluster hydrolyase family of enzymes that catalyze reactions on specific stereoisomers of malate and tartrate. FH from Methanocaldococcus jannaschii accepts only S-malate and S,S-tartrate as substrates while the structurally similar L-TTD from Escherichia coli accepts only R-malate and R,R-tartrate as substrates. Phylogenetic analysis reveals a common evolutionary origin of L-TTDs and two-subunit archaeal FHs suggesting a divergence during evolution that may have led to the switch in substrate stereospecificity preference. Due to the high conservation of their sequences, a molecular basis for switch in stereospecificity is not evident from analysis of crystal structures of FH and predicted structure of L-TTD. The switch in enantiomer preference may be rationalised by invoking conformational plasticity of the amino acids interacting with the substrate, together with substrate reorientation and conformer selection about the C2-C3 bond of the dicarboxylic acid substrates. Although classical models of enzyme-substrate binding are insufficient to explain such a phenomenon, the enantiomer superposition model suggests that a minor reorientation in the active site residues could lead to the switch in substrate stereospecificity. This article is protected by copyright. All rights reserved.

Small-molecule responsive protein switches are powerful tools for controlling cellular processes. These switches are designed to respond rapidly and specifically to their inducer. They have been used in numerous applications, including the regulation of gene expression, post-translational protein modification, and signal transduction. Typically, small-molecule responsive protein switches consist of two proteins that interact with each other in the presence or absence of a small-molecule. Recent advances in computational protein design already contributed to the development of protein switches with an expanded range of small-molecule inducers and increasingly sophisticated switch mechanisms. Further progress in the engineering of small-molecule responsive switches is fueled by cutting-edge computational design approaches, which will enable more complex and precise control over cellular processes and advance synthetic biology applications in biotechnology and medicine. Here, we discuss recent milestones and how technological advances are impacting the development of chemical switches. This article is protected by copyright. All rights reserved.

Recent studies have suggested that neuropilin-1 (NRP1) may serve as a potential receptor in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. However, the biophysical characteristics of interactions between NRP1 and SARS-CoV-2 remain unclear. In this study, we examined the interactions between NRP1 and various SARS-CoV-2 spike (S) fragments, including the receptor-binding domain (RBD) and the S protein trimer in a soluble form or expressed on pseudovirions, using atomic force microscopy (AFM) and structural modeling. Our measurements showed that NRP1 interacts with the RBD and trimer at a higher binding frequency compared to ACE2. This NRP1-RBD interaction has also been predicted and simulated via AlphaFold2 and molecular dynamic simulations, and the results indicate that their binding patterns are very similar to RBD-ACE2 interactions. Additionally, under similar loading rates, the most probable unbinding forces between NRP1 and S trimer (both soluble form and on pseudovirions) are larger than the forces between NRP1 and RBD and between trimer and ACE2. Further analysis indicates that NRP1 has a stronger binding affinity to the SARS-CoV-2 S trimer with a dissociation rate of 0.87 s-1 , four times lower than the dissociation rate of 3.65 s-1 between NRP1 and RBD. Moreover, additional experiments show that RBD-neutralizing antibodies can significantly reduce the binding frequency for both ACE2 and NRP1. Together, the study suggests that NRP1 can be an alternative receptor for SARS-CoV-2 attachment to human cells, and the neutralizing antibodies targeting SARS-CoV-2 RBD can reduce the binding between SARS-CoV-2 and NRP1. This article is protected by copyright. All rights reserved.