Protein structure prediction: do hydrogen bonding and water-mediated interactions suffice?

Copper Forms a PPII Helix-Like Structure with the Catalytic Domains of Bacterial Zinc Metalloproteases

The rapid spread of antibiotic-resistant bacteria continuously raises concerns about the future ineffectiveness of current antimicrobial treatments against infectious diseases. To address this problem, new therapeutic strategies and antimicrobial drugs with unique modes of action are urgently needed. Inhibition of metalloproteases, bacterial virulence factors, is a promising target for the development of antibacterial treatments. In this study, the interaction among Zn(II), Cu(II), and the metal-binding domains of two metalloproteases, AprA (Pseudomonas aureginosa) and CpaA (Acinetobacter baumanii), was investigated. The objective was to determine the coordination sphere of Zn(II) with a peptide model of two zinc-dependent metalloproteases. Additionally, the study explored the formation of Cu(II) complexes with the domains, as Cu(II) has been shown to inhibit metalloproteases. The third aim was to understand the role of nonbinding amino acids in stabilizing the metal complexes formed by these proteases. This work identified specific coordination patterns (HExxHxxxxxH) for both Zn(II) and Cu(II) complexes, with AprA and CpaA exhibiting a higher affinity for Cu(II) compared to Zn(II). The study also found that the CpaA domain has greater stability for both Zn(II) and Cu(II) complexes compared to AprA. The nonbinding amino acids of CpaA surrounding the metal ion contribute to the increased thermodynamic stability of the metal-peptide complex through various intramolecular interactions. These interactions can also influence the secondary structures of the peptides. The presence of certain amino acids, such as tyrosine, arginine, and glutamic acid, and their interactions contribute to the stability and, only in the case of Cu(II) complexes, the formation of a rare protein structure called a left-handed polyproline II helix (PPII), which is known to play a role in the stability and function of various proteins. These findings provide valuable insights into the coordination chemistry of bacterial metalloproteases and expand our understanding of potential mechanisms for inhibiting these enzymes.

Substrate hydrophobicity and enzyme modifiers play a major role in the activity of lipase fromThermomyces lanuginosus

Lipase fromThermomyces lanuginosusdisplays high affinity for long-chain substrates. The chemical modification of this lipase with isothiocyanates and aldehydes was explored to broadening its specificity to chain-length differentiated substrates.

Learning To Fold Proteins Using Energy Landscape Theory

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation.

Coevolutionary signals across protein lineages help capture multiple protein conformations

A long-standing problem in molecular biology is the determination of a complete functional conformational landscape of proteins. This includes not only proteins' native structures, but also all their respective functional states, including functionally important intermediates. Here, we reveal a signature of functionally important states in several protein families, using direct coupling analysis, which detects residue pair coevolution of protein sequence composition. This signature is exploited in a protein structure-based model to uncover conformational diversity, including hidden functional configurations. We uncovered, with high resolution (mean ~1.9 Å rmsd for nonapo structures), different functional structural states for medium to large proteins (200-450 aa) belonging to several distinct families. The combination of direct coupling analysis and the structure-based model also predicts several intermediates or hidden states that are of functional importance. This enhanced sampling is broadly applicable and has direct implications in protein structure determination and the design of ligands or drugs to trap intermediate states.

Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface

Background

Despite computational challenges, elucidating conformations that a protein system assumes under physiologic conditions for the purpose of biological activity is a central problem in computational structural biology. While these conformations are associated with low energies in the energy surface that underlies the protein conformational space, few existing conformational search algorithms focus on explicitly sampling low-energy local minima in the protein energy surface.

Methods

This work proposes a novel probabilistic search framework, PLOW, that explicitly samples low-energy local minima in the protein energy surface. The framework combines algorithmic ingredients from evolutionary computation and computational structural biology to effectively explore the subspace of local minima. A greedy local search maps a conformation sampled in conformational space to a nearby local minimum. A perturbation move jumps out of a local minimum to obtain a new starting conformation for the greedy local search. The process repeats in an iterative fashion, resulting in a trajectory-based exploration of the subspace of local minima.

Results and conclusions

The analysis of PLOW's performance shows that, by navigating only the subspace of local minima, PLOW is able to sample conformations near a protein's native structure, either more effectively or as well as state-of-the-art methods that focus on reproducing the native structure for a protein system. Analysis of the actual subspace of local minima shows that PLOW samples this subspace more effectively that a naive sampling approach. Additional theoretical analysis reveals that the perturbation function employed by PLOW is key to its ability to sample a diverse set of low-energy conformations. This analysis also suggests directions for further research and novel applications for the proposed framework.

AWSEM-MD: protein structure prediction using coarse-grained physical potentials and bioinformatically based local structure biasing

The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained protein force field. AWSEM contains physically motivated terms, such as hydrogen bonding, as well as a bioinformatically based local structure biasing term, which efficiently takes into account many-body effects that are modulated by the local sequence. When combined with appropriate local or global alignments to choose memories, AWSEM can be used to perform de novo protein structure prediction. Herein we present structure prediction results for a particular choice of local sequence alignment method based on short residue sequences called fragments. We demonstrate the model's structure prediction capabilities for three levels of global homology between the target sequence and those proteins used for local structure biasing, all of which assume that the structure of the target sequence is not known. When there are no homologues in the database of structures used for local structure biasing, AWSEM calculations produce structural predictions that are somewhat improved compared with prior works using related approaches. The inclusion of a small number of structures from homologous sequences improves structure prediction only marginally, but when the fragment search is restricted to only homologous sequences, AWSEM can perform high resolution structure prediction and can be used for kinetics and dynamics studies.