CNA web server: rigidity theory-based thermal unfolding simulations of proteins for linking structure, (thermo-)stability, and function

Structural Rigidity and Protein Thermostability in Variants of Lipase A from Bacillus subtilis

Understanding the origin of thermostability is of fundamental importance in protein biochemistry. Opposing views on increased or decreased structural rigidity of the folded state have been put forward in this context. They have been related to differences in the temporal resolution of experiments and computations that probe atomic mobility. Here, we find a significant (p = 0.004) and fair (R² = 0.46) correlation between the structural rigidity of a well-characterized set of 16 mutants of lipase A from Bacillus subtilis (BsLipA) and their thermodynamic thermostability. We apply the rigidity theory-based Constraint Network Analysis (CNA) approach, analyzing directly and in a time-independent manner the statics of the BsLipA mutants. We carefully validate the CNA results on macroscopic and microscopic experimental observables and probe for their sensitivity with respect to input structures. Furthermore, we introduce a robust, local stability measure for predicting thermodynamic thermostability. Our results complement work that showed for pairs of homologous proteins that raising the structural stability is the most common way to obtain a higher thermostability. Furthermore, they demonstrate that related series of mutants with only a small number of mutations can be successfully analyzed by CNA, which suggests that CNA can be applied prospectively in rational protein design aimed at higher thermodynamic thermostability.

A virtual pebble game to ensemble average graph rigidity

Background

The body-bar Pebble Game (PG) algorithm is commonly used to calculate network rigidity properties in proteins and polymeric materials. To account for fluctuating interactions such as hydrogen bonds, an ensemble of constraint topologies are sampled, and average network properties are obtained by averaging PG characterizations. At a simpler level of sophistication, Maxwell constraint counting (MCC) provides a rigorous lower bound for the number of internal degrees of freedom (DOF) within a body-bar network, and it is commonly employed to test if a molecular structure is globally under-constrained or over-constrained. MCC is a mean field approximation (MFA) that ignores spatial fluctuations of distance constraints by replacing the actual molecular structure by an effective medium that has distance constraints globally distributed with perfect uniform density.

Results

The Virtual Pebble Game (VPG) algorithm is a MFA that retains spatial inhomogeneity in the density of constraints on all length scales. Network fluctuations due to distance constraints that may be present or absent based on binary random dynamic variables are suppressed by replacing all possible constraint topology realizations with the probabilities that distance constraints are present. The VPG algorithm is isomorphic to the PG algorithm, where integers for counting “pebbles” placed on vertices or edges in the PG map to real numbers representing the probability to find a pebble. In the VPG, edges are assigned pebble capacities, and pebble movements become a continuous flow of probability within the network. Comparisons between the VPG and average PG results over a test set of proteins and disordered lattices demonstrate the VPG quantitatively estimates the ensemble average PG results well.

Conclusions

The VPG performs about 20% faster than one PG, and it provides a pragmatic alternative to averaging PG rigidity characteristics over an ensemble of constraint topologies. The utility of the VPG falls in between the most accurate but slowest method of ensemble averaging over hundreds to thousands of independent PG runs, and the fastest but least accurate MCC.

γS-crystallin proteins from the Antarctic nototheniid toothfish: a model system for investigating differential resistance to chemical and thermal denaturation

The γS1- and γS2-crystallins, structural eye lens proteins from the Antarctic toothfish (Dissostichus mawsoni), are homologues of the human lens protein γS-crystallin. Although γS1 has the higher thermal stability of the two, it is more susceptible to chemical denaturation by urea. The lower thermodynamic stability of both toothfish crystallins relative to human γS-crystallin is consistent with the current picture of how proteins from organisms endemic to perennially cold environments have achieved low-temperature functionality via greater structural flexibility. In some respects, the sequences of γS1- and γS2-crystallin are typical of psychrophilic proteins; however, their amino acid compositions also reflect their selection for a high refractive index increment. Like their counterparts in the human lens and those of mesophilic fish, both toothfish crystallins are relatively enriched in aromatic residues and methionine and exiguous in aliphatic residues. The sometimes contradictory requirements of selection for cold tolerance and high refractive index make the toothfish crystallins an excellent model system for further investigation of the biophysical properties of structural proteins.

Vibrational entropy differences between mesophile and thermophile proteins and their use in protein engineering

We recently introduced ENCoM, an elastic network atomic contact model, as the first coarse-grained normal mode analysis method that accounts for the nature of amino acids and can predict the effect of mutations on thermostability based on changes vibrational entropy. In this proof-of-concept article, we use pairs of mesophile and thermophile homolog proteins with identical structures to determine if a measure of vibrational entropy based on normal mode analysis can discriminate thermophile from mesophile proteins. We observe that in around 60% of cases, thermophile proteins are more rigid at equivalent temperatures than their mesophile counterpart and this difference can guide the design of proteins to increase their thermostability through series of mutations. We observe that mutations separating thermophile proteins from their mesophile orthologs contribute independently to a decrease in vibrational entropy and discuss the application and implications of this methodology to protein engineering.

Molecular characterization of chicken class II transactivator gene

Class II transactivator (CIITA) is an effective transcriptional factor regulating various genes in the immune system. Since the detection of CIITA in 1993, there has been considerable progress toward understanding its role as an activator of MHC II genes in human and mouse; however, there is little knowledge of this gene in other animals such as chicken. Molecular characterization of the chicken CIITA gene transcript was performed to determine its sequence and expression in different tissues. The CIITA cDNA was first generated through reverse transcriptase-polymerase chain reaction (RT-PCR) from Cobb chicken spleen cell RNA, using oligonucleotide primers based on the predicted cDNA sequence. The effect of the immune system stimulation on the CIITA gene expression in kidney, liver, thymus, and spleen were assessed by semi-quantitative RT-PCR analysis. A partial cDNA sequence (1,688 bp) encoding part of the NACHT domain followed by seven of the transactivator and one of the NLS domains were obtained. Comparison of the deduced amino acid sequence with other CIITAs reveals high level of similarities in amino acid composition, secondary structure and phosphorylation sites. Furthermore, in comparison to the Red Jungle Fowl (RJF) sequence, we found 17 single nucleotide polymorphisms in Cobb broiler chicken, ten of which were reported for the first time. Gene expression analysis indicated that CIITA RNA amounts increased in all the examined tissues following stimulation with Brucella antigen. This investigation may indicate that CIITA molecule has an important role in the chicken immune responses as well as human and other animals.

Family-wide characterization of matrix metalloproteinases from Arabidopsis thaliana reveals their distinct proteolytic activity and cleavage site specificity

The five recombinant MMP-like proteins of Arabidopsis thaliana have specific biochemical properties. Detailed analysis of their sequence specificity using proteomic identification of protease cleavage sites revealed cleavage profiles similar to human MMPs.

Efficient and robust analysis of biomacromolecular flexibility using ensembles of network topologies based on fuzzy noncovalent constraints

We describe an approach (ENT(FNC)) for performing rigidity analyses of biomacromolecules on ensembles of network topologies (ENT) generated from a single input structure. The ENT is based on fuzzy noncovalent constraints, which considers thermal fluctuations of biomacromolecules without actually sampling conformations. Definitions for fuzzy noncovalent constraints were derived from persistency data from molecular dynamics (MD) simulations. A very good agreement between local flexibility and rigidity characteristics from ENT(FNC) and MD simulations-generated ensembles is found. Regarding global characteristics, convincing results were obtained when relative thermostabilities of citrate synthase and lipase A structures were computed. The ENT(FNC) approach significantly improves the robustness of rigidity analyses, is highly efficient, and does not require a protein-specific parameterization. Its low computational demand makes it especially valuable for the analysis of large data sets, e.g., for data-driven protein engineering.

Constraint Network Analysis (CNA): a Python software package for efficiently linking biomacromolecular structure, flexibility, (thermo-)stability, and function

For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity analyses in that phase transition points ("melting points") and unfolding nuclei ("structural weak spots") are determined automatically. Furthermore, CNA robustly handles small-molecule ligands in general. Such advancements are important for applying rigidity analysis to data-driven protein engineering and for estimating the influence of ligand molecules on biomacromolecular stability. CNA maintains the efficiency of FIRST such that the analysis of a single protein structure takes a few seconds for systems of several hundred residues on a single core. These features make CNA an interesting tool for linking biomacromolecular structure, flexibility, (thermo-)stability, and function. CNA is available from http://cpclab.uni-duesseldorf.de/software for nonprofit organizations.