Protein structural robustness to mutations: an in silico investigation

Connected Component Analysis of Dynamical Perturbation Contact Networks

Describing protein dynamical networks through amino acid contacts is a powerful way to analyze complex biomolecular systems. However, due to the size of the systems, identifying the relevant features of protein-weighted graphs can be a difficult task. To address this issue, we present the connected component analysis (CCA) approach that allows for fast, robust, and unbiased analysis of dynamical perturbation contact networks (DPCNs). We first illustrate the CCA method as applied to a prototypical allosteric enzyme, the imidazoleglycerol phosphate synthase (IGPS) enzyme from Thermotoga maritima bacteria. This approach was shown to outperform the clustering methods applied to DPCNs, which could not capture the propagation of the allosteric signal within the protein graph. On the other hand, CCA reduced the DPCN size, providing connected components that nicely describe the allosteric propagation of the signal from the effector to the active sites of the protein. By applying the CCA to the IGPS enzyme in different conditions, i.e., at high temperature and from another organism (yeast IGPS), and to a different enzyme, i.e., a protein kinase, we demonstrated how CCA of DPCNs is an effective and transferable tool that facilitates the analysis of protein-weighted networks.

GCAT: A network model of mutational influences between amino acid positions in PSD95pdz3

Proteins exist for more than 3 billion years: proof of a sustainable design. They have mechanisms coping with internal perturbations (e.g., amino acid mutations), which tie genetic backgrounds to diseases or drug therapy failure. One difficulty to grasp these mechanisms is the asymmetry of amino acid mutational impact: a mutation at position i in the sequence, which impact a position j does not imply that the mutation at position j impacts the position i. Thus, to distinguish the influence of the mutation of i on j from the influence of the mutation of j on i, position mutational influences must be represented with directions. Using the X ray structure of the third PDZ domain of PDS-95 (Protein Data Bank 1BE9) and in silico mutations, we build a directed network called GCAT that models position mutational influences. In the GCAT, a position is a node with edges that leave the node (out-edges) for the influences of the mutation of the position on other positions and edges that enter the position (in-edges) for the influences of the mutation of other positions on the position. 1BE9 positions split into four influence categories called G, C, A and T going from positions influencing on average less other positions and influenced on average by less other positions (category C) to positions influencing on average more others positions and influenced on average by more other positions (category T). The four categories depict position neighborhoods in the protein structure with different tolerance to mutations.

Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.

Linking protein structural and functional change to mutation using amino acid networks

The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.

A computational methodology to diagnose sequence-variant dynamic perturbations by comparing atomic protein structures

MOTIVATION

The objective is to diagnose dynamics perturbations caused by amino-acid mutations as prerequisite to assess protein functional health or drug failure, simply using network models of protein X-ray structures.

RESULTS

We find that the differences in the allocation of the atomic interactions of each amino acid to 1D, 2D, 3D, 4D structural levels between variants structurally robust, recover experimental dynamic perturbations. The allocation measure validated on two B-pentamers variants of AB5 toxins having 17 mutations, also distinguishes dynamic perturbations of pathogenic and non-pathogenic Transthyretin single-mutants. Finally, the main proteases of the coronaviruses SARS-CoV and SARS-CoV-2 exhibit changes in the allocation measure, raising the possibility of drug failure despite the main proteases structural similarity.

AVAILABILITY AND IMPLEMENTATION

The Python code used for the production of the results is available at github.com/lorpac/protein_partitioning_atomic_contacts. The authors will run the analysis on any PDB structures of protein variants upon request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Activation-Induced Reorganization of Energy Transport Networks in the β2 Adrenergic Receptor

We compute energy exchange networks (EENs) through the β₂ adrenergic receptor (β₂AR), a G-protein coupled receptor (GPCR), in inactive and active states, based on the results of molecular dynamics simulations of this membrane bound protein. We introduce a new definition for the reorganization of EENs upon activation that depends on the relative change in rates of energy transfer across noncovalent contacts throughout the protein. On the basis of the reorganized network that we obtain for β₂AR upon activation, we identify a branched pathway between the agonist binding site and the cytoplasmic region, where a G-protein binds to the receptor when activated. The pathway includes all of the motifs containing molecular switches previously identified as contributing to the allosteric transition of β₂AR upon agonist binding. EENs and their reorganization upon activation are compared with structure-based contact networks computed for the inactive and active states of β₂AR.

Topology Results on Adjacent Amino Acid Networks of Oligomeric Proteins

In this chapter, we focus on topology measurements of the adjacent amino acid networks for a data set of oligomeric proteins and some of its subnetworks. The aim is to present many mathematical tools in order to understand the structures of proteins implicitly coded in such networks and subnetworks. We mainly investigate four important networks by computing the number of connected components, the degree distribution, and assortativity measures. We compare each result in order to prove that the four networks have quite independent topologies.

Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

BACKGROUND

How, and the extent to which, evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability is a long-standing open question in the field of molecular evolution. We addressed this issue through the first structurome-scale computational investigation, in which we estimated the change in folding free energy upon all possible single-site mutations introduced in more than 20,000 protein structures, as well as through available experimental stability and fitness data.

RESULTS

At the amino acid level, we found the protein surface to be more robust against random mutations than the core, this difference being stronger for small proteins. The destabilizing and neutral mutations are more numerous in the core and on the surface, respectively, whereas the stabilizing mutations are about 4% in both regions. At the genetic code level, we observed smallest destabilization for mutations that are due to substitutions of base III in the codon, followed by base I, bases I+III, base II, and other multiple base substitutions. This ranking highly anticorrelates with the codon-anticodon mispairing frequency in the translation process. This suggests that the standard genetic code is optimized to limit the impact of random mutations, but even more so to limit translation errors. At the codon level, both the codon usage and the usage bias appear to optimize mutational robustness and translation accuracy, especially for surface residues.

CONCLUSION

Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data.

Analysis of Nanoconfined Protein Dielectric Signals Using Charged Amino Acid Network Models

Protein slow motions involving collective molecular fluctuations on the timescale of microseconds to seconds are difficult to measure and not well understood despite being essential to sustain protein folding and protein function. Broadband dielectric spectroscopy (BDS) is one of the most powerful experimental techniques to monitor, over a broad frequency and temperature range, the molecular dynamics of soft matter through the orientational polarisation of permanent dipole moments that are generated by the chemical structure and morphological organisation of matter. Its typical frequency range goes from 107 Hz down to 10−3 Hz, being thus suitable for investigations on slow motions in proteins. Moreover, BDS has the advantage of providing direct experimental access to molecular fluctuations taking place on different length-scales, from local to cooperative dipolar motions. The unfolding of the cholera toxin B pentamer (CtxB5) after thermal treatment for 3h at 80°C is investigated by BDS under nanoconfined and dehydrated conditions. From the X-ray structure of the toxin pentamer, network-based models are used to infer the toxin dipoles present in the native state and to compute their stability and dielectric properties. Network analyses highlight three domains with distinct dielectric and stability properties that support a model where the toxin unfolds into three conformations after the treatment at 80°C. This novel integrative approach offers some perspective into the investigation of the relation between local perturbations (e.g. mutation, thermal treatment) and larger scale protein conformational changes. It might help ranking protein sequence variants according to their respective scale of dynamics perturbations.

Experimental Protein Molecular Dynamics: Broadband Dielectric Spectroscopy coupled with nanoconfinement

Protein dynamics covers multiple spatiotemporal scale processes, among which slow motions, not much understood even though they are underlying protein folding and protein functions. Protein slow motions are associated with structural heterogeneity, short-lived and poorly populated conformations, hard to detect individually. In addition, they involve collective motions of many atoms, not easily tracked by simulation and experimental devices. Here we propose a biophysical approach, coupling geometrical nanoconfinement and broadband dielectric spectroscopy (BDS), which distinguishes protein conformations by their respective molecular dynamics. In particular, protein-unfolding intermediates, usually poorly populated in macroscopic solutions are detected. The protein dynamics is observed under unusual conditions (sample nanoconfinement and dehydration) highlighting the robustness of protein structure and protein dynamics to a variety of conditions consistent with protein sustainability. The protein dielectric signals evolve with the temperature of thermal treatments indicating sensitivity to atomic and molecular interaction changes triggered by the protein thermal unfolding. As dipole fluctuations depend on both collective large-scale motions and local motions, the approach offers a prospect to track in-depth unfolding events.

Engineering Order and Cooperativity in a Disordered Protein

Structural disorder in proteins arises from a complex interplay between weak hydrophobicity and unfavorable electrostatic interactions. The extent to which the hydrophobic effect contributes to the unique and compact native state of proteins is, however, confounded by large compensation between multiple entropic and energetic terms. Here we show that protein structural order and cooperativity arise as emergent properties upon hydrophobic substitutions in a disordered system with non-intuitive effects on folding and function. Aided by sequence-structure analysis, equilibrium, and kinetic spectroscopic studies, we engineer two hydrophobic mutations in the disordered DNA-binding domain of CytR that act synergistically, but not in isolation, to promote structure, compactness, and stability. The double mutant, with properties of a fully ordered domain, exhibits weak cooperativity with a complex and rugged conformational landscape. The mutant, however, binds cognate DNA with an affinity only marginally higher than that of the wild type, though nontrivial differences are observed in the binding to noncognate DNA. Our work provides direct experimental evidence of the dominant role of non-additive hydrophobic effects in shaping the molecular evolution of order in disordered proteins and vice versa, which could be generalized to even folded proteins with implications for protein design and functional manipulation.

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

In proteins, the structural responses of a position to mutation rely on the Goldilocks principle: not too many links, not too few

A disease has distinct genetic and molecular hallmarks such as sequence variants that are likely to produce the alternative protein structures accountable for individual responses to drugs and disease development.

Computational study to understand the energy transfer pathways within amicyanin

Vibrational energy diffusivities between the residues present in Amicyanin copper protein are calculated and presented in form of communication map. From those results energy flow pathways from the copper metal ion to the inter protein residue Glu31 are identified. Our finding suggests many different pathways are possible and copper metal ion in oxidized and reduced state switches the pathways. Our finding also suggests the cooperative nature of surrounding residues and water molecules towards selecting the pathways. The major transport channels in the oxidised state are, Cu²⁺---> MET28---> LYS29---> TYR30---> GLU31 and Cu²⁺---> MET98---> TYR30--- > GLU31. And in the reduced state Cu⁺---> CYS9---> TYR30---> GLU31 and Cu⁺---> MET28---> LYS2---> TYR30---> GLU31. We studied further the interaction energies between the copper ion and neighbouring residues using B3LYP/QZVP method. Both the methods complement each other in predicting the energy flow pathways and the cooperative nature of residues.

Water-Mediated Energy Dynamics in a Homodimeric Hemoglobin

We examine energy dynamics in the unliganded and liganded states of the homodimeric hemoglobin from Scapharca inaequivalvis (HbI), which exhibits cooperativity mediated by the cluster of water molecules at the interface upon ligand binding and dissociation. We construct and analyze a dynamic network in which nodes representing the residues, hemes, and water cluster are connected by edges that represent energy transport times, as well as a nonbonded network (NBN) indicating regions that respond rapidly to local strain within the protein via nonbonded interactions. One of the two largest NBNs includes the Lys30-Asp89 salt bridge critical for stabilizing the dimer. The other includes the hemes and surrounding residues, as well as, in the unliganded state, the cluster of water molecules between the globules. Energy transport in the protein appears to be controlled by the Lys30-Asp89 salt bridge critical for stabilizing the dimer, as well as the interface water cluster in the unliganded state. Possible connections between energy transport dynamics in response to local strain identified here and allosteric transitions in HbI are discussed.