Sensing and signaling of oxidative stress in chloroplasts by inactivation of the SAL1 phosphoadenosine phosphatase

Intracellular signaling during oxidative stress is complex, with organelle-to-nucleus retrograde communication pathways ill-defined or incomplete. Here we identify the 3'-phosphoadenosine 5'-phosphate (PAP) phosphatase SAL1 as a previously unidentified and conserved oxidative stress sensor in plant chloroplasts. Arabidopsis thaliana SAL1 (AtSAL1) senses changes in photosynthetic redox poise, hydrogen peroxide, and superoxide concentrations in chloroplasts via redox regulatory mechanisms. AtSAL1 phosphatase activity is suppressed by dimerization, intramolecular disulfide formation, and glutathionylation, allowing accumulation of its substrate, PAP, a chloroplast stress retrograde signal that regulates expression of plastid redox associated nuclear genes (PRANGs). This redox regulation of SAL1 for activation of chloroplast signaling is conserved in the plant kingdom, and the plant protein has evolved enhanced redox sensitivity compared with its yeast ortholog. Our results indicate that in addition to sulfur metabolism, SAL1 orthologs have evolved secondary functions in oxidative stress sensing in the plant kingdom.

Human Aquaporin-4 and Molecular Modeling: Historical Perspective and View to the Future

Among the different aquaporins (AQPs), human aquaporin-4 (hAQP4) has attracted the greatest interest in recent years as a new promising therapeutic target. Such a membrane protein is, in fact, involved in a multiple sclerosis-like immunopathology called Neuromyelitis Optica (NMO) and in several disorders resulting from imbalanced water homeostasis such as deafness and cerebral edema. The gap of knowledge in its functioning and dynamics at the atomistic level of detail has hindered the development of rational strategies for designing hAQP4 modulators. The application, lately, of molecular modeling has proved able to fill this gap providing a breeding ground to rationally address compounds targeting hAQP4. In this review, we give an overview of the important advances obtained in this field through the application of Molecular Dynamics (MD) and other complementary modeling techniques. The case studies presented herein are discussed with the aim of providing important clues for computational chemists and biophysicists interested in this field and looking for new challenges.

From structural biology to designing therapy for inborn errors of metabolism

At the SSIEM Symposium in Istanbul 2010, I presented an overview of protein structural approaches in the study of inborn errors of metabolism (Yue and Oppermann 2011). Five years on, the field is going strong with new protein structures, uncovered catalytic functions and novel chemical matters for metabolic enzymes, setting the stage for the next generation of drug discovery. This article aims to update on recent advances and lessons learnt on inborn errors of metabolism via the protein-centric approach, citing examples of work from my group, collaborators and co-workers that cover diverse pathways of transsulfuration, cobalamin and glycogen metabolism. Taking into consideration that many inborn errors of metabolism result in the loss of enzyme function, this presentation aims to outline three key principles that guide the design of small molecule therapy in this technically challenging field: (1) integrating structural, biochemical and cell-based data to evaluate the wide spectrum of mutation-driven enzyme defects in stability, catalysis and protein-protein interaction; (2) studying multi-domain proteins and multi-protein complexes as examples from nature, to learn how enzymes are activated by small molecules; (3) surveying different regions of the enzyme, away from its active site, that can be targeted for the design of allosteric activators and inhibitors.

Effects of missense mutations in sortase A gene on enzyme activity in Streptococcus mutans

Background

Streptococcus mutans (S. mutans) is the major aetiological agent of dental caries, and the transpeptidase Sortase A (SrtA) plays a major role in cariogenicity. The T168G and G470A missense mutations in the srtA gene may be linked to caries susceptibility, as demonstrated in our previous studies. This study aimed to investigate the effects of these missense mutations of the srtA gene on SrtA enzyme activity in S. mutans.

Methods

The point mutated recombinant S.mutans T168G and G470A sortases were expressed in expression plasmid pET32a. S. mutans UA159 sortase coding gene srtA was used as the template for point mutation. Enzymatic activity was assessed by quantifying increases in the fluorescence intensity generated when a substrate Dabcyl-QALPNTGEE-Edans was cleaved by SrtA. The kinetic constants were calculated based on the curve fit for the Michaelis-Menten equation.

Results

SrtA_{△N40(UA159)} and the mutant enzymes, SrtA_△N40(D56E) and SrtA_{△N40(R157H)}, were expressed and purified. A kinetic analysis showed that the affinity of SrtA_△N40(D56E) and SrtA_{△N40(R157H)} remained approximately equal to the affinity of SrtA_{△N40(UA159)}, as determined by the Michaelis constant (K_m). However, the catalytic rate constant (k_cat) and catalytic efficiency (k_cat/K_m) of SrtA_△N40(D56E) were reduced compared with those of SrtA_{△N40(R157H)} and SrtA_{△N40(UA159)}, whereas the k_cat and k_cat/K_m values of SrtA_{△N40(R157H)} were slightly lower than those of SrtA_{△N40(UA159)}.

Conclusions

The findings of this study indicate that the T168G missense mutation of the srtA gene results in a significant reduction in enzymatic activity compared with S. mutans UA159, suggesting that the T168G missense mutation of the srtA gene may be related to low cariogenicity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12903-016-0204-1) contains supplementary material, which is available to authorized users.

Characterization of parasite-specific indels and their proposed relevance for selective anthelminthic drug targeting

Insertions and deletions (indels) are important sequence variants that are considered as phylogenetic markers that reflect evolutionary adaptations in different species. In an effort to systematically study indels specific to the phylum Nematoda and their structural impact on the proteins bearing them, we examined over 340,000 polypeptides from 21 nematode species spanning the phylum, compared them to non-nematodes and identified indels unique to nematode proteins in more than 3000 protein families. Examination of the amino acid composition revealed uneven usage of amino acids for insertions and deletions. The amino acid composition and cost, along with the secondary structure constitution of the indels, were analyzed in the context of their biological pathway associations. Species-specific indels could enable indel-based targeting for drug design in pathogens/parasites. Therefore, we screened the spatial locations of the indels in the parasite's protein 3D structures, determined the location of the indel and identified potential unique drug targeting sites. These indels could be confirmed by RNA-Seq data. Examples are presented illustrating the close proximity of some indels to established small-molecule binding pockets that can potentially facilitate selective targeting to the parasites and bypassing their host, thus reducing or eliminating the toxicity of the potential drugs. This study presents an approach for understanding the adaptation of pathogens/parasites at a molecular level, and outlines a strategy to identify such nematode-selective targets that remain essential to the organism. With further experimental characterization and validation, it opens a possible channel for the development of novel treatments with high target specificity, addressing both host toxicity and resistance concerns.

PinSnps: structural and functional analysis of SNPs in the context of protein interaction networks

Summary: We present a practical computational pipeline to readily perform data analyses of protein–protein interaction networks by using genetic and functional information mapped onto protein structures. We provide a 3D representation of the available protein structure and its regions (surface, interface, core and disordered) for the selected genetic variants and/or SNPs, and a prediction of the mutants’ impact on the protein as measured by a range of methods. We have mapped in total 2587 genetic disorder-related SNPs from OMIM, 587 873 cancer-related variants from COSMIC, and 1 484 045 SNPs from dbSNP. All result data can be downloaded by the user together with an R-script to compute the enrichment of SNPs/variants in selected structural regions.

Availability and Implementation: PinSnps is available as open-access service at http://fraternalilab.kcl.ac.uk/PinSnps/

Contact:franca.fraternali@kcl.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Subsite-specific contributions of different aromatic residues in the active site architecture of glycoside hydrolase family 12

The active site architecture of glycoside hydrolase (GH) is a contiguous subregion of the enzyme constituted by residues clustered in the three-dimensional space, recognizing the monomeric unit of ligand through hydrogen bonds and hydrophobic interactions. Mutations of the key residues in the active site architecture of the GH12 family exerted different impacts on catalytic efficiency. Binding affinities between the aromatic amino acids and carbohydrate rings were quantitatively determined by isothermal titration calorimetry (ITC) and the quantum mechanical (QM) method, showing that the binding capacity order of Tyr>Trp>His (and Phe) was determined by their side-chain properties. The results also revealed that the binding constant of a certain residue remained unchanged when altering its location, while the catalytic efficiency changed dramatically. Increased binding affinity at a relatively distant subsite, such as the mutant of W7Y at the -4 subsite, resulted in a marked increase in the intermediate product of cellotetraose and enhanced the reactivity of endoglucanase by 144%; while tighter binding near the catalytic center, i.e. W22Y at the -2 subsite, enabled the enzyme to bind and hydrolyze smaller oligosaccharides. Clarification of the specific roles of the aromatics at different subsites may pave the way for a more rational design of GHs.

Pan-Nematoda Transcriptomic Elucidation of Essential Intestinal Functions and Therapeutic Targets With Broad Potential

The nematode intestine is continuous with the outside environment, making it easily accessible to anthelmintics for parasite control, but the development of new therapeutics is impeded by limited knowledge of nematode intestinal cell biology. We established the most comprehensive nematode intestinal functional database to date by generating transcriptional data from the dissected intestines of three parasitic nematodes spanning the phylum, and integrating the results with the whole proteomes of 10 nematodes (including 9 pathogens of humans or animals) and 3 host species and 2 outgroup species. We resolved 10,772 predicted nematode intestinal protein families (IntFams), and studied their presence and absence within the different lineages (births and deaths) among nematodes. Conserved intestinal cell functions representing ancestral functions of evolutionary importance were delineated, and molecular features useful for selective therapeutic targeting were identified. Molecular patterns conserved among IntFam proteins demonstrated large potential as therapeutic targets to inhibit intestinal cell functions with broad applications towards treatment and control of parasitic nematodes.

Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations

Systematic mappings of the effects of protein mutations are becoming increasingly popular. Unexpectedly, these experiments often find that proteins are tolerant to most amino acid substitutions, including substitutions in positions that are highly conserved in nature. To obtain a more realistic distribution of the effects of protein mutations, we applied a laboratory drift comprising 17 rounds of random mutagenesis and selection of M.HaeIII, a DNA methyltransferase. During this drift, multiple mutations gradually accumulated. Deep sequencing of the drifted gene ensembles allowed determination of the relative effects of all possible single nucleotide mutations. Despite being averaged across many different genetic backgrounds, about 67% of all nonsynonymous, missense mutations were evidently deleterious, and an additional 16% were likely to be deleterious. In the early generations, the frequency of most deleterious mutations remained high. However, by the 17th generation, their frequency was consistently reduced, and those remaining were accepted alongside compensatory mutations. The tolerance to mutations measured in this laboratory drift correlated with sequence exchanges seen in M.HaeIII’s natural orthologs. The biophysical constraints dictating purging in nature and in this laboratory drift also seemed to overlap. Our experiment therefore provides an improved method for measuring the effects of protein mutations that more closely replicates the natural evolutionary forces, and thereby a more realistic view of the mutational space of proteins.

Understanding and predicting the effects of single nucleotide polymorphisms (SNPs) is of fundamental importance in many fields. Systematic experimental mappings of the effects of such mutations within a given gene/protein comprise an essential experimental tool for determining protein function and for refining models of protein evolution, as well as an important resource for improving prediction algorithms. Here, we present the results of a laboratory system that mimics the manner by which protein sequences diverge in nature: a prolonged process of gradually accumulating random mutations that retain the protein’s structure and function. The change in frequencies of mutations over generations, as obtained by deep sequencing, enabled us to assess the relative effects of all possible SNPs at the background of an accumulating number of mutations. Compared to previous reports, we found that > 80% of all possible amino acid exchanges have potential deleterious effects, with 67% being clearly deleterious. Tolerance vs. purging of mutations in our prolonged drift also showed better correlation with natural diversity. Overall, our experimental setup provides a better understanding of how protein sequences diverge in nature, plus a new basis for improving the prediction accuracy of the effects of protein mutations, and specifically of SNPs.

Anatomy of protein disorder, flexibility and disease-related mutations

Integration of protein structural information with human genetic variation and pathogenic mutations is essential to understand molecular mechanisms associated with the effects of polymorphisms on protein interactions and cellular processes. We investigate occurrences of non-synonymous SNPs in ordered and disordered protein regions by systematic mapping of common variants and disease-related SNPs onto these regions. We show that common variants accumulate in disordered regions; conversely pathogenic variants are significantly depleted in disordered regions. These different occurrences of pathogenic and common SNPs can be attributed to a negative selection on random mutations in structurally highly constrained regions. New approaches in the study of quantitative effects of pathogenic-related mutations should effectively account for all the possible contexts and relative functional constraints in which the sequence variation occurs.

Understanding the functional difference between growth arrest-specific protein 6 and protein S: an evolutionary approach

Although protein S (PROS1) and growth arrest-specific protein 6 (GAS6) proteins are homologous with a high degree of structural similarity, they are functionally different. The objectives of this study were to identify the evolutionary origins from which these functional differences arose. Bioinformatics methods were used to estimate the evolutionary divergence time and to detect the amino acid residues under functional divergence between GAS6 and PROS1. The properties of these residues were analysed in the light of their three-dimensional structures, such as their stability effects, the identification of electrostatic patches and the identification potential protein-protein interaction. The divergence between GAS6 and PROS1 probably occurred during the whole-genome duplications in vertebrates. A total of 78 amino acid sites were identified to be under functional divergence. One of these sites, Asn463, is involved in N-glycosylation in GAS6, but is mutated in PROS1, preventing this post-translational modification. Sites experiencing functional divergence tend to express a greater diversity of stabilizing/destabilizing effects than sites that do not experience such functional divergence. Three electrostatic patches in the LG1/LG2 domains were found to differ between GAS6 and PROS1. Finally, a surface responsible for protein-protein interactions was identified. These results may help researchers to analyse disease-causing mutations in the light of evolutionary and structural constraints, and link genetic pathology to clinical phenotypes.

Biophysics of protein evolution and evolutionary protein biophysics

The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence-structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by 'hidden' conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

A multiparametric computational algorithm for comprehensive assessment of genetic mutations in mucopolysaccharidosis type IIIA (Sanfilippo syndrome)

Mucopolysaccharidosis type IIIA (MPS-IIIA, Sanfilippo syndrome) is a Lysosomal Storage Disease caused by cellular deficiency of N-sulfoglucosamine sulfohydrolase (SGSH). Given the large heterogeneity of genetic mutations responsible for the disease, a comprehensive understanding of the mechanisms by which these mutations affect enzyme function is needed to guide effective therapies. We developed a multiparametric computational algorithm to assess how patient genetic mutations in SGSH affect overall enzyme biogenesis, stability, and function. 107 patient mutations for the SGSH gene were obtained from the Human Gene Mutation Database representing all of the clinical mutations documented for Sanfilippo syndrome. We assessed each mutation individually using ten distinct parameters to give a comprehensive predictive score of the stability and misfolding capacity of the SGSH enzyme resulting from each of these mutations. The predictive score generated by our multiparametric algorithm yielded a standardized quantitative assessment of the severity of a given SGSH genetic mutation toward overall enzyme activity. Application of our algorithm has identified SGSH mutations in which enzymatic malfunction of the gene product is specifically due to impairments in protein folding. These scores provide an assessment of the degree to which a particular mutation could be treated using approaches such as chaperone therapies. Our multiparametric protein biogenesis algorithm advances a key understanding in the overall biochemical mechanism underlying Sanfilippo syndrome. Importantly, the design of our multiparametric algorithm can be tailored to many other diseases of genetic heterogeneity for which protein misfolding phenotypes may constitute a major component of disease manifestation.

Protein domain-level landscape of cancer-type-specific somatic mutations

Identifying driver mutations and their functional consequences is critical to our understanding of cancer. Towards this goal, and because domains are the functional units of a protein, we explored the protein domain-level landscape of cancer-type-specific somatic mutations. Specifically, we systematically examined tumor genomes from 21 cancer types to identify domains with high mutational density in specific tissues, the positions of mutational hotspots within these domains, and the functional and structural context where possible. While hotspots corresponding to specific gain-of-function mutations are expected for oncoproteins, we found that tumor suppressor proteins also exhibit strong biases toward being mutated in particular domains. Within domains, however, we observed the expected patterns of mutation, with recurrently mutated positions for oncogenes and evenly distributed mutations for tumor suppressors. For example, we identified both known and new endometrial cancer hotspots in the tyrosine kinase domain of the FGFR2 protein, one of which is also a hotspot in breast cancer, and found new two hotspots in the Immunoglobulin I-set domain in colon cancer. Thus, to prioritize cancer mutations for further functional studies aimed at more precise cancer treatments, we have systematically correlated mutations and cancer types at the protein domain level.

Epithelial sodium transport and its control by aldosterone: the story of our internal environment revisited

Transcription and translation require a high concentration of potassium across the entire tree of life. The conservation of a high intracellular potassium was an absolute requirement for the evolution of life on Earth. This was achieved by the interplay of P- and V-ATPases that can set up electrochemical gradients across the cell membrane, an energetically costly process requiring the synthesis of ATP by F-ATPases. In animals, the control of an extracellular compartment was achieved by the emergence of multicellular organisms able to produce tight epithelial barriers creating a stable extracellular milieu. Finally, the adaptation to a terrestrian environment was achieved by the evolution of distinct regulatory pathways allowing salt and water conservation. In this review we emphasize the critical and dual role of Na(+)-K(+)-ATPase in the control of the ionic composition of the extracellular fluid and the renin-angiotensin-aldosterone system (RAAS) in salt and water conservation in vertebrates. The action of aldosterone on transepithelial sodium transport by activation of the epithelial sodium channel (ENaC) at the apical membrane and that of Na(+)-K(+)-ATPase at the basolateral membrane may have evolved in lungfish before the emergence of tetrapods. Finally, we discuss the implication of RAAS in the origin of the present pandemia of hypertension and its associated cardiovascular diseases.

Regulators of carcinogenesis: emerging roles beyond their primary functions

Cancers are characterized by aberrant cell signaling that results in accelerated proliferation, suppressed cell death, and reprogrammed metabolism to provide sufficient energy and intermediate metabolites for macromolecular biosynthesis. Here, we summarize the emerging "unconventional" roles of these regulators based on their newly identified interaction partners, different subcellular localizations, and/or structural variants. For example, the epidermal growth factor receptor (EGFR) regulates DNA synthesis, microRNA maturation and drug resistance by interacting with previously undescribed partners; cyclins and cyclin-dependent kinases (CDKs) crosstalk with multiple canonical pathways by phosphorylating novel substrates or by functioning as transcriptional factors; apoptosis executioners play extensive roles in necroptosis, autophagy, and in the self-renewal of stem cells; and various metabolic enzymes and their mutants control carcinogenesis independently of their enzymatic activity. These recent findings will supplement the systemic functional annotation of cancer regulators and provide new rationales for potential molecular targeted cancer treatments.

Understanding the interaction determinants of CAPN1 inhibition by CAST4 from bovines using molecular modeling techniques

HCV-induced CAPN activation and its effects on virus-infected cells in a host-immune system have been studied recently. It has been shown that the HCV-nonstructural 5A protein acts as both an inducer and a substrate for host CAPN protease; it participates in suppressing the TNF-α-induced apoptosis response and downstream IFN-induced antiviral processes. However, little is known regarding the disturbance of antiviral responses generated by bovine CAPN activation by BVDV, which is a surrogate model of HCV and is one of the most destructive diseases leading to great economic losses in cattle herds worldwide. This is also thought to be associated with the effects of either small CAPN inhibitors or the natural inhibitor CAST. They mainly bind to the binding site of CAPN substrate proteins and competitively inhibit the binding of the enzyme substrates to possibly defend against the two viruses (HCV and BVDV) for anti-viral immunity. To devise a new stratagem to discover lead candidates for an anti-BVDV drug, we first attempted to understand the bovine CAPN-CAST interaction sites and the interaction constraints of local binding architectures, were well reflected in the geometry between the pharmacophore features and its shape constraints identified using our modeled bovine CAPN1/CAST4 complex structures. We propose a computer-aided molecular design of an anti-BVDV drug as a mimetic CAST inhibitor to develop a rule-based screening function for adjusting the puzzle of relationship between bovine CAPN1 and the BVDV nonstructural proteins from all of the data obtained in the study.

Protein-protein interaction networks studies and importance of 3D structure knowledge

Protein-protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes' stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype-phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.

Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues

The active sites of enzymes consist of residues necessary for catalysis and structurally important noncatalytic residues that together maintain the architecture and function of the active site. Examples of evolutionary interactions between catalytic and noncatalytic residues have been difficult to define and experimentally validate due to a general intolerance of these residues to substitution. Here, using computational methods to predict coevolving residues, we identify a network of positions consisting of two catalytic metal-binding residues and two adjacent noncatalytic residues in LAGLIDADG homing endonucleases (LHEs). Distinct combinations of the four residues in the network map to distinct LHE subfamilies, with a striking distribution of the metal-binding Asp (D) and Glu (E) residues. Mutation of these four positions in three LHEs--I-LtrI, I-OnuI, and I-HjeMI--indicate that the combinations of residues tolerated are specific to each enzyme. Kinetic analyses under single-turnover conditions revealed that I-LtrI activity could be modulated over an ∼100-fold range by mutation of residues in the coevolving network. I-LtrI catalytic site variants with low activity could be rescued by compensatory mutations at adjacent noncatalytic sites that restore an optimal coevolving network and vice versa. Our results demonstrate that LHE activity is constrained by an evolutionary barrier of residues with strong context-dependent effects. Creation of optimal coevolving active-site networks is therefore an important consideration in engineering of LHEs and other enzymes.

Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface "hot-spots," but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.

Redistribution of flexibility in stabilizing antibody fragment mutants follows Le Châtelier's principle

Le Châtelier's principle is the cornerstone of our understanding of chemical equilibria. When a system at equilibrium undergoes a change in concentration or thermodynamic state (i.e., temperature, pressure, etc.), La Châtelier's principle states that an equilibrium shift will occur to offset the perturbation and a new equilibrium is established. We demonstrate that the effects of stabilizing mutations on the rigidity ⇔ flexibility equilibrium within the native state ensemble manifest themselves through enthalpy-entropy compensation as the protein structure adjusts to restore the global balance between the two. Specifically, we characterize the effects of mutation to single chain fragments of the anti-lymphotoxin-β receptor antibody using a computational Distance Constraint Model. Statistically significant changes in the distribution of both rigidity and flexibility within the molecular structure is typically observed, where the local perturbations often lead to distal shifts in flexibility and rigidity profiles. Nevertheless, the net gain or loss in flexibility of individual mutants can be skewed. Despite all mutants being exclusively stabilizing in this dataset, increased flexibility is slightly more common than increased rigidity. Mechanistically the redistribution of flexibility is largely controlled by changes in the H-bond network. For example, a stabilizing mutation can induce an increase in rigidity locally due to the formation of new H-bonds, and simultaneously break H-bonds elsewhere leading to increased flexibility distant from the mutation site via Le Châtelier. Increased flexibility within the VH β4/β5 loop is a noteworthy illustration of this long-range effect.

The functional importance of co-evolving residues in proteins

Computational approaches for detecting co-evolution in proteins allow for the identification of protein-protein interaction networks in different organisms and the assignment of function to under-explored proteins. The detection of co-variation of amino acids within or between proteins, moreover, allows for the discovery of residue-residue contacts and highlights functional residues that can affect the binding affinity, catalytic activity, or substrate specificity of a protein. To explore the functional impact of co-evolutionary changes in proteins, a combined experimental and computational approach must be recruited. Here, we review recent studies that apply computational and experimental tools to obtain novel insight into the structure, function, and evolution of proteins. Specifically, we describe the application of co-evolutionary analysis for predicting high-resolution three-dimensional structures of proteins. In addition, we describe computational approaches followed by experimental analysis for identifying specificity-determining residues in proteins. Finally, we discuss studies addressing the importance of such residues in terms of the functional divergence of proteins, allowing proteins to evolve new functions while avoiding crosstalk with existing cellular pathways or forming reproductive barriers and hence promoting speciation.

PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

LenVarDB: database of length-variant protein domains

Protein domains are functionally and structurally independent modules, which add to the functional variety of proteins. This array of functional diversity has been enabled by evolutionary changes, such as amino acid substitutions or insertions or deletions, occurring in these protein domains. Length variations (indels) can introduce changes at structural, functional and interaction levels. LenVarDB (freely available at http://caps.ncbs.res.in/lenvardb/) traces these length variations, starting from structure-based sequence alignments in our Protein Alignments organized as Structural Superfamilies (PASS2) database, across 731 structural classification of proteins (SCOP)-based protein domain superfamilies connected to 2 730 625 sequence homologues. Alignment of sequence homologues corresponding to a structural domain is available, starting from a structure-based sequence alignment of the superfamily. Orientation of the length-variant (indel) regions in protein domains can be visualized by mapping them on the structure and on the alignment. Knowledge about location of length variations within protein domains and their visual representation will be useful in predicting changes within structurally or functionally relevant sites, which may ultimately regulate protein function. Non-technical summary: Evolutionary changes bring about natural changes to proteins that may be found in many organisms. Such changes could be reflected as amino acid substitutions or insertions–deletions (indels) in protein sequences. LenVarDB is a database that provides an early overview of observed length variations that were set among 731 protein families and after examining >2 million sequences. Indels are followed up to observe if they are close to the active site such that they can affect the activity of proteins. Inclusion of such information can aid the design of bioengineering experiments.

Molecular dynamics simulations of barley and maize lipid transfer proteins show different ligand binding preferences in agreement with experimental data

Experimental studies of barley and maize lipid transfer proteins (LTPs) show that the two proteins bind the ligand palmitate in opposite orientations in their internal cavities. Moreover, maize LTP is reported to bind the ligand caprate in the internal cavity in a mixture of two orientations with approximately equal occupancy. Six 30 ns molecular dynamics (MD) simulations of maize and barley LTP with ligands bound in two orientations (modes M and B) have been used to understand the different ligand binding preferences. The simulations show that both maize and barley LTP could bind palmitate in the orientation observed experimentally for maize LTP (mode M), with the predominant interaction being a salt bridge between the ligand carboxylate headgroup and a conserved arginine side chain. However, the simulation of barley LTP with palmitate in the mode B orientation shows the most favorable protein-ligand interaction energy. In contrast, the simulations of maize LTP with palmitate and with caprate in the mode B orientation show no persistent ligand binding, the ligands leaving the cavity during the simulations. Sequence differences between maize and barley LTP in the AB loop region, in residues at the base of the hydrophobic cavity, and in the helix A region are identified as contributing to the different behavior. The simulations reproduce well the experimentally observed binding preferences for palmitate and suggest that the experimental data for maize LTP with caprate reflect ligand mobility in binding mode M rather than the population of binding modes M and B.

Molecular mechanisms of disease-causing missense mutations

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies that illustrate the association between missense mutations and diseases. In addition, we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally, we report the current state-of-the-art methodologies that predict the effects of mutations on protein stability, the hydrogen bond network, pH dependence, conformational dynamics and protein function.