Effects Of Amino Acid Mutations Research Articles

MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.

The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.

NAN342K Mutation Enhances the Pathogenicity of Influenza B Virus in Mice

Objective: Influenza B virus is a significant respiratory pathogen responsible for seasonal influenza. In recent years the B/Yamagata lineage has demonstrated a rapid increase, predominantly featuring the neuraminidase (NA)N342K mutation. This study determined the impact of the NAN342K mutation on the pathogenicity of influenza B virus and elucidate the underlying mechanisms. Methods: Gene fragments with specific mutations were generated using site-directed mutagenesis PCR, resulting in recombinant viruses (rAH127 and rAH127/NAN342K). C57BL/6 mice were infected to evaluate the impact of amino acid mutations on virus pathogenicity. Body weight, survival rate, virus replication, and lung pathology were compared among the groups. NA enzyme activity was assessed to determine the mechanisms underlying the effects of amino acid mutations on the pathogenicity of influenza B virus. Results: The NAN342K mutant virus exhibited significantly increased NA enzyme activity (3.19-fold) and viral replication capacity in MDCK cells (6.76-fold) compared to wild-type virus. These changes led to enhanced pathogenicity in mice, characterized by severe weight loss, increased mortality, and heightened lung tissue inflammation. Conclusions: The NAN342K mutation likely enhances virus replication and pathogenicity by increasing NA enzyme activity. These findings contribute to understanding the molecular mechanisms underlying influenza B virus pathogenicity and have implications for targeted therapeutic strategies.

Electrostatics in Computational Biophysics and Its Implications for Disease Effects.

This review outlines the role of electrostatics in computational molecular biophysics and its implication in altering wild-type characteristics of biological macromolecules, and thus the contribution of electrostatics to disease mechanisms. The work is not intended to review existing computational approaches or to propose further developments. Instead, it summarizes the outcomes of relevant studies and provides a generalized classification of major mechanisms that involve electrostatic effects in both wild-type and mutant biological macromolecules. It emphasizes the complex role of electrostatics in molecular biophysics, such that the long range of electrostatic interactions causes them to dominate all other forces at distances larger than several Angstroms, while at the same time, the alteration of short-range wild-type electrostatic pairwise interactions can have pronounced effects as well. Because of this dual nature of electrostatic interactions, being dominant at long-range and being very specific at short-range, their implications for wild-type structure and function are quite pronounced. Therefore, any disruption of the complex electrostatic network of interactions may abolish wild-type functionality and could be the dominant factor contributing to pathogenicity. However, we also outline that due to the plasticity of biological macromolecules, the effect of amino acid mutation may be reduced, and thus a charge deletion or insertion may not necessarily be deleterious.

Radiothermometric Study of the Effect of Amino Acid Mutation on the Characteristics of the Enzymatic System.

The radiothermometry (RTM) study of a cytochrome-containing system (CYP102 A1) has been conducted in order to demonstrate the applicability of RTM for monitoring changes in the functional activity of an enzyme in case of its point mutation. The study has been performed with the example of the wild-type cytochrome (WT) and its mutant type A264K. CYP102 A1 is a nanoscale protein-enzymatic system of about 10 nm in size. RTM uses a radio detector and can record the corresponding brightness temperature (Tbr) of the nanoscale enzyme solution within the 3.4–4.2 GHz frequency range during enzyme functioning. It was found that the enzymatic reaction during the lauric acid hydroxylation at the wild-type CYP102 A1 (WT) concentration of ~10−9 M is accompanied by Tbr fluctuations of ~0.5–1 °C. At the same time, no Tbr fluctuations are observed for the mutated forms of the enzyme CYP102 A1 (A264K), where one amino acid was replaced. We know that the activity of CYP102 A1 (WT) is ~4 orders of magnitude higher than that of CYP102 A1 (A264K). We therefore concluded that the disappearance of the fluctuation of Tbr CYP102 A1 (A264K) is associated with a decrease in the activity of the enzyme. This effect can be used to develop new methods for testing the activity of the enzyme that do not require additional labels and expensive equipment, in comparison with calorimetry and spectral methods. The RTM is beginning to find application in the diagnosis of oncological diseases and for the analysis of biochemical processes.

Virtual Alanine Scan of the Main Protease Active Site in Severe Acute Respiratory Syndrome Coronavirus 2.

Recently, inhibitors of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) have been proposed as potential therapeutic agents for COVID-19. Studying effects of amino acid mutations in the conformation of drug targets is necessary for anticipating drug resistance. In this study, with the structure of the SARS-CoV-2 Mpro complexed with a non-covalent inhibitor, we performed molecular dynamics (MD) simulations to determine the conformation of the complex when single amino acid residue in the active site is mutated. As a model of amino acid mutation, we constructed mutant proteins with one residue in the active site mutated to alanine. This method is called virtual alanine scan. The results of the MD simulations showed that the conformation and configuration of the ligand was changed for mutants H163A and E166A, although the structure of the whole protein and of the catalytic dyad did not change significantly, suggesting that mutations in His163 and Glu166 may be linked to drug resistance.

A thermodynamic model of protein structure evolution explains empirical amino acid substitution matrices.

Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. These evolutionary pressures are sufficiently consistent over time and across protein families to produce substitution patterns, summarized in global amino acid substitution matrices such as BLOSUM, JTT, WAG, and LG, which can be used to successfully detect homologs, infer phylogenies, and reconstruct ancestral sequences. Although the factors that govern the variation of amino acid substitution rates have received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid substitution matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi‐nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex yet universal pattern observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary driver behind the global amino acid substitution patterns observed in proteins throughout the tree of life.

P324A Point Mutation as a Probe to Determine the Binding Site of BHPI on Estrogen Receptor Alpha

BHPI (1,3-Dihydro-3,3-bis(4-hydroxyphenyl)-7-methyl-2H-indol-2-one) is a noncompetitive bio-modulator of estrogen receptor alpha (ERα) that induces necrotic cell death through ATP depletion and has shown promise as a preclinical anticancer drug. BHPI kills cancer cells through activating the anticipatory Unfolded Protein Response (aUPR). The UPR is a stress sensor within the endoplasmic reticulum that is normally a cytoprotective mechanism, but once hyperactivated by BHPI, leads to necrosis. Three ERα PDB structures 1a52, 3ert, and 5u2b were computationally analyzed with a Proline 324 to Alanine (P324A) point mutation to determine where and how BHPI binds to ERα. These studies are important for understanding the effects of amino acid mutations on protein-ligand interaction, as well as the binding affinity of ERα for BHPI.

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.

Maintaining wild type protein–protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein–protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes.

Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab experiments to get a better understanding of protein mutations is costly and time consuming, and because of huge number of possible mutations the need of computational methods that could accurately predict effects of amino acid mutations is of greatest importance. In this research, we present a robust methodology to predict the energy changes of a proteins upon mutations. The proposed prediction scheme is based on two step algorithm that is a Holdout Random Sampler followed by a neural network model for regression. The Holdout Random Sampler is utilized to analysis the energy change, the corresponding uncertainty, and to obtain a set of admissible energy changes, expressed as a cumulative distribution function. These values are further utilized to train a simple neural network model that can predict the energy changes. Results were blindly tested (validated) against experimental energy changes, giving Pearson correlation coefficients of 0.66 for Single Point Mutations and 0.77 for Multiple Point Mutations. These results confirm the successfulness of our method, since it outperforms majority of previous studies in this field.

Mapping DNA sequence to transcription factor binding energy in vivo.

Despite the central importance of transcriptional regulation in biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to decipher the biophysical mechanisms of transcriptional regulation in living cells and determine the energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for dissecting transcriptional regulatory sequences using in vivo methods (massively parallel reporter assays) to formulate quantitative models that map a transcription factor binding site’s DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 kBT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factor’s sequence specificity.

Effect of amino acid mutations on the conformational dynamics of amyloidogenic immunoglobulin light-chains: A combined NMR and in silico study

The conformational dynamics of a pathogenic κ4 human immunoglobulin light-chain variable domain, SMA, associated with AL amyloidosis, were investigated by 15N relaxation dispersion NMR spectroscopy. Compared to a homologous light-chain, LEN, which differs from SMA at eight positions but is non-amyloidogenic in vivo, we find that multiple residues in SMA clustered around the N-terminus and CDR loops experience considerable conformational exchange broadening caused by millisecond timescale protein motions, consistent with a destabilized dimer interface. To evaluate the contribution of each amino acid substitution to shaping the dynamic conformational landscape of SMA, NMR studies were performed for each SMA-like point mutant of LEN followed by in silico analysis for a subset of these proteins. These studies show that a combination of only three mutations located within or directly adjacent to CDR3 loop at the dimer interface, which remarkably include both destabilizing (Q89H and Y96Q) and stabilizing (T94H) mutations, largely accounts for the differences in conformational flexibility between LEN and SMA. Collectively, our studies indicate that a correct combination of stabilizing and destabilizing mutations is key for immunoglobulin light-chains populating unfolded intermediates that result in amyloid formation, and underscore the complex nature of correlations between light-chain conformational flexibility, thermodynamic stability and amyloidogenicity.

Npro His49 and Erns Lys412 mutations in pig bovine viral diarrhea virus type 2 synergistically enhance the cellular antiviral response.

Type I interferons are major components of the innate immune response of hosts, and accordingly, many viruses have evolved mechanisms to modulate the host response during infection. Bovine viral diarrhea virus (BVDV) nonstructural protein Npro and structural protein Erns play important roles in inhibiting type I interferon. The aim of this study was to explore the epistatic effects of amino acid mutations in Npro and Erns in porcine ST cells to characterize the immune response induced by BVDV-2. Plasmids with mutant amino acids His49 (H49), Glu22 (E22) in Npro, and His300 (H300), Lys412 (K412) in Erns which had been changed to Alanine (A) had similar effects on type I interferon production in MDBK and ST cells, but resulted in much greater ISG15, OAS, and Mx production in ST cells. The rescued vASH/NproH49ErnsK412 virus showed the best efficiency with respect to modulating antiviral cytokines, indicating that the amino acids Npro H49 and Erns K412 had highly synergistic effects in abolishing the ability to inhibit type I interferon. These findings have importance practical implications owing to the increasing prevalence of BVDV infections, including persistent infections, in domestic pigs.

Stability-Mediated Epistasis Restricts Accessible Mutational Pathways in the Functional Evolution of Avian Hemoglobin.

If the fitness effects of amino acid mutations are conditional on genetic background, then mutations can have different effects depending on the sequential order in which they occur during evolutionary transitions in protein function. A key question concerns the fraction of possible mutational pathways connecting alternative functional states that involve transient reductions in fitness. Here we examine the functional effects of multiple amino acid substitutions that contributed to an evolutionary transition in the oxygenation properties of avian hemoglobin (Hb). The set of causative changes included mutations at intradimer interfaces of the Hb tetramer. Replacements at such sites may be especially likely to have epistatic effects on Hb function since residues at intersubunit interfaces are enmeshed in networks of salt bridges and hydrogen bonds between like and unlike subunits; mutational reconfigurations of these atomic contacts can affect allosteric transitions in quaternary structure and the propensity for tetramer–dimer dissociation. We used ancestral protein resurrection in conjunction with a combinatorial protein engineering approach to synthesize genotypes representing the complete set of mutational intermediates in all possible forward pathways that connect functionally distinct ancestral and descendent genotypes. The experiments revealed that 1/2 of all possible forward pathways included mutational intermediates with aberrant functional properties because particular combinations of mutations promoted tetramer–dimer dissociation. The subset of mutational pathways with unstable intermediates may be selectively inaccessible, representing evolutionary roads not taken. The experimental results also demonstrate how epistasis for particular functional properties of proteins may be mediated indirectly by mutational effects on quaternary structural stability.

Effect of amino acid mutations on intra-dimer tubulin-tubulin binding strength of microtubules.

Energetic interactions inside αβ-tubulin dimers of a microtubule (MT) with atomic resolutions are of importance in determining the mechanical properties and structural stability of the MT as well as designing self-assembled functional structures from it. Here, we carry out several comprehensive atomistic simulations to investigate the interaction properties within αβ-tubulin dimers and effect of residue mutations on the intra-dimer tubulin-tubulin (IDTT) binding strength. Results indicate that the force-displacement responses of the dimer could be roughly divided into three stages involving increasing, decreasing, and fluctuating forces. Energetic analysis shows that electrostatic interactions dominate the IDTT binding strength. Further per-residue energetic analysis shows that the major part of the interface interaction energy (approximately 72% for α-tubulin and 62% for β-tubulin) comes from amino acid residues with net charges, namely arginine (ARG), lysine (LYS), glutamic acid (GLU), aspartic acid (ASP). Residue mutations are completed for ARG105 on α-tubulin and ASP251 on β-tubulin to study the effect of mutations on the IDTT binding strength. Results indicate that stiffness, rupture force, and interface interaction energy of αβ-tubulin dimer can be improved by up to 28%, 13% and 28%, respectively. Overall, our results provide a thorough atomistic understanding of the IDTT binding strength within αβ-tubulin heterodimers and help pave the way for eventually designing and controlling the self-assembled functional structures from MTs.

Effects of amino acid mutations in the pore-forming domain of the hemolytic lectin CEL-III.

The hemolytic lectin CEL-III forms transmembrane pores in the membranes of target cells. A study on the effect of site-directed mutation at Lys405 in domain 3 of CEL-III indicated that replacements of this residue by relatively smaller residues lead to a marked increase in hemolytic activity, suggesting that moderately destabilizing domain 3 facilitates formation of transmembrane pores through conformational changes.

Random close packing in protein cores.

Shortly after the determination of the first protein x-ray crystal structures, researchers analyzed their cores and reported packing fractions ϕ ≈ 0.75, a value that is similar to close packing of equal-sized spheres. A limitation of these analyses was the use of extended atom models, rather than the more physically accurate explicit hydrogen model. The validity of the explicit hydrogen model was proved in our previous studies by its ability to predict the side chain dihedral angle distributions observed in proteins. In contrast, the extended atom model is not able to recapitulate the side chain dihedral angle distributions, and gives rise to large atomic clashes at side chain dihedral angle combinations that are highly probable in protein crystal structures. Here, we employ the explicit hydrogen model to calculate the packing fraction of the cores of over 200 high-resolution protein structures. We find that these protein cores have ϕ ≈ 0.56, which is similar to results obtained from simulations of random packings of individual amino acids. This result provides a deeper understanding of the physical basis of protein structure that will enable predictions of the effects of amino acid mutations to protein cores and interfaces of known structure.

Causes of molecular convergence and parallelism in protein evolution.

To what extent is the convergent evolution of protein function attributable to convergent or parallel changes at the amino acid level? The mutations that contribute to adaptive protein evolution may represent a biased subset of all possible beneficial mutations owing to mutation bias and/or variation in the magnitude of deleterious pleiotropy. A key finding is that the fitness effects of amino acid mutations are often conditional on genetic background. This context dependence (epistasis) can reduce the probability of convergence and parallelism because it reduces the number of possible mutations that are unconditionally acceptable in divergent genetic backgrounds. Here, I review factors that influence the probability of replicated evolution at the molecular level.

On human disease-causing amino acid variants: statistical study of sequence and structural patterns.

Statistical analysis was carried out on large set of naturally occurring human amino acid variations, and it was demonstrated that there is a preference for some amino acid substitutions to be associated with diseases. At an amino acid sequence level, it was shown that the disease-causing variants frequently involve drastic changes in amino acid physicochemical properties of proteins such as charge, hydrophobicity, and geometry. Structural analysis of variants involved in diseases and being frequently observed in human population showed similar trends: disease-causing variants tend to cause more changes in hydrogen bond network and salt bridges as compared with harmless amino acid mutations. Analysis of thermodynamics data reported in the literature, both experimental and computational, indicated that disease-causing variants tend to destabilize proteins and their interactions, which prompted us to investigate the effects of amino acid mutations on large databases of experimentally measured energy changes in unrelated proteins. Although the experimental datasets were linked neither to diseases nor exclusory to human proteins, the observed trends were the same: amino acid mutations tend to destabilize proteins and their interactions. Having in mind that structural and thermodynamics properties are interrelated, it is pointed out that any large change in any of them is anticipated to cause a disease.

Effect of amino acid mutation at position 127 in 3A of a rabbit-attenuated foot-and-mouth disease virus serotype Asia1 on viral replication and infection.

An amino acid mutation (R127→I) in the 3A non-structural protein of an FMDV serotype Asia1 rabbit-attenuated ZB strain was previously found after attenuation of the virus. To explore the effects of this mutation on viral replication and infection, the amino acid residue isoleucine (I) was changed to arginine (R) in the infectious cDNA clone of the rabbit-attenuated ZB strain by sitedirected mutagenesis, and the R127-mutated virus was rescued. BHK monolayer cells and suckling mice were inoculated with the R127-mutated virus to test its growth property and pathogenicity, respectively. The effects of the R127 mutation on viral replication and virulence were analyzed. The data showed that there was a slight difference in plaque morphology between the R127-mutated and wild-type viruses. The growth rate of the mutated virus was lower in BHK-21 cells and its virulence in suckling mice was also attenuated. This study indicates that the R127 mutation in 3A may play an important role in FMDV replication in vitro and in pathogenicity in suckling mice.

1P263 Analyses of the effects of amino acid mutations on the protein folding segments by means of sequence and evolutionary analyses(22A. Bioinformatics:Structural genomics,Poster,The 52nd Annual Meeting of the Biophysical Society of Japan(BSJ2014))

1P263 Analyses of the effects of amino acid mutations on the protein folding segments by means of sequence and evolutionary analyses(22A. Bioinformatics:Structural genomics,Poster,The 52nd Annual Meeting of the Biophysical Society of Japan(BSJ2014))