In silico energetic and molecular dynamic simulations studies demonstrate potential effect of the point mutations with implications for protein engineering in BDNF.

Protein engineering by directed evolution is time-consuming. Hence, in silico techniques like FoldX-Yasara for ∆∆G calculation, and SNPeffect for predicting propensity for aggregation, amyloid formation, and chaperone binding are employed to design proteins. Here, we used in silico techniques to engineer BDNF-NTF3 interaction and validated it using mutations with known functional implications for NGF dimer. The structures of three mutants representing a positive, negative, or neutral ∆∆G involving two interface residues in BDNF and two mutations representing a neutral and positive ∆∆G in NGF, which is aligned with BDNF, were selected for molecular dynamics (MD) simulation. Our MD results conclude that the secondary structure of individual protomers of the positive and negative mutants displayed a similar or different conformation from the NTF3 monomer, respectively. The positive mutants showed fewer hydrophobic interactions and higher hydrogen bonds compared to the wild-type, negative, and neutral mutants with similar SASA, suggesting solvent-mediated disruption of hydrogen-bonded interactions. Similar results were obtained for mutations with known functional implications for NGF and BDNF. The results suggest that mutations with known effects in homologous proteins could help in validation, and in silico directed evolution experiments could be a viable alternative to the experimental technique used for protein engineering.

Interpreting the effect of mutations to protein binding sites from large-scale genomic screens.

Predicting the functionality of missense mutations is extremely difficult. Large-scale genomic screens are commonly performed to identify mutational correlates or drivers of disease and treatment resistance, but interpretation of how these mutations impact protein function is limited. One such consequence of mutations to a protein is to impact its ability to bind and interact with partners or small molecules such as ATP, thereby modulating its function. Multiple methods exist for predicting the impact of a single mutation on protein-protein binding energy, but it is difficult in the context of a genomic screen to understand if these mutations with large impacts on binding are more common than statistically expected. We present a methodology for taking mutational data from large-scale genomic screens and generating functional and statistical insights into their role in the binding of proteins both with each other and their small molecule ligands. This allows a quantitative and statistical analysis to determine whether mutations impacting protein binding or ligand interactions are occurring more or less frequently than expected by chance. We achieve this by calculating the potential impact of any possible mutation and comparing an expected distribution to the observed mutations. This method is applied to examples demonstrating its ability to interpret mutations involved in protein-protein binding, protein-DNA interactions, and the evolution of therapeutic resistance.

Improving the thermal stability and catalytic activity of ulvan lyase by the combination of FoldX and KnowVolution campaign.

Thermal stability is one of the most important properties of ulvan lyases for their application in algae biomass degradation. The Knowledge gaining directed eVolution (KnowVolution) protein engineering strategy could be employed to improve thermostability of ulvan lyase with less screening effort. Herein, the unfolding free energies (ΔΔG) of the loop region were calculated using FoldX and four sites (D103, G104, T113, Q229) were selected for saturation mutagenesis, resulting in the identification of a favorable single-site mutant Q229M. Subsequently, iteration mutation was carried out with the mutant N57P (previously obtained by our group) to further enhance the performance of ulvan lyase. The results showed that the most beneficial variant N57P/Q229M exhibited a 1.67-fold and 2-fold increase in residual activity compared to the wild type after incubation at 40 °C and 50 °C for 1 h, respectively. In addition, the variant produced 1.06 mg/mL of reducing sugar in 2 h, which was almost four times as much as the wild type. Molecular dynamics simulations revealed that N57P/Q229M mutant enhanced the structural rigidity by augmenting intramolecular hydrogen bonds. Meanwhile, the shorter proton transmission distance between the general base of the enzyme and the substrate contributed to the glycosidic bond breakage. Our research showed that in silico saturation mutagenesis using position scan module in FoldX allowed for faster screening of mutants with improved thermal stability, and combining it with KnowVolution enabled a balanced effect of thermal stability and enzyme activity in protein engineering.

Simultaneous Improvement in the Thermostability and Catalytic Activity of Epoxidase Lsd18 for the Synthesis of Lasalocid A.

Enzymes used in the synthesis of natural products are potent catalysts, capable of efficient and stereoselective chemical transformations. Lsd18 catalyzes two sequential epoxidations during the biosynthesis of lasalocid A, a polyether polyketide natural product. We performed protein engineering on Lsd18 to improve its thermostability and catalytic activity. Utilizing structure-guided methods of FoldX and Rosetta-ddG, we designed 15 mutants of Lsd18. Screening of these mutants using thermal shift assay identified stabilized variants Lsd18-T189M, Lsd18-S195M, and the double mutant Lsd18-T189M-S195M. Trypsin digestion, molecular dynamic simulation, circular dichroism (CD) spectroscopy, and X-ray crystallography provided insights into the molecular basis for the improved enzyme properties. Notably, enhanced hydrophobic interaction within the enzyme core and interaction of the protein with the FAD cofactor appear to be responsible for its better thermostability.

Structure-based prediction of Ras-effector binding affinities and design of "branchegetic" interface mutations.

Ras is a central cellular hub protein controlling multiple cell fates. How Ras interacts with a variety of potential effector proteins is relatively unexplored, with only some key effectors characterized in great detail. Here, we have used homology modeling based on X-ray and AlphaFold2 templates to build structural models for 54 Ras-effector complexes. These models were used to estimate binding affinities using a supervised learning regressor. Furthermore, we systematically introduced Ras "branch-pruning" (or branchegetic) mutations to identify 200 interface mutations that affect the binding energy with at least one of the model structures. The impacts of these branchegetic mutants were integrated into a mathematical model to assess the potential for rewiring interactions at the Ras hub on a systems level. These findings have provided a quantitative understanding of Ras-effector interfaces and their impact on systems properties of a key cellular hub.

PROT-ON: A structure-based detection of designer PROTein interface MutatiONs.

The mutation-induced changes across protein-protein interfaces have often been observed to lead to severe diseases. Therefore, several computational tools have been developed to predict the impact of such mutations. Among these tools, FoldX and EvoEF1 stand out as fast and accurate alternatives. Expanding on the capabilities of these tools, we have developed the PROT-ON (PROTein-protein interface mutatiONs) framework, which aims at delivering the most critical protein interface mutations that can be used to design new protein binders. To realize this aim, PROT-ON takes the 3D coordinates of a protein dimer as an input. Then, it probes all possible interface mutations on the selected protein partner with EvoEF1 or FoldX. The calculated mutational energy landscape is statistically analyzed to find the most enriching and depleting mutations. Afterward, these extreme mutations are filtered out according to stability and optionally according to evolutionary criteria. The final remaining mutation list is presented to the user as the designer mutation set. Together with this set, PROT-ON provides several residue- and energy-based plots, portraying the synthetic energy landscape of the probed mutations. The stand-alone version of PROT-ON is deposited at https://github.com/CSB-KaracaLab/prot-on. The users can also use PROT-ON through our user-friendly web service http://proton.tools.ibg.edu.tr:8001/ (runs with EvoEF1 only). Considering its speed and the range of analysis provided, we believe that PROT-ON presents a promising means to estimate designer mutations.

Defining the HIV Capsid Binding Site of Nucleoporin 153.

The interaction between the HIV-1 capsid and human nucleoporin 153 (NUP153) is vital for delivering the HIV-1 preintegration complex into the nucleus via the nuclear pore complex. The interaction with the capsid requires a phenylalanine/glycine-containing motif in the C-terminus of NUP153 (NUP153C). This study used molecular modeling and biochemical assays to comprehensively determine the amino acids in NUP153 that are important for capsid interaction. Molecular dynamics, FoldX, and PyRosetta simulations delineated the minimal capsid binding motif of NUP153 based on the known structure of NUP153 bound to the HIV-1 capsid hexamer. Computational predictions were experimentally validated by testing the interaction of NUP153 with capsid using an in vitro binding assay and a cell-based TRIM-NUP153C restriction assay. This work identified eight amino acids from P1411 to G1418 that stably engage with capsid, with significant correlations between the interactions predicted by molecular models and empirical experiments. This validated the usefulness of this multidisciplinary approach to rapidly characterize the interaction between human proteins and the HIV-1 capsid. IMPORTANCE The human immunodeficiency virus (HIV) can infect nondividing cells by interacting with the host nuclear pore complex. The host nuclear pore protein NUP153 directly interacts with the HIV capsid to promote viral nuclear entry. This study used a multidisciplinary approach combining computational and experimental techniques to comprehensively map the effect of mutating the amino acids of NUP153 on HIV capsid interaction. This work showed a significant correlation between computational and empirical data sets, revealing that the HIV capsid interacted specifically with only six amino acids of NUP153. The simplicity of the interaction motif suggested other FG-containing motifs could also interact with the HIV-1 capsid. Furthermore, it was predicted that naturally occurring polymorphisms in human and nonhuman primates would disrupt NUP153 interaction with capsid, potentially protecting certain populations from HIV-1 infection.

Recombination of Single Beneficial Substitutions Obtained from Protein Engineering by Computer-Assisted Recombination (CompassR).

A large number of beneficial substitutions can be obtained from a successful directed enzyme evolution campaign and/or (semi)rational design. It is expected that the recombination of some beneficial substitutions leads to a much higher degree of performance through synergistic effect. However, systematic recombination studies show that poorly performing variants are often obtained after recombination of three to four individual beneficial substitutions and this limits protein engineers to exploit nature's potential in generating better performing enzymes. Computer-assisted Recombination (CompassR) strategy allows the recombination of identified beneficial substitutions in an effective and efficient manner in order to generate active enzymes with improved performance. Here, we describe in detail the CompassR procedure with an example of recombining four substitutions and discuss some important practical issues that should be considered (such as the selection of protein structures, number of FoldX runs, evaluation of calculations) for application of the CompassR rule. The core part of this protocol (system setup, ΔΔGfold calculation, and CompassR application) is transferable to other enzymes and any recombination of single beneficial substitutions.

Thermodynamic analysis of amyloid fibril structures reveals a common framework for stability in amyloid polymorphs.

The increasing number of amyloid structures offers an opportunity to investigate the general principles determining amyloid stability and polymorphism. We find that amyloid stability is dominated by ∼30% of residues localized in segments that favor the cross-ß conformation. These correspond to known aggregation-nucleating regions and constitute a stabilizing cross-ß structural framework that is shared among polymorphs. Alternative packing of these segments with structurally frustrated regions within the protofilament results in conformationally different, but energetically similar, polymorphs. Differential analysis of distributions of interatomic distances in amyloid and globular structures revealed that unconventional residue contacts, such as identical charges in close proximity, are located in energetically frustrated segments of amyloids. These observations suggest that polymorphism results from a framework mechanism consisting of conserved stabilizing regions of high cross-ß propensity. These are interspersed by structurally suboptimal regions that are potential sites of conformational plasticity and interaction with stabilizing cofactors such as (poly)ions.

MotSASi: Functional short linear motifs (SLiMs) prediction based on genomic single nucleotide variants and structural data.

Short linear motifs (SLiMs) are key to cell physiology mediating reversible protein-protein interactions. Precise identification of SLiMs remains a challenge, being the main drawback of most bioinformatic prediction tools, their low specificity (high number of false positives). An important, usually overlooked, aspect is the relation between SLiMs mutations and disease. The presence of variants in each residue position can be used to assess the relevance of the corresponding residue(s) for protein function, and its (in)tolerance to change. In the present work, we combined sequence variant information and structural analysis of the energetic impact of single amino acid substitution (SAS) in SLiM-Receptor complex structure, and showed that it improves prediction of true functional SLiMs. Our strategy is based on building a SAS tolerance matrix that shows, for each position, whether one of the possible 19 SAS is tolerated or not. Herein we present the MotSASi strategy and analyze in detail 3 SLiMs involved in intracellular protein trafficking (phospho-independent tyrosine-based motif (NPx[Y/F]), type 1 PDZ-binding motif ([S/T]x[V/I/L]COOH) and tryptophan-acidic motif ([L/M]xW[D/E])). Our results show that inclusion of variant and structure information improves both prediction of true SLiMs and rejection of false positives, while also allowing better classification of variants inside SLiMs, a result with a direct impact in clinical genomics.

Improved thermostability of proteinase K and recognizing the synergistic effect of Rosetta and FoldX approaches.

Proteinase K (PRK) is a proteolytic enzyme that has been widely used in industrial applications. However, poor stability has severely limited the uses of PRK. In this work, we used two structure-guided rational design methods, Rosetta and FoldX, to modify PRK thermostability. Fifty-two single amino acid conversion mutants were constructed based on software predictions of residues that could affect protein stability. Experimental characterization revealed that 46% (21 mutants) exhibited enhanced thermostability. The top four variants, D260V, T4Y, S216Q, and S219Q, showed improved half-lives at 69°C by 12.4-, 2.6-, 2.3-, and 2.2-fold that of the parent enzyme, respectively. We also found that selecting mutations predicted by both methods could increase the predictive accuracy over that of either method alone, with 73% of the shared predicted mutations resulting in higher thermostability. In addition to providing promising new variants of PRK in industrial applications, our findings also show that combining these programs may synergistically improve their predictive accuracy.

Interactions of Globular and Ribbon [γ4E]GID with α4ß2 Neuronal Nicotinic Acetylcholine Receptor.

The α4ß2 nAChR is implicated in a range of diseases and disorders including nicotine addiction, epilepsy and Parkinson's and Alzheimer's diseases. Designing α4ß2 nAChR selective inhibitors could help define the role of the α4ß2 nAChR in such disease states. In this study, we aimed to modify globular and ribbon α-conotoxin GID to selectively target the α4ß2 nAChR through competitive inhibition of the α4(+)ß2(-) or α4(+)α4(-) interfaces. The binding modes of the globular α-conotoxin [γ4E]GID with rat α3ß2, α4ß2 and α7 nAChRs were deduced using computational methods and were validated using published experimental data. The binding mode of globular [γ4E]GID at α4ß2 nAChR can explain the experimental mutagenesis data, suggesting that it could be used to design GID variants. The predicted mutational energy results showed that globular [γ4E]GID is optimal for binding to α4ß2 nAChR and its activity could not likely be further improved through amino-acid substitutions. The binding mode of ribbon GID with the (α4)3(ß2)2 nAChR was deduced using the information from the cryo-electron structure of (α4)3(ß2)2 nAChR and the binding mode of ribbon AuIB. The program FoldX predicted the mutational energies of ribbon [γ4E]GID at the α4(+)α4(-) interface, and several ribbon[γ4E]GID mutants were suggested to have desirable properties to inhibit (α4)3(ß2)2 nAChR.

Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages.

Resistance to drugs used to treat tuberculosis disease (TB) continues to remain a public health burden, with missense point mutations in the underlying Mycobacterium tuberculosis bacteria described for nearly all anti-TB drugs. The post-genomics era along with advances in computational and structural biology provide opportunities to understand the interrelationships between the genetic basis and the structural consequences of M. tuberculosis mutations linked to drug resistance. Pyrazinamide (PZA) is a crucial first line antibiotic currently used in TB treatment regimens. The mutational promiscuity exhibited by the pncA gene (target for PZA) necessitates computational approaches to investigate the genetic and structural basis for PZA resistance development. We analysed 424 missense point mutations linked to PZA resistance derived from ∼35K M. tuberculosis clinical isolates sourced globally, which comprised the four main M. tuberculosis lineages (Lineage 1-4). Mutations were annotated to reflect their association with PZA resistance. Genomic measures (minor allele frequency and odds ratio), structural features (surface area, residue depth and hydrophobicity) and biophysical effects (change in stability and ligand affinity) of point mutations on pncA protein stability and ligand affinity were assessed. Missense point mutations within pncA were distributed throughout the gene, with the majority (>80%) of mutations with a destabilising effect on protomer stability and on ligand affinity. Active site residues involved in PZA binding were associated with multiple point mutations highlighting mutational diversity due to selection pressures at these functionally important sites. There were weak associations between genomic measures and biophysical effect of mutations. However, mutations associated with PZA resistance showed statistically significant differences between structural features (surface area and residue depth), but not hydrophobicity score for mutational sites. Most interestingly M. tuberculosis lineage 1 (ancient lineage) exhibited a distinct protein stability profile for mutations associated with PZA resistance, compared to modern lineages.

Engineering of Biological Pathways: Complex Formation and Signal Transduction.

The rational in silico design of interface mutations within protein complexes is a synthetic biology tool that enables-when introduced into biological systems-the artificial rewiring of biological pathways. Here we describe the three-dimensional structure-based design of "rewiring" mutations using the FoldX force field. Specifically, we provide the protocol for the design and selection of interface mutations in three Ras-effector complex structures (PDB entries 3KUD, 4K81, and 6AMB). Ras mutations that impair binding to some but not all interacting partners are selected.

Cohesin-dockerin code in cellulosomal dual binding modes and its allosteric regulation by proline isomerization.

Cellulose is the most abundant organic molecule on Earth and represents a renewable and practically everlasting feedstock for the production of biofuels and chemicals. Self-assembled owing to the high-affinity cohesin-dockerin interaction, cellulosomes are huge multi-enzyme complexes with unmatched efficiency in the degradation of recalcitrant lignocellulosic substrates. The recruitment of diverse dockerin-borne enzymes into a multicohesin protein scaffold dictates the three-dimensional layout of the complex, and interestingly two alternative binding modes have been proposed. Using single-molecule fluorescence resonance energy transfer and molecular simulations on a range of cohesin-dockerin pairs, we directly detect varying distributions between these binding modes that follow a built-in cohesin-dockerin code. Surprisingly, we uncover a prolyl isomerase-modulated allosteric control mechanism, mediated by the isomerization state of a single proline residue, which regulates the distribution and kinetics of binding modes. Overall, our data provide a novel mechanistic understanding of the structural plasticity and dynamics of cellulosomes.

Structural basis of BMP-2 and BMP-7 interactions with antagonists Gremlin-1 and Noggin in Glioblastoma tumors.

In Glioblastoma (GBM) brain tumors, both Gremlin-1 and Noggin are reported to bind to BMP and inhibit BMP-signaling, thereby allowing the cell to maintain tumorous morphology. Enlisting the interfacial residues important for protein-protein complex formation between BMPs (BMP-2 and BMP-7) and antagonists (Gremlin-1 and Noggin), we analyzed the structural basis of their interactions. We found possible key mutations that destabilize these complexes, which may prevent GBM development. It was also observed that when the interfacial residues were either mutated to histidine or tryptophan, it led to higher destabilization energy values. Besides, our study of the Noggin interactive model of BMP-2 suggested preferential binding at binding site II over binding site I. In the case of Gremlin-1 and BMPs, our research, along with few previous studies, indicates a close-ended cis-trans interactive model.

Design and development of high affinity dual anticancer peptide-inhibitors against p53-MDM2/X interaction.

AIMS: Inhibition of P53-MDM2/X interaction is known as an effective cancer therapy strategy. In this regard, pDI peptide was introduced previously with the potential of targeting MDM2. In this research, the large-scale peptide mutation screening was used to achieve the best sequence of pDI with the highest affinity for inhibition activity against MDM2/X. MAIN METHODS: Three mutant peptides of pDI as dual inhibitor peptides including single mutations of pDIm/4W, pDIm/11M and double mutations of pDIdm/4W11M were presented with the high affinities to inhibit both MDM2/X. The selected mutants were then evaluated comprehensively to confirm their ability as potent MDM2/X inhibitors, using a theoretical simulation approach. KEY FINDINGS: MD simulations analyses confirmed their dual inhibition potential against both MDM2/X interactions with p53 protein. The developed pDIm and mainly pDIdm peptides showed stable conformations over the simulation time with conserved secondary structure and effective interaction with MDM2/X by physical binding such as hydrogen bonding. Besides, umbrella sampling free energy calculation indicated higher binding energy, ΔGbinding, of pDIm-MDM2/X and pDIdm-MDM2/X compared to pDI-MDM2/X. SIGNIFICANCE: The optimized and improved mutant pDI, pDIdm, with more effective ΔGbinding values of -30 and -25 kcal/mol to MDMX and MDM2, respectively, is recommended as a promising anticancer agent and suitable candidate for experimental evaluations.

Computer-Assisted Recombination (CompassR) Teaches us How to Recombine Beneficial Substitutions from Directed Evolution Campaigns.

A main remaining challenge in protein engineering is how to recombine beneficial substitutions. Systematic recombination studies show that poorly performing variants are usually obtained after recombination of 3 to 4 beneficial substitutions. This limits researchers in exploiting nature's potential in generating better enzymes. The Computer-assisted Recombination (CompassR) strategy provides a selection guide for beneficial substitutions that can be recombined to gradually improve enzyme performance by analysis of the relative free energy of folding (ΔΔGfold ). The performance of CompassR was evaluated by analysis of 84 recombinants located on 13 positions of Bacillus subtilis lipase A. The finally obtained variant F17S/V54K/D64N/D91E had a 2.7-fold improved specific activity in 18.3 % (v/v) 1-butyl-3-methylimidazolium chloride ([BMIM][Cl]). In essence, the deducted CompassR rule allows recombination of beneficial substitutions in an iterative manner and empowers researchers to generate better enzymes in a time-efficient manner.

Curating the gnomAD database: Report of novel variants in the globin-coding genes and bioinformatics analysis.

Massive parallel sequencing technologies are facilitating the faster identification of sequence variants with the consequent capability of untangling the molecular bases of many human genetic syndromes. However, it is not always easy to understand the impact of novel variants, especially for missense changes, which can lead to a spectrum of phenotypes. This study presents a custom-designed multistep methodology to evaluate the impact of novel variants aggregated in the genome aggregation database for the HBB, HBA2, and HBA1 genes, by testing and improving its performance with a dataset of previously described alterations affecting those same genes. This approach scored high sensitivity and specificity values and showed an overall better performance than sequence-derived predictors, highlighting the importance of protein conformation and interaction specific analyses in curating variant databases. This study also describes the strengths and limitations of these structural studies and allows identifying residues in the globin chains more prone to tolerate substitutions.

Protein-assisted RNA fragment docking (RnaX) for modeling RNA-protein interactions using ModelX.

RNA-protein interactions are crucial for such key biological processes as regulation of transcription, splicing, translation, and gene silencing, among many others. Knowing where an RNA molecule interacts with a target protein and/or engineering an RNA molecule to specifically bind to a protein could allow for rational interference with these cellular processes and the design of novel therapies. Here we present a robust RNA-protein fragment pair-based method, termed RnaX, to predict RNA-binding sites. This methodology, which is integrated into the ModelX tool suite (http://modelx.crg.es), takes advantage of the structural information present in all released RNA-protein complexes. This information is used to create an exhaustive database for docking and a statistical forcefield for fast discrimination of true backbone-compatible interactions. RnaX, together with the protein design forcefield FoldX, enables us to predict RNA-protein interfaces and, when sufficient crystallographic information is available, to reengineer the interface at the sequence-specificity level by mimicking those conformational changes that occur on protein and RNA mutagenesis. These results, obtained at just a fraction of the computational cost of methods that simulate conformational dynamics, open up perspectives for the engineering of RNA-protein interfaces.