Computational Alanine Scanning Mutagenesis-An Improved Methodological Approach for Protein-DNA Complexes

Structural and functional effects of the L84S mutant in the SARS-COV-2 ORF8 dimer based on microsecond molecular dynamics study

The L84S mutation has been observed frequently in the ORF8 protein of SARS-CoV-2, which is an accessory protein involved in various important functions such as virus propagation, pathogenesis, and evading the immune response. However, the specific effects of this mutation on the dimeric structure of ORF8 and its impacts on interactions with host components and immune responses are not well understood. In this study, we performed one microsecond molecular dynamics (MD) simulation and analyzed the dimeric behavior of the L84S and L84A mutants in comparison to the native protein. The MD simulations revealed that both mutations caused changes in the conformation of the ORF8 dimer, influenced protein folding mechanisms, and affected the overall structural stability. In particular, the 73YIDI76 motif has found to be significantly affected by the L84S mutation, leading to structural flexibility in the region connecting the C-terminal β4 and β5 strands. This flexibility might be responsible for virus immune modulation.  The free energy landscape (FEL) and principle component analysis (PCA) have also supported our investigation. Overall, the L84S and L84A mutations affect the ORF8 dimeric interfaces by reducing the frequency of protein-protein interacting residues (Arg52, Lys53, Arg98, Ile104, Arg115, Val117, Asp119, Phe120, and Ile121) in the ORF8 dimer.  Our findings provide detail insights for further research in designing structure-based therapeutics against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots

Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

The N-terminal Helix-Turn-Helix Motif of Transcription Factors MarA and Rob Drives DNA Recognition

DNA-binding proteins play an important role in gene regulation and cellular function. The transcription factors MarA and Rob are two homologous members of the AraC/XylS family that regulate multidrug resistance. They share a common DNA-binding domain, and Rob possesses an additional C-terminal domain that permits binding of low-molecular weight effectors. Both proteins possess two helix-turn-helix (HTH) motifs capable of binding DNA; however, while MarA interacts with its promoter through both HTH-motifs, prior studies indicate that Rob binding to DNA via a single HTH-motif is sufficient for tight binding. In the present work, we perform microsecond time scale all-atom simulations of the binding of both transcription factors to different DNA sequences to understand the determinants of DNA recognition and binding. Our simulations characterize sequence-dependent changes in dynamical behavior upon DNA binding, showcasing the role of Arg40 of the N-terminal HTH-motif in allowing for specific tight binding. Finally, our simulations demonstrate that an acidic C-terminal loop of Rob can control the DNA binding mode, facilitating interconversion between the distinct DNA binding modes observed in MarA and Rob. In doing so, we provide detailed molecular insight into DNA binding and recognition by these proteins, which in turn is an important step toward the efficient design of antivirulence agents that target these proteins.

Clustering of Zika Viruses Originating from Different Geographical Regions using Computational Sequence Descriptors

BACKGROUND

In this report, we consider a data set, which consists of 310 Zika virus genome sequences taken from different continents, Africa, Asia and South America. The sequences, which were compiled from GenBank, were derived from the host cells of different mammalian species (Simiiformes, Aedes opok, Aedes africanus, Aedes luteocephalus, Aedes dalzieli, Aedes aegypti, and Homo sapiens).

METHODS

For chemometrical treatment, the sequences have been represented by sequence descriptors derived from their graphs or neighborhood matrices. The set was analyzed with three chemometrical methods: Mahalanobis distances, principal component analysis (PCA) and self organizing maps (SOM). A good separation of samples with respect to the region of origin was observed using these three methods.

RESULTS

Study of 310 Zika virus genome sequences from different continents. To characterize and compare Zika virus sequences from around the world using alignment-free sequence comparison and chemometrical methods.

CONCLUSION

Mahalanobis distance analysis, self organizing maps, principal components were used to carry out the chemometrical analyses of the Zika sequence data. Genome sequences are clustered with respect to the region of origin (continent, country). Africa samples are well separated from Asian and South American ones.

Designing Novel Teduglutide Analogues with Improved Binding Affinity: An In Silico Peptide Engineering Approach

INTRODUCTION

Short bowel syndrome (SBS) is a disabling condition that occurs following the loss of substantial portions of the intestine, leading to inadequate absorption of nutrients and fluids. Teduglutide is the only drug that has been FDA-approved for long-term treatment of SBS. This medicine exerts its biological effects through binding to the GLP-2 receptor.

METHODS

The current study aimed to use computational mutagenesis approaches to design novel potent analogues of teduglutide. To this end, the constructed teduglutide-GLP2R 3D model was subjected to the alanine scanning mutagenesis where ARG²⁰, PHE²², ILE²³, LEU²⁶, ILE²⁷ and LYS³⁰ were identified as the key amino acids involved in ligand-receptor interaction. In order to design potent teduglutide analogues, using MAESTROweb machine learning method, the residues of teduglutide were virtually mutated into all naturally occurring amino acids and the affinity improving mutations were selected for further analysis using PDBePISA methodology which interactively investigates the interactions established at the interfaces of macromolecules.

RESULTS

The calculations resulted in D15I, D15L, D15M and N24M mutations, which can improve the binding ability of the ligand to the receptor. The final evaluation of identified mutations was performed by molecular dynamics simulations, indicating that D15I and D15M are the most reliable mutations to increase teduglutide affinity towards its receptor.

CONCLUSION

The findings in the current study may facilitate designing more potent teduglutide analogues leading to the development of novel treatments in short bowel syndrome.

Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions

The binding affinities of protein-nucleic acid interactions could be altered due to missense mutations occurring in DNA- or RNA-binding proteins, therefore resulting in various diseases. Unfortunately, a systematic comparison and prediction of the effects of mutations on protein-DNA and protein-RNA interactions (these two mutation classes are termed MPDs and MPRs, respectively) is still lacking. Here, we demonstrated that these two classes of mutations could generate similar or different tendencies for binding free energy changes in terms of the properties of mutated residues. We then developed regression algorithms separately for MPDs and MPRs by introducing novel geometric partition-based energy features and interface-based structural features. Through feature selection and ensemble learning, similar computational frameworks that integrated energy- and nonenergy-based models were established to estimate the binding affinity changes resulting from MPDs and MPRs, but the selected features for the final models were different and therefore reflected the specificity of these two mutation classes. Furthermore, the proposed methodology was extended to the identification of mutations that significantly decreased the binding affinities. Extensive validations indicated that our algorithm generally performed better than the state-of-the-art methods on both the regression and classification tasks. The webserver and software are freely available at http://liulab.hzau.edu.cn/PEMPNI and https://github.com/hzau-liulab/PEMPNI.

Protein-nucleic acid interactions play important roles in various cellular processes. Missense mutations occurring in DNA- or RNA-binding proteins (termed MPDs and MPRs, respectively) could change the binding affinities of these interactions. Previous studies have compared protein-DNA and protein-RNA interactions from multifaceted viewpoints, but less attention has been given to the similarities and specific differences between the effects of MPDs and MPRs and between the methodologies for predicting the affinity changes induced by the two mutation classes. Therefore, we systematically compared their impacts and demonstrated that MPDs and MPRs could have specific preferences for binding affinity changes. These observations motivated us to construct regression models separately for MPDs and MPRs by introducing novel energy and nonenergy descriptors. Although similar frameworks were developed to estimate these two categories of mutation effects, different descriptors were selected in the regression models and further revealed the specificity of mutation classes. The interplay between the energy and nonenergy modules effectively improved prediction performance. Our algorithm can also be adopted to disentangle mutations significantly decreasing binding affinities from other mutations.

Considerations around the SARS-CoV-2 Spike Protein with Particular Attention to COVID-19 Brain Infection and Neurological Symptoms

Spike protein (S protein) is the virus "key" to infect cells and is able to strongly bind to the human angiotensin-converting enzyme2 (ACE2), as has been reported. In fact, Spike structure and function is known to be highly important for cell infection as well as for entering the brain. Growing evidence indicates that different types of coronaviruses not only affect the respiratory system, but they might also invade the central nervous system (CNS). However, very little evidence has been so far reported on the presence of COVID-19 in the brain, and the potential exploitation, by this virus, of the lung to brain axis to reach neurons has not been completely understood. In this Article, we assessed the SARS-CoV and SARS-CoV-2 Spike protein sequence, structure, and electrostatic potential using computational approaches. Our results showed that the S proteins of SARS-CoV-2 and SARS-CoV are highly similar, sharing a sequence identity of 77%. In addition, we found that the SARS-CoV-2 S protein is slightly more positively charged than that of SARS-CoV since it contains four more positively charged residues and five less negatively charged residues which may lead to an increased affinity to bind to negatively charged regions of other molecules through nonspecific and specific interactions. Analysis the S protein binding to the host ACE2 receptor showed a 30% higher binding energy for SARS-CoV-2 than for the SARS-CoV S protein. These results might be useful for understanding the mechanism of cell entry, blood-brain barrier crossing, and clinical features related to the CNS infection by SARS-CoV-2.

iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots

Background

The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions.

Results

In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ∆SASsa^1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods.

Conclusion

In this study, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.

Application of MM-PBSA Methods in Virtual Screening

Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

Molecular basis of SMAC-XIAP binding and the effect of electrostatic polarization

X-chromosome-linked inhibitor of apoptosis (XIAP) inhibits cell apoptosis. Overexpression of XIAP is widely found in human cancers. Second mitochondria-derived activator of caspase (SMAC) protein inhibits XIAP through binding with Baculovirus Inhibitor of apoptosis protein Repeat (BIR) 3 or BIR2 domain of XIAP. In this study, molecular dynamics (MD) simulations and the alanine scanning calculations by MM-GBSA_IE method were used to investigate the protein-peptide interaction between BIR3 and BIR2 domains of XIAP and SMAC peptide. Energetic contribution of each binding residue is calculated and hotspots on both XIAP and SMAC were identified using computational alanine scanning with interaction entropy method. We found that electrostatic polarization is important in stabilizing the protein-protein complex structure in MD simulation. By using polarized protein-specific charges, much better agreement with experimental result is obtained for calculated binding free energies compared to those using standard (nonpolarizable) AMBER force field. In particular, excellent correlation between calculated binding free energies in alanine scanning with mutational experimental data was obtained for BIR3/SMAC binding.Communicated by Ramaswamy H. Sarma.

Extraction of molecular features for the drug discovery targeting protein-protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2

Half of the world population is infected by the Gram-negative bacterium Helicobacter pylori (H. pylori). It colonizes in the stomach and is associated with severe gastric pathologies including gastric cancer and peptic ulceration. The most virulent factor of H. pylori is the cytotoxin-associated gene A (CagA) that is injected into the host cell. CagA interacts with several host proteins and alters their function, thereby causing several diseases. The most well-known target of CagA is the tumor suppressor protein ASPP2. The subdomain I at the N-terminus of CagA interacts with the proline-rich motif of ASPP2. Here, in this study, we carried out alanine scanning mutagenesis and an extensive molecular dynamics simulation summing up to 3.8 μs to find out hot spot residues and discovered some new protein-protein interaction (PPI)-modulating molecules. Our findings are in line with previous biochemical studies and further suggested new residues that are crucial for binding. The alanine scanning showed that mutation of Y207 and T211 residues to alanine decreased the binding affinity. Likewise, dynamics simulation and molecular mechanics with generalized Born surface area (MMGBSA) analysis also showed the importance of these two residues at the interface. A four-feature pharmacophore model was developed based on these two residues, and top 10 molecules were filtered from ZINC, NCI, and ChEMBL databases. The good binding affinity of the CHEMBL17319 and CHEMBL1183979 molecules shows the reliability of our adopted protocol for binding hot spot residues. We believe that our study provides a new insight for using CagA as the therapeutic target for gastric cancer treatment and provides a platform for a future experimental study.

Computational analysis of hot spots and binding mechanism in the PD-1/PD-L1 interaction

Programmed cell death protein-1 (PD-1) is an important immunological checkpoint and plays a vital role in maintaining the peripheral tolerance of the human body by interacting with its ligand PD-L1. The overexpression of PD-L1 in tumor cells induces local immune suppression and helps the tumor cells to evade the endogenous anti-tumor immunity. Developing monoclonal antibodies against the PD-1/PD-L1 protein–protein interaction to block the PD-1/PD-L1 signaling pathway has demonstrated superior anti-tumor efficacy in a variety of solid tumors and has made a profound impact on the field of cancer immunotherapy in recent years. Although the X-ray crystal structure of the PD-1/PD-L1 complex has been solved, the detailed binding mechanism of the PD-1/PD-L1 interaction is not fully understood from a theoretical point of view. In this study, we performed computational alanine scanning on the PD-1/PD-L1 complex to quantitatively identify the hot spots in the PD-1/PD-L1 interaction and characterize its binding mechanisms at the atomic level. To the best of our knowledge, this is the first time that theoretical calculations have been used to systematically and quantitatively predict the hot spots in the PD-1/PD-L1 interaction. We hope that the predicted hot spots and the energy profile of the PD-1/PD-L1 interaction presented in this work can provide guidance for the design of peptide and small molecule drugs targeting PD-1 or PD-L1.

The hot spots quantitatively predicted by the recently developed MM/GBSA/IE method reveal a hydrophobic core in the PD-1/PD-L1 interaction.

dbAMEPNI: a database of alanine mutagenic effects for protein-nucleic acid interactions

Protein–nucleic acid interactions play essential roles in various biological activities such as gene regulation, transcription, DNA repair and DNA packaging. Understanding the effects of amino acid substitutions on protein–nucleic acid binding affinities can help elucidate the molecular mechanism of protein–nucleic acid recognition. Until now, no comprehensive and updated database of quantitative binding data on alanine mutagenic effects for protein–nucleic acid interactions is publicly accessible. Thus, we developed a new database of Alanine Mutagenic Effects for Protein-Nucleic Acid Interactions (dbAMEPNI). dbAMEPNI is a manually curated, literature-derived database, comprising over 577 alanine mutagenic data with experimentally determined binding affinities for protein–nucleic acid complexes. It contains several important parameters, such as dissociation constant (K_d), Gibbs free energy change (ΔΔG), experimental conditions and structural parameters of mutant residues. In addition, the database provides an extended dataset of 282 single alanine mutations with only qualitative data (or descriptive effects) of thermodynamic information.

Database URL: http://zhulab.ahu.edu.cn/dbAMEPNI

SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots

We present SpotOn, a web server to identify and classify interfacial residues as Hot-Spots (HS) and Null-Spots (NS). SpotON implements a robust algorithm with a demonstrated accuracy of 0.95 and sensitivity of 0.98 on an independent test set. The predictor was developed using an ensemble machine learning approach with up-sampling of the minor class. It was trained on 53 complexes using various features, based on both protein 3D structure and sequence. The SpotOn web interface is freely available at: http://milou.science.uu.nl/services/SPOTON/.

Wing 1 of protein HOP2 is as important as helix 3 in DNA binding by MD simulation

The repair of programmed DNA double-strand breaks through recombination is required for proper association and disjunction of the meiotic homologous chromosomes. Meiosis-specific protein HOP2 plays essential roles in recombination by promoting recombinase activities. The N-terminal domain of HOP2 interacts with DNA through helix 3 (H3) and wing 1 (W1). Mutations in wing 1 (Y65A/K67A/Q68A) slightly weakened the binding but mutations in helices 2 and 3 (Q30A/K44A/K49A) nearly abolished the binding. To better understand such differential effects at atomic level, molecular dynamics simulations were employed. Despite losing some hydrogen bonds, the W1-mutant DNA complex was rescued by stronger hydrophobic interactions. For the wild type and W1-mutant, the protein was found to slide along the DNA grooves as the DNA rolls along its double-helix axis. This motion could be functionally important to facilitate the precise positioning of the single-stranded DNA with the homologous double-stranded DNA. The sliding motion was reduced in the W1-mutant. The H-mutant nearly lost all intermolecular interactions. Moreover, an additional mutation in wing 1 (Y65A/K67A/Q68A/K69A) also caused complete complex dissociation. Therefore, both wing 1 and helix 3 make important contribution to the DNA binding, which could be important to the strand invasion function of HOP2 homodimer and HOP2-MND1 heterodimer. Similar to cocking a medieval crossbow with the archer's foot placed in the stirrup, wing 1 may push the minor groove to cause distortion while helix 3 grabs the major groove.

Characterizing the Hot Spots Involved in RON-MSPβ Complex Formation Using In Silico Alanine Scanning Mutagenesis and Molecular Dynamics Simulation

Purpose: Implication of protein-protein interactions (PPIs) in development of many diseases such as cancer makes them attractive for therapeutic intervention and rational drug design. RON (Recepteur d'Origine Nantais) tyrosine kinase receptor has gained considerable attention as promising target in cancer therapy. The activation of RON via its ligand, macrophage stimulation protein (MSP) is the most common mechanism of activation for this receptor. The aim of the current study was to perform in silico alanine scanning mutagenesis and to calculate binding energy for prediction of hot spots in protein-protein interface between RON and MSPβ chain (MSPβ). Methods: In this work the residues at the interface of RON-MSPβ complex were mutated to alanine and then molecular dynamics simulation was used to calculate binding free energy. Results: The results revealed that Gln¹⁹³, Arg²²⁰, Glu²⁸⁷, Pro²⁸⁸, Glu²⁸⁹, and His⁴²⁴ residues from RON and Arg⁵²¹, His⁵²⁸, Ser⁵⁶⁵, Glu⁶⁵⁸, and Arg⁶⁸³ from MSPβ may play important roles in protein-protein interaction between RON and MSP. Conclusion: Identification of these RON hot spots is important in designing anti-RON drugs when the aim is to disrupt RON-MSP interaction. In the same way, the acquired information regarding the critical amino acids of MSPβ can be used in the process of rational drug design for developing MSP antagonizing agents, the development of novel MSP mimicking peptides where inhibition of RON activation is required, and the design of experimental site directed mutagenesis studies.

Evolutionary Covariance Combined with Molecular Dynamics Predicts a Framework for Allostery in the MutS DNA Mismatch Repair Protein

Mismatch repair (MMR) is an essential, evolutionarily conserved pathway that maintains genome stability by correcting base-pairing errors in DNA. Here we examine the sequence and structure of MutS MMR protein to decipher the amino acid framework underlying its two key activities—recognizing mismatches in DNA and using ATP to initiate repair. Statistical coupling analysis (SCA) identified a network (sector) of coevolved amino acids in the MutS protein family. The potential functional significance of this SCA sector was assessed by performing molecular dynamics (MD) simulations for alanine mutants of the top 5% of 160 residues in the distribution, and control nonsector residues. The effects on three independent metrics were monitored: (i) MutS domain conformational dynamics, (ii) hydrogen bonding between MutS and DNA/ATP, and (iii) relative ATP binding free energy. Each measure revealed that sector residues contribute more substantively to MutS structure–function than nonsector residues. Notably, sector mutations disrupted MutS contacts with DNA and/or ATP from a distance via contiguous pathways and correlated motions, supporting the idea that SCA can identify amino acid networks underlying allosteric communication. The combined SCA/MD approach yielded novel, experimentally testable hypotheses for unknown roles of many residues distributed across MutS, including some implicated in Lynch cancer syndrome.

Key Residues Involved in the Interaction between Cydia pomonella Pheromone Binding Protein 1 (CpomPBP1) and Codlemone

Codlemone exhibited high affinity to CpomPBP1; studying their binding mode can provide insights into the rational design of active semiochemicals. Our findings suggested that residues including Phe12, Phe36, Trp37, Ile52, Ile 94, Ala115, and Phe118 were favorable to the binding of codlemone to CpomPBP1, whereas residues providing unfavorable contributions such as Ser56 were negative to the binding. van der Waals energy and electrostatic energy, mainly derived from the side chains of favorable residues, contributed most to the formation and stability of the CpomPBP1-codlemone complex. Of the residues involved in the interaction between CpomPBP1 and codlemone, Phe12 and Trp37, the mutation of which into Ala caused a significant decrease of CpomPBP1 binding ability, were two key residues in determining the binding affinity of codlemone to CpomPBP1. This study shed light on discovering novel active semiochemicals as well as facilitating chemical modification of lead semiochemicals.

Solvent accessible surface area-based hot-spot detection methods for protein-protein and protein-nucleic acid interfaces

Due to the importance of hot-spots (HS) detection and the efficiency of computational methodologies, several HS detecting approaches have been developed. The current paper presents new models to predict HS for protein-protein and protein-nucleic acid interactions with better statistics compared with the ones currently reported in literature. These models are based on solvent accessible surface area (SASA) and genetic conservation features subjected to simple Bayes networks (protein-protein systems) and a more complex multi-objective genetic algorithm-support vector machine algorithms (protein-nucleic acid systems). The best models for these interactions have been implemented in two free Web tools.