APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

Background  

It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required.     

Magnitude of the hydrophobic effect at central versus peripheral sites in protein-protein interfaces

Hydrophobic interactions are essential for stabilizing protein-protein complexes, whose interfaces generally consist of a central cluster of hot spot residues surrounded by less important peripheral residues. According to the O-ring hypothesis, a condition for high affinity binding is solvent exclusion from interacting residues. This hypothesis predicts that the hydrophobicity at the center is significantly greater than at the periphery, which we estimated at 21 cal mol(-1) A(-2). To measure the hydrophobicity at the center, structures of an antigen-antibody complex where a buried phenylalanine was replaced by smaller hydrophobic residues were determined. By correlating structural changes with binding free energies, we estimate the hydrophobicity at this central site to be 46 cal mol(-1) A(-2), twice that at the periphery. This context dependence of the hydrophobic effect explains the clustering of hot spots at interface centers and has implications for hot spot prediction and the design of small molecule inhibitors.     

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background  

Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.     

A compound‐based computational approach for the accurate determination of hot spots

A plethora of both experimental and computational methods have been proposed in the past 20 years for the identification of hot spots at a protein–protein interface. The experimental determination of a protein–protein complex followed by alanine scanning mutagenesis, though able to determine hot spots with much precision, is expensive and has no guarantee of success while the accuracy of the current computational methods for hot‐spot identification remains low. Here, we present a novel structure‐based computational approach that accurately determines hot spots through docking into a set of proteins homologous to only one of the two interacting partners of a compound capable of disrupting the protein–protein interaction (PPI). This approach has been applied to identify the hot spots of human activin receptor type II (ActRII) critical for its binding toward Cripto‐I. The subsequent experimental confirmation of the computationally identified hot spots portends a potentially accurate method for hot‐spot determination in silico given a compound capable of disrupting the PPI in question. The hot spots of human ActRII first reported here may well become the focal points for the design of small molecule drugs that target the PPI. The determination of their interface may have significant biological implications in that it suggests that Cripto‐I plays an important role in both activin and nodal signal pathways.     

Artificial intelligence based methods for hot spot prediction

Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved in. Any abnormal interactions may cause diseases. Therefore, the identification of small molecules which modulate protein interactions through their interfaces has high therapeutic potential. However, discovering such molecules is challenging. Most protein–protein binding affinity is attributed to a small set of amino acids found in protein interfaces known as hot spots. Recent studies demonstrate that drug-like small molecules specifically may bind to hot spots. Therefore, hot spot prediction is crucial. As experimental data accumulates, artificial intelligence begins to be used for computational hot spot prediction. First, we review machine learning and deep learning for computational hot spot prediction and then explain the significance of hot spots toward drug design.     

Geometrically centered region: A “wet” model of protein binding hot spots not excluding water molecules

A protein interface can be as “wet” as a protein surface in terms of the number of immobilized water molecules. This important water information has not been explicitly taken by computational methods to model and identify protein binding hot spots, overlooking the water role in forming interface hydrogen bonds and in filing cavities. Hot spot residues are usually clustered at the core of the protein binding interfaces. However, traditional machine learning methods often identify the hot spot residues individually, breaking the cooperativity of the energetic contribution. Our idea in this work is to explore the role of immobilized water and meanwhile to capture two essential properties of hot spots: the compactness in contact and the far distance from bulk solvent. Our model is named geometrically centered region (GCR). The detection of GCRs is based on novel tripartite graphs, and atom burial levels which are a concept more intuitive than SASA. Applying to a data set containing 355 mutations, we achieved an F measure of 0.6414 when ΔΔG ≥ 1.0 kcal/mol was used to define hot spots. This performance is better than Robetta, a benchmark method in the field. We found that all but only one of the GCRs contain water to a certain degree, and most of the outstanding hot spot residues have water‐mediated contacts. If the water is excluded, the burial level values are poorly related to the ΔΔG, and the model loses its performance remarkably. We also presented a definition for the O‐ring of a GCR as the set of immediate neighbors of the residues in the GCR. Comparative analysis between the O‐rings and GCRs reveals that the newly defined O‐ring is indeed energetically less important than the GCR hot spot, confirming a long‐standing hypothesis. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Cold spots are universal in protein–protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico‐chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein–protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo‐ and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high‐affinity complexes showing fewer centrally located colds spots than low‐affinity complexes. A larger number of cold spots is also found in non‐cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo‐dimeric complexes compared to hetero‐complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.     

Protein subunit interfaces: a statistical analysis of hot spots in Sm proteins

The distinguishing property of Sm protein associations is very high stability. In order to understand this property, we analyzed the interfaces and compared the properties of Sm protein interfaces with those of a test set, the Binding Interface Database (BID). The comparison revealed that the main differences between the interfaces of Sm proteins and those of the BID set are the content of charged residues, the coordination numbers of the residues, knowledge-based pair potentials, and the conservation scores of hot spots. In Sm proteins, the interfaces have more hydrophobic and fewer charged residues than the surfaces, which is also the case for the BID test set and other proteins. However, in the interfaces, the content of charged residues in Sm proteins (26%) is substantially larger than that in the BID set (22%). Hot spots are residues that make up a small fraction of the interfaces, but they contribute most of the binding energy. These residues are critical to protein–protein interactions. Analyses of knowledge-based pair potentials of hot spot and non-hot spot residues in Sm proteins show that they are significantly different; their mean values are 31.5 and 11.3, respectively. In the BID set, this difference is smaller; in this case, the mean values for hot spot and non-hot spot residues are 20.7 and 12.4, respectively. Hence, the pair potentials of hot spots differ significantly for the Sm and BID data sets. In the interfaces of Sm proteins, the amino acids are tightly packed, and the coordination numbers are larger in Sm proteins than in the BID set for both hot spots and non-hot spots. At the same time, the coordination numbers are higher for hot spots; the average coordination number of the hot spot residues in Sm proteins is 7.7, while it is 6.1 for the non-hot spot residues. The difference in the calculated average conservation score for hot spots and non-hot spots in Sm proteins is significantly larger than it is in the BID set. In Sm proteins, the average conservation score for the hot spots is 7.4. Hot spots are surrounded by residues that are moderately conserved (5.9). The average conservation score for the other interface residues is 5.6. The conservation scores in the BID set do not show a significant distinction between hot and non-hot spots: the mean values for hot and non-hot spot residues are 5.5 and 5.2, respectively. These data show that structurally conserved residues and hot spots are significantly correlated in Sm proteins.     

Protein-protein interactions; coupling of structurally conserved residues and of hot spots across interfaces. Implications for docking

Hot spot residues contribute dominantly to protein-protein interactions. Statistically, conserved residues correlate with hot spots, and their occurrence can distinguish between binding sites and the remainder of the protein surface. The hot spot and conservation analyses have been carried out on one side of the interface. Here, we show that both experimental hot spots and conserved residues tend to couple across two-chain interfaces. Intriguingly, the local packing density around both hot spots and conserved residues is higher than expected. We further observe a correlation between local packing density and experimental deltadeltaG. Favorable conserved pairs include Gly coupled with aromatics, charged and polar residues, as well as aromatic residue coupling. Remarkably, charged residue couples are underrepresented. Overall, protein-protein interactions appear to consist of regions of high and low packing density, with the hot spots organized in the former. The high local packing density in binding interfaces is reminiscent of protein cores.     

蛋白质-蛋白质界面热点残基预测及其在线工具

蛋白质-蛋白质结合热点是界面中对结合自由能有着显著贡献的一小簇残基。捕捉和揭示这类热点残基可以加深对蛋白质间相互作用机制的理解,为蛋白质工程和药物设计提供指导。但实验技术费时费力且代价昂贵。计算工具可用于辅助和补充实验上的尝试。该文较详细、系统地介绍了蛋白质界面热点的特性、计算预测的策略与技术,并应用实例进一步说明这些方法学的特征;还介绍了界面热点的数据库和一些主要的在线预测工具,旨在为设计、挑选和应用这类工具解决特定问题的研究人员提供指南。     

Unraveling hot spots in binding interfaces: progress and challenges

Protein interface hot spots, as revealed by alanine scanning mutagenesis, continue to stimulate interest in the biophysical basis of molecular recognition. Although these regions apparently constitute fertile grounds for intermolecular interactions, no general algorithm has yet been developed that can predict hot spots based solely on their shape or composition. The discovery of structural plasticity in hot spot regions indicates that dynamic simulation techniques may be essential for achieving a predictive understanding of binding interface energetics. Future progress will depend as much on the application of new computational approaches for dissecting protein interfaces as on expanding our empirical databank of mutagenic substitutions and their effects. Despite our current theoretical shortcomings, recent methodological advances provide efficient experimental means of probing hot spots and enable immediate applications for hot spots in drug discovery.     

Exploring the capacity of minimalist protein interfaces: interface energetics and affinity maturation to picomolar KD of a single-domain antibody with a flat paratope

A major architectural class in engineered binding proteins ("antibody mimics") involves the presentation of recognition loops off a single-domain scaffold. This class of binding proteins, both natural and synthetic, has a strong tendency to bind a preformed cleft using a convex binding interface (paratope). To explore their capacity to produce high-affinity interfaces with diverse shape and topography, we examined the interface energetics and explored the affinity limit achievable with a flat paratope. We chose a minimalist paratope limited to two loops found in a natural camelid heavy-chain antibody (VHH) that binds to ribonuclease A. Ala scanning of the VHH revealed only three "hot spot" side chains and additional four residues important for supporting backbone-mediated interactions. The small number of critical residues suggested that this is not an optimized paratope. Using selection from synthetic combinatorial libraries, we enhanced its affinity by >100-fold, resulting in variants with Kd as low as 180 pM with no detectable loss of binding specificity. High-resolution crystal structures revealed that the mutations induced only subtle structural changes but extended the network of interactions. This resulted in an expanded hot spot region including four additional residues located at the periphery of the paratope with a concomitant loss of the so-called "O-ring" arrangement of energetically inert residues. These results suggest that this class of simple, single-domain scaffolds is capable of generating high-performance binding interfaces with diverse shape. More generally, they suggest that highly functional interfaces can be designed without closely mimicking natural interfaces.     

Small-world network approach to identify key residues in protein-protein interaction

We show that protein complexes can be represented as small-world networks, exhibiting a relatively small number of highly central amino-acid residues occurring frequently at protein-protein interfaces. We further base our analysis on a set of different biological examples of protein-protein interactions with experimentally validated hot spots, and show that 83% of these predicted highly central residues, which are conserved in sequence alignments and nonexposed to the solvent in the protein complex, correspond to or are in direct contact with an experimentally annotated hot spot. The remaining 17% show a general tendency to be close to an annotated hot spot. On the other hand, although there is no available experimental information on their contribution to the binding free energy, detailed analysis of their properties shows that they are good candidates for being hot spots. Thus, highly central residues have a clear tendency to be located in regions that include hot spots. We also show that some of the central residues in the protein complex interfaces are central in the monomeric structures before dimerization and that possible information relating to hot spots of binding free energy could be obtained from the unbound structures.     

Microarray image analysis: background estimation using quantile and morphological filters

Background  

In a microarray experiment the difference in expression between genes on the same slide is up to 10³ fold or more. At low expression, even a small error in the estimate will have great influence on the final test and reference ratios. In addition to the true spot intensity the scanned signal consists of different kinds of noise referred to as background. In order to assess the true spot intensity background must be subtracted. The standard approach to estimate background intensities is to assume they are equal to the intensity levels between spots. In the literature, morphological opening is suggested to be one of the best methods for estimating background this way.     

An automated decision-tree approach to predicting protein interaction hot spots

Protein-protein interactions can be altered by mutating one or more "hot spots," the subset of residues that account for most of the interface's binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge-based models that improve the ability to predict hot spots: K-FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K-CON uses biochemical contact features. The combined K-FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta-Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta-Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein-2 (BMP-2)/BMP receptor-type I (BMPR-IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface.     

Improving missing value imputation of microarray data by using spot quality weights

Background  

Microarray technology has become popular for gene expression profiling, and many analysis tools have been developed for data interpretation. Most of these tools require complete data, but measurement values are often missing A way to overcome the problem of incomplete data is to impute the missing data before analysis. Many imputation methods have been suggested, some na?ve and other more sophisticated taking into account correlation in data. However, these methods are binary in the sense that each spot is considered either missing or present. Hence, they are depending on a cutoff separating poor spots from good spots. We suggest a different approach in which a continuous spot quality weight is built into the imputation methods, allowing for smooth imputations of all spots to larger or lesser degree.     

An ecoprofile of thermoplastic protein derived from blood meal Part 2: thermoplastic processing

Purpose  

The purpose of this research was to develop a nonrenewable energy and greenhouse gas emissions ecoprofile of thermoplastic protein derived from blood meal (Novatein thermoplastic protein; NTP). This was intended for comparison with other bioplastics as well as identification of hot spots in its cradle-to-gate production. In Part 1 of this study, the effect of allocation on the blood meal used as a raw material was discussed. The objective of Part 2 was to assess the ecoprofile of the thermoplastic conversion process and to compare the cradle-to-gate portion of the polymer's life cycle to other bioplastics.     

Hot spot prediction in protein-protein interactions by an ensemble system

Background

Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.

Results

This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.

Conclusion

The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction.

Availability

http://deeplearner.ahu.edu.cn/web/HotspotEL.htm.

     

Extended morphological processing: a practical method for automatic spot detection of biological markers from microscopic images

Background  

A reliable extraction technique for resolving multiple spots in light or electron microscopic images is essential in investigations of the spatial distribution and dynamics of specific proteins inside cells and tissues. Currently, automatic spot extraction and characterization in complex microscopic images poses many challenges to conventional image processing methods.