Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014

PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors

Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

ContactPFP: Protein Function Prediction Using Predicted Contact Information

Computational function prediction is one of the most important problems in bioinformatics as elucidating the function of genes is a central task in molecular biology and genomics. Most of the existing function prediction methods use protein sequences as the primary source of input information because the sequence is the most available information for query proteins. There are attempts to consider other attributes of query proteins. Among these attributes, the three-dimensional (3D) structure of proteins is known to be very useful in identifying the evolutionary relationship of proteins, from which functional similarity can be inferred. Here, we report a novel protein function prediction method, ContactPFP, which uses predicted residue-residue contact maps as input structural features of query proteins. Although 3D structure information is known to be useful, it has not been routinely used in function prediction because the 3D structure is not experimentally determined for many proteins. In ContactPFP, we overcome this limitation by using residue-residue contact prediction, which has become increasingly accurate due to rapid development in the protein structure prediction field. ContactPFP takes a query protein sequence as input and uses predicted residue-residue contact as a proxy for the 3D protein structure. To characterize how predicted contacts contribute to function prediction accuracy, we compared the performance of ContactPFP with several well-established sequence-based function prediction methods. The comparative study revealed the advantages and weaknesses of ContactPFP compared to contemporary sequence-based methods. There were many cases where it showed higher prediction accuracy. We examined factors that affected the accuracy of ContactPFP using several illustrative cases that highlight the strength of our method.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

BACKGROUND

The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities.

OBJECTIVE

Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures.

METHODS

We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models.

RESULTS

Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data.

CONCLUSION

Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

One-Pot Biocatalytic Preparation of Enantiopure Unusual α-Amino Acids from α-Hydroxy Acids via a Hydrogen-Borrowing Dual-Enzyme Cascade

Unusual α-amino acids (UAAs) are important fundamental building blocks and play a key role in medicinal chemistry. Here, we constructed a hydrogen-borrowing dual-enzyme cascade for efficient synthesis of UAAs from α-hydroxy acids (α-HAs). D-mandelate dehydrogenase from Lactobacillus brevis (LbMDH) was screened for the catalysis of α-HAs to α-keto acids but with low activity towards aliphatic α-HAs. Therefore, we rational engineered LbMDH to improve its activity towards aliphatic α-HAs. The substitution of residue Leu243 located in the substrate entrance channel with nonpolar amino acids like Met, Trp, and Ile significantly influenced the enzyme activity towards different α-HAs. Compared with wild type (WT), variant L243W showed 103 U/mg activity towards D-α-hydroxybutyric acid, 1.7 times of the WT’s 60.2 U/mg, while its activity towards D-mandelic acid decreased. Variant L243M showed 2.3 times activity towards D-mandelic acid compared to WT, and its half-life at 40 °C increased to 150.2 h comparing with 98.5 h of WT. By combining LbMDH with L-leucine dehydrogenase from Bacillus cereus, the synthesis of structurally diverse range of UAAs from α-HAs was constructed. We achieved 90.7% conversion for L-phenylglycine production and 66.7% conversion for L-α-aminobutyric acid production. This redox self-sufficient cascade provided high catalytic efficiency and generated pure products.

Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0

Computational prediction of protein-ligand interactions is a useful approach that aids the drug discovery process. Two major tasks of computational approaches are to predict the docking pose of a compound in a known binding pocket and to rank compounds in a library according to their predicted binding affinities. There are many computational tools developed in the past decades both in academia and industry. To objectively assess the performance of existing tools, the community has held a blind assessment of computational predictions, the Drug Design Data Resource Grand Challenge. This round, Grand Challenge 4 (GC4), focused on two targets, protein beta-secretase 1 (BACE-1) and cathepsin S (CatS). We participated in GC4 in both BACE-1 and CatS challenges using our molecular surface-based virtual screening method, PL-PatchSurfer2.0. A unique feature of PL-PatchSurfer2.0 is that it uses the three-dimensional Zernike descriptor, a mathematical moment-based shape descriptor, to quantify local shape complementarity between a ligand and a receptor, which properly incorporates molecular flexibility and provides stable affinity assessment for a bound ligand-receptor complex. Since PL-PatchSurfer2.0 does not explicitly build a bound pose of a ligand, we used an external docking program, such as AutoDock Vina, to provide an ensemble of poses, which were then evaluated by PL-PatchSurfer2.0. Here, we provide an overview of our method and report the performance in GC4.

Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates

Background:

Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism is time- and recourse- consuming. In the phase I metabolism of foreign compounds (i.e., most of drugs), cytochrome P450 enzymes play a key role. To help develop drugs with proper ADME properties, computational models are highly desired to predict the ADME properties of drug candidates, particularly for drugs binding to cytochrome P450.

Objective:

This narrative review aims to briefly summarize machine learning techniques used in the prediction of the cytochrome P450 isoform specificity of drug candidates.

Results:

Both single-label and multi-label classification methods have demonstrated good performance on modelling and prediction of the isoform specificity of substrates based on their quantitative descriptors.

Conclusion:

This review provides a guide for researchers to develop machine learning-based methods to predict the cytochrome P450 isoform specificity of drug candidates.

In silico structure-based approaches to discover protein-protein interaction-targeting drugs

A core concept behind modern drug discovery is finding a small molecule that modulates a function of a target protein. This concept has been successfully applied since the mid-1970s. However, the efficiency of drug discovery is decreasing because the druggable target space in the human proteome is limited. Recently, protein-protein interaction (PPI) has been identified asan emerging target space for drug discovery. PPI plays a pivotal role in biological pathways including diseases. Current human interactome research suggests that the number of PPIs is between 130,000 and 650,000, and only a small number of them have been targeted as drug targets. For traditional drug targets, in silico structure-based methods have been successful in many cases. However, their performance suffers on PPI interfaces because PPI interfaces are different in five major aspects: From a geometric standpoint, they have relatively large interface regions, flat geometry, and the interface surface shape tends to fluctuate upon binding. Also, their interactions are dominated by hydrophobic atoms, which is different from traditional binding-pocket-targeted drugs. Finally, PPI targets usually lack natural molecules that bind to the target PPI interface. Here, we first summarize characteristics of PPI interfaces and their known binders. Then, we will review existing in silico structure-based approaches for discovering small molecules that bind to PPI interfaces.

Discovery of Nicotinamide Adenine Dinucleotide Binding Proteins in the Escherichia coli Proteome Using a Combined Energetic- and Structural-Bioinformatics-Based Approach

Protein-ligand interaction plays a critical role in regulating the biochemical functions of proteins. Discovering protein targets for ligands is vital to new drug development. Here, we present a strategy that combines experimental and computational approaches to identify ligand-binding proteins in a proteomic scale. For the experimental part, we coupled pulse proteolysis with filter-assisted sample preparation (FASP) and quantitative mass spectrometry. Under denaturing conditions, ligand binding affected protein stability, which resulted in altered protein abundance after pulse proteolysis. For the computational part, we used the software Patch-Surfer2.0. We applied the integrated approach to identify nicotinamide adenine dinucleotide (NAD)-binding proteins in the Escherichia coli proteome, which has over 4200 proteins. Pulse proteolysis and Patch-Surfer2.0 identified 78 and 36 potential NAD-binding proteins, respectively, including 12 proteins that were consistently detected by the two approaches. Interestingly, the 12 proteins included 8 that are not previously known as NAD binders. Further validation of these eight proteins showed that their binding affinities to NAD computed by AutoDock Vina are higher than their cognate ligands and also that their protein ratios in the pulse proteolysis are consistent with known NAD-binding proteins. These results strongly suggest that these eight proteins are indeed newly identified NAD binders.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.