Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.     

Mutational fitness landscape and drug resistance

Robust technology has been developed to systematically quantify fitness landscapes that provide valuable opportunities to improve our understanding of drug resistance and define new avenues to develop drugs with reduced resistance susceptibility. We outline the critical importance of drug resistance studies and the potential for fitness landscape approaches to contribute to this effort. We describe the major technical advancements in mutational scanning, which is the primary approach used to quantify protein fitness landscapes. There are many complex steps to consider in planning and executing mutational scanning projects including developing a selection scheme, generating mutant libraries, tracking the frequency of variants using next-generation sequencing, and processing and interpreting the data. Key experimental parameters impacting each of these steps are discussed to aid in planning fitness landscape studies. There is a strong need for improved understanding of drug resistance, and fitness landscapes provide a promising new approach.     

A new strategy for improved secondary screening and lead optimization using high-resolution SPR characterization of compound-target interactions

Biophysical label-free assays such as those based on SPR are essential tools in generating high-quality data on affinity, kinetic, mechanistic and thermodynamic aspects of interactions between target proteins and potential drug candidates. Here we show examples of the integration of SPR with bioinformatic approaches and mutation studies in the early drug discovery process. We call this combination 'structure-based biophysical analysis'. Binding sites are identified on target proteins using information that is either extracted from three-dimensional structural analysis (X-ray crystallography or NMR), or derived from a pharmacore model based on known binders. The binding site information is used for in silico screening of a large substance library (e.g. available chemical directory), providing virtual hits. The three-dimensional structure is also used for the design of mutants where the binding site has been impaired. The wild-type target and the impaired mutant are then immobilized on different spots of the sensor chip and the interactions of compounds with the wild-type and mutant are compared in order to identify selective binders for the binding site of the target protein. This method can be used as a cost-effective alternative to high-throughput screening methods in cases when detailed binding site information is available. Here, we present three examples of how this technique can be applied to provide invaluable data during different phases of the drug discovery process.     

Tools for genome-wide strain design and construction

Advances in DNA sequencing and synthesis technologies concurrent with the development of new recombinant DNA approaches have enabled the extension of directed evolution algorithms to the genome-scale. It is now possible to simultaneously map the effect of mutation(s) in each and every gene in the genome onto almost any screenable or selectable phenotype in less than a week. Such maps can be used to direct the design and construction of libraries containing billions of rationally designed combinatorial mutations. Such combinatorial libraries can now also be created and evaluated in less than a week. The review presents and discusses these new technologies within the context of directed evolution and inverse metabolic engineering.     

A Structure-Based Model for Predicting Serum Albumin Binding

One of the many factors involved in determining the distribution and metabolism of a compound is the strength of its binding to human serum albumin. While experimental and QSAR approaches for determining binding to albumin exist, various factors limit their ability to provide accurate binding affinity for novel compounds. Thus, to complement the existing tools, we have developed a structure-based model of serum albumin binding. Our approach for predicting binding incorporated the inherent flexibility and promiscuity known to exist for albumin. We found that a weighted combination of the predicted logP and docking score most accurately distinguished between binders and nonbinders. This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data.     

Fragment-based design of small RNA binders: promising developments and contribution of NMR

Fragment-based drug design has become increasingly popular over the last decade. We review here the use of this approach to design small RNA binders. In addition, we discuss the use of NMR to detect the binding of small molecules on RNA targets and to guide chemists in the design of compounds targeting RNA.     

Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.     

Fragmentation-free LC-MS can identify hundreds of proteins

One of the most common approaches for large-scale protein identification is LC, followed by MS. If more than a few proteins are to be identified, the additional fragmentation of individual peptides has so far been considered as indispensable, and thus, the associated costs, in terms of instrument time and infrastructure, as unavoidable. Here, we present evidence to the contrary. Using a combination of (i) highly accurate and precise mass measurements, (ii) modern retention time prediction, and (iii) a robust scoring algorithm, we were able to identify 257 proteins of Francisella tularensis from a single LC-MS experiment in a fragmentation-free approach (i.e. without experimental fragmentation spectra). This number amounts to 59% of the number of proteins identified in a standard fragmentation-based approach, when executed with the same false discovery rate. Independent evidence supports at least 27 of a set of 31 proteins that were identified only in the fragmentation-free approach. Our results suggest that additional developments in retention time prediction, measurement technology, and scoring algorithms may render fragmentation-free approaches an interesting complement or an alternative to fragmentation-based approaches.     

Hotspot-centric de novo design of protein binders

Protein-protein interactions play critical roles in biology, and computational design of interactions could be useful in a range of applications. We describe in detail a general approach to de novo design of protein interactions based on computed, energetically optimized interaction hotspots, which was recently used to produce high-affinity binders of influenza hemagglutinin. We present several alternative approaches to identify and build the key hotspot interactions within both core secondary structural elements and variable loop regions and evaluate the method's performance in natural-interface recapitulation. We show that the method generates binding surfaces that are more conformationally restricted than previous design methods, reducing opportunities for off-target interactions.     

蛋白质工程：从定向进化到计算设计

定向进化通过建立突变体文库与高通量筛选方法,快速提升蛋白的特定性质,是目前蛋白质工程最为常用的蛋白质设计改造策略。近十年随着计算机运算能力大幅提升以及先进算法不断涌现,计算机辅助蛋白质设计改造得到了极大的重视和发展,成为蛋白质工程新开辟的重要方向。以结构模拟与能量计算为基础的蛋白质计算设计不但能改造酶的底物特异性与热稳定性,还可从头设计具有特定功能的人工酶。近年来机器学习等人工智能技术也被应用于计算机辅助蛋白质设计改造,并取得瞩目的成绩。文中介绍了蛋白质工程的发展历程,重点评述当前计算机辅助蛋白质设计改造方面的进展与应用,并展望其未来发展方向。     

Strategies and computational tools for improving randomized protein libraries

In the last decade, directed evolution has become a routine approach for engineering proteins with novel or altered properties. Concurrently, a trend away from purely 'blind' randomization strategies and towards more 'semi-rational' approaches has also become apparent. In this review, we discuss ways in which structural information and predictive computational tools are playing an increasingly important role in guiding the design of randomized libraries: web servers such as ConSurf-HSSP and SCHEMA allow the prediction of sites to target for producing functional variants, while algorithms such as GLUE, PEDEL and DRIVeR are useful for estimating library completeness and diversity. In addition, we review recent methodological developments that facilitate the construction of unbiased libraries, which are inherently more diverse than biased libraries and therefore more likely to yield improved variants.     

Acoustic microfluidic chip technology to facilitate automation of phage display selection

Modern tools in proteomics require access to large arrays of specific binders for use in multiplex array formats, such as microarrays, to decipher complex biological processes. Combinatorial protein libraries offer a solution to the generation of collections of specific binders, but unit operations in the process to isolate binders from such libraries must be automatable to ensure an efficient procedure. In the present study, we show how a microfluidic concept that utilizes particle separation in an acoustic force field can be used to efficiently separate antigen-bound from unbound members of such libraries in a continuous flow format. Such a technology has the hallmarks for incorporation in a fully automated selection system for the isolation of specific binders.     

Algorithms for the automated selection of fragment-like molecules using single-point surface plasmon resonance measurements

Fragment-based approaches have added to the arsenal of tools used to identify novel developable leads for drug discovery with high ligand efficiencies. A variety of label-free technologies have been developed and used throughout the industry for fragment screening. Using surface plasmon resonance (SPR) as a fragment screening platform is a relatively new approach. The miniaturization and automation of this technology has led to an associated problem: the large volume of raw data often makes it challenging to analyze and integrate the results of SPR data into the workflow of project teams engaged in the discovery process in a timely fashion. As such, several sets of equations were derived and implemented on Merck’s intranet to score single sensorgrams to distinguish stable binders from weak or anomalous binders. This set of equations was optimized and validated on simulated data to both capture “fragment-like” behavior from SPR experiments and filter out much of the anomalous behavior commonly observed. It has subsequently been applied successfully to several in-house discovery programs.     

Developing hybrid molecule therapeutics for diverse enzyme inhibitory action: Active role of coumarin-based structural leads in drug discovery

Hybrid drugs featuring two or more potentially bioactive pharmacophores have been recognized as advanced and superior chemical entities to simultaneously modulate multiple drug targets of multifactorial diseases, thus overcoming the severe side effects associated with a single drug molecule. The selection of these chemical moieties to produce hybrid structures with druggable properties is generally facilitated by the observed and/or anticipated synergistic pharmacological activities of the individual molecules. In this perspective, coumarin template has extensively been studied in pursuit of structurally diverse leads for drug development due to high affinity and specificity to different molecular targets. This review highlights the most commonly exploited approaches conceptualizing the design and construction of hybrid molecules by coupling two or more individual fragments with or without an appropriate linker. In addition to the design strategies, this review also summarizes and reflects on the therapeutic potential of these hybrid molecules for diverse enzyme inhibitory action as well as their observed structure-activity relationship (SAR). Several key features of the synthesized hybrid structures that assert a profound impact on the inhibitory function have also been discussed alongside computational investigations, inhibitor molecular diversity and selectivity toward multiple drug targets. Finally, these drug discovery and development efforts should serve as a handy reference aiming to provide a useful platform for the exploration of new coumarin-based compounds with enhanced enzyme inhibitory profile.     

Molecular diversity in engineered protein libraries

Engineered protein libraries, defined here as a collection of different mutant variants of a single specific protein, are intentionally designed to be rich in molecular diversity and can span ranges from as little as 400 different variants to greater than 10(12) members per library. The goal of engineering libraries is to generate new protein variants, identified upon screening, that possess desired novel properties. Exploitation of the natural organization of the genetic code has led to 'focused' libraries that are lower in overall complexity yet biased towards variants with preferred biophysical properties. An emerging trend, in which computational algorithms are blended with in vivo screens, is also leading towards greater and more rapid success in the field of protein design.     

Progress in computational protein design

Current progress in computational structure-based protein design is reviewed in the areas of methodology and applications. Foundational advances include new potential functions, more efficient ways of computing energetics, flexible treatments of solvent, and useful energy function approximations, as well as ensemble-based approaches to scoring designs for inclusion of entropic effects, improvements to guaranteed and to stochastic search techniques, and methods to design combinatorial libraries for screening and selection. Applications include new approaches and successes in the design of specificity for protein folding, binding, and catalysis, in the redesign of proteins for enhanced binding affinity, and in the application of design technology to study and alter enzyme catalysis. Computational protein design continues to mature and advance.     

A rapid,efficient, and economical inverse polymerase chain reaction-based method for generating a site saturation mutant library

With the development of deep sequencing methodologies, it has become important to construct site saturation mutant (SSM) libraries in which every nucleotide/codon in a gene is individually randomized. We describe methodologies for the rapid, efficient, and economical construction of such libraries using inverse polymerase chain reaction (PCR). We show that if the degenerate codon is in the middle of the mutagenic primer, there is an inherent PCR bias due to the thermodynamic mismatch penalty, which decreases the proportion of unique mutants. Introducing a nucleotide bias in the primer can alleviate the problem. Alternatively, if the degenerate codon is placed at the 5′ end, there is no PCR bias, which results in a higher proportion of unique mutants. This also facilitates detection of deletion mutants resulting from errors during primer synthesis. This method can be used to rapidly generate SSM libraries for any gene or nucleotide sequence, which can subsequently be screened and analyzed by deep sequencing.     

Signaling protein networks as targets of new antineoplastic drugs

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from "-omic" approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, "intelligent" drug candidates. An additional medicinal chemistry strategy (via 40-50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.