Explore the vast range of titles available.

Peptides, or short chains of amino-acid residues, are becoming increasingly important as active ingredients of drugs and as crucial probes and/or tools in medical, biotechnological, and pharmaceutical research. Situated at the interface between small molecules and larger macromolecular systems, they pose a difficult challenge for computational methods. We report an in silico peptide library generation and prioritization workflow using CmDock for identifying tetrapeptide ligands that bind to Fc regions of antibodies that is analogous to known in vitro recombinant peptide libraries’ display and expression systems. The results of our in silico study are in accordance with existing scientific literature on in vitro peptides that bind to antibody Fc regions. In addition, we postulate an evolving in silico library design workflow that will help circumvent the combinatorial problem of in vitro comprehensive peptide libraries by focusing on peptide subunits that exhibit favorable interaction profiles in initial in silico peptide generation and testing.

Phage-display selection of immunoglobulin (IG) or antibody single chain Fragment variable (scFv) from combinatorial libraries is widely used for identifying new antibodies for novel targets. Next-generation sequencing (NGS) has recently emerged as a new method for the high throughput characterization of IG and T cell receptor (TR) immune repertoires both and . However, challenges remain for the NGS sequencing of scFv from combinatorial libraries owing to the scFv length (>800 bp) and the presence of two variable domains [variable heavy (VH) and variable light (VL) for IG] associated by a peptide linker in a single chain. Here, we show that single-molecule real-time (SMRT) sequencing with the Pacific Biosciences RS II platform allows for the generation of full-length scFv reads obtained from an selection of scFv-phages in an animal model of atherosclerosis. We first amplified the DNA of the phagemid inserts from scFv-phages eluted from an aortic section at the third round of the selection. From this amplified DNA, 450,558 reads were obtained from 15 SMRT cells. Highly accurate circular consensus sequences from these reads were generated, filtered by quality and then analyzed by IMGT/HighV-QUEST with the functionality for scFv. Full-length scFv were identified and characterized in 348,659 reads. Full-length scFv sequencing is an absolute requirement for analyzing the associated VH and VL domains enriched during the panning rounds. In order to further validate the ability of SMRT sequencing to provide high quality, full-length scFv sequences, we tracked the reads of an scFv-phage clone P3 previously identified by biological assays and Sanger sequencing. Sixty P3 reads showed 100% identity with the full-length scFv of 767 bp, 53 of them covering the whole insert of 977 bp, which encompassed the primer sequences. The remaining seven reads were identical over a shortened length of 939 bp that excludes the vicinity of primers at both ends. Interestingly these reads were obtained from each of the 15 SMRT cells. Thus, the SMRT sequencing method and the IMGT/HighV-QUEST functionality for scFv provides a straightforward protocol for characterization of full-length scFv from combinatorial phage libraries.

Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures 1 – 4 and ultra-large libraries of virtual compounds 5 , 6 . However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries 7 , new approaches to compound screening are needed 8 , 9 . Here we introduce a modular synthon-based approach—V-SYNTHES—to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold–synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant ( K i ) = 0.9 nM) and CB 2 /CB 1 selectivity (50–200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm. V-SYNTHES, a scalable and computationally cost-effective synthon-based approach to compound screening, identified compounds with a high affinity for CB2 and CB1 in a hierarchical structure-based screen of more than 11 billion compounds.

Combinatorial chemistry invented nearly 40 years ago was welcomed with enthusiasm in the drug research community. The method offered access to a practically unlimited number of new compounds. The new compounds however are mixtures, and methods had to be developed for the identification of the bioactive components. This was one of the reasons why the method could not providethe expected cornucopia of new drugs. Among the different screening methods, two approaches seem to offer the best results. One of them is based on the intrinsic property of the combinatorial split and pool solid‐phase synthesis: One compound forms on each bead of the solid support. Different methods have been developed to encode the beads and identify the structure of compounds formed on them. The most important method applies DNA oligomers for encoding. As a second approach in screening, DNA‐encoded combinatorial libraries are synthesized omitting the solid support and the mixtures are screened in solution using affinity binding methods. Libraries containing billions and even trillions of components are synthesized and successfully tested, which led to the identification of a significant number of new leads. Combinatorial technology comprises the synthesis of a mixture containing a high number of compounds in one procedure and screening the mixture also in one procedure. The left part of the image shows a two‐step synthesis of a nine‐member library using three building blocks and three DNA codes in both steps. The central part shows screening the library by affinity‐based binding. The non‐binding molecules are washed away, and the code of the binding molecule is amplified then sequenced.

To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si–H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee (enantiomeric excess). By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.

Background Biological Computer Aided Design (bioCAD) assists the de novo design and selection of existing genetic components to achieve a desired biological activity, as part of an integrated design-build-test cycle. To meet the emerging needs of Synthetic Biology, bioCAD tools must address the increasing prevalence of combinatorial library design, design rule specification, and scar-less multi-part DNA assembly. Results We report the development and deployment of web-based bioCAD software, DeviceEditor, which provides a graphical design environment that mimics the intuitive visual whiteboard design process practiced in biological laboratories. The key innovations of DeviceEditor include visual combinatorial library design, direct integration with scar-less multi-part DNA assembly design automation, and a graphical user interface for the creation and modification of design specification rules. We demonstrate how biological designs are rendered on the DeviceEditor canvas, and we present effective visualizations of genetic component ordering and combinatorial variations within complex designs. Conclusions DeviceEditor liberates researchers from DNA base-pair manipulation, and enables users to create successful prototypes using standardized, functional, and visual abstractions. Open and documented software interfaces support further integration of DeviceEditor with other bioCAD tools and software platforms. DeviceEditor saves researcher time and institutional resources through correct-by-construction design, the automation of tedious tasks, design reuse, and the minimization of DNA assembly costs.

Combinatorial chemistry and high-throughput techniques are an efficient way of exploring optimal values of elemental composition. Optimal composition can result in high performance in a sequence of material synthesis and characterization. Materials combinatorial libraries are typically encountered in the form of a thin film composition gradient which is produced by simultaneous material deposition on a substrate from two or more sources that are spatially separated and chemically different. Fast spatially resolved techniques are needed to characterize structure, composition, and relevant properties of these combinatorial screening samples. In this work, the capability of a glow discharge optical emission spectroscopy (GD-OES) elemental mapping system is extended to nitrogen-based combinatorial libraries with nonconductive components through the use of pulsed radiofrequency power. The effects of operating parameters of the glow discharge and detection system on the achievable spatial resolution were investigated as it is the first time that an rf source is coupled to a setup featuring a push-broom hyperspectral imaging system and a restrictive anode tube GD source. Spatial-resolution optimized conditions were then used to characterize an aluminum nitride/chromium nitride thin-film composition spread. Qualitative elemental maps could be obtained within 16.8 s, orders of magnitude faster than typical techniques. The use of certified reference materials allowed quantitative elemental analysis maps to be extracted from the emission intensity images. Moreover, the quantitative procedure allowed correcting for the inherent emission intensity inhomogeneity in GD-OES. The results are compared to quantitative depth profiles obtained with a commercial GD-OES instrument.

High-diversity genetically-encoded combinatorial libraries (10 8 −10 13 members) are a rich source of peptide-based binding molecules, identified by affinity selection. Synthetic libraries can access broader chemical space, but typically examine only ~ 10 6 compounds by screening. Here we show that in-solution affinity selection can be interfaced with nano-liquid chromatography-tandem mass spectrometry peptide sequencing to identify binders from fully randomized synthetic libraries of 10 8 members—a 100-fold gain in diversity over standard practice. To validate this approach, we show that binders to a monoclonal antibody are identified in proportion to library diversity, as diversity is increased from 10 6 –10 8 . These results are then applied to the discovery of p53-like binders to MDM2, and to a family of 3–19 nM-affinity, α/β-peptide-based binders to 14-3-3. An X-ray structure of one of these binders in complex with 14-3-3σ is determined, illustrating the role of β-amino acids in facilitating a key binding contact. Synthetic peptide libraries can access broad chemical space, but generally examine only ~ 10 6  compounds. Here, the authors show that in-solution affinity selection, interfaced with nLC-MS/MS sequencing, can identify binders from fully randomized synthetic libraries of 10 8 members.

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY’s potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution. The effective design of cold-start enzyme libraries to balance fitness and diversity enables access to enzyme variants that are readily evolvable and close to the optima in the fitness landscape. Here, the authors develop MODIFY (machine learning-optimized library design with improved fitness and diversity), a machine learning algorithm to co-optimize expected fitness and sequence diversity of starting libraries, enhancing the efficiency of directed evolution in enzyme engineering.

Successful screening campaigns depend on large and structurally diverse collections of compounds. In macrocycle screening, variation of the molecular scaffold is important for structural diversity, but so far it has been challenging to diversify this aspect in large combinatorial libraries. Here, we report the cyclization of peptides with two chemical bridges to provide rapid access to thousands of different macrocyclic scaffolds in libraries that are easy to synthesize, screen and decode. Application of this strategy to phage-encoded libraries allowed for the screening of an unprecedented structural diversity of macrocycles against plasma kallikrein, which is important in the swelling disorder hereditary angioedema. These libraries yielded inhibitors with remarkable binding properties (subnanomolar Ki, >1,000-fold selectivity) despite the small molecular mass (~1,200 Da). An interlaced bridge format characteristic of this strategy provided high proteolytic stability (t1/2 in plasma of >3 days), making double-bridged peptides potentially amenable to topical or oral delivery.