Dissimilarity-based algorithms for selecting structurally diverse sets of compounds.

This paper commences with a brief introduction to modern techniques for the computational analysis of molecular diversity and the design of combinatorial libraries. It then reviews dissimilarity-based algorithms for the selection of structurally diverse sets of compounds in chemical databases. Procedures are described for selecting a diverse subset of an entire database, and for selecting diverse combinatorial libraries using both reagent-based and product-based selection.     

Combinatorial methods in molecular imprinting

Molecular imprinting is a general method for synthesizing robust, network polymers with highly specific binding sites for small molecules. Recently, combinatorial and computational approaches have been employed to select an optimal molecularly imprinted polymer (MIP) formulation for a targeted analyte. The use of MIPs in the combinatorial field, specifically their use for screening libraries of small molecules, has also been developed.     

Computational Chemistry in Lead Identification,Library Design and Lead Optimisation

Abstract

We describe a variety of the computational techniques which we use in the drug discovery and design process. Some of these computational methods are designed to support the new experimental technologies of high-throughput screening and combinatorial chemistry. We also consider some new approaches to problems of long-standing interest such as protein-ligand docking and the prediction of free energies of binding.     

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.     

Computational methods for sequence mapping of large combinatorial libraries and deduced sequence signatures

Here we describe a computational approach for the high-throughput sequence mapping of combinatorial libraries obtained by DNA shuffling. Original algorithms and their software implementation were developed for the automated and reliable analysis of hybridization data of differentially labeled oligonucleotide probes with PCR products spotted on DNA microarrays. This novel approach allows a context-dependent sequence attribution tolerant to fluctuations in experimental conditions and is well adapted to hybridization signals of variable qualities resulting from high-throughput PCR amplification from colonies. In addition, the analysis permits the calculation of sequence signatures that are characteristic of combinatorial library structure, defects, and diversity. The approach is of interest for the characterization and the equalization (library reduction to nonredundant structures) of combinatorial libraries involved in directed evolution and could be extrapolated to high-throughput polymorphism analysis.     

Microbial pathway engineering for industrial processes: evolution, combinatorial biosynthesis and rational design

Microbial pathway engineering has made significant progress in multiple areas. Many examples of successful pathway engineering for specialty and fine chemicals have been reported in the past two years. Novel carotenoids and polyketides have been synthesized using molecular evolution and combinatorial strategies. In addition, rational design approaches based on metabolic control have been reported to increase metabolic flux to specific products. Experimental and computational tools have been developed to aid in design, reconstruction and analysis of non-native pathways. It is expected that a hybrid of evolutionary, combinatorial and rational design approaches will yield significant advances in the near future.     

Energy estimation in protein design

The progress achieved by several groups in the field of computational protein design shows that successful design methods include two major features: efficient algorithms to deal with the combinatorial exploration of sequence space and optimal energy functions to rank sequences according to their fitness for the given fold.     

Structure‐based design of combinatorial mutagenesis libraries

The development of protein variants with improved properties (thermostability, binding affinity, catalytic activity, etc.) has greatly benefited from the application of high‐throughput screens evaluating large, diverse combinatorial libraries. At the same time, since only a very limited portion of sequence space can be experimentally constructed and tested, an attractive possibility is to use computational protein design to focus libraries on a productive portion of the space. We present a general‐purpose method, called “Structure‐based Optimization of Combinatorial Mutagenesis ” (SOCoM ), which can optimize arbitrarily large combinatorial mutagenesis libraries directly based on structural energies of their constituents. SOCoM chooses both positions and substitutions, employing a combinatorial optimization framework based on library‐averaged energy potentials in order to avoid explicitly modeling every variant in every possible library. In case study applications to green fluorescent protein, β‐lactamase, and lipase A, SOCoM optimizes relatively small, focused libraries whose variants achieve energies comparable to or better than previous library design efforts, as well as larger libraries (previously not designable by structure‐based methods) whose variants cover greater diversity while still maintaining substantially better energies than would be achieved by representative random library approaches. By allowing the creation of large‐scale combinatorial libraries based on structural calculations, SOCoM promises to increase the scope of applicability of computational protein design and improve the hit rate of discovering beneficial variants. While designs presented here focus on variant stability (predicted by total energy), SOCoM can readily incorporate other structure‐based assessments, such as the energy gap between alternative conformational or bound states.     

MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data

MetaReg http://acgt.cs.tau.ac.il/metareg/application.html is a computational tool that models cellular networks and integrates experimental results with such models. MetaReg represents established knowledge about a biological system, available today mostly in informal form in the literature, as probabilistic network models with underlying combinatorial regulatory logic. MetaReg enables contrasting predictions with measurements, model improvements and studying what-if scenarios. By summarizing prior knowledge and providing visual and computational aids, it helps the expert explore and understand her system better.     

IPRO: an iterative computational protein library redesign and optimization procedure

A number of computational approaches have been developed to reengineer promising chimeric proteins one at a time through targeted point mutations. In this article, we introduce the computational procedure IPRO (iterative protein redesign and optimization procedure) for the redesign of an entire combinatorial protein library in one step using energy-based scoring functions. IPRO relies on identifying mutations in the parental sequences, which when propagated downstream in the combinatorial library, improve the average quality of the library (e.g., stability, binding affinity, specific activity, etc.). Residue and rotamer design choices are driven by a globally convergent mixed-integer linear programming formulation. Unlike many of the available computational approaches, the procedure allows for backbone movement as well as redocking of the associated ligands after a prespecified number of design iterations. IPRO can also be used, as a limiting case, for the redesign of a single or handful of individual sequences. The application of IPRO is highlighted through the redesign of a 16-member library of Escherichia coli/Bacillus subtilis dihydrofolate reductase hybrids, both individually and through upstream parental sequence redesign, for improving the average binding energy. Computational results demonstrate that it is indeed feasible to improve the overall library quality as exemplified by binding energy scores through targeted mutations in the parental sequences.     

Computational methods for analysis of cellular functions and pathways collectively targeted by differentially expressed microRNA.

This report presents computational methods of analysis of cellular processes, functions, and pathways affected by differentially expressed microRNA, a statistical basis of the gene enrichment analysis method, a modification of enrichment analysis method accounting for combinatorial targeting of Gene Ontology categories by multiple miRNAs and examples of the global functional profiling of predicted targets of differentially expressed miRNAs in cancer. We have also summarized an application of Ingenuity Pathway Analysis tools for in depth analysis of microRNA target sets that may be useful for the biological interpretation of microRNA profiling data. To illustrate the utility of these methods, we report the main results of our recent computational analysis of five published datasets of aberrantly expressed microRNAs in five human cancers (pancreatic cancer, breast cancer, colon cancer, lung cancer, and lymphoma). Using a combinatorial target prediction algorithm and statistical enrichment analysis, we have determined Gene Ontology categories as well as biological functions, disease categories, toxicological categories, and signaling pathways that are: targeted by multiple microRNAs; statistically significantly enriched with target genes; and known to be affected in specific cancers. Our recent computational analysis of predicted targets of co-expressed miRNAs in five human cancers suggests that co-expressed miRNAs provide systemic compensatory response to the abnormal phenotypic changes in cancer cells by targeting a broad range of functional categories and signaling pathways reportedly affected in a particular cancer.     

Biomolecular computing: Is it ready to take off?

Biomolecular computing is an emerging field at the interface of computer science, biological science and engineering. It uses DNA and other biological materials as the building blocks for construction of living computational machines to solve difficult combinatorial problems. In this article, notable advances in the biomolecular computing are reviewed and challenges associated with this multidisciplinary research are addressed. Finally, several perspectives are given based on the review of biomolecular computing.     

Tailoring new enzyme functions by rational redesign

Site-directed mutagenesis is still a very efficient strategy to elaborate improved enzymes. Recently, advances have been made in developing rational strategies aimed at reshaping enzyme specificities and mechanisms, and at engineering biocatalysts through molecular assembling. These knowledge-based studies greatly benefit from the most recent computational analyses of enzyme structures and functions. The combination of rational and combinatorial methods opens up new vistas in the design of stable and efficient enzymes.     

Sensitive detection of sequence similarity using combinatorial pattern discovery: a challenging study of two distantly related protein families

We investigate the performance of combinatorial pattern discovery to detect remote sequence similarities in terms of both biological accuracy and computational efficiency for a pair of distantly related families, as a case study. The two families represent the cupredoxins and multicopper oxidases, both containing blue copper-binding domains. These families present a challenging case due to low sequence similarity, different local structure, and variable sequence conservation at their copper-binding active sites. In this study, we investigate a new approach for automatically identifying weak sequence similarities that is based on combinatorial pattern discovery. We compare its performance with a traditional, HMM-based scheme and obtain estimates for sensitivity and specificity of the two approaches. Our analysis suggests that pattern discovery methods can be substantially more sensitive in detecting remote protein relationships while at the same time guaranteeing high specificity.     

Trawler: de novo regulatory motif discovery pipeline for chromatin immunoprecipitation

We developed Trawler, the fastest computational pipeline to date, to efficiently discover over-represented motifs in chromatin immunoprecipitation (ChIP) experiments and to predict their functional instances. When we applied Trawler to data from yeast and mammals, 83% of the known binding sites were accurately called, often with other additional binding sites, providing hints of combinatorial input. Newly discovered motifs and their features (identity, conservation, position in sequence) are displayed on a web interface.     

EMILiO: a fast algorithm for genome-scale strain design

Systems-level design of cell metabolism is becoming increasingly important for renewable production of fuels, chemicals, and drugs. Computational models are improving in the accuracy and scope of predictions, but are also growing in complexity. Consequently, efficient and scalable algorithms are increasingly important for strain design. Previous algorithms helped to consolidate the utility of computational modeling in this field. To meet intensifying demands for high-performance strains, both the number and variety of genetic manipulations involved in strain construction are increasing. Existing algorithms have experienced combinatorial increases in computational complexity when applied toward the design of such complex strains. Here, we present EMILiO, a new algorithm that increases the scope of strain design to include reactions with individually optimized fluxes. Unlike existing approaches that would experience an explosion in complexity to solve this problem, we efficiently generated numerous alternate strain designs producing succinate, l-glutamate and l-serine. This was enabled by successive linear programming, a technique new to the area of computational strain design.     

The death factors: a combinatorial analysis

This paper presents theoretical and computational tools to understand how a small group of proteins, the death factors, are involved in widely different behavior of the cell. Experiments were done using a virtual laboratory that can simulate cellular response to different external stimuli. WARNING: It is not certain which of the theoretical protein clusters described here really occur in nature. In addition, the rules of cluster assembly are combinatorial, and thus an oversimplification to describe the real situation.