Bionano-Interfaces through Peptide Design

The clinical success of restoring bone and tooth function through implants critically depends on the maintenance of an infection-free, integrated interface between the host tissue and the biomaterial surface. The surgical site infections, which are the infections within one year of surgery, occur in approximately 160,000-300,000 cases in the US annually. Antibiotics are the conventional treatment for the prevention of infections. They are becoming ineffective due to bacterial antibiotic-resistance from their wide-spread use. There is an urgent need both to combat bacterial drug resistance through new antimicrobial agents and to limit the spread of drug resistance by limiting their delivery to the implant site. This work aims to reduce surgical site infections from implants by designing of chimeric antimicrobial peptides to integrate a novel and effective delivery method. In recent years, antimicrobial peptides (AMPs) have attracted interest as natural sources for new antimicrobial agents. By being part of the immune system in all life forms, they are examples of antibacterial agents with successfully maintained efficacy across evolutionary time. Both natural and synthetic AMPs show significant promise for solving the antibiotic resistance problems. In this work, AMP1 and AMP2 was shown to be active against three different strains of pathogens in Chapter 4. In the literature, these peptides have been shown to be effective against multi-drug resistant bacteria. However, their effective delivery to the implantation site limits their clinical use. In recent years, different groups adapted covalent chemistry-based or non-specific physical adsorption methods for antimicrobial peptide coatings on implant surfaces. Many of these procedures use harsh chemical conditions requiring multiple reaction steps. Furthermore, none of these methods allow the orientation control of these molecules on the surfaces, which is an essential consideration for biomolecules. In the last few decades, solid binding peptides attracted high interest due to their material specificity and self-assembly properties. These peptides offer robust surface adsorption and assembly in diverse applications. In this work, a design method for chimeric antimicrobial peptides that can self-assemble and self-orient onto biomaterial surfaces was demonstrated. Three specific aims used to address this two-fold strategy of self-assembly and self-orientation are: 1) Develop classification and design methods using rough set theory and genetic algorithm search to customize antibacterial peptides; 2) Develop chimeric peptides by designing spacer sequences to improve the activity of antimicrobial peptides on titanium surfaces; 3) Verify the approach as an enabling technology by expanding the chimeric design approach to other biomaterials. In Aim 1, a peptide classification tool was developed because the selection of an antimicrobial peptide for an application was difficult among the thousands of peptide sequences available. A rule-based rough-set theory classification algorithm was developed to group antimicrobial peptides by chemical properties. This work is the first time that rough set theory has been applied to peptide activity analysis. The classification method on benchmark data sets resulted in low false discovery rates. The novel rough set theory method was combined with a novel genetic algorithm search, resulting in a method for customizing active antibacterial peptides using sequence-based relationships. Inspired by the fact that spacer sequences play critical roles between functional protein domains, in Aim 2, chimeric peptides were designed to combine solid binding functionality with antimicrobial functionality. To improve how these functions worked together in the same peptide sequence, new spacer sequences were engineered. The rough set theory method from Aim 1 was used to find structure-based relationships to discover new spacer sequences which improved the antimicrobial activity of the chimeric peptides. In Aim 3, the proposed approach is demonstrated as an enabling technology. In this work, calcium phosphate was tested and verified the modularity of the chimeric antimicrobial self-assembling peptide approach. Other chimeric peptides were designed for common biomaterials zirconia and urethane polymer. Finally, an antimicrobial peptide was engineered for a dental adhesive system toward applying spacer design concepts to optimize the antimicrobial activity

Protein sequences classification by means of feature extraction with substitution matrices

<p>Abstract</p> <p>Background</p> <p>This paper deals with the preprocessing of protein sequences for supervised classification. Motif extraction is one way to address that task. It has been largely used to encode biological sequences into feature vectors to enable using well-known machine-learning classifiers which require this format. However, designing a suitable feature space, for a set of proteins, is not a trivial task. For this purpose, we propose a novel encoding method that uses amino-acid substitution matrices to define similarity between motifs during the extraction step.</p> <p>Results</p> <p>In order to demonstrate the efficiency of such approach, we compare several encoding methods using some machine learning classifiers. The experimental results showed that our encoding method outperforms other ones in terms of classification accuracy and number of generated attributes. We also compared the classifiers in term of accuracy. Results indicated that SVM generally outperforms the other classifiers with any encoding method. We showed that SVM, coupled with our encoding method, can be an efficient protein classification system. In addition, we studied the effect of the substitution matrices variation on the quality of our method and hence on the classification quality. We noticed that our method enables good classification accuracies with all the substitution matrices and that the variances of the obtained accuracies using various substitution matrices are slight. However, the number of generated features varies from a substitution matrix to another. Furthermore, the use of already published datasets allowed us to carry out a comparison with several related works.</p> <p>Conclusions</p> <p>The outcomes of our comparative experiments confirm the efficiency of our encoding method to represent protein sequences in classification tasks.</p

Protein secondary structure prediction using BLAST and relaxed threshold rule induction from coverings

Protein structure prediction has always been an important research area in bioinformatics and biochemistry. Despite the recent breakthrough of combining multiple sequence alignment information and artificial intelligence algorithms to predict protein secondary structure, the Q₃ accuracy of various computational prediction methods rarely has exceeded 75%; this status has changed little since 2003 when Rost stated that the currently best methods reach a level around 77% three-state per-residue accuracy. The application of artificial neural network methods to this problem is revolutionary in the sense that those techniques employ the homologues of proteins for training and prediction. In this dissertation, a different approach, RT-RICO (Relaxed Threshold Rule Induction from Coverings), is presented that instead uses association rule mining. This approach still makes use of the fundamental principle that structure is more conserved than sequence. However, rules between each known secondary structure element and its neighboring amino acid residues are established to perform the predictions. This dissertation consists of five research articles that discuss different prediction techniques and detailed rule-generation algorithms. The most recent prediction approach, BLAST-RT-RICO, achieved a Q₃ accuracy score of 89.93% on the standard test dataset RS126 and a Q₃ score of 87.71% on the standard test dataset CB396, an improvement over comparable computational methods. Herein one research article also discusses the results of examining those RT-RICO rules using an existing association rule visualization tool, modified to account for the non-Boolean characterization of protein secondary structure --Abstract, page iv

Towards protein function annotations for matching remote homologs

Identifying functional similarities for proteins with low sequence identity and low structure similarity often suffers from high false positives and false negatives results. To improve the functional prediction ability based on the local protein structures, we proposed two different refinement and filtering approaches. We built a statistical model (known as Markov Random Field) to describe protein functional site structure. We also developed filters that consider the local environment around the active sites to remove the false positives. Our experimental results, as evaluated in five sets of enzyme families with less than 40% sequence identity, demonstrated that our methods can obtain more remote homologs that could not be detected by traditional sequence-based methods. At the same time, our method could reduce large amount of random matches. Our methods could improve up to 70% of the functional annotation ability (measured by their Area under the ROC curve) in extended motif method

Discovery and Extraction of Protein Sequence Motif Information that Transcends Protein Family Boundaries

Protein sequence motifs are gathering more and more attention in the field of sequence analysis. The recurring patterns have the potential to determine the conformation, function and activities of the proteins. In our work, we obtained protein sequence motifs which are universally conserved across protein family boundaries. Therefore, unlike most popular motif discovering algorithms, our input dataset is extremely large. As a result, an efficient technique is essential. We use two granular computing models, Fuzzy Improved K-means (FIK) and Fuzzy Greedy K-means (FGK), in order to efficiently generate protein motif information. After that, we develop an efficient Super Granular SVM Feature Elimination model to further extract the motif information. During the motifs searching process, setting up a fixed window size in advance may simplify the computational complexity and increase the efficiency. However, due to the fixed size, our model may deliver a number of similar motifs simply shifted by some bases or including mismatches. We develop a new strategy named Positional Association Super-Rule to confront the problem of motifs generated from a fixed window size. It is a combination approach of the super-rule analysis and a novel Positional Association Rule algorithm. We use the super-rule concept to construct a Super-Rule-Tree (SRT) by a modified HHK clustering, which requires no parameter setup to identify the similarities and dissimilarities between the motifs. The positional association rule is created and applied to search similar motifs that are shifted some residues. By analyzing the motifs results generated by our approaches, we realize that these motifs are not only significant in sequence area, but also in secondary structure similarity and biochemical properties

Engineering proteinase K using machine learning and synthetic genes

BACKGROUND: Altering a protein's function by changing its sequence allows natural proteins to be converted into useful molecular tools. Current protein engineering methods are limited by a lack of high throughput physical or computational tests that can accurately predict protein activity under conditions relevant to its final application. Here we describe a new synthetic biology approach to protein engineering that avoids these limitations by combining high throughput gene synthesis with machine learning-based design algorithms. RESULTS: We selected 24 amino acid substitutions to make in proteinase K from alignments of homologous sequences. We then designed and synthesized 59 specific proteinase K variants containing different combinations of the selected substitutions. The 59 variants were tested for their ability to hydrolyze a tetrapeptide substrate after the enzyme was first heated to 68°C for 5 minutes. Sequence and activity data was analyzed using machine learning algorithms. This analysis was used to design a new set of variants predicted to have increased activity over the training set, that were then synthesized and tested. By performing two cycles of machine learning analysis and variant design we obtained 20-fold improved proteinase K variants while only testing a total of 95 variant enzymes. CONCLUSION: The number of protein variants that must be tested to obtain significant functional improvements determines the type of tests that can be performed. Protein engineers wishing to modify the property of a protein to shrink tumours or catalyze chemical reactions under industrial conditions have until now been forced to accept high throughput surrogate screens to measure protein properties that they hope will correlate with the functionalities that they intend to modify. By reducing the number of variants that must be tested to fewer than 100, machine learning algorithms make it possible to use more complex and expensive tests so that only protein properties that are directly relevant to the desired application need to be measured. Protein design algorithms that only require the testing of a small number of variants represent a significant step towards a generic, resource-optimized protein engineering process

The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4

In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.Comment: 230 pages report; 181 pages for main content

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

This work introduces a number of algebraic topology approaches, such as multicomponent persistent homology, multi-level persistent homology and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. Multicomponent persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for chemical and biological problems. Extensive numerical experiments involving more than 4,000 protein-ligand complexes from the PDBBind database and near 100,000 ligands and decoys in the DUD database are performed to test respectively the scoring power and the virtual screening power of the proposed topological approaches. It is demonstrated that the present approaches outperform the modern machine learning based methods in protein-ligand binding affinity predictions and ligand-decoy discrimination