Utilization of polymers as biomaterials has greatly impacted the advancement of modern medicine. Specifically, polymeric biomaterials that are biodegradable provide the significant advantage of being able to be broken down and removed after they have served their function. Applications are wide ranging with degradable polymers being used clinically as surgical sutures and implants. To fit functional demand, materials with desired physical, chemical, biological, biomechanical, and degradation properties must be selected. Fortunately, a wide range of natural and synthetic degradable polymers has been investigated for biomedical applications with novel materials constantly being developed to meet new challenges. This review summarizes the most recent advances in the field over the past 4 years, specifically highlighting new and interesting discoveries in tissue engineering and drug delivery applications.

Biomedical applications of biodegradable polymers

Proceedings of the fifth annual workshop on Computational learning theory

Information about macromolecular structure of protein complexes and related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automated deep learning-based method for fast de novo multichain protein complex structure determination from high-resolution cryoelectron microscopy (cryo-EM) maps. We applied DeepTracer on a previously published set of 476 raw experimental cryo-EM maps and compared the results with a current state of the art method. The residue coverage increased by over 30% using DeepTracer, and the rmsd value improved from 1.29 A to 1.18 A. Additionally, we applied DeepTracer on a set of 62 coronavirus-related cryo-EM maps, among them 10 with no deposited structure available in EMDataResource. We observed an average residue match of 84% with the deposited structures and an average rmsd of 0.93 A. Additional tests with related methods further exemplify DeepTracer's competitive accuracy and efficiency of structure modeling. DeepTracer allows for exceptionally fast computations, making it possible to trace around 60,000 residues in 350 chains within only 2 h. The web service is globally accessible at https://deeptracer.uw.edu.

https://www.pnas.org/content/pnas/118/2/e2017525118.full.pdf

DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes.

Lignocellulosic biomass is the earth’s most abundant renewable feedstock alternative that comprises of cellulose, hemi-cellulose and lignin. The synergistic action of cellulolytic/xylanolytic enzymes produced by lignocellulolytic micro-organisms such as bacteria, algae and fungi are capable of robust cellulosic biomass deconstruction. Most of the microorganisms dwelling in extreme environmental habitats such as rumen environment, hot/cold springs, deep ocean trenches, acidic/alkaline pH environment have been considered as an attractive producers of hemi/cellulolytic lignocellulolytic and other biotechnological enzymes with enhanced bio-chemical properties essential for industrial bioconversion processes. However, the potential microbial sources of cellulolytic enzymes and the underlying mechanism to achieve this is not fully elucidated. In this review article, first we detail the composition of lignocellulosic biomass. Next, we describe the structure and functions of divergent hydrolytic enzymes (cellulolytic and xylanolytic enzymes) involved in cellulosic biomass degradation. Third, we analyze, outline and unveil the prospective source of microbes encoding exceptionally diverse set of biotechnologically relevant cellulolytic enzymes which are critical to answer the specific ecological question of by whom, where and how cellulosic biomass is degraded in the environment. Finally, this review article features the snapshot about the future developments and perspectives on microbial enzymes, high-throughput techniques and molecular tools that could be exploited to derive those enzymes from the potential sources.

Microbial cellulolytic enzymes: diversity and biotechnology with reference to lignocellulosic biomass degradation

Lipases are industrially important biocatalyst, particularly microbial lipases and therefore, screening, production and purification of lipase enzyme from microbial strains are continuously emerging to fulfil the needs of pharmaceutical and food industries. More recently, various cost effective and efficient approaches are being attempted to increase the production of lipases in microbial strains. In this junction, this article attempt to connect production of lipases using various microbial strains such as bacteria, fungi and yeast has been highlighted and discussed, in order to enable researchers to choose an appropriate strains for lipase production. Then, screening of lipase producer and production of lipases under different fermentation system was discussed. Later, impact of various factors such as carbon sources, nitrogen sources, pH and temperature has been assessed, and presented in the perspective of increasing its production from microbial strains. Finally, purification techniques for lipase enzymes was summarized and recent literature pertaining to lipase purification from microbial stains are reviewed, and summarized.

Microbial lipases: An overview of screening, production and purification

The accuracy of the secondary structure element (SSE) identification from volumetric protein density maps is critical for de-novo backbone structure derivation in electron cryo-microscopy (cryoEM) It is still challenging to detect the SSE automatically and accurately from the density maps at medium resolutions (∼5–10 A) We present a machine learning approach, SSELearner, to automatically identify helices and β-sheets by using the knowledge from existing volumetric maps in the Electron Microscopy Data Bank We tested our approach using 10 simulated density maps The averaged specificity and sensitivity for the helix detection are 949% and 958%, respectively, and those for the β-sheet detection are 867% and 964%, respectively We have developed a secondary structure annotator, SSID, to predict the helices and β-strands from the backbone Cα trace With the help of SSID, we tested our SSELearner using 13 experimentally derived cryo-EM density maps The machine learning approach shows the specificity and sensitivity of 918% and 745%, respectively, for the helix detection and 852% and 865% respectively for the β-sheet detection in cryoEM maps of Electron Microscopy Data Bank The reduced detection accuracy reveals the challenges in SSE detection when the cryoEM maps are used instead of the simulated maps Our results suggest that it is effective to use one cryoEM map for learning to detect the SSE in another cryoEM map of similar quality © 2012 Wiley Periodicals, Inc Biopolymers 97: 698–708, 2012

A Machine Learning Approach for the Identification of Protein Secondary Structure Elements from Electron Cryo-Microscopy Density Maps†

Microbes are ubiquitously distributed in nature and are a critical part of the holobiont fitness. They are perceived as the most potential biochemical reservoir of inordinately diverse and multi-functional enzymes. The robust nature of the microbial enzymes with thermostability, pH stability and multi-functionality make them potential candidates for the efficient biotechnological processes under diverse physio-chemical conditions. The need for sustainable solutions to various environmental challenges has further surged the demand for industrial enzymes. Fueled by the recent advent of recombinant DNA technology, genetic engineering, and high-throughput sequencing and omics techniques, numerous microbial enzymes have been developed and further exploited for various industrial and therapeutic applications. Most of the hydrolytic enzymes (protease being the dominant hydrolytic enzyme) have broad range of industrial uses such as food and feed processing, polymer synthesis, production of pharmaceuticals, manufactures of detergents, paper and textiles, and bio-fuel refinery. In this review article, after a short overview of microbial enzymes, an approach has been made to highlight and discuss their potential relevance in biotechnological applications and industrial bio-processes, significant biochemical characteristics of the microbial enzymes, and various tools that are revitalizing the novel enzymes discovery.

Biochemical Characteristics of Microbial Enzymes and Their Significance from Industrial Perspectives

Electron cryomicroscopy is becoming a major experimental technique in solving the structures of large molecular assemblies. More and more three-dimensional images have been obtained at the medium resolutions between 5 and $10 \AA$ . At this resolution range, major $\alpha$ -helices can be detected as cylindrical sticks and β-sheets can be detected as plain-like regions. A critical question in de novo modeling from cryo-EM images is to determine the match between the detected secondary structures from the image and those on the protein sequence. We formulate this matching problem into a constrained graph problem and present an $O({\rm \Delta }^2 N^2 2^N)$ algorithm to this NP-Hard problem. The algorithm incorporates the dynamic programming approach into a constrained $K$ -shortest path algorithm. Our method, DP-TOSS, has been tested using α-proteins with maximum 33 helices and $\alpha{\hbox{-}}\beta$ proteins up to five helices and 12 $\beta$ -strands. The correct match was ranked within the top 35 for 19 of the 20 α-proteins and all nine $\alpha {\hbox{-}}\beta$ proteins tested. The results demonstrate that DP-TOSS improves accuracy, time and memory space in deriving the topologies of the secondary structure elements for proteins with a large number of secondary structures and a complex skeleton.

Solving the secondary structure matching problem in cryo-EM de novo modeling using a constrained K-shortest path graph algorithm

Electron cryo-microscopy is a fast advancing biophysical technique to derive three-dimensional structures of large protein complexes. Using this technique, many density maps have been generated at intermediate resolution such as 6–10 A resolution. Although it is challenging to derive the backbone of the protein directly from such density maps, secondary structure elements such as helices and β-sheets can be computationally detected. Our work in this paper provides an approach to enumerate the top-ranked possible topologies instead of enumerating the entire population of the topologies. This approach is particularly practical for large proteins. We developed a directed weighted graph, the topology graph, to represent the secondary structure assignment problem. We prove that the problem of finding the valid topology with the minimum cost is NP hard. We developed an O(N2 2N) dynamic programming algorithm to identify the topology with the minimum cost. The test of 15 proteins suggests that our dynamic programming approach is feasible to work with proteins of much larger size than we could before. The largest protein in the test contains 18 helical sticks detected from the density map out of 33 helices in the protein.

Ranking valid topologies of the secondary structure elements using a constraint graph.

Cryo-electron Microscopy (cryoEM) is an advanced imaging technique that produces volume maps at different resolutions. This technique is capable of visualizing large molecular complexes such as viruses and ribosomes. At the medium resolutions, such as 5 to 10A, the location and orientation of the secondary structure elements (SSEs) can be computationally identified. However, there is no registration between the detected SSEs and the protein sequence, and therefore it is challenging to derive the atomic structure from such volume data. We present, in this paper, the preliminary results of the full-atom protein chains using our de novo modeling framework. The framework has multiple components including the ranking of topologies, the construction of helices and loops along the density traces, and the energy evaluation of the structure. A test containing thirteen simulated density maps and two experimentally derived density maps show that the true topology was ranked among the top 35 of the huge topological space. The best atomic model of the true topology was ranked within the top 40 for twelve of the fifteen proteins tested. The average backbone RMSD100 of these models is about 4A for the fifteen proteins.

Kamal Al Nasr

Papers

A Machine Learning Approach for the Identification of Protein Secondary Structure Elements from Electron Cryo-Microscopy Density Maps†

Biochemical Characteristics of Microbial Enzymes and Their Significance from Industrial Perspectives

Solving the secondary structure matching problem in cryo-EM de novo modeling using a constrained K-shortest path graph algorithm

Ranking valid topologies of the secondary structure elements using a constraint graph.

Building the initial chain of the proteins through de novo modeling of the cryo-electron microscopy volume data at the medium resolutions