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PubMed2002

From knowledge-based potentials to combinatorial lead design in silico.

Computational methods are becoming increasingly used in the drug discovery process. In this Account, we review a novel computational method for lead discovery. This method, called CombiSMoG for "combinatorial small molecule growth", is based on two components: a fast and accurate knowledge-based scoring function used to predict binding affinities of protein-ligand complexes, and a Monte Carlo combinatorial growth algorithm that generates large numbers of low-free-energy ligands in the binding site of a protein. We illustrate the advantages of the method by describing its application in the design of picomolar inhibitors for human carbonic anhydrase.

Algorithms↗

PubMed2005

Development of a ligand knowledge base, part 1: computational descriptors for phosphorus donor ligands.

A prototype collection of knowledge on ligands in metal complexes, termed a ligand knowledge base (LKB), has been developed. This contribution describes the design of DFT-calculated descriptors for monodentate phosphorus(III) donor ligands in a range of representative complexes. Using the resulting data, a ligand space is mapped and predictive models are derived for metal complexes. Important characteristics, including chemical, computational and statistical robustness for the generation and exploitation of such an LKB are described. Chemical robustness ensures transferability of the descriptors, as well as comprehensive sampling of ligand space. To make the calculations amenable to automation in an e-science setting, a reliable, well-defined computational approach has been sought from which the descriptors can be readily extracted. The LKB has been explored with multivariate statistical methods. Principal component analysis (PCA) is used for the mapping of chemical space, projecting multiple descriptors into scatter plots which illustrate the clustering of chemically similar ligands. Interpretation of the resulting principal components in terms of established steric and electronic properties and the importance of its statistical robustness to variations in the ligand set are discussed. Multiple linear regression (MLR) models have been derived, demonstrating the versatility of the descriptors for modeling varied experimentally determined parameters (bond lengths, reaction enthalpies and bond-stretching frequencies). The importance of re-sampling methods for testing the robustness of predictions is highlighted. A strategy for the construction of a robust LKB suitable for the modeling of ligand and complex behavior is outlined based on these observations.

Journal Article↗

PubMed2005

The key role of atom types, reference states, and interaction cutoff radii in the knowledge-based method: new variational approach.

We present a variational method to derive knowledge-based potentials. The method is based on an optimization procedure of objective variables: atom types, reference states, and interaction cutoff radii. We suggest and apply new unsymmetrical reference states. The cutoff radii and atom types are optimized to improve docking accuracy of the corresponding potentials. The atom types are varied along an atom type tree, with 6 root and 49 top atom types, and the set of 18 optimal atom types is obtained. We demonstrate strong dependence between the choice of atom types and the docking accuracy of the potentials derived with these atom types. The averaged root-mean square deviations (RMSDs) of the ligand docked positions relative to the experimentally determined positions decrease when the elements C, N, O are split into the optimal types.

Algorithms↗

PubMedDate not supplied

BHB: a simple knowledge-based scoring function to improve the efficiency of database screening.

A new knowledge-based scoring function was developed in this work to facilitate the rapid ranking of ligands in databases. The acronym of the method is BHB based on the descriptors it utilizes: buriedness, hydrogen bonding, and binding energy. Receptor buriedness is a measure of how well molecules occupy the binding pocket in comparison to known high-affinity ligands or, alternatively, whether they have contact with identified residues in the pocket. The possibility of hydrogen bond formation is checked for selected residues that are recognized as being important in the binding of known ligands. The approximate binding energy is calculated from the thermodynamic cycle using the optimized bound and free solvent conformations of the ligand-receptor system. The information necessary for the scoring function can ideally be gleaned from the 3D structure of the receptor-ligand complex. Alternatively, the descriptors can be derived from the 3D structure of the unbound receptor, provided this receptor has a known ligand that binds to the given site with nanomolar activity. We show that the new scoring functions provide up to 12 times improvement in enrichment compared to the popular commercial docking program GOLD.

Journal Article↗

PubMed1997

The use of knowledge-based systems in medicine in developing countries: a luxury or a necessity?

Knowledge-based systems (KBSs) in medicine have received much attention over the past two decades, mainly because of the potential benefits that can be gained from using them. They may facilitate in increasing productivity in a medical environment, support the making of diagnoses and other types of medical decisions, assist in the training of medical professionals, and can even handle some routine tasks in a medical environment. However, some critical problems in this field have also been identified. For example, research indicated that some problems can be solved partially, but not completely, with existing artificial intelligence techniques. Another problem is that many of the existing medical information systems do not support the integration of KBSs in a natural way. Furthermore, the routine use of a medical KBS is complicated by legal issues. These and other problems contribute to what we experience today: a large proportion of the medical KB applications that are developed is never actually used in practice. This justifies questions such as: Should developing countries, having limited infrastructure and research resources, invest in medical KBSs research and development, or should this field be regarded as a luxury that only belongs to developed countries?, and: Can developing countries really benefit from the use of these systems? These questions are discussed in this paper. We highlight the main problems surrounding the development and use of medical KBSs. With the focus on developing countries we discuss potential benefits that could be obtained by investing in these systems and we offer guidelines for focusing research and development of medical KBSs.

Artificial Intelligence↗

PubMed2000

The utility of event-based knowledge elicitation.

The purpose of this investigation was to describe and evaluate an event-based knowledge elicitation technique. With this approach experts are provided with deliberate and controlled job situations, allowing investigation of specific task aspects and the comparison of expert responses. For this effort a videotape was developed showing an instructor pilot and student conducting a training mission. Various job situations were depicted in the video to gather information pertinent to understanding team situational awareness. The videotape was shown to 10 instructors and 10 student aviators in the community, and responses to the videotape were collected using a questionnaire at predetermined stop points. Consistent with expectations, the results showed that more experienced respondents (i.e., instructors) identified a richer database of cues and were more likely than students to identify strategies for responding to the situations depicted, providing some empirical evidence for the validity of the event-based technique. This method may serve as a useful knowledge elicitation technique, especially in the later stages of a job analysis when focused information is sought.

Cognitive Science↗

PubMed1991

Data and decisions: can mental health management be knowledge-based?

Three major factors suggest a healthy future for data-based decision making within mental health authorities: (1) the improved knowledge base related to the treatment and management of serious mental illness, (2) advances in data-processing technology and (3) conceptual advances in management information system design, most notably the National Institute of Mental Health (NIMH) Mental Health Statistics Improvement Package. This paper briefly outlines these three factors and goes on to examine information needed by state mental health authorities (SMHAs) to enhance decision making. The client-level data necessary for data-based policy decisions, while still scarce, are increasingly available and are increasingly finding homes within SMHA management information systems. As SMHAs improve their information systems to accommodate such data, they face substantial implementation challenges and substantial payoffs in terms of increased knowledge for decision making.

Decision Support Systems, Management↗

PubMed1996

Nationally speaking. Life history and narrative research: generating a humanistic knowledge base for occupational therapy.

As a profession, occupational therapists are guided in practice by the accumulated knowledge of occupational therapy. This article demonstrates the contributions of life-history and narrative research to this knowledge base. We are suggesting that in response to our humanistic roots, we must pursue additional knowledge, principles of practice, and ethical philosophies that support practice. We have argued that our ideologic concern for the client must guide our choice of epistemologies to investigate the lived experience to those whom we serve. "What is at stake here is the ethic that is embedded in the epistemology that gives rise to kinds of research" (Brock, 1995, p.157) that meet the societal demands for professionalism and support a humanistic practice.

Ethics, Professional↗

PubMed2006

Improving the stability of an antibody variable fragment by a combination of knowledge-based approaches: validation and mechanisms.

Numerous approaches have been described to obtain variable fragments of antibodies (Fv or scFv) that are sufficiently stable for their applications. Here, we combined several knowledge-based methods to increase the stability of pre-existing scFvs by design. Firstly, the consensus sequence approach was used in a non-stringent way to predict a large basic set of potentially stabilizing mutations. These mutations were then prioritized by other methods of design, mainly the formation of additional hydrogen bonds, an increase in the hydrophilicity of solvent exposed residues, and previously described mutations in other antibodies. We validated this combined method with antibody mAbD1.3, directed against lysozyme. Fourteen potentially stabilizing mutations were designed and introduced into scFvD1.3 by site-directed mutagenesis, either individually or in combinations. We characterized the effects of the mutations on the thermodynamic stability of scFvD1.3 by experiments of unfolding with urea, monitored by spectrofluorometry, and tested the additivity of their effects by double-mutant cycles. We also quantified the individual contributions of the resistance to denaturation ([urea](1/2)) and cooperativity of unfolding (m) to the variations of stability and the energy of coupling between mutations by a novel approach. Most mutations (75%) were stabilizing and none was destabilizing. The progressive recombination of the mutations into the same molecule of scFvD1.3 showed that their effects were mostly additive or synergistic, provided a large overall increase in protein stability (9.1 kcal/mol), and resulted in a highly stable scFvD1.3 derivative. The mechanisms of the mutations and of their combinations involved variations in the resistance to denaturation, cooperativity of unfolding, and likely residual structures of the denatured state, which was constrained by two disulfide bonds. This combined method should be applicable to any recombinant antibody fragment, through a single step of mutagenesis.

Animals↗

PubMed2001

A distance-dependent atomic knowledge-based potential for improved protein structure selection.

A heavy atom distance-dependent knowledge-based pairwise potential has been developed. This statistical potential is first evaluated and optimized with the native structure z-scores from gapless threading. The potential is then used to recognize the native and near-native structures from both published decoy test sets, as well as decoys obtained from our group's protein structure prediction program. In the gapless threading test, there is an average z-score improvement of 4 units in the optimized atomic potential over the residue-based quasichemical potential. Examination of the z-scores for individual pairwise distance shells indicates that the specificity for the native protein structure is greatest at pairwise distances of 3.5-6.5 A, i.e., in the first solvation shell. On applying the current atomic potential to test sets obtained from the web, composed of native protein and decoy structures, the current generation of the potential performs better than residue-based potentials as well as the other published atomic potentials in the task of selecting native and near-native structures. This newly developed potential is also applied to structures of varying quality generated by our group's protein structure prediction program. The current atomic potential tends to pick lower RMSD structures than do residue-based contact potentials. In particular, this atomic pairwise interaction potential has better selectivity especially for near-native structures. As such, it can be used to select near-native folds generated by structure prediction algorithms as well as for protein structure refinement.

Computational Biology↗

PubMed1993

A knowledge-based alarm system for monitoring cardiac operated patients--technical construction and evaluation.

A knowledge-based alarm system for intensive care monitoring was designed, built, tested on-line, and evaluated. The system is a functional prototype of a highly specific patient monitor providing alarms on hypovolemia, hyperdynamic state, left ventricular failure and hypoventilation. These intelligent alarm functions aim to maintain the quality of patient monitoring even if nurses' attention is temporarily reduced or focused elsewhere. The alarm system has an electronic access to data available in a multichannel patient monitor and the patient data management system of the intensive care unit. Median filtering, trend estimation, and rule-based reasoning are applied when processing the measured variables and estimating the patient's state.

Artificial Intelligence↗

PubMed2000

Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: application to the human procarboxypeptidase A2.

Knowledge-based energy profiles combined with secondary structure prediction have been applied to molecular modelling refinement. To check the procedure, three different models of human procarboxypeptidase A2 (hPCPA2) have been built using the 3D structures of procarboxypeptidase A1 (pPCPA1) and bovine procarboxypeptidase A (bPCPA) as templates. The results of the refinement can be tested against the X-ray structure of hPCPA2 which has been recently determined. Regions miss-modelled in the activation segment of hPCPA2 were detected by means of pseudo-energies using Prosa II and modified afterwards according to the secondary structure prediction. Moreover, models obtained by automated methods as COMPOSER, MODELLER and distance restraints have also been compared, where it was found possible to find out the best model by means of pseudo-energies. Two general conclusions can be elicited from this work: (1) on a given set of putative models it is possible to distinguish among them the one closest to the crystallographic structure, and (2) within a given structure it is possible to find by means of pseudo-energies those regions that have been defectively modelled.

Amino Acid Sequence↗

PubMed1996

Evaluation of a knowledge-based system providing ventilatory management and decision for extubation.

We evaluated whether a knowledge-based system (KBS) connected to a ventilator in pressure support mode could correctly predict the ability of patients to tolerate total withdrawal from ventilatory support. The KBS was designed to continuously adapt ventilatory assistance to the needs of the patient, to manage a strategy of gradually decreasing ventilatory assistance, and to indicate when the patient was able to breathe without assistance. Thirty-eight patients for whom weaning was being considered were evaluated using a conventional battery of parameters, including weaning criteria, tolerance of a T-piece trial, and outcome 48h after permanent withdrawal of ventilation. The results of this evaluation were compared with the suggestions made by the KBS at the end of a period of KBS-driven mechanical ventilation inserted in the conventional weaning procedure. The positive predictive value of the KBS was 89%, versus 77% for the conventional procedure and 81% for the rapid shallow breathing index alone. The KBS correctly predicted the course of five patients who tolerated a T-piece trial but required ventilation within 48 h. We conclude that our KBS ensured appropriate patient management during the weaning period and improved our ability to predict responses to weaning.

Adult↗

PubMedDate not supplied

Knowledge-based approaches to the maintenance of a large controlled medical terminology.

OBJECTIVE: Develop a knowledge-based representation for a controlled terminology of clinical information to facilitate creation, maintenance, and use of the terminology. DESIGN: The Medical Entities Dictionary (MED) is a semantic network, based on the Unified Medical Language System (UMLS), with a directed acyclic graph to represent multiple hierarchies. Terms from four hospital systems (laboratory, electrocardiography, medical records coding, and pharmacy) were added as nodes in the network. Additional knowledge about terms, added as semantic links, was used to assist in integration, harmonization, and automated classification of disparate terminologies. RESULTS: The MED contains 32,767 terms and is in active clinical use. Automated classification was successfully applied to terms for laboratory specimens, laboratory tests, and medications. One benefit of the approach has been the automated inclusion of medications into multiple pharmacologic and allergenic classes that were not present in the pharmacy system. Another benefit has been the reduction of maintenance efforts by 90%. CONCLUSION: The MED is a hybrid of terminology and knowledge. It provides domain coverage, synonymy, consistency of views, explicit relationships, and multiple classification while preventing redundancy, ambiguity (homonymy) and misclassification.

Computer Simulation↗

PubMed1987

Intelligent physiologic modeling: an application of knowledge based systems technology to medical education.

This article describes the design and implementation of a knowledge-based physiologic modeling system (KPBMS) and a preliminary evaluation of its use as a learning resource within the context of an experimental medical curriculum--the Harvard New Pathway. KBPMS possesses combined numeric and qualitative simulation capabilities and can provide explanations of its knowledge and behavior. It has been implemented on a microcomputer with a user interface incorporating interactive graphics. The preliminary evaluation of KBPMS is based on anecdotal data which suggests that the system might have pedagogic potential. Much work remains to be done in enhancing and further evaluating KBPMS.

Artificial Intelligence↗

PubMed2004

A knowledge-based clustering algorithm driven by Gene Ontology.

We have developed an algorithm for inferring the degree of similarity between genes by using the graph-based structure of Gene Ontology (GO). We applied this knowledge-based similarity metric to a clique-finding algorithm for detecting sets of related genes with biological classifications. We also combined it with an expression-based distance metric to produce a co-cluster analysis, which accentuates genes with both similar expression profiles and similar biological characteristics and identifies gene clusters that are more stable and biologically meaningful. These algorithms are demonstrated in the analysis of MPRO cell differentiation time series experiments.

Algorithms↗

PubMed1994

A methodology for evaluation of knowledge-based systems in medicine.

Evaluation is critical to the development and successful integration of knowledge-based systems into their application environment. This is of particular importance in the medical domain--not only for reasons of safety and correctness, but also to reinforce the users' confidence in these systems. In this paper we describe an iterative, four-phased development evaluation cycle covering the following areas: (i) early prototype development, (ii) validity of the system, (iii) functionality of the system, and (iv) impact of the system.

Artificial Intelligence↗

PubMed1995

Image-oriented rule generating tool for medical knowledge base.

This paper describes an automatic programming tool for the end users in the medical knowledge representation. When end users work with the rule generating tool (RGT) that we have designed, they can represent an idea using an Image-oriented interface such as a picture, movie, audio reference or two dimensional bar code. Such representations can be valuable tools for medical study, diagnosis, decision making and treatment monitoring to supplement the expertise of medical personnel. The RGT will automatically generate rules in the logic programming language Prolog and then add them to the knowledge base.

Algorithms↗